Translation
Transformers
PyTorch
ONNX
Safetensors
m2m_100
text2text-generation
small100
flores101
gsarti/flores_101
tico19
gmnlp/tico19
tatoeba
Instructions to use alirezamsh/small100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alirezamsh/small100 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="alirezamsh/small100")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("alirezamsh/small100") model = AutoModelForSeq2SeqLM.from_pretrained("alirezamsh/small100", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
File size: 890 Bytes
8aa0892 c7f0122 8aa0892 | 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 | {
"_name_or_path": "/checkpoints/alirezamsh/fairseq/hf_small100",
"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": 0.0,
"decoder_layers": 3,
"decoder_start_token_id": 2,
"dropout": 0.1,
"encoder_attention_heads": 16,
"encoder_ffn_dim": 4096,
"encoder_layerdrop": 0.0,
"encoder_layers": 12,
"eos_token_id": 2,
"init_std": 0.02,
"is_encoder_decoder": true,
"max_position_embeddings": 1024,
"model_type": "m2m_100",
"num_hidden_layers": 12,
"pad_token_id": 1,
"scale_embedding": true,
"torch_dtype": "float32",
"transformers_version": "4.23.1",
"use_cache": true,
"vocab_size": 128112,
"max_length": 256,
"num_beams": 5
}
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