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@@ -67,14 +67,14 @@ BLASER 2.0 is our latest model-based evaluation metric for multimodal translatio
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We provide the extensive evaluation results of seamlessM4T-Large and SeamlessM4T-Medium reported in the paper (as averages) in the `metrics` files above.
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## Evaluating SeamlessM4T models
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-To reproduce our results, or to evaluate using the same metrics over your own test sets, please check out [README here](https://github.com/fairinternal/seamless_communication/blob/main/docs/m4t/eval_README.md).
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+To reproduce our results, or to evaluate using the same metrics over your own test sets, please check out [README here](https://github.com/facebookresearch/seamless_communication/blob/main/docs/m4t/eval_README.md).
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## Finetuning SeamlessM4T models
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TODO
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## On-device models
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-Apart from Seamless-M4T large (2.3B) and medium (1.2B) models, we are also releasing a small model (281M) targeted for on-device inference. To learn more about the usage and model details check out [README here](https://github.com/fairinternal/seamless_communication/blob/main/docs/m4t/on_device_README.md)
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+Apart from Seamless-M4T large (2.3B) and medium (1.2B) models, we are also releasing a small model (281M) targeted for on-device inference. To learn more about the usage and model details check out [README here](https://github.com/facebookresearch/seamless_communication/blob/main/docs/m4t/on_device_README.md)
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# Citation
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If you use SeamlessM4T in your work or any models/datasets/artifacts published in SeamlessM4T, please cite :
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Ning