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: en
+widget:
+- text: "I love AutoTrain 🤗"
+datasets:
+- Sathira/autotrain-data-mbtiNlp
+co2_eq_emissions: 121.67185089502216
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Multi-class Classification
+- Model ID: 798824628
+- CO2 Emissions (in grams): 121.67185089502216
+
+## Validation Metrics
+
+- Loss: 0.5046824812889099
+- Accuracy: 0.8472124039775673
+- Macro F1: 0.7812978033330673
+- Micro F1: 0.8472124039775673
+- Weighted F1: 0.8464983956259307
+- Macro Precision: 0.812208631055716
+- Micro Precision: 0.8472124039775673
+- Weighted Precision: 0.8478968364150775
+- Macro Recall: 0.7593223884993787
+- Micro Recall: 0.8472124039775673
+- Weighted Recall: 0.8472124039775673
+
+
+## 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/Sathira/autotrain-mbtiNlp-798824628
+```
+
+Or Python API:
+
+```
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("Sathira/autotrain-mbtiNlp-798824628", use_auth_token=True)
+
+tokenizer = AutoTokenizer.from_pretrained("Sathira/autotrain-mbtiNlp-798824628", 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": 16,
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "ENFJ",
+    "1": "ENFP",
+    "2": "ENTJ",
+    "3": "ENTP",
+    "4": "ESFJ",
+    "5": "ESFP",
+    "6": "ESTJ",
+    "7": "ESTP",
+    "8": "INFJ",
+    "9": "INFP",
+    "10": "INTJ",
+    "11": "INTP",
+    "12": "ISFJ",
+    "13": "ISFP",
+    "14": "ISTJ",
+    "15": "ISTP"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "ENFJ": 0,
+    "ENFP": 1,
+    "ENTJ": 2,
+    "ENTP": 3,
+    "ESFJ": 4,
+    "ESFP": 5,
+    "ESTJ": 6,
+    "ESTP": 7,
+    "INFJ": 8,
+    "INFP": 9,
+    "INTJ": 10,
+    "INTP": 11,
+    "ISFJ": 12,
+    "ISFP": 13,
+    "ISTJ": 14,
+    "ISTP": 15
+  },
+  "max_length": 512,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "padding": "max_length",
+  "problem_type": "single_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.15.0",
+  "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": "DistilBertTokenizer"}