Instructions to use damgomz/ft_16_2e6_cv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use damgomz/ft_16_2e6_cv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="damgomz/ft_16_2e6_cv")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("damgomz/ft_16_2e6_cv") model = AutoModelForSequenceClassification.from_pretrained("damgomz/ft_16_2e6_cv") - Notebooks
- Google Colab
- Kaggle
Environmental Impact (CODE CARBON DEFAULT)
| Metric | Value |
|---|---|
| Duration (in seconds) | 83354.60116434097 |
| Emissions (Co2eq in kg) | 0.0504391260759982 |
| CPU power (W) | 42.5 |
| GPU power (W) | [No GPU] |
| RAM power (W) | 3.75 |
| CPU energy (kWh) | 0.9840448831700616 |
| GPU energy (kWh) | [No GPU] |
| RAM energy (kWh) | 0.0868267772192754 |
| Consumed energy (kWh) | 1.070871660389339 |
| 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) | 0.16045760724135635 |
| Emissions (Co2eq in kg) | 0.032647218789366876 |
Note
21 May 2024
My Config
| Config | Value |
|---|---|
| checkpoint | damgomz/ThunBERT_bs32_lr5 |
| model_name | ft_16_2e6_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 | 0.561226 | 0.447885 | 0.796405 | 0.848743 |
| 1 | 0.393072 | 0.373661 | 0.833234 | 0.856584 |
| 2 | 0.329678 | 0.364563 | 0.836034 | 0.860685 |
| 3 | 0.288565 | 0.371531 | 0.839861 | 0.832672 |
| 4 | 0.244927 | 0.390422 | 0.833969 | 0.845587 |
| 5 | 0.189810 | 0.424411 | 0.829697 | 0.833529 |
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