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: ja
+widget:
+- text: "I love AutoTrain 🤗"
+datasets:
+- vabadeh213/autotrain-data-iine_classification10
+co2_eq_emissions: 7.351885824089346
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Binary Classification
+- Model ID: 737422470
+- CO2 Emissions (in grams): 7.351885824089346
+
+## Validation Metrics
+
+- Loss: 0.39456263184547424
+- Accuracy: 0.8279088689991864
+- Precision: 0.6869806094182825
+- Recall: 0.17663817663817663
+- AUC: 0.7937892215111646
+- F1: 0.2810198300283286
+
+## 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/vabadeh213/autotrain-iine_classification10-737422470
+```
+
+Or Python API:
+
+```
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("vabadeh213/autotrain-iine_classification10-737422470", use_auth_token=True)
+
+tokenizer = AutoTokenizer.from_pretrained("vabadeh213/autotrain-iine_classification10-737422470", 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": 2,
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "0",
+    "1": "1"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "0": 0,
+    "1": 1
+  },
+  "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",
+  "tokenizer_class": "BertJapaneseTokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.15.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 32000
+}

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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+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "do_lower_case": false, "do_word_tokenize": true, "do_subword_tokenize": true, "word_tokenizer_type": "mecab", "subword_tokenizer_type": "wordpiece", "never_split": null, "mecab_kwargs": null, "model_max_length": 512, "special_tokens_map_file": null, "tokenizer_file": null, "name_or_path": "AutoTrain", "tokenizer_class": "BertJapaneseTokenizer"}