File size: 2,890 Bytes
9d15158 | 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 | ---
tags:
- generated_from_trainer
datasets:
- bc4chemd
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: electramed-small-BC4CHEMD-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: bc4chemd
type: bc4chemd
config: bc4chemd
split: train
args: bc4chemd
metrics:
- name: Precision
type: precision
value: 0.7715624436835465
- name: Recall
type: recall
value: 0.6760888102832959
- name: F1
type: f1
value: 0.7206773498518718
- name: Accuracy
type: accuracy
value: 0.9770623458780496
---
<!-- 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. -->
# electramed-small-BC4CHEMD-ner
This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the bc4chemd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0655
- Precision: 0.7716
- Recall: 0.6761
- F1: 0.7207
- Accuracy: 0.9771
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0882 | 1.0 | 1918 | 0.1058 | 0.6615 | 0.3942 | 0.4940 | 0.9635 |
| 0.0555 | 2.0 | 3836 | 0.0820 | 0.7085 | 0.5133 | 0.5954 | 0.9689 |
| 0.0631 | 3.0 | 5754 | 0.0769 | 0.6892 | 0.5743 | 0.6266 | 0.9699 |
| 0.0907 | 4.0 | 7672 | 0.0682 | 0.7623 | 0.5923 | 0.6666 | 0.9740 |
| 0.0313 | 5.0 | 9590 | 0.0675 | 0.7643 | 0.6223 | 0.6860 | 0.9749 |
| 0.0306 | 6.0 | 11508 | 0.0662 | 0.7654 | 0.6398 | 0.6970 | 0.9754 |
| 0.0292 | 7.0 | 13426 | 0.0656 | 0.7694 | 0.6552 | 0.7077 | 0.9763 |
| 0.1025 | 8.0 | 15344 | 0.0658 | 0.7742 | 0.6687 | 0.7176 | 0.9769 |
| 0.0394 | 9.0 | 17262 | 0.0662 | 0.7741 | 0.6731 | 0.7201 | 0.9770 |
| 0.0378 | 10.0 | 19180 | 0.0655 | 0.7716 | 0.6761 | 0.7207 | 0.9771 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|