Instructions to use kumarprashant556/checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kumarprashant556/checkpoints with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("kumarprashant556/checkpoints") model = AutoModelForSeq2SeqLM.from_pretrained("kumarprashant556/checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from kumarprashant556/checkpoints: direct link, hf CLI and curl.
- Browser
- Download file 1.04 kB
-
https://huggingface.co/kumarprashant556/checkpoints/resolve/main/README.md
- Command line
-
hf download hf://kumarprashant556/checkpoints/README.md
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curl -L -o README.md https://huggingface.co/kumarprashant556/checkpoints/resolve/main/README.md
1.04 kB
metadata
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: checkpoints
results: []
checkpoints
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ro on the None 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.8.1+cpu
- Datasets 2.8.0
- Tokenizers 0.13.2