Commit From AutoTrain

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
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+*.bin.* 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: unk
+widget:
+- text: "I love AutoTrain 🤗"
+datasets:
+- justpyschitry/autotrain-data-Psychiatry_Article_Identifier
+co2_eq_emissions: 13.4308931494349
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Multi-class Classification
+- Model ID: 990132822
+- CO2 Emissions (in grams): 13.4308931494349
+
+## Validation Metrics
+
+- Loss: 0.3777158558368683
+- Accuracy: 0.9177471636952999
+- Macro F1: 0.9082952086962773
+- Micro F1: 0.9177471636952999
+- Weighted F1: 0.9175376430905807
+- Macro Precision: 0.9175123149319843
+- Micro Precision: 0.9177471636952999
+- Weighted Precision: 0.9185452324503698
+- Macro Recall: 0.9042000199743617
+- Micro Recall: 0.9177471636952999
+- Weighted Recall: 0.9177471636952999
+
+
+## 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/justpyschitry/autotrain-Psychiatry_Article_Identifier-990132822
+```
+
+Or Python API:
+
+```
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("justpyschitry/autotrain-Psychiatry_Article_Identifier-990132822", use_auth_token=True)
+
+tokenizer = AutoTokenizer.from_pretrained("justpyschitry/autotrain-Psychiatry_Article_Identifier-990132822", 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": 20,
+  "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": "Certain infectious or parasitic diseases",
+    "1": "Developmental anaomalies",
+    "2": "Diseases of the blood or blood forming organs",
+    "3": "Diseases of the genitourinary system",
+    "4": "Mental behavioural or neurodevelopmental disorders",
+    "5": "Neoplasms",
+    "6": "certain conditions originating in the perinatal period",
+    "7": "conditions related to sexual health",
+    "8": "diseases of the circulatroy system",
+    "9": "diseases of the digestive system",
+    "10": "diseases of the ear or mastoid process",
+    "11": "diseases of the immune system",
+    "12": "diseases of the musculoskeletal system or connective tissue",
+    "13": "diseases of the nervous system",
+    "14": "diseases of the respiratory system",
+    "15": "diseases of the skin",
+    "16": "diseases of the visual system",
+    "17": "endocrine  nutritional or metabolic diseases",
+    "18": "pregnanacy  childbirth or the puerperium",
+    "19": "sleep-wake disorders"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Certain infectious or parasitic diseases": 0,
+    "Developmental anaomalies": 1,
+    "Diseases of the blood or blood forming organs": 2,
+    "Diseases of the genitourinary system": 3,
+    "Mental behavioural or neurodevelopmental disorders": 4,
+    "Neoplasms": 5,
+    "certain conditions originating in the perinatal period": 6,
+    "conditions related to sexual health": 7,
+    "diseases of the circulatroy system": 8,
+    "diseases of the digestive system": 9,
+    "diseases of the ear or mastoid process": 10,
+    "diseases of the immune system": 11,
+    "diseases of the musculoskeletal system or connective tissue": 12,
+    "diseases of the nervous system": 13,
+    "diseases of the respiratory system": 14,
+    "diseases of the skin": 15,
+    "diseases of the visual system": 16,
+    "endocrine  nutritional or metabolic diseases": 17,
+    "pregnanacy  childbirth or the puerperium": 18,
+    "sleep-wake disorders": 19
+  },
+  "layer_norm_eps": 1e-12,
+  "max_length": 192,
+  "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"}