Instructions to use transZ/M2M_Vi_Ba with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use transZ/M2M_Vi_Ba 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="transZ/M2M_Vi_Ba")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("transZ/M2M_Vi_Ba") model = AutoModelForSeq2SeqLM.from_pretrained("transZ/M2M_Vi_Ba", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| language: | |
| - vi | |
| - ba | |
| tags: | |
| - translation | |
| datasets: | |
| - custom dataset | |
| metrics: | |
| - bleu | |
| - sacrebleu | |
| # How to run the model | |
| ```python | |
| from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer | |
| model = M2M100ForConditionalGeneration.from_pretrained("transZ/M2M_Vi_Ba") | |
| tokenizer = M2M100Tokenizer.from_pretrained("transZ/M2M_Vi_Ba") | |
| tokenizer.src_lang = "vi" | |
| vi_text = "Hôm nay ba đi chợ." | |
| encoded_vi = tokenizer(vi_text, return_tensors="pt") | |
| generated_tokens = model.generate(**encoded_vi, forced_bos_token_id=tokenizer.get_lang_id("ba")) | |
| translate = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0] | |
| print(translate) | |
| ``` |