File size: 2,169 Bytes
90f79a7 0aa4dbe 90f79a7 0aa4dbe |
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 78 79 80 |
---
license: mit
tags:
- generated_from_trainer
datasets:
- sagawa/ZINC-canonicalized
metrics:
- accuracy
model-index:
- name: ZINC-deberta-base-output
results:
- task:
name: Masked Language Modeling
type: fill-mask
dataset:
name: sagawa/ZINC-canonicalized
type: sagawa/ZINC-canonicalized
metrics:
- name: Accuracy
type: accuracy
value: 0.9900059572833486
---
<!-- 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. -->
# ZINC-deberta-base-output
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co./microsoft/deberta-base) on the sagawa/ZINC-canonicalized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0237
- Accuracy: 0.9900
## 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: 20
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:------:|:--------:|:---------------:|
| 0.045 | 1.06 | 100000 | 0.9842 | 0.0409 |
| 0.0372 | 2.13 | 200000 | 0.9864 | 0.0346 |
| 0.0337 | 3.19 | 300000 | 0.9874 | 0.0314 |
| 0.0318 | 4.25 | 400000 | 0.9882 | 0.0293 |
| 0.0296 | 5.31 | 500000 | 0.0277 | 0.9887 |
| 0.0289 | 6.38 | 600000 | 0.0264 | 0.9891 |
| 0.0267 | 7.44 | 700000 | 0.0253 | 0.9894 |
| 0.0261 | 8.5 | 800000 | 0.0243 | 0.9898 |
| 0.025 | 9.57 | 900000 | 0.0238 | 0.9900 |
### Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.12.0
- Datasets 2.4.1.dev0
- Tokenizers 0.11.6
|