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---
library_name: transformers
license: mit
base_model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract
tags:
- generated_from_trainer
datasets:
- source_data
metrics:
- precision
- recall
- f1
model-index:
- name: SourceData_NER_v1_0_0_PubMedBERT_base
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: source_data
type: source_data
config: NER
split: validation
args: NER
metrics:
- name: Precision
type: precision
value: 0.8140302498537645
- name: Recall
type: recall
value: 0.8535940649005462
- name: F1
type: f1
value: 0.8333428384042887
---
<!-- 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. -->
# SourceData_NER_v1_0_0_PubMedBERT_base
This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract) on the source_data dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1432
- Accuracy Score: 0.9557
- Precision: 0.8140
- Recall: 0.8536
- F1: 0.8333
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 128
- seed: 42
- optimizer: Use adafactor and the args are:
No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy Score | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:---------:|:------:|:------:|
| 0.1092 | 1.0 | 864 | 0.1403 | 0.9520 | 0.8061 | 0.8293 | 0.8175 |
| 0.075 | 2.0 | 1728 | 0.1432 | 0.9557 | 0.8140 | 0.8536 | 0.8333 |
### Framework versions
- Transformers 4.46.3
- Pytorch 1.13.1+cu117
- Datasets 3.1.0
- Tokenizers 0.20.3
|