File size: 2,406 Bytes
ba06bc3 527d747 ba06bc3 03f1d01 ba06bc3 527d747 ba06bc3 527d747 03f1d01 ba06bc3 |
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 |
---
license: apache-2.0
base_model: bert-large-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results_bert-large-uncased
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. -->
# results_bert-large-uncased
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2128
- Accuracy: 0.9141
- Precision: 0.9182
- Recall: 0.9421
- F1: 0.9300
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6415 | 0.09 | 50 | 0.5315 | 0.7175 | 0.6981 | 0.9394 | 0.8010 |
| 0.4007 | 0.18 | 100 | 0.7702 | 0.7243 | 0.9892 | 0.5505 | 0.7074 |
| 0.5158 | 0.28 | 150 | 0.4075 | 0.8591 | 0.8904 | 0.8748 | 0.8825 |
| 0.3934 | 0.37 | 200 | 0.2809 | 0.8763 | 0.9354 | 0.8546 | 0.8932 |
| 0.2691 | 0.46 | 250 | 0.3406 | 0.8832 | 0.8837 | 0.9294 | 0.9060 |
| 0.2814 | 0.55 | 300 | 0.2582 | 0.8768 | 0.8512 | 0.9651 | 0.9046 |
| 0.2735 | 0.64 | 350 | 0.2715 | 0.8953 | 0.8708 | 0.9711 | 0.9182 |
| 0.2411 | 0.74 | 400 | 0.2389 | 0.9103 | 0.9242 | 0.9279 | 0.9260 |
| 0.2371 | 0.83 | 450 | 0.2081 | 0.9104 | 0.9212 | 0.9316 | 0.9264 |
| 0.1974 | 0.92 | 500 | 0.2128 | 0.9141 | 0.9182 | 0.9421 | 0.9300 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|