File size: 1,950 Bytes
989666f a1fed0a 989666f a1fed0a 989666f 43d0c8c 989666f e4c4530 989666f a1fed0a 989666f |
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 |
---
tags:
- generated_from_trainer
datasets:
- null
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: bert-srb-ner-setimes
results:
- task:
name: Token Classification
type: token-classification
metric:
name: Accuracy
type: accuracy
value: 0.9557318926219821
---
<!-- 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. -->
# bert-srb-ner-setimes
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1490
- Precision: 0.7643
- Recall: 0.7972
- F1: 0.7804
- Accuracy: 0.9557
## 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: 32
- 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 104 | 0.2357 | 0.6485 | 0.6922 | 0.6697 | 0.9325 |
| No log | 2.0 | 208 | 0.1876 | 0.7036 | 0.7440 | 0.7232 | 0.9447 |
| No log | 3.0 | 312 | 0.1651 | 0.7389 | 0.7739 | 0.7560 | 0.9515 |
| No log | 4.0 | 416 | 0.1530 | 0.7562 | 0.7888 | 0.7722 | 0.9544 |
| 0.2044 | 5.0 | 520 | 0.1490 | 0.7643 | 0.7972 | 0.7804 | 0.9557 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0
- Datasets 1.11.0
- Tokenizers 0.10.1
|