init

.gitignore

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+test.py

README.md

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+---
+language: 
+  - fr
+
+license: mit
+
+pipeline_tag: "token-classification"
+
+datasets:
+  - squadFR
+  - fquad
+  - piaf
+
+tags:
+  - camembert
+  - answer extraction 
+---
+
+# Extraction de réponse
+
+Ce modèle est _fine tuné_ à partir du modèle [camembert-base](https://huggingface.co/camembert-base) pour la tâche de classification de tokens. 
+L'objectif est d'identifier les suites de tokens probables qui pourrait être l'objet d'une question.
+
+
+## Données d'apprentissage
+
+La base d'entrainement est la concatenation des bases SquadFR, [fquad](https://huggingface.co/datasets/fquad), [piaf](https://huggingface.co/datasets/piaf). 
+Les réponses de chaque contexte ont été labelisées avec le label "ANS". 
+
+Volumétrie (nombre de contexte):
+* train: 24 652
+* test: 1 370
+* valid: 1 370
+
+
+## Entrainement
+
+L'apprentissage s'est effectué sur une carte Tesla V100.
+
+* Batch size: 16
+* Weight decay: 0.01
+* Learning rate: 2x10-5 (décroit linéairement)
+* Paramètres par défaut de la classe [TrainingArguments](https://huggingface.co/transformers/main_classes/trainer.html#trainingarguments)
+* Total steps: 1 000
+
+Le modèle semble sur apprendre au delà :
+
+![Loss](assets/loss_m_sl_sota_2.PNG)  
+
+
+## Critiques
+
+Le modèle n'a pas de bonnes performances et doit être corrigé après prédiction pour être cohérent. La tâche de classification n'est pas évidente car le modèle doit identifier des groupes de token _sachant_ qu'une question peut être posée.
+
+![Performances](assets/perfs_m_sl_sota_2.PNG)
+
+## Utilisation
+
+_Le modèle est un POC, nous garantissons pas ses performances_
+
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+import numpy as np
+
+model_name = "lincoln/camembert-squadFR-fquad-piaf-answer-extraction"
+
+loaded_tokenizer = AutoTokenizer.from_pretrained(model_path)
+loaded_model = AutoModelForTokenClassification.from_pretrained(model_path)
+text = "La science des données est un domaine interdisciplinaire qui utilise des méthodes, des processus,\
+    des algorithmes et des systèmes scientifiques pour extraire des connaissances et des idées de nombreuses données structurelles et non structurées.\
+        Elle est souvent associée aux données massives et à l'analyse des données."
+
+inputs = loaded_tokenizer(text, return_tensors="pt", return_offsets_mapping=True)
+outputs = loaded_model(inputs.input_ids).logits
+probs = 1 / (1 + np.exp(-outputs.detach().numpy()))
+probs[:, :, 1][0] = np.convolve(probs[:, :, 1][0], np.ones(2), 'same') / 2
+
+sentences = loaded_tokenizer.tokenize(text, add_special_tokens=False)
+prob_answer_tokens = probs[:, 1:-1, 1].flatten().tolist()
+offset_start_mapping = inputs.offset_mapping[:, 1:-1, 0].flatten().tolist()
+offset_end_mapping = inputs.offset_mapping[:, 1:-1, 1].flatten().tolist()
+threshold = 0.4
+
+entities = []
+for ix, (token, prob_ans, offset_start, offset_end) in enumerate(zip(sentences, prob_answer_tokens, offset_start_mapping, offset_end_mapping)):
+    entities.append({
+        'entity': 'ANS' if prob_ans > threshold else 'O',
+        'score': prob_ans, 
+        'index': ix,
+        'word': token,
+        'start': offset_start,
+        'end': offset_end
+    })
+
+for p in entities:
+    print(p)
+
+# {'entity': 'O', 'score': 0.3118681311607361, 'index': 0, 'word': '▁La', 'start': 0, 'end': 2}
+# {'entity': 'O', 'score': 0.37866950035095215, 'index': 1, 'word': '▁science', 'start': 3, 'end': 10}
+# {'entity': 'ANS', 'score': 0.45018652081489563, 'index': 2, 'word': '▁des', 'start': 11, 'end': 14}
+# {'entity': 'ANS', 'score': 0.4615934491157532, 'index': 3, 'word': '▁données', 'start': 15, 'end': 22}
+# {'entity': 'O', 'score': 0.35033443570137024, 'index': 4, 'word': '▁est', 'start': 23, 'end': 26}
+# {'entity': 'O', 'score': 0.24779987335205078, 'index': 5, 'word': '▁un', 'start': 27, 'end': 29}
+# {'entity': 'O', 'score': 0.27084410190582275, 'index': 6, 'word': '▁domaine', 'start': 30, 'end': 37}
+# {'entity': 'O', 'score': 0.3259460926055908, 'index': 7, 'word': '▁in', 'start': 38, 'end': 40}
+# {'entity': 'O', 'score': 0.371802419424057, 'index': 8, 'word': 'terdisciplinaire', 'start': 40, 'end': 56}
+# {'entity': 'O', 'score': 0.3140853941440582, 'index': 9, 'word': '▁qui', 'start': 57, 'end': 60}
+# {'entity': 'O', 'score': 0.2629334330558777, 'index': 10, 'word': '▁utilise', 'start': 61, 'end': 68}
+# {'entity': 'O', 'score': 0.2968383729457855, 'index': 11, 'word': '▁des', 'start': 69, 'end': 72}
+# {'entity': 'O', 'score': 0.33898216485977173, 'index': 12, 'word': '▁méthodes', 'start': 73, 'end': 81}
+# {'entity': 'O', 'score': 0.3776060938835144, 'index': 13, 'word': ',', 'start': 81, 'end': 82}
+# {'entity': 'O', 'score': 0.3710060119628906, 'index': 14, 'word': '▁des', 'start': 83, 'end': 86}
+# {'entity': 'O', 'score': 0.35908180475234985, 'index': 15, 'word': '▁processus', 'start': 87, 'end': 96}
+# {'entity': 'O', 'score': 0.3890596628189087, 'index': 16, 'word': ',', 'start': 96, 'end': 97}
+# {'entity': 'O', 'score': 0.38341325521469116, 'index': 17, 'word': '▁des', 'start': 101, 'end': 104}
+# {'entity': 'O', 'score': 0.3743852376937866, 'index': 18, 'word': '▁', 'start': 105, 'end': 106}
+# {'entity': 'O', 'score': 0.3943936228752136, 'index': 19, 'word': 'algorithme', 'start': 105, 'end': 115}
+# {'entity': 'O', 'score': 0.39456743001937866, 'index': 20, 'word': 's', 'start': 115, 'end': 116}
+# {'entity': 'O', 'score': 0.3846966624259949, 'index': 21, 'word': '▁et', 'start': 117, 'end': 119}
+# {'entity': 'O', 'score': 0.367380827665329, 'index': 22, 'word': '▁des', 'start': 120, 'end': 123}
+# {'entity': 'O', 'score': 0.3652925491333008, 'index': 23, 'word': '▁systèmes', 'start': 124, 'end': 132}
+# {'entity': 'O', 'score': 0.3975735306739807, 'index': 24, 'word': '▁scientifiques', 'start': 133, 'end': 146}
+# {'entity': 'O', 'score': 0.36417365074157715, 'index': 25, 'word': '▁pour', 'start': 147, 'end': 151}
+# {'entity': 'O', 'score': 0.32438698410987854, 'index': 26, 'word': '▁extraire', 'start': 152, 'end': 160}
+# {'entity': 'O', 'score': 0.3416857123374939, 'index': 27, 'word': '▁des', 'start': 161, 'end': 164}
+# {'entity': 'O', 'score': 0.3674810230731964, 'index': 28, 'word': '▁connaissances', 'start': 165, 'end': 178}
+# {'entity': 'O', 'score': 0.38362061977386475, 'index': 29, 'word': '▁et', 'start': 179, 'end': 181}
+# {'entity': 'O', 'score': 0.364640474319458, 'index': 30, 'word': '▁des', 'start': 182, 'end': 185}
+# {'entity': 'O', 'score': 0.36050117015838623, 'index': 31, 'word': '▁idées', 'start': 186, 'end': 191}
+# {'entity': 'O', 'score': 0.3768993020057678, 'index': 32, 'word': '▁de', 'start': 192, 'end': 194}
+# {'entity': 'O', 'score': 0.39184248447418213, 'index': 33, 'word': '▁nombreuses', 'start': 195, 'end': 205}
+# {'entity': 'ANS', 'score': 0.4091200828552246, 'index': 34, 'word': '▁données', 'start': 206, 'end': 213}
+# {'entity': 'ANS', 'score': 0.41234123706817627, 'index': 35, 'word': '▁structurelle', 'start': 214, 'end': 226}
+# {'entity': 'ANS', 'score': 0.40243157744407654, 'index': 36, 'word': 's', 'start': 226, 'end': 227}
+# {'entity': 'ANS', 'score': 0.4007353186607361, 'index': 37, 'word': '▁et', 'start': 228, 'end': 230}
+# {'entity': 'ANS', 'score': 0.40597623586654663, 'index': 38, 'word': '▁non', 'start': 231, 'end': 234}
+# {'entity': 'ANS', 'score': 0.40272021293640137, 'index': 39, 'word': '▁structurée', 'start': 235, 'end': 245}
+# {'entity': 'O', 'score': 0.392631471157074, 'index': 40, 'word': 's', 'start': 245, 'end': 246}
+# {'entity': 'O', 'score': 0.34266412258148193, 'index': 41, 'word': '.', 'start': 246, 'end': 247}
+# {'entity': 'O', 'score': 0.26178646087646484, 'index': 42, 'word': '▁Elle', 'start': 255, 'end': 259}
+# {'entity': 'O', 'score': 0.2265639454126358, 'index': 43, 'word': '▁est', 'start': 260, 'end': 263}
+# {'entity': 'O', 'score': 0.22844195365905762, 'index': 44, 'word': '▁souvent', 'start': 264, 'end': 271}
+# {'entity': 'O', 'score': 0.2475772500038147, 'index': 45, 'word': '▁associée', 'start': 272, 'end': 280}
+# {'entity': 'O', 'score': 0.3002186715602875, 'index': 46, 'word': '▁aux', 'start': 281, 'end': 284}
+# {'entity': 'O', 'score': 0.3875720798969269, 'index': 47, 'word': '▁données', 'start': 285, 'end': 292}
+# {'entity': 'ANS', 'score': 0.445063054561615, 'index': 48, 'word': '▁massive', 'start': 293, 'end': 300}
+# {'entity': 'ANS', 'score': 0.4419114589691162, 'index': 49, 'word': 's', 'start': 300, 'end': 301}
+# {'entity': 'ANS', 'score': 0.4240635633468628, 'index': 50, 'word': '▁et', 'start': 302, 'end': 304}
+# {'entity': 'O', 'score': 0.3900952935218811, 'index': 51, 'word': '▁à', 'start': 305, 'end': 306}
+# {'entity': 'O', 'score': 0.3784807324409485, 'index': 52, 'word': '▁l', 'start': 307, 'end': 308}
+# {'entity': 'O', 'score': 0.3459452986717224, 'index': 53, 'word': "'", 'start': 308, 'end': 309}
+# {'entity': 'O', 'score': 0.37636008858680725, 'index': 54, 'word': 'analyse', 'start': 309, 'end': 316}
+# {'entity': 'ANS', 'score': 0.4475618302822113, 'index': 55, 'word': '▁des', 'start': 317, 'end': 320}
+# {'entity': 'ANS', 'score': 0.43845775723457336, 'index': 56, 'word': '▁données', 'start': 321, 'end': 328}
+# {'entity': 'O', 'score': 0.3761221170425415, 'index': 57, 'word': '.', 'start': 328, 'end': 329}
+```

config.json

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+{
+  "_name_or_path": "/mnt/qaqg_playground/Users/colin.davidson/qa-qg-playground/data/huggingface/args/sl/m-sl-sota-2-test/checkpoint-1000",
+  "architectures": [
+    "CamembertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 5,
+  "classifier_dropout": null,
+  "eos_token_id": 6,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "O",
+    "1": "ANS"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "ANS": 1,
+    "O": 0
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "camembert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.11.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32005
+}

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special_tokens_map.json

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+{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": false}, "additional_special_tokens": ["<s>NOTUSED", "</s>NOTUSED"]}

tokenizer_config.json

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+{"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "additional_special_tokens": ["<s>NOTUSED", "</s>NOTUSED"], "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "/mnt/qaqg_playground/Users/colin.davidson/qa-qg-playground/data/huggingface/args/sl/m-sl-sota-2-test/checkpoint-1000", "tokenizer_class": "CamembertTokenizer"}