File size: 2,523 Bytes
a0ad3d5 8f78dda a0ad3d5 8f78dda a0ad3d5 |
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 |
---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: biobert-ner-finetuned-con-kaggle
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. -->
# biobert-ner-finetuned
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0894
- Precision: 0.9293
- Recall: 0.9551
- F1: 0.9420
- Accuracy: 0.9795
## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 306 | 0.2575 | 0.7864 | 0.8034 | 0.7948 | 0.9322 |
| 0.8692 | 2.0 | 612 | 0.0949 | 0.9170 | 0.9451 | 0.9308 | 0.9759 |
| 0.8692 | 3.0 | 918 | 0.0854 | 0.9234 | 0.9607 | 0.9417 | 0.9791 |
| 0.1096 | 4.0 | 1224 | 0.0768 | 0.9333 | 0.9585 | 0.9457 | 0.9809 |
| 0.0656 | 5.0 | 1530 | 0.0772 | 0.9320 | 0.9562 | 0.9439 | 0.9806 |
| 0.0656 | 6.0 | 1836 | 0.0810 | 0.9360 | 0.9575 | 0.9466 | 0.9806 |
| 0.0468 | 7.0 | 2142 | 0.0827 | 0.9308 | 0.9580 | 0.9442 | 0.9803 |
| 0.0468 | 8.0 | 2448 | 0.0890 | 0.9248 | 0.9568 | 0.9405 | 0.9788 |
| 0.038 | 9.0 | 2754 | 0.0859 | 0.9345 | 0.9579 | 0.9460 | 0.9806 |
| 0.031 | 10.0 | 3060 | 0.0894 | 0.9293 | 0.9551 | 0.9420 | 0.9795 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|