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:
+- abhishek/autonlp-data-prodigy-10
+co2_eq_emissions: 5.340540212393564
+---
+
+# Model Trained Using AutoNLP
+
+- Problem type: Entity Extraction
+- Model ID: 3362554
+- CO2 Emissions (in grams): 5.340540212393564
+
+## Validation Metrics
+
+- Loss: 0.14167872071266174
+- Accuracy: 0.9587076867229332
+- Precision: 0.7351351351351352
+- Recall: 0.7923728813559322
+- F1: 0.7626816212082591
+
+## 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-prodigy-10-3362554
+```
+
+Or Python API:
+
+```
+from transformers import AutoModelForTokenClassification, AutoTokenizer
+
+model = AutoModelForTokenClassification.from_pretrained("abhishek/autonlp-prodigy-10-3362554", use_auth_token=True)
+
+tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-prodigy-10-3362554", 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": 9,
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "B-LOCATION",
+    "1": "B-ORG",
+    "2": "B-PERSON",
+    "3": "B-PRODUCT",
+    "4": "I-LOCATION",
+    "5": "I-ORG",
+    "6": "I-PERSON",
+    "7": "I-PRODUCT",
+    "8": "O"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "B-LOCATION": 0,
+    "B-ORG": 1,
+    "B-PERSON": 2,
+    "B-PRODUCT": 3,
+    "I-LOCATION": 4,
+    "I-ORG": 5,
+    "I-PERSON": 6,
+    "I-PRODUCT": 7,
+    "O": 8
+  },
+  "layer_norm_eps": 1e-12,
+  "max_length": 512,
+  "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",
+  "transformers_version": "4.8.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

pytorch_model.bin

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+size 1336566961

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"}