Update README.md

README.md

@@ -9,27 +9,15 @@ model-index:
   results: []
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-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # bert-paper-classifier
 
-This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 1.0016
-- Accuracy: 0.6706
-
-## Model description
-
-More information needed
+This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract) on the dataset from [González-Márquez et al., 2023](https://www.biorxiv.org/content/10.1101/2023.04.10.536208v1).
 
 ## Intended uses & limitations
 
-More information needed
+This model is intended to predict the category given the paper title (and optionally its abstract) — for the biomedical papers. For example, it is likely to predict `virology` as a category for the paper with a title containing `COVID-19`.
 
-## Training and evaluation data
-
-More information needed
+So far only a subset of the PubMed dataset has been used for training. Future improvements to this model can come with using the full dataset with a combination of titles and abstracts for the fine-tuning as well as extending the training set to the preprints from bioRxiv and/or arXiv.
 
 ## Training procedure
 
@@ -37,25 +25,17 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 5e-05
-- train_batch_size: 256
+- train_batch_size: 128
 - eval_batch_size: 32
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - num_epochs: 3
 
-### Training results
-
-| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
-|:-------------:|:-----:|:-----:|:---------------:|:--------:|
-| 1.0579        | 1.0   | 3516  | 1.0306          | 0.6587   |
-| 0.9142        | 2.0   | 7032  | 0.9958          | 0.6694   |
-| 0.8156        | 3.0   | 10548 | 1.0016          | 0.6706   |
-
 
 ### Framework versions
 
 - Transformers 4.28.1
 - Pytorch 2.0.0+cu117
 - Datasets 2.11.0
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3