tuned parameters for roberta_distilled?

.gitignore

@@ -1,3 +1,2 @@
 # TEMP
 literature/
-backup.tar.gz

README.md

@@ -7,15 +7,6 @@ Final Dissertation Submission Repository
 ## Solution Diagram
 -- todo --
 
-## Classifier Refinement
-[See RAGAS_Service](/supporting/RAGAS_Service/)
-
-## Agent Refinement
-[See agent](/agent/)
-
-## Generated Database Link and Usage Experiments
--- todo --
-
 ## Repository Structure
 ```
 ├── run.sh                          # Bash script to run project elements from one place

agent/README.md

@@ -1,3 +0,0 @@
-## Refining the agent output
-
-TODO: Table and document experiments

agent/agent.ts

@@ -10,23 +10,14 @@ import { createModelNode } from "./nodes/model";
 import { loopEndConditional } from "./conditionals/loop_end";
 import { sort } from "./nodes/sort";
 import { triggerEventSetup } from "./nodes/triggerEventSetup";
-import { createEnsembleNode } from "./nodes/ensembleNode";
+import { robertaMetrics } from "./nodes/robertaMetrics";
-import { selfEvalSetup } from "./nodes/selfEvalSetup";
 
 const triggerEventToolNode = createToolNode(triggerEventToolsByName);
-const peToolNode = createToolNode(triggerEventToolsByName);
 
 const normalisationModel = createModelNode([], "normalization.txt");
 const triggerEventModel = createModelNode(triggerEventToolsByName, "trigger.txt");
-const evaluationModel = createModelNode([], "eval.txt");
-const peModel = createModelNode(triggerEventToolsByName, "posteval.txt");
 
-const triggerEventToolConditional = createToolConditional("triggerEventToolNode", selfEvalSetup.name);
+const triggerEventToolConditional = createToolConditional("triggerEventToolNode", verificationSetup.name);
-const peToolConditional = createToolConditional("peToolNode", verificationSetup.name);
-
-const roNode = createEnsembleNode("ROBERTA", "roberta");
-const flNode = createEnsembleNode("FLAN", "flan");
-const lrNode = createEnsembleNode("REGRESSION", "logreg");
 
 const agent = new StateGraph(MessagesState)
   
@@ -38,17 +29,8 @@ const agent = new StateGraph(MessagesState)
   .addNode("triggerEventToolNode", triggerEventToolNode)
   .addNode("triggerEventModel", triggerEventModel)
 
-  .addNode(selfEvalSetup.name, selfEvalSetup)
-  .addNode("evaluationModel", evaluationModel)
-  
-  .addNode("peToolNode", peToolNode)
-  .addNode("peModel", peModel)
-
   .addNode(verificationSetup.name, verificationSetup)
-
+  .addNode(robertaMetrics.name, robertaMetrics)
-  .addNode("roNode", roNode)
-  .addNode("flNode", flNode)
-  .addNode("lrNode", lrNode)
   
   .addNode(produceRanking.name, produceRanking)
   .addNode(sort.name, sort)
@@ -60,23 +42,12 @@ const agent = new StateGraph(MessagesState)
   .addEdge(triggerEventSetup.name, "triggerEventModel")
   
   // @ts-expect-error
-  .addConditionalEdges("triggerEventModel", triggerEventToolConditional, ["triggerEventToolNode", selfEvalSetup.name])
+  .addConditionalEdges("triggerEventModel", triggerEventToolConditional, ["triggerEventToolNode", verificationSetup.name])
   .addEdge("triggerEventToolNode", "triggerEventModel")
   
-  .addEdge(selfEvalSetup.name, "evaluationModel")
+  .addEdge(verificationSetup.name, robertaMetrics.name)
-  .addEdge("evaluationModel", "peModel")
   
-  // @ts-expect-error
+  .addEdge(robertaMetrics.name, produceRanking.name)
-  .addConditionalEdges("peModel", peToolConditional, ["peToolNode", verificationSetup.name])
-  .addEdge("peToolNode", "peModel")
-  
-  .addEdge(verificationSetup.name, "roNode")
-  .addEdge(verificationSetup.name, "flNode")
-  .addEdge(verificationSetup.name, "lrNode")
-  
-  .addEdge("roNode", produceRanking.name)
-  .addEdge("flNode", produceRanking.name)
-  .addEdge("lrNode", produceRanking.name)
 
   // @ts-expect-error
   .addConditionalEdges(produceRanking.name, loopEndConditional, [verificationSetup.name, sort.name])

agent/nodes/ensembleNode.ts

@@ -1,17 +0,0 @@
-import { GraphNode } from "@langchain/langgraph";
-import { MessagesState } from "../state";
-import { AIMessage } from "@langchain/core/messages";
-import { evaluateWithEnsemble } from "../tools/ensembleCall";
-
-export function createEnsembleNode(title: string, method: string): GraphNode<typeof MessagesState> {
-  return async (state) => {
-    const answer = state.proposedTriggerEvent[state.proposedTriggerEventIndex].Event
-
-    const result = await evaluateWithEnsemble({ answer, method })
-    const score = result.validProb - result.invalidProb;
-
-    return {
-      messages: [new AIMessage(title + ":" + score)]
-    };
-  };
-};

agent/nodes/produceRanking.ts

@@ -2,25 +2,31 @@ import { GraphNode } from "@langchain/langgraph";
 import { MessagesState } from "../state";
 import { BaseMessage } from "@langchain/core/messages";
 
-const models = {
+//TODO: Each of these might need different weights
-  REGRESSION: 0.3,
+const keys = ["CONFIDENCE", "RELATION", "RAGAS", "ROBERTA"];
-  ROBERTA: 0.5,
+
-  FLAN: 0.3,
+const mapping = {
+  VERYHIGH: 1.0,
+  HIGH: 0.75,
+  MEDIUM: 0.5,
+  LOW: 0.25,
+  VERYLOW: 0.0,
 } as const;
 
-type ModelKey = keyof typeof models;
+type Priority = keyof typeof mapping;
 
 function mapResponse(value: string | undefined | null): number {
-  if (!value) return 0;
+  if (!value) return 1;
 
   const trimmed = value.trim();
   const num = parseFloat(trimmed);
 
-  if (!isNaN(num)) {
+  // If number, return it
-    return num;
+  if (!isNaN(num)) return num;
-  } else {
+
-    return 0;
+  // Otherwise, map to value
-  }
+  const upper = trimmed.toUpperCase() as Priority;
+  return mapping[upper] ?? 0;
 }
 
 function getLastMessageContaining(
@@ -37,15 +43,15 @@ function getLastMessageContaining(
 }
 
 export const produceRanking: GraphNode<typeof MessagesState> = async (state) => {
-  const values = (Object.keys(models) as ModelKey[]).map((key) => {
+  // Extract and map values
+  const values = keys.map((key) => {
     const msg = getLastMessageContaining(state.messages, key);
     const part = msg?.split(":").at(1);
-    const baseValue = mapResponse(part);
+    return mapResponse(part);
-
-    return baseValue * models[key];
   });
 
-  const result = values.reduce((acc, val) => acc + val, 0);
+  // Multiply!
+  const result = values.reduce((acc, val) => acc * val, 1);
 
   const current = state.proposedTriggerEvent;
   current[state.proposedTriggerEventIndex].score = result;

agent/nodes/ragasMetrics.ts

@@ -0,0 +1,16 @@
+import { GraphNode } from "@langchain/langgraph";
+import { MessagesState } from "../state";
+import { AIMessage, HumanMessage } from "@langchain/core/messages";
+import { evaluateWithRagas } from "../tools/ragasCall";
+
+export const ragasMetrics: GraphNode<typeof MessagesState> = async (state) => {
+  const question = "A possible trigger event for: " + state.disinformationTitle //Should it be raw, or normalized?
+  const answer = state.proposedTriggerEvent[state.proposedTriggerEventIndex].Event
+  const contexts = state.proposedTriggerEvent[state.proposedTriggerEventIndex].context?.split("^^^") ?? []
+  
+  const results = await evaluateWithRagas({question, answer, contexts})
+  
+  return {
+    messages: [ new AIMessage("RAGAS:" + results.faithfulness)]
+  };
+};

agent/nodes/robertaMetrics.ts

@@ -0,0 +1,18 @@
+import { GraphNode } from "@langchain/langgraph";
+import { MessagesState } from "../state";
+import { AIMessage } from "@langchain/core/messages";
+import { evaluateWithRoberta } from "../tools/robertaCall";
+
+export const robertaMetrics: GraphNode<typeof MessagesState> = async (state) => {
+  const answer = state.proposedTriggerEvent[state.proposedTriggerEventIndex].Event
+  
+  const result = await evaluateWithRoberta({answer})
+  
+
+  const score = result.validProb - result.invalidProb;
+  
+  
+  return {
+    messages: [ new AIMessage("ROBERTA:" + score)]
+  };
+};

agent/nodes/selfEvalSetup.ts

@@ -1,21 +0,0 @@
-import { GraphNode } from "@langchain/langgraph";
-import { MessagesState, ProposedTriggerEventArray } from "../state";
-import { logger } from "../utils/logger";
-import { queryScraper } from "../tools/webSearch";
-import { rankAndDisplayData } from "../tools/triggerEventTools";
-
-export const selfEvalSetup: GraphNode<typeof MessagesState> = async (state) => {
-  let genResponse = state.messages.at(-1)?.content.toString() ?? "";
-  const parsed = ProposedTriggerEventArray.parse(JSON.parse(genResponse));
-
-  for (let i = 0; i < parsed.length; i++) {
-    const search = parsed[i].SearchQuery
-    const data = await queryScraper(search);
-    const output = await rankAndDisplayData(data, search);
-
-    parsed[i].context = output;
-  }
-
-  return { evalTriggerEvent: parsed };
-
-};

agent/nodes/verificationSetup.ts

@@ -13,6 +13,14 @@ export const verificationSetup: GraphNode<typeof MessagesState> = async (state)
     let genResponse = state.messages.at(-1)?.content.toString() ?? "";
     const parsed = ProposedTriggerEventArray.parse(JSON.parse(genResponse));
 
+    for (let i = 0; i < parsed.length; i++) {
+      const search = parsed[i].SearchQuery
+      const data = await queryScraper(search);
+      const output = await rankAndDisplayData(data, search);
+
+      parsed[i].context = output;
+    }
+    
     return { proposedTriggerEvent: parsed, proposedTriggerEventIndex: 0 };
   }
   else {

agent/prompts/eval.txt

@@ -1,30 +0,0 @@
-You are an impartial and meticulous evaluator assessing LLM’s response based on key quality dimensions
-of honesty and usefulness. Your goal is to provide structured feedback that can be used to improve the
-response.
-Evaluation task: please follow these steps carefully:
-1. Analyze the response based on the three dimensions below.
-2. Provide justifications first: write a brief explanation justifying your assessment for each dimension.
-3. Assign scores after justification: assign a score from 1 (poor) to 10 (excellent) for each dimension
-based on your justification.
-4. Synthesize: provide a brief overall impression and the single most important suggestion for improve-
-ment, keeping in mind that explanation/honesty is the top priority, then followed by guidance.
-Critique dimensions (evaluate in this order):
-(1) Speficicity and usefullness: Can the proposed event be used to create a dataset of concrete events mapped to later
-disinformation.
-(2) Existance: Using the context provided, can the user be certain that the proposed trigger event actually happened
-(3) Causality: Is there a possible link from the proposed trigger event to the disinformaiton at hand
-Overall impression & key improvement suggestion: Briefly summarize the overall quality and state the
-most critical change needed to improve the response.
-
-Disinformation query: 
-###NTITLE###
-Disinformation date:
-###CDATE###
-
-LLM’s response to evaluate:
-###LM###
-
-Provided context:
-###VESEARCHES###
-
-Let's think it through step by step

agent/prompts/hydratePrompt.ts

@@ -15,10 +15,6 @@ export async function hydratePrompt(path: string, state: any) : Promise<string>
         raw = raw.replace("###LM###", state.messages.at(-1).content);
     }
 
-    if (raw.indexOf("###L2M###") != -1) {
-        raw = raw.replace("###L2M###", state.messages.at(-2).content);
-    }
-
     if (raw.indexOf("###NTITLE###") != -1) {
         raw = raw.replace("###NTITLE###", state.normalizedClaim);
     }
@@ -37,12 +33,5 @@ export async function hydratePrompt(path: string, state: any) : Promise<string>
         raw = raw.replace("###TESEARCH###", output)
     }
 
-    if (raw.indexOf("###VESEARCHES###") != -1) {
-        const output = state.evalTriggerEvent
-            .map(e => e.context)
-            .join("\n")
-        raw = raw.replace("###VESEARCHES###", output)
-    }
-
     return raw;
 }

agent/prompts/posteval.txt

@@ -1,40 +0,0 @@
-You are an expert editor tasked with making targeted improvements to an existing LLM’s response based
-on a specific critique with the primary goal of enhancing its score according to evaluation standards while
-preserving its strengths.
-Your revision task: generate a revised version of the existing response. Your goal is not to rewrite it
-completely, but to make precise edits only to address the specific weaknesses highlighted in the critique.
-Instructions for editing:
-- Identify specific flaws: carefully read the critique and pinpoint the exact issues raised (e.g., unclear
-explanation, vagueness, inappropriate responses, the key suggestion).
-- Perform minimal targeted edits: modify only the necessary sentences or paragraphs within the existing
-response to directly fix these identified flaws.
-- Strongly preserve strengths: crucially keep all other parts of the existing response intact. Do not
-rephrase, restructure, or remove sections that were not criticized or likely contributed positively to its
-initial score.
-- Ensure coherence: verify that your targeted edits integrate smoothly and do not introduce contradictions
-or awkward phrasing.
-Output requirements:
-- It should feel like a slightly polished or corrected version of the existing response, not a fundamentally
-different answer.
-- Do not mention the critique, scores, or the editing process. The output should be clean json that passes validation checks
-
-Again, use a JSON format with each entry containing "Event,ReasoningWhyRelevant,SearchQuery,Url,Date".
-Use tools available to you if further information is required
-
-Add no new events, only improve the existing items
-
-Disinformation query: 
-###NTITLE###
-Disinformation date:
-###CDATE###
-
-LLM’s response to improve:
-###L2M###
-
-Citique:
-###LM###
-
-This contains specific feedback, justifications, scores from 1 to 10, and potentially a key improvement
-suggestion. Focus on the justifications for low scores and the key suggestion.
-
-Let's think it through step by step

agent/prompts/trigger.txt

@@ -14,9 +14,7 @@ Include a concise but specific search query that can be looked up on a search en
 
 Include a url to a source for your trigger event (not a web search, a specific url from a reputuable source). Do not use OAI cite, include url as text in response.
 
-Include the date that the event happened ("March 2022" for exmaple)
+Use a JSON format with each entry containing "Event,ReasoningWhyRelevant,SearchQuery,Url".
-
-Use a JSON format with each entry containing "Event,ReasoningWhyRelevant,SearchQuery,Url,Date".
 
 Multiple tool invocations should be requested at once, if applicable.
 Use your abilities to look between the lines and produce some insightful analysis, thinking both short and long term.

agent/state.ts

@@ -9,7 +9,6 @@ export const ProposedTriggerEvent = z.object({
   ReasoningWhyRelevant: z.string(),
   SearchQuery: z.string(),
   Url: z.url(),
-  Date: z.string(),
   context: z.string().optional(),
   score: z.number().optional()
 })
@@ -21,7 +20,6 @@ export const MessagesState = new StateSchema({
   date: z.string(),
   messages: MessagesValue,
   proposedTriggerEvent: ProposedTriggerEventArray,
-  evalTriggerEvent: ProposedTriggerEventArray,
   proposedTriggerEventIndex: z.int(),
   normalizedClaim: z.string(),
 });

agent/tools/retreiveExamples.ts

@@ -15,8 +15,6 @@ const CACHE_PATH = "../data/csv.cache.json";
 
 const JSONL_PATH = "../data/input.jsonl"
 
-const BM25_MIN_DOCS = 3;
-
 type EmbeddingCache = {
   rawtexts: string[];
   cleantexts: string[];
@@ -289,20 +287,8 @@ async function embedText(text: string): Promise<number[]> {
 }
 
 function buildBM25(texts: string[]) {
-  let paddedTexts = texts;
+  logger.info("Building BM25 index (%s docs)...", texts.length);
 
-  if (texts.length < BM25_MIN_DOCS) {
-    const needed = BM25_MIN_DOCS - texts.length;
-    logger.error(
-      "Corpus too small for BM25 (%s docs, need %s+), padding with %s dummy doc(s)",
-      texts.length,
-      BM25_MIN_DOCS,
-      needed
-    );
-    paddedTexts = [...texts, ...Array(needed).fill("placeholder dummy document")];
-  }
-
-  logger.info("Building BM25 index (%s docs)...", paddedTexts.length);
   const bm25 = bm25Factory();
 
   bm25.defineConfig({
@@ -316,7 +302,7 @@ function buildBM25(texts: string[]) {
     nlp.tokens.removeWords,
   ]);
 
-  paddedTexts.forEach((text, i) => {
+  texts.forEach((text, i) => {
     bm25.addDoc({ text }, i);
   });
 

agent/tools/robertaCall.ts

@@ -1,16 +1,13 @@
 import axios from "axios";
 
-export async function evaluateWithEnsemble({
+export async function evaluateWithRoberta({
-  answer,
+  answer
-  method
 }: {
   answer: string;
-  method: string
 }): Promise<{ validProb: number; invalidProb: number; }> {
   const res = await axios.post("http://localhost:8000/evaluate", {
-    answer,
+    answer
-    method
+  });
-  }, {timeout: 0});
   // console.log(res.data)
   const validProb = res.data["probabilities"][0][0]
   const invalidProb = res.data["probabilities"][0][1] + res.data["probabilities"][0][2]

agent/tools/webpageFetch.ts

@@ -1,92 +1,32 @@
 import { Builder, Browser } from "selenium-webdriver";
 import firefox from "selenium-webdriver/firefox";
-import { backOff } from "exponential-backoff";
-import { logger } from "../utils/logger";
 
-export async function extractWebpageContent(url: string): Promise<string[]> {
+export async function extractWebpageContent(url: string) : Promise<string[]>{
-  try {
-    const response = await backOff(async () => {
-      return await extractWebpageContentWorker(url);
-    }, {
-      numOfAttempts: 10,
-      startingDelay: 500,
-      timeMultiple: 2,
-      jitter: "full",
-      maxDelay: 50000,
-    });
-    return response;
-  } catch (err: any) {
-    logger.error(`Failed out of retry loop for URL "${url}", returning placeholder to pipeline`);
-    return ["API EXCEPTION"];
-  }
-}
-
-async function extractWebpageContentWorker(url: string): Promise<string[]> {
-  let driver;
-  try {
     const options = new firefox.Options();
     options.addArguments("--headless");
-    driver = await new Builder()
-      .forBrowser(Browser.FIREFOX)
-      .setFirefoxOptions(options)
-      .build();
-  } catch (err: any) {
-    const desc = `Failed to launch Firefox driver: ${err.message}`;
-    logger.error(desc);
-    throw new Error(desc);
-  }
 
-  try {
+    let driver = await new Builder().forBrowser(Browser.FIREFOX).setFirefoxOptions(options).build()
     try {
-      await driver.get(url);
+        await driver.get(url)
-    } catch (err: any) {
+        await driver.wait(async () => {
-      const desc = `Failed to navigate to URL "${url}": ${err.message}`;
+            return await driver.executeScript(
-      logger.error(desc);
+                "return document.readyState === 'complete'"
-      throw new Error(desc);
+            );
-    }
+        }, 5000);
 
-    try {
+        const readableText = await driver.executeScript(
-      await driver.wait(async () => {
+            "return document.body.innerText;"
-        return await driver.executeScript(
+        ) as string;
-          "return document.readyState === 'complete'"
-        );
-      }, 5000);
-    } catch (err: any) {
-      logger.error(`Page load timed out for "${url}", attempting to read partial content: ${err.message}`);
-      // do not throw, attempt to read
-    }
 
-    let readableText: string;
+        const filteredLines = readableText
-    try {
+            .split(/\r?\n/)
-      readableText = await driver.executeScript(
+            .map(line => line.trim())
-        "return document.body.innerText;"
+            .filter(line => line.split(/\s+/).length > 1); 
-      ) as string;
-    } catch (err: any) {
-      const desc = `Failed to extract page text from "${url}": ${err.message}`;
-      logger.error(desc);
-      throw new Error(desc);
-    }
         
-    const filteredLines = readableText
+        return filteredLines;
-      .split(/\r?\n/)
+    } finally {
-      .map(line => line.trim())
+        await driver.quit()
-      .filter(line => line.split(/\s+/).length > 1);
-
-    if (filteredLines.length === 0) {
-      const desc = `No content extracted from "${url}"`;
-      logger.error(desc);
-      throw new Error(desc);
     }
-
-    return filteredLines;
-  } finally {
-    try {
-      await driver.quit();
-    } catch (err: any) {
-      logger.error(`Failed to quit Firefox driver cleanly: ${err.message}`);
-    }
-  }
 }
 
-// console.log(await extractWebpageContent("https://www.bbc.co.uk/news/live/c74wd01egvyt"))
+//console.log(await extractWebpageContent("https://www.bbc.co.uk/news/live/c74wd01egvyt"))
-// console.log(await extractWebpageContent("https://badcertificate.int.jeynes.uk/"))

agent/verify.ts

@@ -1,35 +1,38 @@
 import { END, START, StateGraph } from "@langchain/langgraph";
 import { MessagesState } from "./state";
 import { verificationSetup } from "./nodes/verificationSetup";
+import { ragasMetrics } from "./nodes/ragasMetrics";
 import { produceRanking } from "./nodes/produceRanking";
+import { createModelNode } from "./nodes/model";
 import { loopEndConditional } from "./conditionals/loop_end";
 import { sort } from "./nodes/sort";
-import { createEnsembleNode } from "./nodes/ensembleNode";
+import { robertaMetrics } from "./nodes/robertaMetrics";
 
-const roNode = createEnsembleNode("ROBERTA", "roberta");
+const verificationModel = createModelNode([], "verify.txt");
-const flNode = createEnsembleNode("FLAN", "flan");
+const relationModel = createModelNode([], "relation.txt");
-const lrNode = createEnsembleNode("REGRESSION", "logreg");
 
 const agent = new StateGraph(MessagesState)
   
   //NODES
   .addNode(verificationSetup.name, verificationSetup)
-  .addNode("roNode", roNode)
+  // .addNode("verificationModel", verificationModel)
-  .addNode("flNode", flNode)
+  // .addNode(ragasMetrics.name, ragasMetrics)
-  .addNode("lrNode", lrNode)
+  .addNode(robertaMetrics.name, robertaMetrics)
+  // .addNode("relationModel", relationModel)
   
   .addNode(produceRanking.name, produceRanking)
   .addNode(sort.name, sort)
   
   .addEdge(START, verificationSetup.name)
+  // .addEdge(verificationSetup.name, "verificationModel")
+  // .addEdge(verificationSetup.name, ragasMetrics.name)
+  .addEdge(verificationSetup.name, robertaMetrics.name)
+  // .addEdge(verificationSetup.name, "relationModel")
 
-  .addEdge(verificationSetup.name, "roNode")
+  // .addEdge(ragasMetrics.name, produceRanking.name)
-  .addEdge(verificationSetup.name, "flNode")
+  .addEdge(robertaMetrics.name, produceRanking.name)
-  .addEdge(verificationSetup.name, "lrNode")
+  // .addEdge("verificationModel", produceRanking.name)
-
+  // .addEdge("relationModel", produceRanking.name)
-  .addEdge("roNode", produceRanking.name)
-  .addEdge("flNode", produceRanking.name)
-  .addEdge("lrNode", produceRanking.name)
 
   // @ts-expect-error
   .addConditionalEdges(produceRanking.name, loopEndConditional, [verificationSetup.name, sort.name])

run.sh

@@ -8,10 +8,10 @@ run_agent () {
     npx @langchain/langgraph-cli dev
 }
 
-run_ensemble_service () {
+run_ragas_service () {
-    echo "Starting Ensemble service..."
+    echo "Starting RAGAS service..."
     cd "supporting/RAGAS_Service"
-    .venv/bin/uvicorn ensemble_service:app --timeout-keep-alive 300
+    .venv/bin/uvicorn ragas_service:app --port 8001
 }
 
 run_frontend () {
@@ -34,13 +34,13 @@ run_wrapper () {
 
 case "$1" in
     agent) run_agent ;;
-    ensemble_service) run_ensemble_service ;;
+    ragas_service) run_ragas_service ;;
     frontend) run_frontend ;;
     fetch) run_fetch ;;
     wrapper) run_wrapper ;;
     *)
         echo "Unknown command: $1"
-        echo "Usage: ./runproject [agent|ensemble_service|frontend|fetch|wrapper]"
+        echo "Usage: ./runproject [agent|ragas_service|frontend|fetch|wrapper]"
         exit 1
         ;;
 esac

supporting/RAGAS_Service/.gitignore

@@ -1,9 +1,7 @@
 # -- OURS --
 results/
 roberta_classifier/
-roberta_distilled_classifier/
 roberta_classifier*/
-*.pt
 output*
 
 # -- THEIRS --

supporting/RAGAS_Service/README.md

@@ -1,25 +0,0 @@
-# Classifier work for evaluating model quality
-
-Made using a dataset of 1000 labeled claims from MVP pipeline.
-
-Roberta model trained on an augmented dataset with LLM generated adversarial examples for low frequency labels.
-
-Flan model trained using raw labelled claims, inherrent natural language ability allows for pattern recognition without the need for fake data.
-
-Regression model trained using the roberta dataset.
-
-Used ensemble model in the final version, with the component models available on Hugging Face. 
-
-| Model                                                      | % Correct | % Valid taken forward|Used in ensemble|Link
-|------------------------------------------------------------|-----------|----------------------|----------------|-
-| Original                                                   | 53.22     | 61.72                |
-| Original (RAGAS)                                           | 56.01     | 57.73                |
-| Roberta (base)                                             | 75        | 70                   |
-| Roberta (Generated Data)                                   | 76        | 71                   |
-| Roberta (Generated Data + Back Translation)                | 74        | 71                   |
-| Roberta (Generated Data + Back Translation + Thresholding) | 77        | 90                   |Y|[Here](https://huggingface.co/WillJeynes/LLMsForDisinformationAnalysis)
-| Distilled Roberta                                          | 72.73     | 69.57                |
-| Flan                                                       | 79.17     | 85.71                |Y|[Here](https://huggingface.co/WillJeynes/LLMsForDisinformationAnalysis-Flan)
-| Simple Regression Model                                    | 74.77     | 85.71                |Y|[Here](https://huggingface.co/WillJeynes/LLMsForDisinformationAnalysis-Regression)
-| Ensemble Model (weighted confidence score sum)             | 84.21     | 83.33                |
-| Ensemble Model (majority voting)                           | 80.2      | 95.12                |

supporting/RAGAS_Service/ensemble_service.py

@@ -1,224 +0,0 @@
-from pydantic import BaseModel
-from fastapi import FastAPI
-import torch
-import torch.nn as nn
-import os
-from sentence_transformers import SentenceTransformer
-from huggingface_hub import hf_hub_download
-from transformers import RobertaTokenizer, RobertaForSequenceClassification
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-
-app = FastAPI()
-
-############################################
-# SCHEMA
-############################################
-
-class EvalRequest(BaseModel):
-    answer: str
-    method: str  # "logreg", "roberta", "flan"
-
-
-############################################
-# REGRESSION MODEL
-############################################
-
-HF_REPO_ID = "WillJeynes/LLMsForDisinformationAnalysis-Regression"
-MODEL_FILENAME = "logreg_classifier.pt"
-CACHE_DIR = "./model_cache"
-
-
-def load_checkpoint(repo_id: str, filename: str, cache_dir: str) -> dict:
-    local_path = os.path.join(cache_dir, filename)
-    if not os.path.exists(local_path):
-        os.makedirs(cache_dir, exist_ok=True)
-        hf_hub_download(repo_id=repo_id, filename=filename, local_dir=cache_dir)
-    return torch.load(local_path, map_location="cpu")
-
-
-class LogisticNet(nn.Module):
-    def __init__(self, input_dim, hidden_dim, num_classes, dropout):
-        super().__init__()
-        self.net = nn.Sequential(
-            nn.Linear(input_dim, hidden_dim),
-            nn.BatchNorm1d(hidden_dim),
-            nn.ReLU(),
-            nn.Dropout(dropout),
-            nn.Linear(hidden_dim, num_classes),
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-
-checkpoint = load_checkpoint(HF_REPO_ID, MODEL_FILENAME, CACHE_DIR)
-
-encoder = SentenceTransformer(checkpoint["embedding_model"])
-
-logreg_model = LogisticNet(
-    checkpoint["input_dim"],
-    checkpoint["hidden_dim"],
-    checkpoint["num_classes"],
-    checkpoint["dropout"],
-)
-logreg_model.load_state_dict(checkpoint["model_state"])
-logreg_model.eval()
-
-
-############################################
-# ROBERTA
-############################################
-
-ROBERTA_PATH = "WillJeynes/LLMsForDisinformationAnalysis"
-
-roberta_tokenizer = RobertaTokenizer.from_pretrained(ROBERTA_PATH)
-roberta_model = RobertaForSequenceClassification.from_pretrained(ROBERTA_PATH)
-roberta_model.eval()
-
-
-############################################
-# FLAN
-############################################
-
-FLAN_PATH = "WillJeynes/LLMsForDisinformationAnalysis-Flan"
-
-INT_TO_LABEL = {
-    0: "perfect",
-    1: "story",
-    2: "not specific",
-}
-LABEL_TO_INT = {v: k for k, v in INT_TO_LABEL.items()}
-
-flan_tokenizer = AutoTokenizer.from_pretrained(FLAN_PATH)
-flan_model = AutoModelForSeq2SeqLM.from_pretrained(FLAN_PATH)
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-flan_model.to(device)
-flan_model.eval()
-
-label_token_ids = {
-    label: flan_tokenizer(label, add_special_tokens=False).input_ids[0]
-    for label in LABEL_TO_INT.keys()
-}
-
-
-def format_prompt(text: str) -> str:
-    return (
-        "Classify the following event into one of these categories: "
-        "perfect, story, not specific.\n\n"
-        f"Event: {text}\n\n"
-        "Category:"
-    )
-
-
-def parse_generated_label(generated: str):
-    generated = generated.strip().lower()
-    for label_text, label_int in LABEL_TO_INT.items():
-        if label_text in generated:
-            return label_int
-    return None
-
-
-############################################
-# ENDPOINT
-############################################
-
-@app.post("/evaluate")
-def evaluate(req: EvalRequest):
-    method = req.method.lower()
-
-    ########################################
-    # LOGREG
-    ########################################
-    if method == "logreg":
-        embedding = encoder.encode(
-            [req.answer],
-            normalize_embeddings=True,
-            show_progress_bar=False,
-        )
-
-        x = torch.tensor(embedding, dtype=torch.float32)
-
-        with torch.no_grad():
-            logits = logreg_model(x)
-
-        probs = torch.softmax(logits, dim=1)
-
-        return {
-            "method": "logreg",
-            "probabilities": probs.cpu().numpy().tolist()
-        }
-
-    ########################################
-    # ROBERTA
-    ########################################
-    elif method == "roberta":
-        inputs = roberta_tokenizer(
-            req.answer,
-            return_tensors="pt",
-            truncation=True,
-            padding=True
-        )
-
-        with torch.no_grad():
-            logits = roberta_model(**inputs).logits
-
-        probs = torch.softmax(logits, dim=1)
-
-        return {
-            "method": "roberta",
-            "probabilities": probs.cpu().numpy().tolist()
-        }
-
-    ########################################
-    # FLAN
-    ########################################
-    elif method == "flan":
-        prompt = format_prompt(req.answer)
-
-        inputs = flan_tokenizer(
-            prompt,
-            return_tensors="pt",
-            truncation=True,
-            padding=True,
-            max_length=256,
-        ).to(device)
-
-        with torch.no_grad():
-            outputs = flan_model.generate(**inputs, max_new_tokens=8)
-
-            decoder_input_ids = torch.tensor(
-                [[flan_model.config.decoder_start_token_id]]
-            ).to(device)
-
-            logits_output = flan_model(
-                **inputs,
-                decoder_input_ids=decoder_input_ids
-            )
-
-            logits = logits_output.logits[:, 0, :]
-
-        generated_text = flan_tokenizer.decode(
-            outputs[0],
-            skip_special_tokens=True
-        )
-
-        label_logits = torch.tensor(
-            [logits[0, tid].item() for tid in label_token_ids.values()]
-        )
-
-        label_probs = torch.softmax(label_logits, dim=0).tolist()
-
-        return {
-            "method": "flan",
-            "generated": generated_text,
-            "probabilities": [label_probs],
-        }
-
-    ########################################
-    # INVALID METHOD
-    ########################################
-    else:
-        return {
-            "error": "Invalid method. Use 'logreg', 'roberta', or 'flan'."
-        }

supporting/RAGAS_Service/flan_service.py

@@ -1,89 +0,0 @@
-from pydantic import BaseModel
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-import torch
-from fastapi import FastAPI
-
-app = FastAPI()
-
-MODEL_PATH = "WillJeynes/LLMsForDisinformationAnalysis-Flan"
-
-INT_TO_LABEL = {
-    0: "perfect",
-    1: "story",
-    2: "not specific",
-}
-
-LABEL_TO_INT = {v: k for k, v in INT_TO_LABEL.items()}
-
-tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
-model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_PATH)
-model.eval()
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device)
-
-
-def format_prompt(text: str) -> str:
-    return (
-        "Classify the following event into one of these categories: "
-        "perfect, story, not specific.\n\n"
-        f"Event: {text}\n\n"
-        "Category:"
-    )
-
-
-def parse_generated_label(generated: str) -> int | None:
-    generated = generated.strip().lower()
-    for label_text, label_int in LABEL_TO_INT.items():
-        if label_text in generated:
-            return label_int
-    return None
-
-
-class EvalRequest(BaseModel):
-    answer: str
-
-
-@app.post("/evaluate")
-def evaluate(req: EvalRequest):
-    prompt = format_prompt(req.answer)
-
-    inputs = tokenizer(
-        prompt,
-        return_tensors="pt",
-        truncation=True,
-        padding=True,
-        max_length=256,
-    ).to(device)
-
-    with torch.no_grad():
-        # Get the generated label
-        outputs = model.generate(
-            **inputs,
-            max_new_tokens=8,
-        )
-
-        # Produce a confidence score
-        decoder_input_ids = torch.tensor([[model.config.decoder_start_token_id]]).to(device)
-        logits_output = model(**inputs, decoder_input_ids=decoder_input_ids)
-        logits = logits_output.logits[:, 0, :]
-
-    # Decode the generated text label
-    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    predicted_int = parse_generated_label(generated_text)
-
-    # Extract probabilities
-    label_token_ids = {
-        label: tokenizer(label, add_special_tokens=False).input_ids[0]
-        for label in LABEL_TO_INT.keys()
-    }
-
-    label_logits = torch.tensor(
-        [logits[0, tid].item() for tid in label_token_ids.values()]
-    )
-    label_probs = torch.softmax(label_logits, dim=0).tolist()
-
-    return {
-        "generated": generated_text,
-        "probabilities": [label_probs],
-    }

supporting/RAGAS_Service/regression_service.py

@@ -1,82 +0,0 @@
-from pydantic import BaseModel
-from sentence_transformers import SentenceTransformer
-from fastapi import FastAPI
-import torch
-import torch.nn as nn
-from huggingface_hub import hf_hub_download
-import os
-
-app = FastAPI()
-
-HF_REPO_ID = "WillJeynes/LLMsForDisinformationAnalysis-Regression"
-MODEL_FILENAME = "logreg_classifier.pt"
-CACHE_DIR = "./model_cache"
-
-
-def load_checkpoint(repo_id: str, filename: str, cache_dir: str) -> dict:
-    local_path = os.path.join(cache_dir, filename)
-    if not os.path.exists(local_path):
-        print(f"Downloading {filename} from {repo_id}...")
-        os.makedirs(cache_dir, exist_ok=True)
-        downloaded = hf_hub_download(
-            repo_id=repo_id,
-            filename=filename,
-            local_dir=cache_dir,
-        )
-        print(f"Saved to {downloaded}")
-    else:
-        print(f"Using cached model at {local_path}")
-    return torch.load(local_path, map_location="cpu")
-
-
-class LogisticNet(nn.Module):
-    def __init__(self, input_dim: int, hidden_dim: int, num_classes: int, dropout: float):
-        super().__init__()
-        self.net = nn.Sequential(
-            nn.Linear(input_dim, hidden_dim),
-            nn.BatchNorm1d(hidden_dim),
-            nn.ReLU(),
-            nn.Dropout(dropout),
-            nn.Linear(hidden_dim, num_classes),
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-
-checkpoint = load_checkpoint(HF_REPO_ID, MODEL_FILENAME, CACHE_DIR)
-
-encoder = SentenceTransformer(checkpoint["embedding_model"])
-
-model = LogisticNet(
-    input_dim   = checkpoint["input_dim"],
-    hidden_dim  = checkpoint["hidden_dim"],
-    num_classes = checkpoint["num_classes"],
-    dropout     = checkpoint["dropout"],
-)
-model.load_state_dict(checkpoint["model_state"])
-model.eval()
-
-
-class EvalRequest(BaseModel):
-    answer: str
-
-
-@app.post("/evaluate")
-def evaluate(req: EvalRequest):
-    embedding = encoder.encode(
-        [req.answer],
-        normalize_embeddings=True,
-        show_progress_bar=False,
-    )
-
-    x = torch.tensor(embedding, dtype=torch.float32)
-
-    with torch.no_grad():
-        logits = model(x)
-
-    probs = torch.softmax(logits, dim=1)
-
-    return {
-        "probabilities": probs.cpu().numpy().tolist()
-    }

supporting/RAGAS_Service/roberta_service.py

@@ -5,7 +5,7 @@ from fastapi import FastAPI
 
 app = FastAPI()
 
-MODEL_PATH = "WillJeynes/LLMsForDisinformationAnalysis"
+MODEL_PATH = "./roberta_classifier"
 
 tokenizer = RobertaTokenizer.from_pretrained(MODEL_PATH)
 model = RobertaForSequenceClassification.from_pretrained(MODEL_PATH)

supporting/RAGAS_Service/train_flan.py

@@ -1,227 +0,0 @@
-from sklearn.utils import compute_class_weight
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, Seq2SeqTrainer, Seq2SeqTrainingArguments, DataCollatorForSeq2Seq
-import torch
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
-from collections import Counter
-import sys
-import csv
-import numpy as np
-
-NUM_CLASSES = 3
-model_name = "google/flan-t5-base"
-
-INT_TO_LABEL = {
-    0: "perfect",
-    1: "story",
-    2: "not specific",
-}
-LABEL_TO_INT = {v: k for k, v in INT_TO_LABEL.items()}
-
-LABEL_PRIORITY = [
-    ("PERFECT", 0),
-    ("STORY", 1),
-    ("NSPECIFIC", 2),
-    ("REWORDING", 1),
-    ("TINCORRECT", -1),
-    ("DUPLICATE", -1),
-    ("", 0),
-]
-
-def label_to_int(extra_info: str) -> int:
-    if extra_info is None:
-        extra_info = ""
-    extra_info = extra_info.strip()
-    if extra_info == "":
-        for key, value in LABEL_PRIORITY:
-            if key == "":
-                return value
-        raise ValueError("Empty extra_info but no empty mapping defined")
-    tokens = set(extra_info.upper().split())
-    for key, value in LABEL_PRIORITY:
-        if key == "" :
-            continue
-        if key in tokens:
-            return value
-    raise ValueError(f"Unknown label content: '{extra_info}'")
-
-
-def load_dataset_from_csv(path):
-    texts = []
-    labels = []
-    removed_rows = 0
-    with open(path, newline="", encoding="utf-8") as f:
-        reader = csv.DictReader(f)
-        for i, row in enumerate(reader, start=1):
-            text = row["event"]
-            label_str = row["extra_info"]
-            try:
-                label_int = label_to_int(label_str)
-            except Exception as e:
-                print(f"ERROR converting label on line {i}: {label_str}")
-                print(e)
-                sys.exit(1)
-            if label_int == -1:
-                removed_rows += 1
-                continue
-            texts.append(text)
-            labels.append(label_int)
-    print(f"Loaded {len(texts)} samples (removed {removed_rows})")
-    return texts, labels
-
-
-def format_prompt(text: str) -> str:
-    return (
-        "Classify the following event into one of these categories: "
-        "perfect, story, not specific.\n\n"
-        f"Event: {text}\n\n"
-        "Category:"
-    )
-
-
-def parse_generated_label(generated: str) -> int:
-    generated = generated.strip().lower()
-    for label_text, label_int in LABEL_TO_INT.items():
-        if label_text in generated:
-            return label_int
-    print("invlid label:" +  generated)
-    return -1  # unknown / unparseable output
-
-
-class GenerativeTextDataset(torch.utils.data.Dataset):
-    def __init__(self, texts, labels, tokenizer, max_input_length=256, max_target_length=8):
-        self.tokenizer = tokenizer
-        self.max_input_length = max_input_length
-        self.max_target_length = max_target_length
-
-        self.inputs = [format_prompt(t) for t in texts]
-        # Convert integer labels to their text equivalents for the target sequence
-        self.targets = [INT_TO_LABEL[l] for l in labels]
-        self.int_labels = labels  # keep originals for metric computation
-
-    def __len__(self):
-        return len(self.inputs)
-
-    def __getitem__(self, idx):
-        model_inputs = self.tokenizer(
-            self.inputs[idx],
-            max_length=self.max_input_length,
-            truncation=True,
-            padding=False,
-        )
-        target_encoding = self.tokenizer(
-            self.targets[idx],
-            max_length=self.max_target_length,
-            truncation=True,
-            padding=False,
-        )
-        # Seq2Seq convention: labels use -100 to ignore padding tokens in loss
-        labels = target_encoding["input_ids"]
-        labels = [token if token != self.tokenizer.pad_token_id else -100 for token in labels]
-
-        model_inputs["labels"] = labels
-        return {k: torch.tensor(v) for k, v in model_inputs.items()}
-
-
-def compute_metrics_generative(eval_pred, tokenizer):
-    predictions, label_ids = eval_pred
-
-    # Decode predictions 
-    # Replace -100 in labels before decoding
-    label_ids = np.where(label_ids != -100, label_ids, tokenizer.pad_token_id)
-
-    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)
-    decoded_labels = tokenizer.batch_decode(label_ids, skip_special_tokens=True)
-
-    # Map decoded text back to integer labels
-    pred_ints = [parse_generated_label(p) for p in decoded_preds]
-    true_ints = [parse_generated_label(l) for l in decoded_labels]
-
-    # Filter out any rows where parsing failed
-    valid = [(p, t) for p, t in zip(pred_ints, true_ints) if t != -1]
-    if not valid:
-        return {"accuracy": 0.0, "f1": 0.0, "precision": 0.0, "recall": 0.0}
-
-    preds_filtered, true_filtered = zip(*valid)
-
-    return {
-        "accuracy": accuracy_score(true_filtered, preds_filtered),
-        "f1": f1_score(true_filtered, preds_filtered, average="weighted", zero_division=0),
-        "precision": precision_score(true_filtered, preds_filtered, average="weighted", zero_division=0),
-        "recall": recall_score(true_filtered, preds_filtered, average="weighted", zero_division=0),
-    }
-
-
-def main():
-    torch.multiprocessing.set_start_method('spawn', force=True) 
-    print("CUDA available:", torch.cuda.is_available())
-    print("CUDA device count:", torch.cuda.device_count())
-
-    texts, labels = load_dataset_from_csv("../../data/classify.csv")
-
-    print("Dataset size:", len(texts))
-    print("Label distribution:", Counter(labels))
-
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-
-    train_texts, val_texts, train_labels, val_labels = train_test_split(
-        texts, labels,
-        test_size=0.2,
-        random_state=42,
-        stratify=labels
-    )
-
-    train_dataset = GenerativeTextDataset(train_texts, train_labels, tokenizer)
-    val_dataset = GenerativeTextDataset(val_texts, val_labels, tokenizer)
-
-    data_collator = DataCollatorForSeq2Seq(
-        tokenizer=tokenizer,
-        model=model,
-        padding=True,
-        label_pad_token_id=-100,
-    )
-
-    training_args = Seq2SeqTrainingArguments(
-        output_dir="./results",
-        learning_rate=5e-5,
-        per_device_train_batch_size=16,
-        per_device_eval_batch_size=16,
-        num_train_epochs=10,
-        weight_decay=0.01,
-        eval_strategy="epoch",
-        save_strategy="epoch",
-        load_best_model_at_end=True,
-        metric_for_best_model="f1",
-        greater_is_better=True,
-        predict_with_generate=True,
-        generation_max_length=8,
-        dataloader_num_workers=0,
-        dataloader_pin_memory=False,
-        fp16=False,
-        max_grad_norm=1.0,
-    )
-
-    trainer = Seq2SeqTrainer(
-        model=model,
-        args=training_args,
-        train_dataset=train_dataset,
-        eval_dataset=val_dataset,
-        processing_class=tokenizer,
-        data_collator=data_collator,
-        compute_metrics=lambda ep: compute_metrics_generative(ep, tokenizer),
-    )
-
-    trainer.train()
-
-    metrics = trainer.evaluate()
-    print("\nFinal evaluation metrics:")
-    for k, v in metrics.items():
-        print(f"  {k}: {v}")
-
-    trainer.save_model("./flan_classifier")
-    tokenizer.save_pretrained("./flan_classifier")
-
-
-if __name__ == "__main__":
-    main()

supporting/RAGAS_Service/train_regression.py

@@ -1,209 +0,0 @@
-from sentence_transformers import SentenceTransformer
-from sklearn.model_selection import train_test_split
-from sklearn.utils import compute_class_weight
-from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
-from collections import Counter
-import torch
-import torch.nn as nn
-import torch.optim as optim
-from torch.utils.data import DataLoader, TensorDataset
-import numpy as np
-import csv
-import sys
-
-NUM_CLASSES    = 3
-EMBEDDING_MODEL = "all-mpnet-base-v2"
-HIDDEN_DIM     = 256
-DROPOUT        = 0.4
-LEARNING_RATE  = 2e-3
-WEIGHT_DECAY   = 1e-4
-BATCH_SIZE     = 64
-NUM_EPOCHS     = 30
-PATIENCE       = 5
-
-LABEL_PRIORITY = [
-    ("PERFECT",    0),
-    ("STORY",      1),
-    ("NSPECIFIC",  2),
-    ("REWORDING",  1),
-    ("TINCORRECT", -1),
-    ("DUPLICATE",  -1),
-    ("",           0),   # fallback to PERFECT
-]
-
-
-def label_to_int(extra_info: str) -> int:
-    if extra_info is None:
-        extra_info = ""
-    extra_info = extra_info.strip()
-
-    if extra_info == "":
-        for key, value in LABEL_PRIORITY:
-            if key == "":
-                return value
-        raise ValueError("No empty-string fallback defined in LABEL_PRIORITY")
-
-    tokens = set(extra_info.upper().split())
-    for key, value in LABEL_PRIORITY:
-        if key and key in tokens:
-            return value
-
-    raise ValueError(f"Unknown label content: '{extra_info}'")
-
-
-def load_dataset_from_csv(path: str):
-    texts, labels = [], []
-    removed = 0
-
-    with open(path, newline="", encoding="utf-8") as f:
-        for i, row in enumerate(csv.DictReader(f), start=1):
-            try:
-                label_int = label_to_int(row["extra_info"])
-            except Exception as e:
-                print(f"ERROR on line {i}: {row['extra_info']!r}")
-                print(e)
-                sys.exit(1)
-
-            if label_int == -1:
-                removed += 1
-                continue
-
-            texts.append(row["event"])
-            labels.append(label_int)
-
-    print(f"Loaded {len(texts)} samples  (removed {removed})")
-    return texts, labels
-
-
-class LogisticNet(nn.Module):
-    def __init__(self, input_dim: int, hidden_dim: int, num_classes: int, dropout: float):
-        super().__init__()
-        self.net = nn.Sequential(
-            nn.Linear(input_dim, hidden_dim),
-            nn.BatchNorm1d(hidden_dim),
-            nn.ReLU(),
-            nn.Dropout(dropout),
-            nn.Linear(hidden_dim, num_classes),   # raw logits – loss handles softmax
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-
-def evaluate(model, loader, device):
-    model.eval()
-    all_preds, all_labels = [], []
-
-    with torch.no_grad():
-        for xb, yb in loader:
-            xb, yb = xb.to(device), yb.to(device)
-            logits = model(xb)
-            preds  = logits.argmax(dim=1).cpu().numpy()
-            all_preds.extend(preds)
-            all_labels.extend(yb.cpu().numpy())
-
-    return {
-        "accuracy":  accuracy_score(all_labels, all_preds),
-        "f1":        f1_score(all_labels, all_preds, average="weighted", zero_division=0),
-        "precision": precision_score(all_labels, all_preds, average="weighted", zero_division=0),
-        "recall":    recall_score(all_labels, all_preds, average="weighted", zero_division=0),
-    }
-
-
-def main():
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    print(f"Using device: {device}")
-
-    texts, labels = load_dataset_from_csv("../../data/classify.csv")
-    print("Label distribution:", Counter(labels))
-
-    print(f"\nEncoding with '{EMBEDDING_MODEL}' …")
-    encoder = SentenceTransformer(EMBEDDING_MODEL)
-    embeddings = encoder.encode(texts, batch_size=64, show_progress_bar=True, normalize_embeddings=True)
-    input_dim = embeddings.shape[1]
-    print(f"Embedding dim: {input_dim}")
-
-    X_train, X_val, y_train, y_val = train_test_split(
-        embeddings, labels, test_size=0.2, random_state=42, stratify=labels
-    )
-
-    class_weights = compute_class_weight("balanced", classes=np.unique(y_train), y=y_train)
-    weight_tensor  = torch.tensor(class_weights, dtype=torch.float).to(device)
-    print("Class weights:", class_weights)
-
-    def make_loader(X, y, shuffle=False):
-        ds = TensorDataset(
-            torch.tensor(X, dtype=torch.float32),
-            torch.tensor(y, dtype=torch.long),
-        )
-        return DataLoader(ds, batch_size=BATCH_SIZE, shuffle=shuffle)
-
-    train_loader = make_loader(X_train, y_train, shuffle=True)
-    val_loader   = make_loader(X_val,   y_val,   shuffle=False)
-
-    model     = LogisticNet(input_dim, HIDDEN_DIM, NUM_CLASSES, DROPOUT).to(device)
-    criterion = nn.CrossEntropyLoss(weight=weight_tensor)
-    optimizer = optim.AdamW(model.parameters(), lr=LEARNING_RATE, weight_decay=WEIGHT_DECAY)
-    scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=NUM_EPOCHS)
-
-    best_f1       = 0.0
-    best_state    = None
-    epochs_no_imp = 0
-
-    print("\n Training:")
-    for epoch in range(1, NUM_EPOCHS + 1):
-        model.train()
-        total_loss = 0.0
-
-        for xb, yb in train_loader:
-            xb, yb = xb.to(device), yb.to(device)
-            optimizer.zero_grad()
-            loss = criterion(model(xb), yb)
-            loss.backward()
-            optimizer.step()
-            total_loss += loss.item() * len(yb)
-
-        scheduler.step()
-        avg_loss = total_loss / len(train_loader.dataset)
-        val_metrics = evaluate(model, val_loader, device)
-
-        print(
-            f"Epoch {epoch:3d}/{NUM_EPOCHS} | "
-            f"loss {avg_loss:.4f} | "
-            f"val_acc {val_metrics['accuracy']:.4f} | "
-            f"val_f1 {val_metrics['f1']:.4f}"
-        )
-
-        # Early stopping on weighted F1
-        if val_metrics["f1"] > best_f1:
-            best_f1    = val_metrics["f1"]
-            best_state = {k: v.clone() for k, v in model.state_dict().items()}
-            epochs_no_imp = 0
-        else:
-            epochs_no_imp += 1
-            if epochs_no_imp >= PATIENCE:
-                print(f"Early stopping at epoch {epoch} (no improvement for {PATIENCE} epochs)")
-                break
-
-    print("\n Final evaluation:")
-    model.load_state_dict(best_state)
-    final = evaluate(model, val_loader, device)
-    for k, v in final.items():
-        print(f"  {k}: {v:.4f}")
-
-    torch.save(
-        {
-            "model_state":   best_state,
-            "input_dim":     input_dim,
-            "hidden_dim":    HIDDEN_DIM,
-            "num_classes":   NUM_CLASSES,
-            "dropout":       DROPOUT,
-            "embedding_model": EMBEDDING_MODEL,
-        },
-        "logreg_classifier.pt"
-    )
-    print("\n Model saved to logreg_classifier.pt")
-
-
-if __name__ == "__main__":
-    main()

supporting/RAGAS_Service/train_roberta.py

@@ -1,6 +1,6 @@
 from sklearn.utils import compute_class_weight
 from torch.nn import CrossEntropyLoss
-from transformers import RobertaTokenizer, RobertaForSequenceClassification, Trainer, TrainingArguments
+from transformers import RobertaTokenizer, RobertaForSequenceClassification, Trainer, TrainingArguments, AutoTokenizer, AutoModelForSequenceClassification
 import torch
 from sklearn.model_selection import train_test_split
 from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
@@ -10,7 +10,7 @@ import csv
 import numpy as np
 
 NUM_CLASSES = 3
-model_name = "roberta-base"
+model_name = "distilbert/distilroberta-base"
 
 LABEL_PRIORITY = [
     ("PERFECT", 0),
@@ -29,12 +29,21 @@ class WeightedTrainer(Trainer):
 
     def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
         labels = inputs.get("labels")
+        # print("DBG: Before forward")
         outputs = model(**inputs)
+        # print("DBG: After forward")
         logits = outputs.get("logits")
 
-        loss_fct = CrossEntropyLoss(weight=self.class_weights.to(logits.device))
+        # loss_fct = CrossEntropyLoss(weight=self.class_weights.to(logits.device))
-        loss = loss_fct(logits, labels)
+        loss_fct = CrossEntropyLoss(
+            weight=self.class_weights.to(logits.device).to(logits.dtype)
+        )
+        # loss_fct = CrossEntropyLoss()
         
+        # print("DBG: Before loss")
+        loss = loss_fct(logits, labels)
+        # loss.backward()
+        # print("DBG: After loss")
         return (loss, outputs) if return_outputs else loss
 
 def label_to_int(extra_info: str) -> int:
@@ -120,17 +129,23 @@ def main():
     print("Current device:", torch.cuda.current_device() if torch.cuda.is_available() else "CPU")
     texts, labels = load_dataset_from_csv("../../data/classify.csv")
 
-    tokenizer = RobertaTokenizer.from_pretrained(model_name, hidden_dropout_prob=0.2,attention_probs_dropout_prob=0.2)
+    # tokenizer = RobertaTokenizer.from_pretrained(model_name, hidden_dropout_prob=0.2,attention_probs_dropout_prob=0.2)
-    model = RobertaForSequenceClassification.from_pretrained(
+    # model = RobertaForSequenceClassification.from_pretrained(
+    #     model_name,
+    #     num_labels=NUM_CLASSES
+    # )
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+    model = AutoModelForSequenceClassification.from_pretrained(
         model_name,
         num_labels=NUM_CLASSES
     )
 
-    for param in model.roberta.parameters():
+    # for param in model.deberta.parameters():
-        param.requires_grad = False
+    #     param.requires_grad = True
 
-    for param in model.roberta.encoder.layer[-6:].parameters():
+    # for param in model.deberta.encoder.layer[-6:].parameters():
-        param.requires_grad = True
+    #     param.requires_grad = True
 
     print("Dataset size:", len(texts))
     print("Label distribution:")
@@ -140,7 +155,8 @@ def main():
         texts,
         labels,
         test_size=0.2,
-        random_state=42
+        random_state=42,
+        stratify=labels
     )
 
 
@@ -173,6 +189,7 @@ def main():
             self.labels = labels
 
         def __getitem__(self, idx):
+            # print(f"DBG: Loading item {idx}")
             item = {
                 key: torch.tensor(val[idx])
                 for key, val in self.encodings.items()
@@ -187,7 +204,8 @@ def main():
         output_dir="./results",
         learning_rate=2e-5,
         per_device_train_batch_size=32,
-        num_train_epochs=5,
+        # gradient_accumulation_steps=2,
+        num_train_epochs=15,
         weight_decay=0.01,
         load_best_model_at_end=True,
         eval_strategy="epoch",
@@ -195,7 +213,8 @@ def main():
         metric_for_best_model="f1",
         greater_is_better=True,
         dataloader_num_workers=4,
-        dataloader_pin_memory=True
+        dataloader_pin_memory=True,
+        # warmup_steps=100,
     )
 
     train_dataset = TextDataset(train_encodings, train_labels)
@@ -218,8 +237,8 @@ def main():
     for k, v in metrics.items():
         print(f"{k}: {v}")
 
-    trainer.save_model("./roberta_classifier")
+    trainer.save_model("./roberta_distilled_classifier")
-    tokenizer.save_pretrained("./roberta_classifier")
+    tokenizer.save_pretrained("./roberta_distilled_classifier")
 
 
 

supporting/Wrapper/run.ts

@@ -118,7 +118,7 @@ async function processRecord(record: any): Promise<ResultRecord> {
       input: buildAgentInput(record),
       streamMode: "values",
       config: {
-        recursion_limit: 100
+        recursion_limit: 50
       }
     });
 

supporting/dbkf/fetch.py

@@ -16,18 +16,18 @@ BASE_URL = "https://dbkf.ontotext.com/rest-api/search/documents"
 
 # "documentTypes": "http://schema.org/Claim",
 DEFAULT_PARAMS = [
-    ("documentTypes", "http://schema.org/Claim"),
+    ("concept", "http://weverify.eu/resource/Concept/Q212"),
     ("from", "2000-01-01"),
     ("to", "2026-02-19"),
     ("lang", "en"),
-    ("limit", 7000),
+    ("limit", 5000),
     ("page", 1),
     ("orderBy", "date"),
-    ("organization", "http://weverify.eu/resource/Organization/128573c5d49d37558706194e755f152d"), # Science Direct
     ("organization", "http://weverify.eu/resource/Organization/3727f7b2aa90ec0716693e5464b28d18"), # StopFake
+    ("organization", "http://weverify.eu/resource/Organization/c71953fa6cf24ac4178f751c77862070"), # CheckYourFact
 ]
 
-NUM_RANDOM_CLAIMS = 200
+NUM_RANDOM_CLAIMS = 40
 
 INPUT_FILE = "../../data/input.jsonl"
 OUTPUT_FILE = "../../data/claims.json"

supporting/scorer/views/stats.py

@@ -5,6 +5,7 @@ import streamlit as st
 import pandas as pd
 import matplotlib.pyplot as plt
 
+# THRESH = 0.4
 THRESH = 0.6
 
 def page_title() -> str:
@@ -60,18 +61,6 @@ def render():
         return
 
     for file_path in jsonl_files:
-        thresh = THRESH
-        if ("flan" in file_path.name):
-            thresh = 0.94
-        if ("regression" in file_path.name):
-            thresh = 0.75
-        if ("ensemble" in file_path.name):
-            thresh = 0.1
-        if ("ensemble" in file_path.name and "2" in file_path.name):
-            thresh = 0.4
-        if ("ensemble" in file_path.name and "vot" in file_path.name):
-            thresh = 0.7
-
         st.subheader(f"File: {file_path.name}")
 
         confidence_counter = Counter()
@@ -97,15 +86,15 @@ def render():
                             dup_counter += 1
                         elif "ranked" not in event:
                             "ignore for now"
-                        elif score > thresh and extra_lower == "perfect":
+                        elif score > THRESH and extra_lower == "perfect":
                             confidence_counter["Correct-PERFECT"] += 1
-                        elif score > thresh and extra_lower == "":
+                        elif score > THRESH and extra_lower == "":
                             confidence_counter["Correct-FINE"] += 1
-                        elif score > thresh and extra_lower != "perfect" and extra_lower != "":
+                        elif score > THRESH and extra_lower != "perfect" and extra_lower != "":
                             confidence_counter["Over-confident"] += 1
                             wrong_counter[extra_lower] += 1
                             overconfident_docs.append(doc_id)
-                        elif score < thresh and (extra_lower == "perfect" or extra_lower == ""):
+                        elif score < THRESH and (extra_lower == "perfect" or extra_lower == ""):
                             confidence_counter["Under-confident"] += 1
                             underconfident_docs.append(doc_id)
                         else:

supporting/scorer/views/stats2.py

@@ -1,78 +0,0 @@
-from collections import Counter
-from pathlib import Path
-import json
-import streamlit as st
-import pandas as pd
-import matplotlib.pyplot as plt
-
-THRESH = 0.4
-
-def page_title() -> str:
-    return "Statistics 2"
-
-def render():
-    st.header("Statistics 2")
-
-    path = Path("../../data/refinement")
-
-    if not path.exists() or not path.is_dir():
-        st.error("Invalid folder path.")
-        return
-
-    jsonl_files = sorted(path.glob("*.jsonl"))
-    if not jsonl_files:
-        st.info("No .jsonl files found in this folder.")
-        return
-
-    for file_path in jsonl_files:
-        thresh = THRESH
-        st.subheader(f"File: {file_path.name}")
-
-        confidence_counter = Counter()
-        
-        # ---- Read file line by line ----
-        with open(file_path, "r", encoding="utf-8") as f:
-            for line in f:
-                try:
-                    entry = json.loads(line)
-                except json.JSONDecodeError:
-                    continue
-                if (entry.get("status") != "success"):
-                    confidence_counter["Crash"] += 1
-                for event in entry.get("events", []):
-                    score = event.get("score", None)
-                    
-                    if score is not None:
-                        if score == -1:
-                            confidence_counter["BAD-1"] += 1
-                        elif score > thresh:
-                            confidence_counter["PERFECT"] += 1
-                        else:
-                            confidence_counter["BAD"] += 1
-                        
-        if confidence_counter:
-            df_conf = pd.DataFrame(
-                confidence_counter.items(),
-                columns=["Category", "Count"]
-            )
-
-            fig, ax = plt.subplots()
-            ax.pie(
-                df_conf["Count"],
-                labels=df_conf["Category"],
-                autopct="%1.1f%%",
-                startangle=90
-            )
-            ax.axis("equal")
-            ax.set_title(file_path.name)
-
-            total = sum(confidence_counter.values())
-            correct = confidence_counter["PERFECT"]
-
-            corr_percent = (correct / total) * 100
-            
-            st.markdown(f"**Correct: {corr_percent:.2f}% ({correct}/{total})**")
-            st.markdown(f"**Crash: {confidence_counter["Crash"]}**")
-            st.pyplot(fig, width=500)
-        else:
-            st.info("No score data available in this file.")