Commit From AutoNLP

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
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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: autonlp
+language: en
+widget:
+- text: "I love AutoNLP 🤗"
+datasets:
+- abhishek/autonlp-data-bbc-news-classification
+co2_eq_emissions: 5.448567309047846
+---
+
+# Model Trained Using AutoNLP
+
+- Problem type: Multi-class Classification
+- Model ID: 37229289
+- CO2 Emissions (in grams): 5.448567309047846
+
+## Validation Metrics
+
+- Loss: 0.07081354409456253
+- Accuracy: 0.9867109634551495
+- Macro F1: 0.9859067529980614
+- Micro F1: 0.9867109634551495
+- Weighted F1: 0.9866417220968429
+- Macro Precision: 0.9868771404595043
+- Micro Precision: 0.9867109634551495
+- Weighted Precision: 0.9869289511551576
+- Macro Recall: 0.9853173241852486
+- Micro Recall: 0.9867109634551495
+- Weighted Recall: 0.9867109634551495
+
+
+## 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 AutoNLP"}' https://api-inference.huggingface.co/models/abhishek/autonlp-bbc-news-classification-37229289
+```
+
+Or Python API:
+
+```
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-bbc-news-classification-37229289", use_auth_token=True)
+
+tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-bbc-news-classification-37229289", use_auth_token=True)
+
+inputs = tokenizer("I love AutoNLP", return_tensors="pt")
+
+outputs = model(**inputs)
+```

config.json

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+{
+  "_name_or_path": "AutoNLP",
+  "_num_labels": 5,
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "business",
+    "1": "entertainment",
+    "2": "politics",
+    "3": "sport",
+    "4": "tech"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "business": 0,
+    "entertainment": 1,
+    "politics": 2,
+    "sport": 3,
+    "tech": 4
+  },
+  "layer_norm_eps": 1e-12,
+  "max_length": 128,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "padding": "max_length",
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "transformers_version": "4.8.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

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