File size: 3,059 Bytes
e18a82c |
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: climatebert/distilroberta-base-climate-f
tags:
- generated_from_trainer
model-index:
- name: TAPP-multilabel-climatebert
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. -->
# TAPP-multilabel-climatebert
This model is a fine-tuned version of [climatebert/distilroberta-base-climate-f](https://huggingface.co./climatebert/distilroberta-base-climate-f) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6527
- Precision-micro: 0.7368
- Precision-samples: 0.7425
- Precision-weighted: 0.7469
- Recall-micro: 0.8044
- Recall-samples: 0.7744
- Recall-weighted: 0.8044
- F1-micro: 0.7691
- F1-samples: 0.7384
- F1-weighted: 0.7721
## 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: 3.06e-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: cosine
- lr_scheduler_warmup_steps: 200
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision-micro | Precision-samples | Precision-weighted | Recall-micro | Recall-samples | Recall-weighted | F1-micro | F1-samples | F1-weighted |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|:-----------------:|:------------------:|:------------:|:--------------:|:---------------:|:--------:|:----------:|:-----------:|
| 0.7627 | 0.8 | 500 | 0.6471 | 0.6232 | 0.6727 | 0.6384 | 0.7989 | 0.7741 | 0.7989 | 0.7002 | 0.6929 | 0.7062 |
| 0.5542 | 1.59 | 1000 | 0.6114 | 0.6393 | 0.6754 | 0.6671 | 0.8154 | 0.7833 | 0.8154 | 0.7167 | 0.6999 | 0.7279 |
| 0.4219 | 2.39 | 1500 | 0.6145 | 0.7196 | 0.7236 | 0.7311 | 0.7989 | 0.7645 | 0.7989 | 0.7572 | 0.7231 | 0.7613 |
| 0.3268 | 3.19 | 2000 | 0.6363 | 0.7272 | 0.7383 | 0.7358 | 0.8053 | 0.7738 | 0.8053 | 0.7643 | 0.7374 | 0.7672 |
| 0.2477 | 3.99 | 2500 | 0.6509 | 0.7315 | 0.7351 | 0.7439 | 0.8007 | 0.7689 | 0.8007 | 0.7646 | 0.7319 | 0.7686 |
| 0.1989 | 4.78 | 3000 | 0.6527 | 0.7368 | 0.7425 | 0.7469 | 0.8044 | 0.7744 | 0.8044 | 0.7691 | 0.7384 | 0.7721 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|