AA

README.md

@@ -1,57 +1,13 @@
-# MLOPS_Project
-
-FASE 1) 
-    - Riuscire ad allenare un modello
-
-FASE 2)
-
-
-Public Colab notebook (single link)
-
-Loads a ready model: cardiffnlp/twitter-roberta-base-sentiment-latest (or -sep2022).
-
-Loads a public dataset (e.g., tweet_eval/sentiment).
-
-Runs inference + evaluation (accuracy, F1 macro, recall macro).
-
-(Optional but easy) light fine-tuning on a fraction of the data (small batch, few epochs).
-
-Shows a tiny monitoring demo: aggregate % positive/neutral/negative over a sample and plot a time series (synthetic timestamps are fine).
-
-Links to your GitHub repo at the top.
-
-Public GitHub repo
-
-src/ with:
-
-train.py — fine-tuning script (works on CPU/MPS/CUDA; small batch + gradient accumulation).
-
-eval.py — evaluate a model checkpoint on validation/test.
-
-infer.py — batch inference from CSV/JSONL.
-
-app.py — (optional) Gradio mini UI.
-
-data_utils.py — your subset functions + tokenization helpers.
-
-requirements.txt
-
-README.md — how to run locally + what the project does.
-
-.github/workflows/ci.yml — CI runs lint + tests + a tiny dry-run of training (e.g., 500 samples, 1 epoch).
-
-MODEL_CARD.md — brief model card (data, metrics, limits/bias).
-
-tests/test_smoke.py — imports + 10-sample training/eval smoke test.
-
-Minimal documentation (in README)
-
-Goal: monitor social sentiment for MachineInnovators Inc.
-
-Model choice: use pre-trained RoBERTa; FastText kept as optional baseline.
-
-Pipeline overview: data → tokenize → (optional fine-tune) → evaluate → artifact save → (optional deploy).
-
-How to reproduce: exact commands.
-
-Monitoring idea: log predictions; compute daily sentiment mix; simple drift check (distribution shift of logits).
+---
+title: Sentiment Analysis API
+emoji: 📊
+colorFrom: blue
+colorTo: purple
+sdk: docker
+app_port: 7860
+pinned: false
+---
+
+# Sentiment Analysis API
+
+(rest of your README here…)