Create README.md

README.md

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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)