Commit From AutoTrain

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+*.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:
+- reachosen/autotrain-data-in-basket-3.42
+co2_eq_emissions:
+  emissions: 0.7228932272231364
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Multi-class Classification
+- Model ID: 83100142189
+- CO2 Emissions (in grams): 0.7229
+
+## Validation Metrics
+
+- Loss: 0.584
+- Accuracy: 0.851
+- Macro F1: 0.841
+- Micro F1: 0.851
+- Weighted F1: 0.847
+- Macro Precision: 0.851
+- Micro Precision: 0.851
+- Weighted Precision: 0.853
+- Macro Recall: 0.843
+- Micro Recall: 0.851
+- Weighted Recall: 0.851
+
+
+## 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/reachosen/autotrain-in-basket-3.42-83100142189
+```
+
+Or Python API:
+
+```
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("reachosen/autotrain-in-basket-3.42-83100142189", use_auth_token=True)
+
+tokenizer = AutoTokenizer.from_pretrained("reachosen/autotrain-in-basket-3.42-83100142189", 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": 31,
+  "architectures": [
+    "DebertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Abdominal Nonurgent",
+    "1": "Abdominal Urgent",
+    "2": "BrkB N",
+    "3": "BrkB U",
+    "4": "Dig N",
+    "5": "Dig U",
+    "6": "Digestive Nonurgent",
+    "7": "Face/Tongue/Lip Swell Nonurgent",
+    "8": "Face/Tongue/Lip Swell Urgent",
+    "9": "Hd N",
+    "10": "Hd U",
+    "11": "Lg Ank Swling N",
+    "12": "Lg Ank Swling U",
+    "13": "Limb: fnc/sen N",
+    "14": "Limb: fnc/sen U",
+    "15": "Mov  U",
+    "16": "Mov N",
+    "17": "Musculoskeletal Nonurgent",
+    "18": "Musculoskeletal Urgent",
+    "19": "Respiratory Nonurgent",
+    "20": "Respiratory Urgent",
+    "21": "Seizure Nonurgent",
+    "22": "Seizure Urgent",
+    "23": "Sk N",
+    "24": "Sk U",
+    "25": "Stand up/Bear Weight Nonurgent",
+    "26": "Stand up/Bear Weight Urgent",
+    "27": "Throat Nonurgent",
+    "28": "Throat Urgent",
+    "29": "Vision Nonurgent",
+    "30": "Vision Urgent"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Abdominal Nonurgent": 0,
+    "Abdominal Urgent": 1,
+    "BrkB N": 2,
+    "BrkB U": 3,
+    "Dig N": 4,
+    "Dig U": 5,
+    "Digestive Nonurgent": 6,
+    "Face/Tongue/Lip Swell Nonurgent": 7,
+    "Face/Tongue/Lip Swell Urgent": 8,
+    "Hd N": 9,
+    "Hd U": 10,
+    "Lg Ank Swling N": 11,
+    "Lg Ank Swling U": 12,
+    "Limb: fnc/sen N": 13,
+    "Limb: fnc/sen U": 14,
+    "Mov  U": 15,
+    "Mov N": 16,
+    "Musculoskeletal Nonurgent": 17,
+    "Musculoskeletal Urgent": 18,
+    "Respiratory Nonurgent": 19,
+    "Respiratory Urgent": 20,
+    "Seizure Nonurgent": 21,
+    "Seizure Urgent": 22,
+    "Sk N": 23,
+    "Sk U": 24,
+    "Stand up/Bear Weight Nonurgent": 25,
+    "Stand up/Bear Weight Urgent": 26,
+    "Throat Nonurgent": 27,
+    "Throat Urgent": 28,
+    "Vision Nonurgent": 29,
+    "Vision Urgent": 30
+  },
+  "layer_norm_eps": 1e-07,
+  "max_length": 64,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "padding": "max_length",
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 768,
+  "pos_att_type": [
+    "c2p",
+    "p2c"
+  ],
+  "position_biased_input": false,
+  "relative_attention": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.29.2",
+  "type_vocab_size": 0,
+  "vocab_size": 50265
+}

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pytorch_model.bin

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special_tokens_map.json

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tokenizer.json

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tokenizer_config.json

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