Token Classification
Transformers
Safetensors
English
distilbert
named-entity-recognition
ner
bio
Eval Results (legacy)
Instructions to use Hasanblanka/polygraf-ner-stage3-first-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hasanblanka/polygraf-ner-stage3-first-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Hasanblanka/polygraf-ner-stage3-first-model")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Hasanblanka/polygraf-ner-stage3-first-model") model = AutoModelForTokenClassification.from_pretrained("Hasanblanka/polygraf-ner-stage3-first-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "activation": "gelu", | |
| "architectures": [ | |
| "DistilBertForTokenClassification" | |
| ], | |
| "attention_dropout": 0.1, | |
| "bos_token_id": null, | |
| "dim": 768, | |
| "dropout": 0.1, | |
| "dtype": "float32", | |
| "eos_token_id": null, | |
| "hidden_dim": 3072, | |
| "id2label": { | |
| "0": "O", | |
| "1": "B-PERSON", | |
| "2": "I-PERSON", | |
| "3": "B-ORGANIZATION", | |
| "4": "I-ORGANIZATION", | |
| "5": "B-LOCATION", | |
| "6": "I-LOCATION", | |
| "7": "B-TIMEDATE", | |
| "8": "I-TIMEDATE", | |
| "9": "B-PRODUCT", | |
| "10": "I-PRODUCT", | |
| "11": "B-WORKOFART", | |
| "12": "I-WORKOFART", | |
| "13": "B-JOB", | |
| "14": "I-JOB", | |
| "15": "B-AMOUNT", | |
| "16": "I-AMOUNT" | |
| }, | |
| "initializer_range": 0.02, | |
| "label2id": { | |
| "B-AMOUNT": 15, | |
| "B-JOB": 13, | |
| "B-LOCATION": 5, | |
| "B-ORGANIZATION": 3, | |
| "B-PERSON": 1, | |
| "B-PRODUCT": 9, | |
| "B-TIMEDATE": 7, | |
| "B-WORKOFART": 11, | |
| "I-AMOUNT": 16, | |
| "I-JOB": 14, | |
| "I-LOCATION": 6, | |
| "I-ORGANIZATION": 4, | |
| "I-PERSON": 2, | |
| "I-PRODUCT": 10, | |
| "I-TIMEDATE": 8, | |
| "I-WORKOFART": 12, | |
| "O": 0 | |
| }, | |
| "max_position_embeddings": 512, | |
| "model_type": "distilbert", | |
| "n_heads": 12, | |
| "n_layers": 6, | |
| "output_past": true, | |
| "pad_token_id": 0, | |
| "qa_dropout": 0.1, | |
| "seq_classif_dropout": 0.2, | |
| "sinusoidal_pos_embds": false, | |
| "tie_weights_": true, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.7.0", | |
| "vocab_size": 28996 | |
| } | |