YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
English to Tamil Translation Model
This model translates English sentences into Tamil using a fine-tuned version of the Mr-Vicky available on the Hugging Face model hub.
About the Authors
This model was developed by suriya7 in collaboration with Mr-Vicky.
Usage
To use this model, you can either directly use the Hugging Face transformers library or you can use the model via the Hugging Face inference API.
Model Information
Training Details
- This model has been fine-tuned for English to Tamil translation.
- Training Duration: Over 10 hours
- Loss Achieved: 0.6
- Model Architecture
- The model architecture is based on the Transformer architecture, specifically optimized for sequence-to-sequence tasks.
Installation
To use this model, you'll need to have the transformers library installed. You can install it via pip:
pip install transformers
Via Transformers Library
You can use this model in your Python code like this:
Inference
- How to use the model in our notebook:
# Load model directly
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
checkpoint = "suriya7/English-to-Tamil"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
def language_translator(text):
tokenized = tokenizer([text], return_tensors='pt')
out = model.generate(**tokenized, max_length=128)
return tokenizer.decode(out[0],skip_special_tokens=True)
text_to_translate = "hardwork never fail"
output = language_translator(text_to_translate)
print(output)
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