|
--- |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- enoriega/odinsynth_dataset |
|
model-index: |
|
- name: rule_learning_margin_3mm_many_negatives_spanpred_attention |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# rule_learning_margin_3mm_many_negatives_spanpred_attention |
|
|
|
This model is a fine-tuned version of [enoriega/rule_softmatching](https://huggingface.co/enoriega/rule_softmatching) on the enoriega/odinsynth_dataset dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2196 |
|
- Margin Accuracy: 0.8969 |
|
|
|
## Model description |
|
|
|
More information needed |
|
|
|
## Intended uses & limitations |
|
|
|
More information needed |
|
|
|
## Training and evaluation data |
|
|
|
More information needed |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 5e-05 |
|
- train_batch_size: 4 |
|
- eval_batch_size: 4 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 2000 |
|
- total_train_batch_size: 8000 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 3.0 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Margin Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------------:| |
|
| 0.3149 | 0.16 | 60 | 0.3098 | 0.8608 | |
|
| 0.2754 | 0.32 | 120 | 0.2725 | 0.8733 | |
|
| 0.2619 | 0.48 | 180 | 0.2512 | 0.8872 | |
|
| 0.2378 | 0.64 | 240 | 0.2391 | 0.8925 | |
|
| 0.2451 | 0.8 | 300 | 0.2305 | 0.8943 | |
|
| 0.2357 | 0.96 | 360 | 0.2292 | 0.8949 | |
|
| 0.2335 | 1.12 | 420 | 0.2269 | 0.8952 | |
|
| 0.2403 | 1.28 | 480 | 0.2213 | 0.8957 | |
|
| 0.2302 | 1.44 | 540 | 0.2227 | 0.8963 | |
|
| 0.2353 | 1.6 | 600 | 0.2222 | 0.8961 | |
|
| 0.2271 | 1.76 | 660 | 0.2207 | 0.8964 | |
|
| 0.228 | 1.92 | 720 | 0.2218 | 0.8967 | |
|
| 0.2231 | 2.08 | 780 | 0.2201 | 0.8967 | |
|
| 0.2128 | 2.24 | 840 | 0.2219 | 0.8967 | |
|
| 0.2186 | 2.4 | 900 | 0.2202 | 0.8967 | |
|
| 0.2245 | 2.56 | 960 | 0.2205 | 0.8969 | |
|
| 0.2158 | 2.72 | 1020 | 0.2196 | 0.8969 | |
|
| 0.2106 | 2.88 | 1080 | 0.2192 | 0.8968 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.19.2 |
|
- Pytorch 1.11.0 |
|
- Datasets 2.2.1 |
|
- Tokenizers 0.12.1 |
|
|
