File size: 2,900 Bytes
df5554d |
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:
- species_800
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: electramed-small-SPECIES800-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: species_800
type: species_800
config: species_800
split: train
args: species_800
metrics:
- name: Precision
type: precision
value: 0.6221498371335505
- name: Recall
type: recall
value: 0.7470664928292047
- name: F1
type: f1
value: 0.6789099526066352
- name: Accuracy
type: accuracy
value: 0.9831434110359828
---
<!-- 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-SPECIES800-ner
This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the species_800 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0513
- Precision: 0.6221
- Recall: 0.7471
- F1: 0.6789
- Accuracy: 0.9831
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0536 | 1.0 | 359 | 0.0971 | 0.6138 | 0.5554 | 0.5832 | 0.9795 |
| 0.0309 | 2.0 | 718 | 0.0692 | 0.6175 | 0.6063 | 0.6118 | 0.9808 |
| 0.0563 | 3.0 | 1077 | 0.0582 | 0.6424 | 0.6910 | 0.6658 | 0.9819 |
| 0.0442 | 4.0 | 1436 | 0.0553 | 0.5900 | 0.7523 | 0.6613 | 0.9814 |
| 0.0069 | 5.0 | 1795 | 0.0511 | 0.6291 | 0.7497 | 0.6841 | 0.9827 |
| 0.0141 | 6.0 | 2154 | 0.0505 | 0.6579 | 0.7471 | 0.6996 | 0.9837 |
| 0.0052 | 7.0 | 2513 | 0.0513 | 0.5965 | 0.7458 | 0.6628 | 0.9826 |
| 0.0573 | 8.0 | 2872 | 0.0509 | 0.6140 | 0.7445 | 0.6730 | 0.9828 |
| 0.0203 | 9.0 | 3231 | 0.0516 | 0.6118 | 0.7458 | 0.6722 | 0.9830 |
| 0.0101 | 10.0 | 3590 | 0.0513 | 0.6221 | 0.7471 | 0.6789 | 0.9831 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|