Commit From AutoTrain

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.tar.gz filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+tags: autotrain
+language: en
+widget:
+- text: "I love AutoTrain 🤗"
+datasets:
+- ndavid/autotrain-data-trec-fine-bert
+co2_eq_emissions: 0.02238820299105448
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Multi-class Classification
+- Model ID: 739422530
+- CO2 Emissions (in grams): 0.02238820299105448
+
+## Validation Metrics
+
+- Loss: 0.36623290181159973
+- Accuracy: 0.9321753515301903
+- Macro F1: 0.9066706944656866
+- Micro F1: 0.9321753515301903
+- Weighted F1: 0.9314858667247282
+- Macro Precision: 0.9489233194839841
+- Micro Precision: 0.9321753515301903
+- Weighted Precision: 0.9347346558570125
+- Macro Recall: 0.8842587178845419
+- Micro Recall: 0.9321753515301903
+- Weighted Recall: 0.9321753515301903
+
+
+## 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/ndavid/autotrain-trec-fine-bert-739422530
+```
+
+Or Python API:
+
+```
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("ndavid/autotrain-trec-fine-bert-739422530", use_auth_token=True)
+
+tokenizer = AutoTokenizer.from_pretrained("ndavid/autotrain-trec-fine-bert-739422530", 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": 47,
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "abb",
+    "1": "animal",
+    "2": "body",
+    "3": "city",
+    "4": "code",
+    "5": "color",
+    "6": "count",
+    "7": "country",
+    "8": "cremat",
+    "9": "currency",
+    "10": "date",
+    "11": "def",
+    "12": "desc",
+    "13": "dismed",
+    "14": "dist",
+    "15": "event",
+    "16": "exp",
+    "17": "food",
+    "18": "gr",
+    "19": "ind",
+    "20": "instru",
+    "21": "lang",
+    "22": "letter",
+    "23": "manner",
+    "24": "money",
+    "25": "mount",
+    "26": "ord",
+    "27": "other",
+    "28": "perc",
+    "29": "period",
+    "30": "plant",
+    "31": "product",
+    "32": "reason",
+    "33": "religion",
+    "34": "speed",
+    "35": "sport",
+    "36": "state",
+    "37": "substance",
+    "38": "symbol",
+    "39": "techmeth",
+    "40": "temp",
+    "41": "termeq",
+    "42": "title",
+    "43": "veh",
+    "44": "volsize",
+    "45": "weight",
+    "46": "word"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "abb": 0,
+    "animal": 1,
+    "body": 2,
+    "city": 3,
+    "code": 4,
+    "color": 5,
+    "count": 6,
+    "country": 7,
+    "cremat": 8,
+    "currency": 9,
+    "date": 10,
+    "def": 11,
+    "desc": 12,
+    "dismed": 13,
+    "dist": 14,
+    "event": 15,
+    "exp": 16,
+    "food": 17,
+    "gr": 18,
+    "ind": 19,
+    "instru": 20,
+    "lang": 21,
+    "letter": 22,
+    "manner": 23,
+    "money": 24,
+    "mount": 25,
+    "ord": 26,
+    "other": 27,
+    "perc": 28,
+    "period": 29,
+    "plant": 30,
+    "product": 31,
+    "reason": 32,
+    "religion": 33,
+    "speed": 34,
+    "sport": 35,
+    "state": 36,
+    "substance": 37,
+    "symbol": 38,
+    "techmeth": 39,
+    "temp": 40,
+    "termeq": 41,
+    "title": 42,
+    "veh": 43,
+    "volsize": 44,
+    "weight": 45,
+    "word": 46
+  },
+  "layer_norm_eps": 1e-12,
+  "max_length": 64,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "padding": "max_length",
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.15.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

pytorch_model.bin

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sample_input.pkl

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special_tokens_map.json

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

tokenizer_config.json

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