Update README.md

README.md

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 # rttl-ai/BIOptimus v.0.4
 
 ## Model Details
-**Model Description:** BIOptimus v.0.4 model pretrained on [PubMed](https://pubmed.ncbi.nlm.nih.gov/) abstracts. 
-It is introduced in the paper BIOptimus: Pre-training an Optimal Biomedical Language Model with Curriculum Learning for Named Entity Recognition (BioNLP workshop @ ACL 2023).  More information is available in [this repository](https://github.com/rttl-ai/BIOptimus).
+**Model Description:** BIOptimus v.0.4 model is a BERT-like model pre-trained on [PubMed](https://pubmed.ncbi.nlm.nih.gov/) abstracts. 
+It is a biomedical language model pre-trained using contextualized weight distillation and Curriculum Learning. 
+This model achieves state-of-the-art performance on several biomedical NER datasets from [BLURB benchmark](https://microsoft.github.io/BLURB/).
 
-This model achieves state-of-the-art performance on several biomedical NER datasets from [BLURB](https://microsoft.github.io/BLURB/).
 
 - **Developed by:** rttl-ai
-- **Model Type:** Text Classification
+- **Model Type:** Language model
 - **Language(s):** English
 - **License:** Apache-2.0
 - **Resources for more information:**
- - The model was pre-trained with task-adaptive pre-training [TAPT](https://arxiv.org/pdf/2004.10964.pdf) with an increased masking rate, no corruption strategy, and using WWM, following [this paper](https://aclanthology.org/2023.eacl-main.217.pdf)
-    - fine-tuned on sst with subtrees 
-    - fine-tuned on sst2
+ - It is introduced in the paper BIOptimus: Pre-training an Optimal Biomedical Language Model with Curriculum Learning for Named Entity Recognition (BioNLP workshop @ ACL 2023).
+ - More information is available in [this repository](https://github.com/rttl-ai/BIOptimus).