Commit From AutoTrain

.gitattributes

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+*.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:
+- jurader/autotrain-data-livedoor_news
+co2_eq_emissions: 0.02886635131127639
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Multi-class Classification
+- Model ID: 732022289
+- CO2 Emissions (in grams): 0.02886635131127639
+
+## Validation Metrics
+
+- Loss: 0.19849611818790436
+- Accuracy: 0.9471186440677966
+- Macro F1: 0.9441816841379956
+- Micro F1: 0.9471186440677966
+- Weighted F1: 0.9470801715002611
+- Macro Precision: 0.945983665608131
+- Micro Precision: 0.9471186440677966
+- Weighted Precision: 0.9475574732458715
+- Macro Recall: 0.9429694962141204
+- Micro Recall: 0.9471186440677966
+- Weighted Recall: 0.9471186440677966
+
+
+## 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/jurader/autotrain-livedoor_news-732022289
+```
+
+Or Python API:
+
+```
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("jurader/autotrain-livedoor_news-732022289", use_auth_token=True)
+
+tokenizer = AutoTokenizer.from_pretrained("jurader/autotrain-livedoor_news-732022289", 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": 9,
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "dokujo-tsushin",
+    "1": "it-life-hack",
+    "2": "kaden-channel",
+    "3": "livedoor-homme",
+    "4": "movie-enter",
+    "5": "peachy",
+    "6": "smax",
+    "7": "sports-watch",
+    "8": "topic-news"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "dokujo-tsushin": 0,
+    "it-life-hack": 1,
+    "kaden-channel": 2,
+    "livedoor-homme": 3,
+    "movie-enter": 4,
+    "peachy": 5,
+    "smax": 6,
+    "sports-watch": 7,
+    "topic-news": 8
+  },
+  "layer_norm_eps": 1e-12,
+  "max_length": 128,
+  "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": 32768
+}

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": {"mecab_dic": "unidic_lite"}, "special_tokens_map_file": null, "tokenizer_file": null, "name_or_path": "AutoTrain", "tokenizer_class": "BertJapaneseTokenizer"}