File size: 2,073 Bytes
231396e |
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 |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: filtered_cause_extraction_bert_because
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. -->
# filtered_cause_extraction_bert_because
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4768
- Precision: 0.25
- Recall: 0.3878
- F1: 0.304
- Accuracy: 0.8087
- Cause P: 0.25
- Cause R: 0.3878
- Cause F1: 0.304
## 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: 4e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Cause P | Cause R | Cause F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------:|:-------:|:--------:|
| 0.7298 | 0.41 | 20 | 0.5714 | 0.0843 | 0.3010 | 0.1317 | 0.6191 | 0.0843 | 0.3010 | 0.1317 |
| 0.7298 | 0.82 | 40 | 0.4815 | 0.1528 | 0.3010 | 0.2027 | 0.7796 | 0.1528 | 0.3010 | 0.2027 |
| 0.7298 | 1.22 | 60 | 0.4449 | 0.2061 | 0.3776 | 0.2667 | 0.7979 | 0.2061 | 0.3776 | 0.2667 |
| 0.7298 | 1.63 | 80 | 0.4607 | 0.2444 | 0.3929 | 0.3014 | 0.8052 | 0.2444 | 0.3929 | 0.3014 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.3.1.post100
- Datasets 2.20.0
- Tokenizers 0.15.1
|