Commit From AutoTrain

.gitattributes

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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:
+- qualitydatalab/autotrain-data-car-review-project
+co2_eq_emissions: 0.21529888368377176
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Multi-class Classification
+- Model ID: 966432121
+- CO2 Emissions (in grams): 0.21529888368377176
+
+## Validation Metrics
+
+- Loss: 0.6013365983963013
+- Accuracy: 0.737791286727457
+- Macro F1: 0.729171012281939
+- Micro F1: 0.737791286727457
+- Weighted F1: 0.729171012281939
+- Macro Precision: 0.7313770127538427
+- Micro Precision: 0.737791286727457
+- Weighted Precision: 0.7313770127538428
+- Macro Recall: 0.737791286727457
+- Micro Recall: 0.737791286727457
+- Weighted Recall: 0.737791286727457
+
+
+## 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/qualitydatalab/autotrain-car-review-project-966432121
+```
+
+Or Python API:
+
+```
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("qualitydatalab/autotrain-car-review-project-966432121", use_auth_token=True)
+
+tokenizer = AutoTokenizer.from_pretrained("qualitydatalab/autotrain-car-review-project-966432121", 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": 3,
+  "architectures": [
+    "RobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "great",
+    "1": "ok",
+    "2": "poor"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "great": 0,
+    "ok": 1,
+    "poor": 2
+  },
+  "layer_norm_eps": 1e-05,
+  "max_length": 192,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "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": 50265
+}

pytorch_model.bin

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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": false}}

tokenizer_config.json

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+{"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "add_prefix_space": false, "errors": "replace", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": "<mask>", "trim_offsets": true, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "AutoTrain", "tokenizer_class": "RobertaTokenizer"}