Instructions to use vania2911/barto_exp1_10partition_modelo_msl6000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vania2911/barto_exp1_10partition_modelo_msl6000 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("vania2911/barto_exp1_10partition_modelo_msl6000") model = AutoModelForSeq2SeqLM.from_pretrained("vania2911/barto_exp1_10partition_modelo_msl6000") - Notebooks
- Google Colab
- Kaggle
File size: 1,041 Bytes
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"activation_dropout": 0.1,
"activation_function": "gelu",
"architectures": [
"BartForConditionalGeneration"
],
"attention_dropout": 0.1,
"bos_token_id": 0,
"classifier_dropout": 0.1,
"d_model": 768,
"decoder_attention_heads": 12,
"decoder_ffn_dim": 3072,
"decoder_layerdrop": 0.0,
"decoder_layers": 6,
"decoder_start_token_id": 2,
"dropout": 0.1,
"encoder_attention_heads": 12,
"encoder_ffn_dim": 3072,
"encoder_layerdrop": 0.0,
"encoder_layers": 6,
"eos_token_id": 2,
"forced_eos_token_id": 2,
"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": null,
"num_beams": null,
"num_hidden_layers": 6,
"pad_token_id": 1,
"scale_embedding": false,
"torch_dtype": "float32",
"transformers_version": "4.51.3",
"use_cache": true,
"vocab_size": 50268
}
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