Instructions to use gowtham58/T_TL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gowtham58/T_TL with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("gowtham58/T_TL") model = AutoModelForSeq2SeqLM.from_pretrained("gowtham58/T_TL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from gowtham58/T_TL: direct link, hf CLI and curl.
- Browser
- Download file 1.56 kB
-
https://huggingface.co/gowtham58/T_TL/resolve/main/README.md
- Command line
-
hf download hf://gowtham58/T_TL/README.md
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curl -L -o README.md https://huggingface.co/gowtham58/T_TL/resolve/main/README.md
1.56 kB
metadata
license: mit
base_model: facebook/m2m100_418M
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: T_TL
results: []
T_TL
This model is a fine-tuned version of facebook/m2m100_418M on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6574
- Bleu: 44.6034
- Gen Len: 32.5553
Model description
The Model can Generate Tamil Sentences from Transliterated Tamil words typed usin English Alphabets
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: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| No log | 1.0 | 496 | 0.7856 | 37.1055 | 32.574 |
| 1.5361 | 2.0 | 992 | 0.6574 | 44.6034 | 32.5553 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1