Instructions to use dtorber/NAS-bilingue with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dtorber/NAS-bilingue with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("summarization", model="dtorber/NAS-bilingue")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("dtorber/NAS-bilingue") model = AutoModelForSeq2SeqLM.from_pretrained("dtorber/NAS-bilingue", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,152 Bytes
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"_name_or_path": "./NAS-bilingue/checkpoint-2546384",
"activation_dropout": 0.0,
"activation_function": "gelu",
"architectures": [
"BartForConditionalGeneration"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"classifier_dropout": 0.0,
"d_model": 1024,
"decoder_attention_heads": 16,
"decoder_ffn_dim": 4096,
"decoder_layerdrop": 0.0,
"decoder_layers": 12,
"decoder_start_token_id": 1,
"dropout": 0.1,
"encoder_attention_heads": 16,
"encoder_ffn_dim": 4096,
"encoder_layerdrop": 0.0,
"encoder_layers": 12,
"eos_token_id": 2,
"forced_eos_token_id": 2,
"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
},
"mask_sent_token_id": 6,
"mask_token_id": 5,
"max_position_embeddings": 512,
"model_type": "bart",
"num_hidden_layers": 12,
"pad_token_id": 0,
"scale_embedding": false,
"torch_dtype": "float32",
"transformers_version": "4.26.1",
"unk_token_id": 3,
"use_cache": true,
"vocab_size": 60002
}
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