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") - Inference
- Notebooks
- Google Colab
- Kaggle
Match evaluation settings in `config.json`
Hi,
I am trying to use the model but as reported in the Github repo currently the max_length is set to 20 by default. Would it be possible to add a more lenient default limit (e.g. 128 tokens), and maybe also the num_beams = 5 used in the paper to the model config for default usage?
Thanks in advance!
Hi,
Thanks for your message and concern. I updated the model card to include our default generation config. We use beam size of 5 and maximum target length of 256 tokens.
Please let me know if you have further concerns!
Best,
Alireza
Thank you for your answer! Wouldn't it be more practical to have these as defaults in the config.json file?
Sure. Added. Thanks for your message.
I am closing the case, please don't hesitate to open it if you have further concerns. Thanks.