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:
+- it
+widget:
+- text: "I love AutoTrain"
+datasets:
+- kolkata97/autotrain-data-pe-llm-0.6
+co2_eq_emissions:
+  emissions: 0.022138627441573373
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Multi-class Classification
+- Model ID: 89942144050
+- CO2 Emissions (in grams): 0.0221
+
+## Validation Metrics
+
+- Loss: 0.841
+- Accuracy: 0.761
+- Macro F1: 0.644
+- Micro F1: 0.761
+- Weighted F1: 0.750
+- Macro Precision: 0.679
+- Micro Precision: 0.761
+- Weighted Precision: 0.748
+- Macro Recall: 0.635
+- Micro Recall: 0.761
+- Weighted Recall: 0.761
+
+
+## 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/kolkata97/autotrain-pe-llm-0.6-89942144050
+```
+
+Or Python API:
+
+```
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("kolkata97/autotrain-pe-llm-0.6-89942144050", use_auth_token=True)
+
+tokenizer = AutoTokenizer.from_pretrained("kolkata97/autotrain-pe-llm-0.6-89942144050", 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": 15,
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "ID",
+    "1": "M&A",
+    "2": "acceptance",
+    "3": "competence",
+    "4": "data protection",
+    "5": "date",
+    "6": "intellectual property",
+    "7": "liability",
+    "8": "license",
+    "9": "object",
+    "10": "party",
+    "11": "remedy",
+    "12": "term",
+    "13": "termination",
+    "14": "warranty"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "ID": 0,
+    "M&A": 1,
+    "acceptance": 2,
+    "competence": 3,
+    "data protection": 4,
+    "date": 5,
+    "intellectual property": 6,
+    "liability": 7,
+    "license": 8,
+    "object": 9,
+    "party": 10,
+    "remedy": 11,
+    "term": 12,
+    "termination": 13,
+    "warranty": 14
+  },
+  "layer_norm_eps": 1e-12,
+  "max_length": 256,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "padding": "max_length",
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.29.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 31102
+}

model.safetensors

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pytorch_model.bin

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special_tokens_map.json

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+{
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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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+{
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "max_len": 512,
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+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}