Update README.md

README.md

@@ -1,3 +1,16 @@
+---
+language:
+- en
+- hi
+- te
+- ta
+- mr
+- kn
+- ml
+pipeline_tag: translation
+tags:
+- indic_trans_v2
+---
 # IndicTrans2 HF Compatible Models
 
 In this section, we provide details on how to use our [IndicTrans2](https://github.com/AI4Bharat/IndicTrans2) models which were originally trained with the [fairseq](https://github.com/facebookresearch/fairseq) to [HuggingFace transformers](https://huggingface.co/docs/transformers/index) for inference purpose. Our scripts for HuggingFace compatible models are adapted from [M2M100 repository](https://github.com/huggingface/transformers/tree/main/src/transformers/models/m2m_100).
@@ -19,17 +32,6 @@ source install.sh
 > Note: The `install.sh` script in this directory is specifically for running HF compatible models for inference.
 
 
-### Converting
-
-In order to convert the fairseq checkpoint to a PyTorch checkpoint that is compatible with HuggingFace Transformers, use the following command:
-
-```bash
-python3 convert_indictrans_checkpoint_to_pytorch.py --fairseq_path <fairseq_checkpoint_best.pt> --pytorch_dump_folder_path <hf_output_dir>
-```
-- `<fairseq_checkpoint_best.pt>`: path to the fairseq `checkpoint_best.pt` that needs to be converted to HF compatible models
-- `<hf_output_dir>`: path to the output directory where the HF compatible models will be saved
-
-
 ### Models
 
 | Model    | 🤗 HuggingFace Checkpoints        |
@@ -59,4 +61,4 @@ Feel free to modify the `example.py` script to suit your translation needs.
   year    = {2023},
   journal = {arXiv preprint arXiv: 2305.16307}
 }
-```
+```