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malayalam-ULMFit-Seq2Seq
hugginglearners/malayalam-ULMFit-Seq2Seq
malayalam-ULMFit-Seq2Seq is a machine learning model from hugginglearners. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for fastai.
[](https://www.kaggle.com/code/rajeshradhakrishnan/ml-ulmfit-seq2seq-translation)
Access
Public
Updated Jul 16, 2022
Repo size
450 MB
Likes
1
Public
Repo makeup
Click a slice to open those files.
.pkl151 MB · 100%
At a glance
- Library
- fastai
- Access
- Public
- Created
- Jul 16, 2022
- Updated
- Jul 16, 2022
- SHA
- fe657d29
Try a prompt
Model card
From the Hugging Face model README
മലയാളം - English ULMFit translationmodel. (Working in Progress)
malayalam-ULMFit-Seq2Seq (Traslation model)
malayalam-ULMFit-Seq2Seq model is pre-trained on Malyalam_Language_Model_ULMFiT using fastai Language Model using fastai
Tokenized using Sentencepiece with a vocab size of 10000 the language model is upload to kaggle dataset
Usage
!pip install -Uqq huggingface_hub["fastai"]
from huggingface_hub import from_pretrained_fastai
learner = from_pretrained_fastai(repo_id)
original_xtext = 'കേൾക്കുന്ന എല്ലാ കാര്യങ്ങളും എനിക്കു മനസിലായില്ല'
original_ytext = 'I didnt understand all this'
predicted_text = learner.predict(original_xtext)
print(f'original text: {original_xtext}')
print(f'original answer: {original_ytext}')
print(f'predicted text: {predicted_text}')
Intended uses & limitations
It's not fine tuned to the state of the art accuracy
Training and evaluation data
Malayalam Samanantar Dataset - uploaded to kaggle with english - malayalam
- Library
- fastai
- Languages
- ml
- Created
- Jul 16, 2022
- Updated
- Jul 16, 2022