File size: 3,945 Bytes
1e96d3e |
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 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: SST2_DistilBERT_5E
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. -->
# SST2_DistilBERT_5E
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4125
- Accuracy: 0.8933
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6744 | 0.12 | 50 | 0.6094 | 0.66 |
| 0.4942 | 0.23 | 100 | 0.3772 | 0.8667 |
| 0.3857 | 0.35 | 150 | 0.3256 | 0.8867 |
| 0.3483 | 0.46 | 200 | 0.3634 | 0.84 |
| 0.3235 | 0.58 | 250 | 0.3338 | 0.8733 |
| 0.3129 | 0.69 | 300 | 0.3482 | 0.8667 |
| 0.3573 | 0.81 | 350 | 0.3632 | 0.8333 |
| 0.3266 | 0.92 | 400 | 0.3274 | 0.86 |
| 0.2615 | 1.04 | 450 | 0.3400 | 0.8667 |
| 0.2409 | 1.15 | 500 | 0.3541 | 0.8467 |
| 0.2508 | 1.27 | 550 | 0.2997 | 0.88 |
| 0.2442 | 1.39 | 600 | 0.3654 | 0.86 |
| 0.2625 | 1.5 | 650 | 0.3302 | 0.8667 |
| 0.1983 | 1.62 | 700 | 0.3184 | 0.8867 |
| 0.2356 | 1.73 | 750 | 0.3239 | 0.8867 |
| 0.2078 | 1.85 | 800 | 0.2968 | 0.9 |
| 0.2343 | 1.96 | 850 | 0.3148 | 0.8933 |
| 0.1544 | 2.08 | 900 | 0.3535 | 0.9 |
| 0.1407 | 2.19 | 950 | 0.3603 | 0.8733 |
| 0.187 | 2.31 | 1000 | 0.3843 | 0.88 |
| 0.144 | 2.42 | 1050 | 0.4546 | 0.8467 |
| 0.1786 | 2.54 | 1100 | 0.3681 | 0.88 |
| 0.1315 | 2.66 | 1150 | 0.3806 | 0.8867 |
| 0.1399 | 2.77 | 1200 | 0.3880 | 0.8867 |
| 0.1905 | 2.89 | 1250 | 0.3944 | 0.8733 |
| 0.2043 | 3.0 | 1300 | 0.3974 | 0.8733 |
| 0.1081 | 3.12 | 1350 | 0.3731 | 0.9067 |
| 0.1055 | 3.23 | 1400 | 0.3809 | 0.8867 |
| 0.1092 | 3.35 | 1450 | 0.3568 | 0.9 |
| 0.0981 | 3.46 | 1500 | 0.3610 | 0.9133 |
| 0.109 | 3.58 | 1550 | 0.4126 | 0.8867 |
| 0.1001 | 3.7 | 1600 | 0.3831 | 0.9 |
| 0.1027 | 3.81 | 1650 | 0.4064 | 0.9 |
| 0.133 | 3.93 | 1700 | 0.3845 | 0.9 |
| 0.1031 | 4.04 | 1750 | 0.3915 | 0.9 |
| 0.0772 | 4.16 | 1800 | 0.3988 | 0.8867 |
| 0.0785 | 4.27 | 1850 | 0.3962 | 0.9 |
| 0.1059 | 4.39 | 1900 | 0.3969 | 0.9 |
| 0.0668 | 4.5 | 1950 | 0.4095 | 0.8933 |
| 0.0915 | 4.62 | 2000 | 0.4077 | 0.8933 |
| 0.1413 | 4.73 | 2050 | 0.4004 | 0.9067 |
| 0.0727 | 4.85 | 2100 | 0.4100 | 0.8933 |
| 0.0724 | 4.97 | 2150 | 0.4125 | 0.8933 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
|