Commit From AutoNLP

.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: autonlp
+language: en
+widget:
+- text: "I love AutoNLP 🤗"
+datasets:
+- bgoel4132/autonlp-data-tweet-disaster-classifier
+co2_eq_emissions: 27.22397099134103
+---
+
+# Model Trained Using AutoNLP
+
+- Problem type: Multi-class Classification
+- Model ID: 28716412
+- CO2 Emissions (in grams): 27.22397099134103
+
+## Validation Metrics
+
+- Loss: 0.4146720767021179
+- Accuracy: 0.8066924731182795
+- Macro F1: 0.7835463282531184
+- Micro F1: 0.8066924731182795
+- Weighted F1: 0.7974252447208724
+- Macro Precision: 0.8183917344767431
+- Micro Precision: 0.8066924731182795
+- Weighted Precision: 0.8005510296861892
+- Macro Recall: 0.7679676081852519
+- Micro Recall: 0.8066924731182795
+- Weighted Recall: 0.8066924731182795
+
+
+## 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/bgoel4132/autonlp-tweet-disaster-classifier-28716412
+```
+
+Or Python API:
+
+```
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("bgoel4132/autonlp-tweet-disaster-classifier-28716412", use_auth_token=True)
+
+tokenizer = AutoTokenizer.from_pretrained("bgoel4132/autonlp-tweet-disaster-classifier-28716412", 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": 13,
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "accident",
+    "1": "cyclone",
+    "2": "earthquake",
+    "3": "explosion",
+    "4": "fire",
+    "5": "flood",
+    "6": "hurricane",
+    "7": "medical",
+    "8": "other",
+    "9": "pollution",
+    "10": "tornado",
+    "11": "typhoon",
+    "12": "volcano"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "accident": 0,
+    "cyclone": 1,
+    "earthquake": 2,
+    "explosion": 3,
+    "fire": 4,
+    "flood": 5,
+    "hurricane": 6,
+    "medical": 7,
+    "other": 8,
+    "pollution": 9,
+    "tornado": 10,
+    "typhoon": 11,
+    "volcano": 12
+  },
+  "max_length": 32,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "padding": "max_length",
+  "problem_type": "single_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "transformers_version": "4.8.0",
+  "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": "DistilBertTokenizer"}