Instructions to use bbaaaa/myfork2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bbaaaa/myfork2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="bbaaaa/myfork2")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("bbaaaa/myfork2") model = AutoModel.from_pretrained("bbaaaa/myfork2") - Notebooks
- Google Colab
- Kaggle
File size: 1,713 Bytes
e658f90 b0c3779 73413dd e658f90 c5eee23 e658f90 73413dd e658f90 73413dd c5eee23 e423709 e658f90 2557745 73413dd b2ec1a3 e423709 e658f90 c5eee23 d0af988 c5eee23 e658f90 c5eee23 e658f90 60823c5 e658f90 b2ec1a3 c5eee23 b2ec1a3 b0c3779 c5eee23 00201dc e658f90 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | {
"_name_or_path": "bart-base",
"activation_dropout": 0.3,
"activation_function": "gelu",
"add_bias_logits": false,
"add_final_layer_norm": false,
"architectures": [
"BartModel"
],
"attention_dropout": 0.3,
"bos_token_id": 0,
"classif_dropout": 0.3,
"classifier_dropout": 0.0,
"d_model": 512,
"decoder_attention_heads": 8,
"decoder_ffn_dim": 2048,
"decoder_layerdrop": 0.0,
"decoder_layers": 6,
"decoder_start_token_id": 2,
"dropout": 0.3,
"early_stopping": true,
"encoder_attention_heads": 8,
"encoder_ffn_dim": 2048,
"encoder_layerdrop": 0.0,
"encoder_layers": 6,
"eos_token_id": 2,
"forced_eos_token_id": 2,
"forced_bos_token_id": 0,
"gradient_checkpointing": false,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1",
"2": "LABEL_2"
},
"init_std": 0.02,
"is_encoder_decoder": true,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1,
"LABEL_2": 2
},
"max_position_embeddings": 1024,
"model_type": "bart",
"no_repeat_ngram_size": 3,
"normalize_before": false,
"normalize_embedding": true,
"num_beams": 5,
"num_hidden_layers": 6,
"pad_token_id": 1,
"scale_embedding": false,
"task_specific_params": {
"summarization": {
"length_penalty": 1.0,
"max_length": 128,
"min_length": 12,
"num_beams": 4
},
"summarization_cnn": {
"length_penalty": 2.0,
"max_length": 142,
"min_length": 56,
"num_beams": 4
},
"summarization_xsum": {
"length_penalty": 1.0,
"max_length": 62,
"min_length": 11,
"num_beams": 6
}
},
"torch_dtype": "float32",
"transformers_version": "4.12.0.dev0",
"use_cache": true,
"vocab_size": 8000
}
|