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
File size: 880 Bytes
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"add_cross_attention": false,
"architectures": [
"EsmForTokenClassification"
],
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"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",
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"num_hidden_layers": 12,
"pad_token_id": 1,
"position_embedding_type": "rotary",
"rope_theta": 10000.0,
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"token_dropout": true,
"transformers_version": "5.13.1",
"use_cache": false,
"vocab_list": null,
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}
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