giacomorossojakala
/

dbmdz-bert-base-italian-xxl-cased-eutekne-filtri-materia-lv2

@@ -1,199 +1,103 @@
 ---
-library_name: transformers
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 ---
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 ---
+tags:
+  - multi-label
+  - text-classification
 ---
+# Model Description
+This model is a fine-tuned dbmdz/bert-base-italian-xxl-cased for multi-label text classification on Eutekne domande_fitlri_materie train dataset.
+# Intended Use
+This model is intended for classifying legal questions into 216 categories. These categories are based on the Eutekne domande_fitlri_materie.
+# Training Details
+## Base Model
+This model is a finetuning of: **dbmdz/bert-base-italian-xxl-cased**
+## Training Data
+```json
+{
+  "train_samples": 4044,
+  "val_samples": 867,
+  "test_samples": 867,
+  "num_labels": 216
+}
+```
+## Training Hyperparameters
+```json
+{
+  "batch_size": 32,
+  "learning_rate": 2e-05,
+  "num_epochs": 5,
+  "max_length": 512,
+  "threshold": 0.5
+}
+```
+## Evaluation Results
+```json
+|                      |   validation_results |   test_results |
+|:---------------------|---------------------:|---------------:|
+| eval_exact_match     |           0.310265   |     0.303345   |
+| eval_hamming_loss    |           0.00932868 |     0.00976654 |
+| eval_f1_micro        |           0.589038   |     0.566896   |
+| eval_f1_macro        |           0.142712   |     0.157367   |
+| eval_precision_micro |           0.773795   |     0.746259   |
+| eval_precision_macro |           0.206659   |     0.234683   |
+| eval_recall_micro    |           0.475503   |     0.457045   |
+| eval_recall_macro    |           0.118758   |     0.13034    |
+```
+```json
+|     |   hit_rate |   precision |   recall |     f1 |   ndcg |   coverage |    mrr |
+|:----|-----------:|------------:|---------:|-------:|-------:|-----------:|-------:|
+| @1  |     0.7255 |      0.7255 |   0.2076 | 0.302  | 0.7255 |     0.2076 | 0.2076 |
+| @3  |     0.8316 |      0.5398 |   0.4278 | 0.4389 | 0.6316 |     0.4278 | 0.3084 |
+| @5  |     0.8674 |      0.3852 |   0.4841 | 0.3924 | 0.5862 |     0.4841 | 0.3214 |
+| @10 |     0.9193 |      0.2261 |   0.546  | 0.2955 | 0.5756 |     0.546  | 0.3297 |
+```
+## How to Use
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from huggingface_hub import hf_hub_download
+import pickle
+import torch
+import numpy as np
+repo_id = "giacomorossojakala/dbmdz-bert-base-italian-xxl-cased-eutekne-filtri-materia-lv2"
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+model = AutoModelForSequenceClassification.from_pretrained(repo_id)
+# Download and load label encoder
+downloaded_path = hf_hub_download(repo_id=repo_id, filename="label_encoder.pkl")
+with open(downloaded_path, 'rb') as f:
+    mlb = pickle.load(f)
+custom_text = "agevolazioni acquisto prima casa"
+inputs = tokenizer(custom_text, truncation=True, padding=True, max_length=512, return_tensors="pt")
+model.eval()
+with torch.no_grad():
+    outputs = model(**inputs)
+logits = outputs.logits
+probabilities = torch.sigmoid(logits)
+predictions = (probabilities > 0.75).int().cpu().numpy()
+predicted_labels = mlb.inverse_transform(predictions)
+ranked_idexes = np.argsort(probabilities.cpu().numpy(), axis=1)[:, ::-1]
+ranked_labels = np.array(mlb.classes_)[ranked_idexes]
+print(f"Custom Text: agevolazioni acquisto prima casa")
+print(f"Predicted Labels: ('V',)")
+print(f"Ranked Labels: {'[' +', '.join(ranked_labels[0, :5]) + '...]'}")
+```