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:
+- CH0KUN/autotrain-data-TNC_Data2500_WangchanBERTa
+co2_eq_emissions: 0.07293362913158113
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Multi-class Classification
+- Model ID: 928030564
+- CO2 Emissions (in grams): 0.07293362913158113
+
+## Validation Metrics
+
+- Loss: 0.4989683926105499
+- Accuracy: 0.8445845697329377
+- Macro F1: 0.8407629450432429
+- Micro F1: 0.8445845697329377
+- Weighted F1: 0.8407629450432429
+- Macro Precision: 0.8390327354531153
+- Micro Precision: 0.8445845697329377
+- Weighted Precision: 0.8390327354531154
+- Macro Recall: 0.8445845697329377
+- Micro Recall: 0.8445845697329377
+- Weighted Recall: 0.8445845697329377
+
+
+## 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/CH0KUN/autotrain-TNC_Data2500_WangchanBERTa-928030564
+```
+
+Or Python API:
+
+```
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("CH0KUN/autotrain-TNC_Data2500_WangchanBERTa-928030564", use_auth_token=True)
+
+tokenizer = AutoTokenizer.from_pretrained("CH0KUN/autotrain-TNC_Data2500_WangchanBERTa-928030564", 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": 8,
+  "architectures": [
+    "CamembertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Applied Science",
+    "1": "Arts",
+    "2": "Belief & Thought",
+    "3": "Commerce & Finance",
+    "4": "History",
+    "5": "Imaginative",
+    "6": "Natural & Pure Science",
+    "7": "Social Science "
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Applied Science": 0,
+    "Arts": 1,
+    "Belief & Thought": 2,
+    "Commerce & Finance": 3,
+    "History": 4,
+    "Imaginative": 5,
+    "Natural & Pure Science": 6,
+    "Social Science ": 7
+  },
+  "layer_norm_eps": 1e-12,
+  "max_length": 256,
+  "max_position_embeddings": 512,
+  "model_type": "camembert",
+  "num_attention_head": 12,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "padding": "max_length",
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.15.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 25005
+}

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sample_input.pkl

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special_tokens_map.json

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+{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true}, "additional_special_tokens": ["<s>NOTUSED", "</s>NOTUSED", "<_>"]}

tokenizer_config.json

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+{"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "additional_special_tokens": ["<s>NOTUSED", "</s>NOTUSED", "<_>"], "special_tokens_map_file": null, "name_or_path": "AutoTrain", "sp_model_kwargs": {}, "tokenizer_class": "CamembertTokenizer"}