Commit From AutoTrain

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.tar.gz filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,49 @@
+---
+tags:
+- autotrain
+- token-classification
+language:
+- fr
+widget:
+- text: "I love AutoTrain"
+datasets:
+- davanstrien/autotrain-data-french-ner-blank-model
+co2_eq_emissions:
+  emissions: 0.11647938304211661
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Entity Extraction
+- Model ID: 69856137957
+- CO2 Emissions (in grams): 0.1165
+
+## Validation Metrics
+
+- Loss: 1.510
+- Accuracy: 0.706
+- Precision: 0.648
+- Recall: 0.679
+- F1: 0.663
+
+## Usage
+
+You can use cURL to access this model:
+
+```
+$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/davanstrien/autotrain-french-ner-blank-model-69856137957
+```
+
+Or Python API:
+
+```
+from transformers import AutoModelForTokenClassification, AutoTokenizer
+
+model = AutoModelForTokenClassification.from_pretrained("davanstrien/autotrain-french-ner-blank-model-69856137957", use_auth_token=True)
+
+tokenizer = AutoTokenizer.from_pretrained("davanstrien/autotrain-french-ner-blank-model-69856137957", use_auth_token=True)
+
+inputs = tokenizer("I love AutoTrain", return_tensors="pt")
+
+outputs = model(**inputs)
+```

config.json

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+{
+  "_name_or_path": "AutoTrain",
+  "_num_labels": 13,
+  "architectures": [
+    "CamembertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "B-Dosage",
+    "1": "B-Drug",
+    "2": "B-Duration",
+    "3": "B-Form",
+    "4": "B-Frequency",
+    "5": "B-Strength",
+    "6": "I-Dosage",
+    "7": "I-Drug",
+    "8": "I-Duration",
+    "9": "I-Form",
+    "10": "I-Frequency",
+    "11": "I-Strength",
+    "12": "O"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
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+    "B-Drug": 1,
+    "B-Duration": 2,
+    "B-Form": 3,
+    "B-Frequency": 4,
+    "B-Strength": 5,
+    "I-Dosage": 6,
+    "I-Drug": 7,
+    "I-Duration": 8,
+    "I-Form": 9,
+    "I-Frequency": 10,
+    "I-Strength": 11,
+    "O": 12
+  },
+  "layer_norm_eps": 1e-05,
+  "max_length": 64,
+  "max_position_embeddings": 514,
+  "model_type": "camembert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "padding": "max_length",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.29.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32005
+}

model.safetensors

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pytorch_model.bin

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sentencepiece.bpe.model

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special_tokens_map.json

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+{
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+    "<s>NOTUSED",
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+  "unk_token": "<unk>"
+}

tokenizer.json

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tokenizer_config.json

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+{
+  "additional_special_tokens": [
+    "<s>NOTUSED",
+    "</s>NOTUSED"
+  ],
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "__type": "AddedToken",
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "CamembertTokenizer",
+  "unk_token": "<unk>"
+}