Instructions to use damgomz/fp_bs16_lr2e4_x1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use damgomz/fp_bs16_lr2e4_x1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="damgomz/fp_bs16_lr2e4_x1")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("damgomz/fp_bs16_lr2e4_x1") model = AutoModelForMaskedLM.from_pretrained("damgomz/fp_bs16_lr2e4_x1") - Notebooks
- Google Colab
- Kaggle
Environmental Impact (CODE CARBON DEFAULT)
| Metric | Value |
|---|---|
| Duration (in seconds) | 123018.00080919266 |
| Emissions (Co2eq in kg) | 0.0925471051534231 |
| CPU power (W) | 42.5 |
| GPU power (W) | [No GPU] |
| RAM power (W) | 15.0 |
| CPU energy (kWh) | 1.4522934853661444 |
| GPU energy (kWh) | [No GPU] |
| RAM energy (kWh) | 0.512571488910914 |
| Consumed energy (kWh) | 1.9648649742770457 |
| Country name | Switzerland |
| Cloud provider | nan |
| Cloud region | nan |
| CPU count | 6 |
| 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) | 0.23680965155769587 |
| Emissions (Co2eq in kg) | 0.04818205031693379 |
Note
5 juillet 2024 !
My Config
| Config | Value |
|---|---|
| checkpoint | albert-base-v2 |
| model_name | fp_bs16_lr2e4_x1 |
| sequence_length | 400 |
| num_epoch | 6 |
| learning_rate | 0.0002 |
| 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 | 41539 |
Training and Testing steps
| Epoch | Train Loss | Test Loss |
|---|---|---|
| 0.0 | 10.138601 | 4.677505 |
| 0.5 | 6.875287 | 7.005647 |
| 1.0 | 7.000262 | 7.003398 |
| 1.5 | 6.987953 | 6.991044 |
| 2.0 | 6.982683 | 6.987339 |
| 2.5 | 6.973123 | 6.974583 |
| 3.0 | 6.966743 | 6.964363 |
| 3.5 | 6.962001 | 6.969299 |
| 4.0 | 6.956372 | 6.955241 |
| 4.5 | 6.956191 | 6.957408 |
| 5.0 | 6.949049 | 6.946764 |
| 5.5 | 6.941440 | 6.947819 |
| 6.0 | 6.935547 | 6.941309 |
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