Commit From AutoTrain

.gitattributes

@@ -32,3 +32,6 @@ 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:
+- unk
+widget:
+- text: "I love AutoTrain 🤗"
+datasets:
+- teacookies/autotrain-data-21-12-2022_exam_part4_1
+co2_eq_emissions:
+  emissions: 17.974413129541247
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Entity Extraction
+- Model ID: 2558278221
+- CO2 Emissions (in grams): 17.9744
+
+## Validation Metrics
+
+- Loss: 0.001
+- Accuracy: 1.000
+- Precision: 0.984
+- Recall: 0.985
+- F1: 0.984
+
+## 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/teacookies/autotrain-21-12-2022_exam_part4_1-2558278221
+```
+
+Or Python API:
+
+```
+from transformers import AutoModelForTokenClassification, AutoTokenizer
+
+model = AutoModelForTokenClassification.from_pretrained("teacookies/autotrain-21-12-2022_exam_part4_1-2558278221", use_auth_token=True)
+
+tokenizer = AutoTokenizer.from_pretrained("teacookies/autotrain-21-12-2022_exam_part4_1-2558278221", 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": 21,
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "B-tec_L_E_model_no",
+    "1": "B-tec_braking_force_max",
+    "2": "B-tec_braking_force_min",
+    "3": "B-tec_buffer",
+    "4": "B-tec_guided_rail_condition",
+    "5": "B-tec_lift",
+    "6": "B-tec_safety_gear",
+    "7": "B-tec_total_mass_max",
+    "8": "B-tec_total_mass_min",
+    "9": "B-tec_tripping_speed_max",
+    "10": "I-tec_L_E_model_no",
+    "11": "I-tec_braking_force_max",
+    "12": "I-tec_braking_force_min",
+    "13": "I-tec_buffer",
+    "14": "I-tec_guided_rail_condition",
+    "15": "I-tec_lift",
+    "16": "I-tec_safety_gear",
+    "17": "I-tec_total_mass_max",
+    "18": "I-tec_total_mass_min",
+    "19": "I-tec_tripping_speed_max",
+    "20": "O"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "B-tec_L_E_model_no": 0,
+    "B-tec_braking_force_max": 1,
+    "B-tec_braking_force_min": 2,
+    "B-tec_buffer": 3,
+    "B-tec_guided_rail_condition": 4,
+    "B-tec_lift": 5,
+    "B-tec_safety_gear": 6,
+    "B-tec_total_mass_max": 7,
+    "B-tec_total_mass_min": 8,
+    "B-tec_tripping_speed_max": 9,
+    "I-tec_L_E_model_no": 10,
+    "I-tec_braking_force_max": 11,
+    "I-tec_braking_force_min": 12,
+    "I-tec_buffer": 13,
+    "I-tec_guided_rail_condition": 14,
+    "I-tec_lift": 15,
+    "I-tec_safety_gear": 16,
+    "I-tec_total_mass_max": 17,
+    "I-tec_total_mass_min": 18,
+    "I-tec_tripping_speed_max": 19,
+    "O": 20
+  },
+  "layer_norm_eps": 1e-12,
+  "max_length": 512,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "padding": "max_length",
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.25.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 105879
+}

pytorch_model.bin

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special_tokens_map.json

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+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer.json

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tokenizer_config.json

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+{
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "name_or_path": "AutoTrain",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "special_tokens_map_file": null,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}