Update README.md

README.md

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 This model transcribes speech into lowercase Esperanto alphabet including spaces and apostroph. The model was obtained by finetuning from English SSL-pretrained model on Mozilla Common Voice Esperanto 11.0 dataset. It is a non-autoregressive "large" variant of Conformer [1], with around 120 million parameters. See the [model architecture](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#conformer-ctc) for complete architecture details.
 It is also compatible with NVIDIA Riva for [production-grade server deployments](#deployment-with-nvidia-riva). 
 
-The Esperanto model utilizes a Google SentencePiece [2] tokenizer with vocabulary size 128
-
 ## Usage
 
 The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for finetuning on another dataset.
@@ -103,9 +101,9 @@ Conformer-CTC model is a non-autoregressive variant of Conformer model [1] for A
 
 ## Training
 
-The NeMo toolkit [3] was used for finetuning from English SSL model for three hundred epochs. The model is finetuning with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/conformer/conformer_ctc_bpe.yaml). As pretrained English SSL model we use [ssl_en_conformer_large](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/ssl_en_conformer_large) which was trained using LibriLight corpus (~56k hrs of unlabeled English speech).
+The NeMo toolkit [3] was used for finetuning from English SSL model for over several hundred epochs. The model is finetuning with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/conformer/conformer_ctc_bpe.yaml). As pretrained English SSL model we use [ssl_en_conformer_large](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/ssl_en_conformer_large) which was trained using LibriLight corpus (~56k hrs of unlabeled English speech).
 
-The tokenizer (BPE vocab size 128) for the model was built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).
+The tokenizer for the model was built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).
 
 Full config can be found inside the .nemo files.
 
@@ -125,7 +123,7 @@ The list of the available models in this collection is shown in the following ta
 
 | Version | Tokenizer             | Vocabulary Size | Dev WER| Test WER| Train Dataset   |
 |---------|-----------------------|-----------------|--------|---------|-----------------|
-| 1.14.0  | SentencePiece BPE     | 128             |  2.9   |    4.8  | MCV-11.0 Train set |
+| 1.14.0  | SentencePiece [2] BPE     | 128             |  2.9   |    4.8  | MCV-11.0 Train set |
 
 ## Limitations
 Since this model was trained on publicly available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.