Update README.md

README.md

@@ -1,199 +1,120 @@
 ---
-library_name: transformers
-tags: []
+library_name: peft
+license: cc-by-4.0
+datasets:
+- SPRINGLab/shiksha
+- SPRINGLab/BPCC_cleaned
+language:
+- bn
+- gu
+- hi
+- mr
+- ml
+- kn
+- ta
+- te
+- en
+metrics:
+- bleu
+base_model:
+- facebook/nllb-200-3.3B
+pipeline_tag: translation
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Shiksha MT Model Card
 
 ## Model Details
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
+### 1. Model Description
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+- **Developed by:** [SPRING Lab](https://asr.iitm.ac.in)
+- **Model type:** LoRA Adaptor
+- **Language(s) (NLP):** Bengali, Gujarati, Hindi, Marathi, Malayalam, Kannada, Tamil, Telugu
+- **License:** CC-BY-4.0
+- **Finetuned from model:** [NLLB-200 3.3B](https://huggingface.co/facebook/nllb-200-3.3B)
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+### 2. Model Sources
 
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Paper:** https://arxiv.org/abs/2412.09025
+- **Demo:** https://asr.iitm.ac.in/demo/ttt
 
 ## Uses
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
+```python
+import torch
+from peft import AutoPeftModelForSeq2SeqLM
+from transformers import NllbTokenizerFast
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
-#### Speeds, Sizes, Times [optional]
+# Load model and tokenizer from local checkpoint
+model = AutoPeftModelForSeq2SeqLM.from_pretrained("SPRINGLab/shiksha-MT-nllb-3.3B", device_map=device)
+tokenizer = NllbTokenizerFast.from_pretrained("facebook/nllb-200-3.3B")
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+input_text = "Welcome back to the lecture series in Cell Culture."
 
-[More Information Needed]
+# Lang codes: https://github.com/facebookresearch/flores/tree/main/flores200
+tgt_lang = "hin_Deva"
 
-## Evaluation
+inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True)
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+output = model.generate(input_ids=inputs["input_ids"].to(device), max_new_tokens=256, forced_bos_token_id=tokenizer.convert_tokens_to_ids(tgt_lang))
 
-### Testing Data, Factors & Metrics
+output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
 
-#### Testing Data
+print(output_text[0])
+```
 
-<!-- This should link to a Dataset Card if possible. -->
 
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+## Training Details
 
-## Technical Specifications [optional]
+### 1. Training Data
 
-### Model Architecture and Objective
+We used the following datasets for training this adapter:
 
-[More Information Needed]
+Shiksha: https://huggingface.co/datasets/SPRINGLab/shiksha
+<br>
+BPCC-cleaned: https://huggingface.co/datasets/SPRINGLab/BPCC_cleaned
 
-### Compute Infrastructure
 
-[More Information Needed]
+#### 2. Training Hyperparameters
 
-#### Hardware
+- peft-type: LORA
+- rank: 256
+- lora alpha: 256
+- lora dropout: 0.1
+- rslora: True
+- target modules: all-linear
+- learning rate: 4e-5
+- optimizer: adafactor
+- data-type: BF-16
+- epochs: 1
 
-[More Information Needed]
 
-#### Software
+### 3. Compute Infrastructure
 
-[More Information Needed]
+We used 8 x A100 40GB GPUs for training this adapter. We would like to thank [CDAC](https://cdac.in) for providing the compute resources.
 
-## Citation [optional]
+## Citation
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+If you use this model in your work, please cite us:
 
 **BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+```bibtex
+@misc{joglekar2024shikshatechnicaldomainfocused,
+      title={Shiksha: A Technical Domain focused Translation Dataset and Model for Indian Languages}, 
+      author={Advait Joglekar and Srinivasan Umesh},
+      year={2024},
+      eprint={2412.09025},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2412.09025}, 
+}
+```