File size: 2,748 Bytes
22e9e81 0bf315b 22e9e81 0bf315b 22e9e81 0bf315b 22e9e81 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
---
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
|