Token Classification
Transformers
Safetensors
Bengali
electra
ner
bangla
bengali
Eval Results (legacy)
Instructions to use arafatfahim/BanglaTag with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arafatfahim/BanglaTag with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="arafatfahim/BanglaTag")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("arafatfahim/BanglaTag") model = AutoModelForTokenClassification.from_pretrained("arafatfahim/BanglaTag") - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_cross_attention": false, | |
| "architectures": [ | |
| "ElectraForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": null, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "embedding_size": 768, | |
| "eos_token_id": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "O", | |
| "1": "B-PER", | |
| "2": "I-PER", | |
| "3": "B-LOC", | |
| "4": "I-LOC", | |
| "5": "B-ORG", | |
| "6": "I-ORG", | |
| "7": "B-POL", | |
| "8": "I-POL", | |
| "9": "B-DATE", | |
| "10": "I-DATE", | |
| "11": "B-TIME", | |
| "12": "I-TIME", | |
| "13": "B-EVENT", | |
| "14": "I-EVENT", | |
| "15": "B-CRIME", | |
| "16": "I-CRIME", | |
| "17": "B-TITLE", | |
| "18": "I-TITLE", | |
| "19": "B-NUM", | |
| "20": "I-NUM", | |
| "21": "B-SYMBOL", | |
| "22": "I-SYMBOL", | |
| "23": "B-CONSTITUENCY", | |
| "24": "I-CONSTITUENCY", | |
| "25": "B-INST", | |
| "26": "I-INST" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "is_decoder": false, | |
| "label2id": { | |
| "B-CONSTITUENCY": 23, | |
| "B-CRIME": 15, | |
| "B-DATE": 9, | |
| "B-EVENT": 13, | |
| "B-INST": 25, | |
| "B-LOC": 3, | |
| "B-NUM": 19, | |
| "B-ORG": 5, | |
| "B-PER": 1, | |
| "B-POL": 7, | |
| "B-SYMBOL": 21, | |
| "B-TIME": 11, | |
| "B-TITLE": 17, | |
| "I-CONSTITUENCY": 24, | |
| "I-CRIME": 16, | |
| "I-DATE": 10, | |
| "I-EVENT": 14, | |
| "I-INST": 26, | |
| "I-LOC": 4, | |
| "I-NUM": 20, | |
| "I-ORG": 6, | |
| "I-PER": 2, | |
| "I-POL": 8, | |
| "I-SYMBOL": 22, | |
| "I-TIME": 12, | |
| "I-TITLE": 18, | |
| "O": 0 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "electra", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "summary_activation": "gelu", | |
| "summary_last_dropout": 0.1, | |
| "summary_type": "first", | |
| "summary_use_proj": true, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.5.0", | |
| "type_vocab_size": 2, | |
| "use_cache": false, | |
| "vocab_size": 32000 | |
| } | |