Instructions to use SarwarShafee/BanglaBert_with_TFModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SarwarShafee/BanglaBert_with_TFModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SarwarShafee/BanglaBert_with_TFModel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SarwarShafee/BanglaBert_with_TFModel") model = AutoModelForSequenceClassification.from_pretrained("SarwarShafee/BanglaBert_with_TFModel") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "sHafee/TFModel", | |
| "activation": "gelu", | |
| "architectures": [ | |
| "DistilBertForSequenceClassification" | |
| ], | |
| "attention_dropout": 0.1, | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "dim": 768, | |
| "dropout": 0.1, | |
| "embedding_size": 768, | |
| "hidden_act": "gelu", | |
| "hidden_dim": 3072, | |
| "hidden_dropout_prob": 0.1, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "distilbert", | |
| "n_heads": 12, | |
| "n_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "qa_dropout": 0.1, | |
| "seq_classif_dropout": 0.2, | |
| "sinusoidal_pos_embds": false, | |
| "summary_activation": "gelu", | |
| "summary_last_dropout": 0.1, | |
| "summary_type": "first", | |
| "summary_use_proj": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.31.0", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 32000 | |
| } | |