Update README.md

README.md

@@ -54,7 +54,7 @@ The compression module is a light-weight transformer that takes as input the hid
 
 ## Version
 
-This version of ZeroSwot is trained with ASR data from CommonVoice, and adapted [wav2vec2.0-large](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) to the [nllb-200-distilled-1.3B_covost2](https://huggingface.co/facebook/nllb-200-distilled-600M_covost2_en-to-15) model, which was first finetuned on CoVoST2 MT data.
+This version of ZeroSwot is trained with ASR data from CommonVoice. It adapts [wav2vec2.0-large](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) to the embedding space of the [nllb-200-distilled-1.3B_covost2](https://huggingface.co/johntsi/nllb-200-distilled-600M_covost2_en-to-15) model, which is a multilingually finetuned NLLB on MuST-C MT data.
 
 We have more versions available:
 
@@ -91,7 +91,7 @@ def load_and_resample_audio(audio_path, target_sr=16000):
 
 # Load processors and tokenizers
 processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")
-tokenizer = NllbTokenizer.from_pretrained("johntsi/nllb-200-distilled-600M_covost2_en-to-15")
+tokenizer = NllbTokenizer.from_pretrained("johntsi/nllb-200-distilled-1.3B_covost2_en-to-15")
 
 # Load ZeroSwot Encoder
 commit_hash = "762878c55bf91406318983c724db22590a828e96"
@@ -102,7 +102,7 @@ zeroswot_encoder.eval()
 zeroswot_encoder.to("cuda")
 
 # Load NLLB Model
-nllb_model = AutoModelForSeq2SeqLM.from_pretrained("johntsi/nllb-200-distilled-600M_covost2_en-to-15")
+nllb_model = AutoModelForSeq2SeqLM.from_pretrained("johntsi/nllb-200-distilled-1.3B_covost2_en-to-15")
 nllb_model.eval()
 nllb_model.to("cuda")