Commit From AutoNLP

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* 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: autonlp
+language: en
+widget:
+- text: "I love AutoNLP 🤗"
+datasets:
+- emekaboris/autonlp-data-txc
+co2_eq_emissions: 610.861733873082
+---
+
+# Model Trained Using AutoNLP
+
+- Problem type: Multi-class Classification
+- Model ID: 17923129
+- CO2 Emissions (in grams): 610.861733873082
+
+## Validation Metrics
+
+- Loss: 0.2319454699754715
+- Accuracy: 0.9264228741381642
+- Macro F1: 0.6730537318152493
+- Micro F1: 0.9264228741381642
+- Weighted F1: 0.9251493598895151
+- Macro Precision: 0.7767479491141245
+- Micro Precision: 0.9264228741381642
+- Weighted Precision: 0.9277971545757154
+- Macro Recall: 0.6617262519071917
+- Micro Recall: 0.9264228741381642
+- Weighted Recall: 0.9264228741381642
+
+
+## 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/emekaboris/autonlp-txc-17923129
+```
+
+Or Python API:
+
+```
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("emekaboris/autonlp-txc-17923129", use_auth_token=True)
+
+tokenizer = AutoTokenizer.from_pretrained("emekaboris/autonlp-txc-17923129", 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": 24,
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "directionality": "bidi",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "1.0",
+    "1": "10.0",
+    "2": "11.0",
+    "3": "12.0",
+    "4": "13.0",
+    "5": "14.0",
+    "6": "15.0",
+    "7": "16.0",
+    "8": "17.0",
+    "9": "18.0",
+    "10": "19.0",
+    "11": "2.0",
+    "12": "20.0",
+    "13": "21.0",
+    "14": "22.0",
+    "15": "23.0",
+    "16": "24.0",
+    "17": "3.0",
+    "18": "4.0",
+    "19": "5.0",
+    "20": "6.0",
+    "21": "7.0",
+    "22": "8.0",
+    "23": "9.0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
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+    "9.0": 23
+  },
+  "layer_norm_eps": 1e-12,
+  "max_length": 192,
+  "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",
+  "transformers_version": "4.8.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28996
+}

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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