Update README.md

README.md

@@ -3,20 +3,21 @@ tags:
 - biology
 ---
 
-# xTrimoPGLM-1B-MLM
+# ProteinPGLM-1B-MLM
 
 ## Model Introduction
 
-**xTrimoPGLM-1B-MLM** is the open-source version of the latest masked protein language models designed to protein understanding tasks. The xTrimoPGLM family models are developed by BioMap and Tsinghua University. Along with this, we have released the int4 quantization xTrimoPGLM-100B weights and other xTrimo-series small models, which include: 1B, 3B, and 10B models trained with masked language modeling for protein understanding, and 1B, 3B, and 7B causal language models aimed at protein design.
+**ProteinPGLM-1B-MLM** is the open-source version of the latest masked protein language models designed to protein understanding tasks. The ProteinPGLM family models are developed by Tsinghua University. Along with this, we have released the int4 quantization ProteinPGLM-100B weights and other small models, which include: 1B, 3B, and 10B models trained with masked language modeling for protein understanding, and 1B, 3B, and 7B causal language models aimed at protein design.
 
 ### Out-of-Distribution Perplexity Evaluation
 
-We evaluated the xTrimoPGLM-MLM (xTMLM) and xTrimoPGLM(100B) models on two OOD test sets, one with sequence identity lower than 0.9 with the training set (<0.9 ID) and the other with sequence identity lower than 0.5 with the training set (<0.5 ID). Each OOD dataset comprises approximately 10,000 protein sequences. The perplexity results, compared against ESM2-3B and ESM2-15B, are as follows (lower is better):
+We evaluated the ProteinPGLM-MLM (PGLM) and ProteinPGLM-INT4(100B) models on two OOD test sets, one with sequence identity lower than 0.9 with the training set (<0.9 ID) and the other with sequence identity lower than 0.5 with the training set (<0.5 ID). Each OOD dataset comprises approximately 10,000 protein sequences. The perplexity results, compared against ESM2-3B and ESM2-15B, are as follows (lower is better):
 
-| Model               | ESM2(3B)| ESM2 (15B) | xTMLM (1B) | xTMLM (3B) | xTMLM (10B) | xT (100B)-INT4 |
+| Model               | ESM2(3B)| ESM2 (15B) | PGLM (1B) | PGLM (3B) | PGLM (10B) |  PGLM-INT4 (100B) |
 |:--------------------|:----------:|:----------:|:----------:|:----------:|:--------------------:|:--------------------:|
-| < 0.9 ID           |   7.7   |   7.3    | 9.3    |    7.8    |      7.6           |  **6.8**         | 
-| < 0.5 ID            |  11.5 |  11.0  |  13.5 |   11.9  |   11.6             | **10.8**          |
+| < 0.9 ID           |   7.7   |   7.3    | 9.3    |    7.8    |      7.6    |             **6.8**         | 
+| < 0.5 ID            |  11.5 |  11.0  |  13.5 |   11.9  |   11.6   |         **10.8**          |
+
 
 
 ## Downstream Protein Understanding Tasks Evaluation
@@ -30,8 +31,8 @@ We evaluated the xTrimoPGLM-MLM (xTMLM) and xTrimoPGLM(100B) models on two OOD t
 from transformers import AutoModelForMaskedLM, AutoModelForSequenceClassification, AutoModelForTokenClassification, AutoTokenizer, AutoConfig
 import torch
 
-tokenizer  = AutoTokenizer.from_pretrained("biomap-research/xtrimopglm-1b-mlm", trust_remote_code=True, use_fast=True)
-model = AutoModelForMaskedLM.from_pretrained("biomap-research/xtrimopglm-1b-mlm", trust_remote_code=True, torch_dtype=torch.bfloat16)
+tokenizer  = AutoTokenizer.from_pretrained("Bo1015/proteinglm-1b-mlm", trust_remote_code=True, use_fast=True)
+model = AutoModelForMaskedLM.from_pretrained("Bo1015/proteinglm-1b-mlm",  trust_remote_code=True, torch_dtype=torch.bfloat16)
 if torch.cuda.is_available():
     model = model.cuda()
 model.eval()
@@ -44,16 +45,13 @@ with torch.inference_mode():
 
 
 # model for the sequence-level tasks
-model = AutoModelForSequenceClassification.from_pretrained("biomap-research/xtrimopglm-1b-mlm", trust_remote_code=True, torch_dtype=torch.bfloat16)
+model = AutoModelForSequenceClassification.from_pretrained("Bo1015/proteinglm-10b-mlm",  trust_remote_code=True, torch_dtype=torch.bfloat16)
 
 # model for the token-level tasks
-model = AutoModelForTokenClassification.from_pretrained("biomap-research/xtrimopglm-1b-mlm", trust_remote_code=True, torch_dtype=torch.bfloat16)
+model = AutoModelForTokenClassification.from_pretrained("Bo1015/proteinglm-10b-mlm",  trust_remote_code=True, torch_dtype=torch.bfloat16)
 
 ```
 
-
-For more inference or fine-tuning code, datasets, and requirements, please visit our [GitHub page](https://github.com/biomap-research/xTrimoPGLM).
-
 ## LICENSE
 
 The code in this repository is open source under the [Creative Commons Attribution-NonCommercial 4.0 International License](./LICENSE).