Instructions to use usaf/ztranslate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use usaf/ztranslate with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("usaf/ztranslate") model = AutoModelForSeq2SeqLM.from_pretrained("usaf/ztranslate", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from usaf/ztranslate: direct link, hf CLI and curl.
- Browser
- Download file 1.24 kB
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https://huggingface.co/usaf/ztranslate/resolve/main/README.md
- Command line
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hf download hf://usaf/ztranslate/README.md
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curl -L -o README.md https://huggingface.co/usaf/ztranslate/resolve/main/README.md
1.24 kB
metadata
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: ztranslate
results: []
ztranslate
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-sw on an unknown dataset.
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 |
|---|---|---|---|---|---|
| No log | 1.0 | 113 | 0.9276 | 48.8401 | 19.9436 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1