File size: 4,558 Bytes
946ef85 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 |
---
license: mit
base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
tags:
- generated_from_trainer
model-index:
- name: JNLPBA_PubMedBERT_NER
results: []
---
<!-- 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. -->
# JNLPBA_PubMedBERT_NER
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1450
- Seqeval classification report: precision recall f1-score support
DNA 0.75 0.83 0.79 955
RNA 0.80 0.83 0.82 1144
cell_line 0.76 0.79 0.78 5330
cell_type 0.86 0.91 0.88 2518
protein 0.87 0.85 0.86 926
micro avg 0.80 0.83 0.81 10873
macro avg 0.81 0.84 0.82 10873
weighted avg 0.80 0.83 0.81 10873
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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 | Seqeval classification report |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 0.2726 | 1.0 | 582 | 0.1526 | precision recall f1-score support
DNA 0.73 0.82 0.77 955
RNA 0.79 0.82 0.81 1144
cell_line 0.75 0.78 0.76 5330
cell_type 0.86 0.86 0.86 2518
protein 0.86 0.84 0.85 926
micro avg 0.79 0.81 0.80 10873
macro avg 0.80 0.82 0.81 10873
weighted avg 0.79 0.81 0.80 10873
|
| 0.145 | 2.0 | 1164 | 0.1473 | precision recall f1-score support
DNA 0.73 0.82 0.77 955
RNA 0.85 0.78 0.81 1144
cell_line 0.77 0.78 0.78 5330
cell_type 0.85 0.92 0.88 2518
protein 0.88 0.83 0.85 926
micro avg 0.80 0.82 0.81 10873
macro avg 0.81 0.83 0.82 10873
weighted avg 0.80 0.82 0.81 10873
|
| 0.1276 | 3.0 | 1746 | 0.1450 | precision recall f1-score support
DNA 0.75 0.83 0.79 955
RNA 0.80 0.83 0.82 1144
cell_line 0.76 0.79 0.78 5330
cell_type 0.86 0.91 0.88 2518
protein 0.87 0.85 0.86 926
micro avg 0.80 0.83 0.81 10873
macro avg 0.81 0.84 0.82 10873
weighted avg 0.80 0.83 0.81 10873
|
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|