File size: 2,778 Bytes
a612576 | 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 | ---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- fin
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: fin3
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: fin
type: fin
config: default
split: train
args: default
metrics:
- name: Precision
type: precision
value: 0.944
- name: Recall
type: recall
value: 0.9402390438247012
- name: F1
type: f1
value: 0.9421157684630739
- name: Accuracy
type: accuracy
value: 0.9921209540034072
---
<!-- 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. -->
# fin3
This model is a fine-tuned version of [nlpaueb/sec-bert-base](https://huggingface.co/nlpaueb/sec-bert-base) on the fin dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0748
- Precision: 0.944
- Recall: 0.9402
- F1: 0.9421
- Accuracy: 0.9921
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 129 | 0.0669 | 0.8821 | 0.9243 | 0.9027 | 0.9883 |
| No log | 2.0 | 258 | 0.0568 | 0.9289 | 0.9363 | 0.9325 | 0.9913 |
| No log | 3.0 | 387 | 0.0565 | 0.9141 | 0.9323 | 0.9231 | 0.9904 |
| 0.0556 | 4.0 | 516 | 0.0617 | 0.9237 | 0.9163 | 0.92 | 0.9904 |
| 0.0556 | 5.0 | 645 | 0.0658 | 0.9243 | 0.9243 | 0.9243 | 0.9904 |
| 0.0556 | 6.0 | 774 | 0.0695 | 0.944 | 0.9402 | 0.9421 | 0.9921 |
| 0.0556 | 7.0 | 903 | 0.0731 | 0.932 | 0.9283 | 0.9301 | 0.9917 |
| 0.0016 | 8.0 | 1032 | 0.0750 | 0.9283 | 0.9283 | 0.9283 | 0.9917 |
| 0.0016 | 9.0 | 1161 | 0.0737 | 0.944 | 0.9402 | 0.9421 | 0.9921 |
| 0.0016 | 10.0 | 1290 | 0.0748 | 0.944 | 0.9402 | 0.9421 | 0.9921 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|