indictrans2-indic-en-1B Fine-tuned for [Your Task]

This model is a fine-tuned version of ai4bharat/indictrans2-indic-en-1B specifically trained for [Your Task, e.g., Indic to English translation, Indic text classification, etc.]. It has been fine-tuned on the [Dataset Name] dataset, resulting in improved performance on [Specific Metrics or Aspects, e.g., translation quality, classification accuracy, etc.].

Model Details
Intended Use and Limitations
Training Data
Evaluation
How to Use
Citation
License
Contact

Model Details

Model Type: Sequence-to-Sequence Language Model (Fine-tuned)
Original Model: ai4bharat/indictrans2-indic-en-1B
Fine-tuning Task: [Your Task, e.g., Indic to English translation, Indic text classification, etc.]
Language(s): [List languages, e.g., Hindi, Bengali, Tamil, English, etc.]
Training Framework: Transformers (Hugging Face)
PEFT Method: LoRA (Low-Rank Adaptation)

Intended Use and Limitations

This model is intended for [Describe intended use, e.g., translating Indic languages to English, classifying Indic text sentiment, etc.]. It is best suited for [Specific Domains or Types of Text].

Limitations:

The model's performance may vary depending on the specific Indic language and the domain of the text.
It may not perform well on text that is significantly different from the training data.
[Add any other limitations you are aware of, e.g., bias in the data, computational requirements, etc.]

Training Data

The model was fine-tuned on the [Dataset Name] dataset ([Hugging Face Dataset Card URL](If applicable)). This dataset consists of [Describe the data, e.g., parallel text for translation, labeled text for classification, etc.]. The dataset contains approximately [Number] examples for training, [Number] examples for validation, and [Number] examples for testing.

Evaluation

The model was evaluated on the [Dataset Name] test set using the [Evaluation Metrics, e.g., BLEU score for translation, Accuracy/F1-score for classification]. The model achieved the following results:

[Add more metrics as needed]

How to Use

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch

model_path = "[Your Model Path or Hub Name]"  # Replace with your model path or Hugging Face Hub name
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)

# Example Usage (Adapt to your specific task)
inputs = tokenizer("[Your Input Text]", return_tensors="pt")
outputs = model.generate(**inputs)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

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