---
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-transition-physical-risk-prediction-v1
  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-transition-physical-risk-prediction-v1

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: 0.0003
- 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   | 57   | 0.0072          | 1.0      |
| No log        | 2.0   | 114  | 0.0010          | 1.0      |
| No log        | 3.0   | 171  | 0.0006          | 1.0      |
| No log        | 4.0   | 228  | 0.0005          | 1.0      |
| No log        | 5.0   | 285  | 0.0004          | 1.0      |
| No log        | 6.0   | 342  | 0.0003          | 1.0      |
| No log        | 7.0   | 399  | 0.0003          | 1.0      |
| No log        | 8.0   | 456  | 0.0003          | 1.0      |
| 0.0243        | 9.0   | 513  | 0.0003          | 1.0      |
| 0.0243        | 10.0  | 570  | 0.0003          | 1.0      |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1