File size: 2,065 Bytes
4266502 |
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 |
---
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: SciBERT_25K_steps_bs64
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. -->
# SciBERT_25K_steps_bs64
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0177
- Accuracy: 0.9941
- Precision: 0.7990
- Recall: 0.5288
- F1: 0.6364
- Hamming: 0.0059
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 25000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Hamming |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.0467 | 0.16 | 5000 | 0.0416 | 0.9902 | 0.0 | 0.0 | 0.0 | 0.0098 |
| 0.0236 | 0.32 | 10000 | 0.0223 | 0.9932 | 0.8192 | 0.3929 | 0.5311 | 0.0068 |
| 0.0198 | 0.47 | 15000 | 0.0190 | 0.9939 | 0.8015 | 0.4934 | 0.6108 | 0.0061 |
| 0.0185 | 0.63 | 20000 | 0.0180 | 0.9940 | 0.7974 | 0.5220 | 0.6310 | 0.0060 |
| 0.0181 | 0.79 | 25000 | 0.0177 | 0.9941 | 0.7990 | 0.5288 | 0.6364 | 0.0059 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.7.1
- Tokenizers 0.14.1
|