fairinternal -> facebookresearch (#5)

- fairinternal -> facebookresearch (ac892e67742089154e09deebd802e63a73f44bf7)

README.md

@@ -3,6 +3,7 @@ inference: false
 tags:
 - SeamlessM4T
 license: cc-by-nc-4.0
+library_name: fairseq2
 ---
 
 # SeamlessM4T Large
@@ -24,21 +25,19 @@ This is the "large" variant of the unified model, which enables multiple tasks w
 
 ## SeamlessM4T models
 
-The SeamlessM4T models come in two checkpoints of different size: 
-
-| Model Name | #params | checkpoint | metrics |
-| - | - | - | - |
-| [SeamlessM4T-Medium]((https://huggingface.co/facebook/seamless-m4t-medium) | 1.2B | [checkpoint](https://huggingface.co/facebook/seamless-m4t-medium/resolve/main/multitask_unity_medium.pt) | [metrics]() |
-| [SeamlessM4T-Large](https://huggingface.co/facebook/seamless-m4t-large) | 2.3B | [checkpoint](https://huggingface.co/facebook/seamless-m4t-large/resolve/main/multitask_unity_large.pt) | [metrics]() |
+| Model Name         | #params | checkpoint                                                                              | metrics                                                                              |
+| ------------------ | ------- | --------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------ |
+| SeamlessM4T-Large  | 2.3B    | [🤗 Model card](https://huggingface.co/facebook/seamless-m4t-large) - [checkpoint](https://huggingface.co/facebook/seamless-m4t-large/resolve/main/multitask_unity_large.pt)   | [metrics](https://dl.fbaipublicfiles.com/seamlessM4T/metrics/seamlessM4T_large.zip)  |
+| SeamlessM4T-Medium | 1.2B    | [🤗 Model card](https://huggingface.co/facebook/seamless-m4t-medium) - [checkpoint](https://huggingface.co/facebook/seamless-m4t-medium/resolve/main/multitask_unity_medium.pt) | [metrics](https://dl.fbaipublicfiles.com/seamlessM4T/metrics/seamlessM4T_medium.zip) |
 
 We provide extensive evaluation results of SeamlessM4T-Medium and SeamlessM4T-Large in the SeamlessM4T paper (as averages) in the `metrics` files above.
 
 ## Instructions to run inference with SeamlessM4T models
 
 The SeamlessM4T models are currently available through the `seamless_communication` package. The `seamless_communication` 
-package can be installed by following the instructions outlined here: [Installation](https://github.com/fairinternal/seamless_communication/tree/main#installation).
+package can be installed by following the instructions outlined here: [Installation](https://github.com/facebookresearch/seamless_communication/tree/main#installation).
 
-Once installed, a [`Translator`](https://github.com/fairinternal/seamless_communication/blob/590547965b343b590d15847a0aa25a6779fc3753/src/seamless_communication/models/inference/translator.py#L47) 
+Once installed, a [`Translator`](https://github.com/facebookresearch/seamless_communication/blob/590547965b343b590d15847a0aa25a6779fc3753/src/seamless_communication/models/inference/translator.py#L47) 
 object can be instantiated to perform all five of the spoken langauge tasks. The `Translator` is instantiated with three arguments:
 1. **model_name_or_card**: SeamlessM4T checkpoint. Can be either `seamlessM4T_medium` for the medium model, or `seamlessM4T_large` for the large model
 2. **vocoder_name_or_card**: vocoder checkpoint (`vocoder_36langs`)