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
MT5-small is finetuned with large corups of Nepali Health Question-Answering Dataset.
Training Procedure
The model was trained for 30 epochs with the following training parameters:
- Learning Rate: 2e-4
- Batch Size: 2
- Gradient Accumulation Steps: 8
- FP16 (mixed-precision training): Disabled
- Optimizer: AdamW with weight decay
The training loss consistently decreased, indicating successful learning.
Use Case
!pip install transformers sentencepiece
from transformers import MT5ForConditionalGeneration, AutoTokenizer
# Load the trained model
model = MT5ForConditionalGeneration.from_pretrained("Chhabi/mt5-small-finetuned-Nepali-Health-50k-2")
# Load the tokenizer for generating new output
tokenizer = AutoTokenizer.from_pretrained("Chhabi/mt5-small-finetuned-Nepali-Health-50k-2",use_fast=True)
query = "म धेरै थकित महसुस गर्छु र मेरो नाक बगिरहेको छ। साथै, मलाई घाँटी दुखेको छ र अलि टाउको दुखेको छ। मलाई के भइरहेको छ?"
input_text = f"answer: {query}"
inputs = tokenizer(input_text,return_tensors='pt',max_length=256,truncation=True).to("cuda")
print(inputs)
generated_text = model.generate(**inputs,max_length=512,min_length=256,length_penalty=3.0,num_beams=10,top_p=0.95,top_k=100,do_sample=True,temperature=0.7,num_return_sequences=3,no_repeat_ngram_size=4)
print(generated_text)
# generated_text
generated_response = tokenizer.batch_decode(generated_text,skip_special_tokens=True)[0]
tokens = generated_response.split(" ")
filtered_tokens = [token for token in tokens if not token.startswith("<extra_id_")]
print(' '.join(filtered_tokens))
Evaluation
BLEU score:
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