Instructions to use damgomz/ft_1_16e6_x12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use damgomz/ft_1_16e6_x12 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="damgomz/ft_1_16e6_x12")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("damgomz/ft_1_16e6_x12") model = AutoModelForSequenceClassification.from_pretrained("damgomz/ft_1_16e6_x12") - Notebooks
- Google Colab
- Kaggle
Environmental Impact (CODE CARBON DEFAULT)
| Metric | Value |
|---|---|
| Duration (in seconds) | 96214.49659323692 |
| Emissions (Co2eq in kg) | 0.0582209088697825 |
| CPU power (W) | 42.5 |
| GPU power (W) | [No GPU] |
| RAM power (W) | 3.75 |
| CPU energy (kWh) | 1.135863712835643 |
| GPU energy (kWh) | [No GPU] |
| RAM energy (kWh) | 0.1002227581476171 |
| Consumed energy (kWh) | 1.2360864709832615 |
| 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) | 0.18521290594198106 |
| Emissions (Co2eq in kg) | 0.03768401116568446 |
Note
12 juillet 2024
My Config
| Config | Value |
|---|---|
| checkpoint | damgomz/fp_bs8_lr1e4_x12 |
| model_name | ft_1_16e6_x12 |
| sequence_length | 400 |
| num_epoch | 6 |
| learning_rate | 1.6e-05 |
| batch_size | 1 |
| 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 | 0.711051 | 0.747852 |
| 1 | 0.274838 | 0.211746 | 0.935131 |
| 2 | 0.139967 | 0.241108 | 0.918968 |
| 3 | 0.048871 | 0.316788 | 0.923798 |
| 4 | 0.011017 | 0.488954 | 0.925787 |
| 5 | 0.003908 | 0.541524 | 0.927690 |
| 6 | 0.001621 | 0.561884 | 0.918366 |
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