Instructions to use chamdentimem/Final-DeepLearning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chamdentimem/Final-DeepLearning with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("chamdentimem/Final-DeepLearning") model = AutoModelForSeq2SeqLM.from_pretrained("chamdentimem/Final-DeepLearning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from chamdentimem/Final-DeepLearning: direct link, hf CLI and curl.
- Browser
- Download file 1.43 kB
-
https://huggingface.co/chamdentimem/Final-DeepLearning/resolve/main/README.md
- Command line
-
hf download hf://chamdentimem/Final-DeepLearning/README.md
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curl -L -o README.md https://huggingface.co/chamdentimem/Final-DeepLearning/resolve/main/README.md
1.43 kB
metadata
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-vi
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: Final-DeepLearning
results: []
Final-DeepLearning
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-vi on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.6884
- Bleu: 26.3182
- Gen Len: 11.991
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| 1.697 | 1.0 | 31250 | 1.6884 | 26.3182 | 11.991 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1