|
--- |
|
base_model: hfl/chinese-roberta-wwm-ext |
|
library_name: transformers |
|
license: apache-2.0 |
|
metrics: |
|
- accuracy |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: chinese-roberta-climate-risk-opportunity-prediction-3 |
|
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. --> |
|
|
|
# chinese-roberta-climate-risk-opportunity-prediction-3 |
|
|
|
This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0000 |
|
- Accuracy: 1.0 |
|
|
|
## 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 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| No log | 1.0 | 113 | 0.0000 | 1.0 | |
|
| No log | 2.0 | 226 | 0.0000 | 1.0 | |
|
| No log | 3.0 | 339 | 0.0000 | 1.0 | |
|
| No log | 4.0 | 452 | 0.0000 | 1.0 | |
|
| 0.0 | 5.0 | 565 | 0.0000 | 1.0 | |
|
| 0.0 | 6.0 | 678 | 0.0000 | 1.0 | |
|
| 0.0 | 7.0 | 791 | 0.0000 | 1.0 | |
|
| 0.0 | 8.0 | 904 | 0.0000 | 1.0 | |
|
| 0.0 | 9.0 | 1017 | 0.0000 | 1.0 | |
|
| 0.0 | 10.0 | 1130 | 0.0000 | 1.0 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.44.2 |
|
- Pytorch 2.4.0+cu121 |
|
- Datasets 3.0.0 |
|
- Tokenizers 0.19.1 |
|
|