Instructions to use js0309/lab1_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use js0309/lab1_finetuning with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("js0309/lab1_finetuning") model = AutoModelForSeq2SeqLM.from_pretrained("js0309/lab1_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
js0309/lab1_finetuning
Browse files
README.md
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metrics:
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- name: Bleu
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type: bleu
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value: 48.
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.0256
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- Model Preparation Time: 0.
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- Bleu: 48.
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## Model description
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metrics:
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- name: Bleu
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type: bleu
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value: 48.955053735035584
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.0256
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- Model Preparation Time: 0.0112
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- Bleu: 48.9551
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## Model description
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