|
|
--- |
|
|
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) |
