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ft_32_13e6_base_x4
damgomz/ft_32_13e6_base_x4
ft_32_13e6_base_x4 is a text classification model from damgomz. Use it when you need a label for a piece of text. It is set up for transformers.
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64
Public
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32.9M
3.8 GB on disk
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Repo makeup
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.safetensors132 MB · 98%
At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- albert
- Access
- Public
- Created
- Jun 23, 2024
- Updated
- Jun 24, 2024
- SHA
- e1bfad59
Try a prompt
Model card
From the Hugging Face model README
Environmental Impact (CODE CARBON DEFAULT)
| Metric | Value |
|---|---|
| Duration (in seconds) | 58472.9090526104 |
| Emissions (Co2eq in kg) | 0.0353828713730564 |
| CPU power (W) | 42.5 |
| GPU power (W) | [No GPU] |
| RAM power (W) | 3.75 |
| CPU energy (kWh) | 0.6903039364420729 |
| GPU energy (kWh) | [No GPU] |
| RAM energy (kWh) | 0.0609088058151305 |
| Consumed energy (kWh) | 0.7512127422572015 |
| 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) | 0.112560349926275 |
| Emissions (Co2eq in kg) | 0.02290188937893907 |
Note
19 juin 2024
My Config
| Config | Value |
|---|---|
| checkpoint | albert-base-v2 |
| model_name | ft_32_13e6_base_x4 |
| sequence_length | 400 |
| num_epoch | 6 |
| learning_rate | 1.3e-05 |
| batch_size | 32 |
| weight_decay | 0.0 |
| warm_up_prop | 0.0 |
| drop_out_prob | 0.1 |
| packing_length | 100 |
| train_test_split | 0.2 |
| num_steps | 29328 |
Training and Testing steps
| Epoch | Train Loss | Test Loss | F-beta Score |
|---|---|---|---|
| 0 | 0.000000 | 0.769925 | 0.338207 |
| 1 | 0.318355 | 0.234563 | 0.926135 |
| 2 | 0.195246 | 0.224179 | 0.909345 |
| 3 | 0.144236 | 0.233321 | 0.929572 |
| 4 | 0.095344 | 0.285724 | 0.918945 |
| 5 | 0.062180 | 0.348233 | 0.910088 |
| 6 | 0.041635 | 0.362033 | 0.907800 |
- Task
- Text Classification
- Library
- transformers
- Type
- albert
- Languages
- en
- Created
- Jun 23, 2024
- Updated
- Jun 24, 2024