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---
datasets:
- zainabfatima097/My_Dataset
language:
- en
- hi
library_name: transformers
---
# 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.].
## Table of Contents
- [Model Details](#model-details)
- [Intended Use and Limitations](#intended-use-and-limitations)
- [Training Data](#training-data)
- [Evaluation](#evaluation)
- [How to Use](#how-to-use)
- [Citation](#citation)
- [License](#license)
- [Contact](#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](https://huggingface.co/))
- **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:
- [Metric 1]: [Value]
- [Metric 2]: [Value]
- [Add more metrics as needed]
## How to Use
```python
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) |