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ft_16_2e6_mlm_cv
damgomz/ft_16_2e6_mlm_cv
ft_16_2e6_mlm_cv is a fill-mask model from damgomz. Use it when you need the model to fill a missing word. It is set up for transformers.
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30
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11.7M
1.4 GB on disk
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Repo makeup
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At a glance
- Task
- Fill-Mask
- Library
- transformers
- Model type
- albert
- Access
- Public
- Created
- May 21, 2024
- Updated
- May 22, 2024
- SHA
- 9f26d852
Try a prompt
Model card
From the Hugging Face model README
Environmental Impact (CODE CARBON DEFAULT)
| Metric | Value |
|---|---|
| Duration (in seconds) | 67783.15809178352 |
| Emissions (Co2eq in kg) | 0.0410166344361076 |
| CPU power (W) | 42.5 |
| GPU power (W) | [No GPU] |
| RAM power (W) | 3.75 |
| CPU energy (kWh) | 0.8002162253196032 |
| GPU energy (kWh) | [No GPU] |
| RAM energy (kWh) | 0.0706067813279726 |
| Consumed energy (kWh) | 0.8708230066475792 |
| Country name | Switzerland |
| Cloud provider | nan |
| Cloud region | nan |
| CPU count | 2 |
| CPU model | Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz |
| GPU count | nan |
| GPU model | nan |
Environmental Impact (for one core)
| Metric | Value |
|---|---|
| CPU energy (kWh) | 0.13048257932668325 |
| Emissions (Co2eq in kg) | 0.026548403585948545 |
Note
21 May 2024
My Config
| Config | Value |
|---|---|
| checkpoint | damgomz/ThunBERT_bs16_lr5_MLM |
| model_name | ft_16_2e6_mlm_cv |
| sequence_length | 400 |
| num_epoch | 6 |
| learning_rate | 2e-06 |
| batch_size | 16 |
| weight_decay | 0.0 |
| warm_up_prop | 0.0 |
| drop_out_prob | 0.1 |
| packing_length | 100 |
| train_test_split | 0.2 |
| num_steps | 32586 |
Training and Testing steps
| Epoch | Train Loss | Test Loss | Accuracy | Recall |
|---|---|---|---|---|
| 0 | 0.504354 | 0.392917 | 0.823365 | 0.864496 |
| 1 | 0.357782 | 0.348270 | 0.847230 | 0.864104 |
| 2 | 0.319041 | 0.351701 | 0.843102 | 0.848042 |
| 3 | 0.290859 | 0.349991 | 0.844281 | 0.877908 |
| 4 | 0.263249 | 0.358632 | 0.842073 | 0.826910 |
| 5 | 0.229067 | 0.364421 | 0.845903 | 0.846295 |
- Task
- Fill-Mask
- Library
- transformers
- Type
- albert
- Languages
- en
- Created
- May 21, 2024
- Updated
- May 22, 2024