Instructions to use yq0522/lab1_random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yq0522/lab1_random with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("yq0522/lab1_random") model = AutoModelForSeq2SeqLM.from_pretrained("yq0522/lab1_random", device_map="auto") - Notebooks
- Google Colab
- Kaggle
yq0522/lab1_random
Browse files- README.md +3 -3
- generation_config.json +0 -1
README.md
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metrics:
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- name: Bleu
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type: bleu
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value:
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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:
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- Bleu:
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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: 7.896215171598384
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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: 5.2522
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- Bleu: 7.8962
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## Model description
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generation_config.json
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"max_length": 512,
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"num_beams": 4,
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"pad_token_id": 59513,
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"renormalize_logits": true,
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"transformers_version": "4.48.3"
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}
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"max_length": 512,
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"num_beams": 4,
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"pad_token_id": 59513,
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"transformers_version": "4.48.3"
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}
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