Instructions to use abk20031218/checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abk20031218/checkpoints with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="abk20031218/checkpoints")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("abk20031218/checkpoints") model = AutoModelForTokenClassification.from_pretrained("abk20031218/checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_cross_attention": false, | |
| "architectures": [ | |
| "EsmForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.0, | |
| "bos_token_id": null, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "emb_layer_norm_before": false, | |
| "eos_token_id": 2, | |
| "esmfold_config": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.0, | |
| "hidden_size": 480, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1920, | |
| "is_decoder": false, | |
| "is_folding_model": false, | |
| "layer_norm_eps": 1e-05, | |
| "mask_token_id": 32, | |
| "max_position_embeddings": 1026, | |
| "model_type": "esm", | |
| "num_attention_heads": 20, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "rotary", | |
| "rope_theta": 10000.0, | |
| "tie_word_embeddings": true, | |
| "token_dropout": true, | |
| "transformers_version": "5.13.1", | |
| "use_cache": false, | |
| "vocab_list": null, | |
| "vocab_size": 33 | |
| } | |