Instructions to use masakhane/m2m100_418M_hau_en_rel_ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use masakhane/m2m100_418M_hau_en_rel_ft with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("masakhane/m2m100_418M_hau_en_rel_ft") model = AutoModelForSeq2SeqLM.from_pretrained("masakhane/m2m100_418M_hau_en_rel_ft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download all_results.json from masakhane/m2m100_418M_hau_en_rel_ft: direct link, hf CLI and curl.
- Browser
- Download file 433 Bytes
-
https://huggingface.co/masakhane/m2m100_418M_hau_en_rel_ft/resolve/main/all_results.json
- Command line
-
hf download hf://masakhane/m2m100_418M_hau_en_rel_ft/all_results.json
-
curl -L -o all_results.json https://huggingface.co/masakhane/m2m100_418M_hau_en_rel_ft/resolve/main/all_results.json
433 Bytes
| { | |
| "epoch": 3.0, | |
| "predict_bleu": 17.38, | |
| "predict_gen_len": 35.144, | |
| "predict_loss": 2.0691263675689697, | |
| "predict_runtime": 574.8468, | |
| "predict_samples": 1500, | |
| "predict_samples_per_second": 2.609, | |
| "predict_steps_per_second": 0.261, | |
| "train_loss": 1.9193934286794354, | |
| "train_runtime": 353.0158, | |
| "train_samples": 3098, | |
| "train_samples_per_second": 26.327, | |
| "train_steps_per_second": 2.634 | |
| } |