Translation
Transformers
Safetensors
multilingual
m2m_100
text2text-generation
nllb
seq2seq
endpoints-template
Instructions to use Resilient-Coders/baseline-nllb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Resilient-Coders/baseline-nllb with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" 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("translation", model="Resilient-Coders/baseline-nllb")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Resilient-Coders/baseline-nllb") model = AutoModelForSeq2SeqLM.from_pretrained("Resilient-Coders/baseline-nllb") - Notebooks
- Google Colab
- Kaggle
File size: 817 Bytes
4ac6fc1 | 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 | {
"activation_dropout": 0.0,
"activation_function": "relu",
"architectures": [
"M2M100ForConditionalGeneration"
],
"attention_dropout": 0.1,
"bos_token_id": 0,
"d_model": 1024,
"decoder_attention_heads": 16,
"decoder_ffn_dim": 4096,
"decoder_layerdrop": 0,
"decoder_layers": 12,
"decoder_start_token_id": 2,
"dropout": 0.1,
"dtype": "float32",
"encoder_attention_heads": 16,
"encoder_ffn_dim": 4096,
"encoder_layerdrop": 0,
"encoder_layers": 12,
"eos_token_id": 2,
"init_std": 0.02,
"is_encoder_decoder": true,
"max_position_embeddings": 1024,
"model_type": "m2m_100",
"pad_token_id": 1,
"scale_embedding": true,
"tie_word_embeddings": true,
"tokenizer_class": "NllbTokenizer",
"transformers_version": "5.6.0",
"use_cache": true,
"vocab_size": 256206
}
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