File size: 1,991 Bytes
3e68696 ecf285b 3e68696 ecf285b 256cc90 3e68696 256cc90 3e68696 256cc90 3e68696 ecf285b 256cc90 3e68696 ecf285b 3e68696 |
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 |
---
license: apache-2.0
base_model: dslim/distilbert-NER
tags:
- generated_from_trainer
datasets:
- conll2012_ontonotesv5
metrics:
- accuracy
- f1
model-index:
- name: distilbert-NER-finetuned
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2012_ontonotesv5
type: conll2012_ontonotesv5
config: english_v4
split: validation
args: english_v4
metrics:
- name: Accuracy
type: accuracy
value: 0.867816091954023
- name: F1
type: f1
value: 0.4862665310274669
---
<!-- 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. -->
# distilbert-NER-finetuned
This model is a fine-tuned version of [dslim/distilbert-NER](https://huggingface.co/dslim/distilbert-NER) on the conll2012_ontonotesv5 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5043
- Accuracy: 0.8678
- F1: 0.4863
## 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: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.9019 | 1.0 | 61 | 0.6286 | 0.8406 | 0.4223 |
| 0.5594 | 2.0 | 122 | 0.5302 | 0.8605 | 0.4567 |
| 0.4537 | 3.0 | 183 | 0.5043 | 0.8678 | 0.4863 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
|