Commit From AutoTrain

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.tar.gz filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,56 @@
+---
+tags:
+- autotrain
+- text-classification
+language:
+- en
+widget:
+- text: "I love AutoTrain 🤗"
+datasets:
+- badalsahani/autotrain-data-text-classification
+co2_eq_emissions:
+  emissions: 7.761992510873142
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Multi-class Classification
+- Model ID: 3486594647
+- CO2 Emissions (in grams): 7.7620
+
+## Validation Metrics
+
+- Loss: 0.008
+- Accuracy: 1.000
+- Macro F1: 1.000
+- Micro F1: 1.000
+- Weighted F1: 1.000
+- Macro Precision: 1.000
+- Micro Precision: 1.000
+- Weighted Precision: 1.000
+- Macro Recall: 1.000
+- Micro Recall: 1.000
+- Weighted Recall: 1.000
+
+
+## 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/badalsahani/autotrain-text-classification-3486594647
+```
+
+Or Python API:
+
+```
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("badalsahani/autotrain-text-classification-3486594647", use_auth_token=True)
+
+tokenizer = AutoTokenizer.from_pretrained("badalsahani/autotrain-text-classification-3486594647", 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": 13,
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "biology",
+    "1": "business-studies",
+    "2": "chemistry",
+    "3": "computer science",
+    "4": "economics",
+    "5": "english literature",
+    "6": "geography",
+    "7": "history",
+    "8": "mathematics",
+    "9": "physics",
+    "10": "political science",
+    "11": "psychology",
+    "12": "sociology"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "biology": 0,
+    "business-studies": 1,
+    "chemistry": 2,
+    "computer science": 3,
+    "economics": 4,
+    "english literature": 5,
+    "geography": 6,
+    "history": 7,
+    "mathematics": 8,
+    "physics": 9,
+    "political science": 10,
+    "psychology": 11,
+    "sociology": 12
+  },
+  "layer_norm_eps": 1e-12,
+  "max_length": 512,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "padding": "max_length",
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.25.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28996
+}

pytorch_model.bin

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special_tokens_map.json

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+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer.json

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tokenizer_config.json

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+{
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "name_or_path": "AutoTrain",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "special_tokens_map_file": null,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}