File size: 2,378 Bytes
edbbaaf 449cab1 edbbaaf 83fe4a1 edbbaaf 4fea786 edbbaaf 83fe4a1 edbbaaf 2cbcb10 edbbaaf b416a8c edbbaaf b416a8c edbbaaf b416a8c edbbaaf b416a8c edbbaaf 4fea786 edbbaaf 449cab1 edbbaaf b416a8c edbbaaf b416a8c 2f2f50f edbbaaf b416a8c edbbaaf 2f2f50f b416a8c edbbaaf 6fd4b09 edbbaaf 4fea786 6fd4b09 |
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 |
---
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_trainer
datasets:
- ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Bert-NER
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ner
type: ner
config: indian_names
split: test
args: indian_names
metrics:
- name: Precision
type: precision
value: 0.9779481031086752
- name: Recall
type: recall
value: 0.950199700449326
- name: F1
type: f1
value: 0.96387423507069
- name: Accuracy
type: accuracy
value: 0.977337411889879
---
<!-- 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-NER
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0518
- Precision: 0.9779
- Recall: 0.9502
- F1: 0.9639
- Accuracy: 0.9773
## 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: 32
- eval_batch_size: 32
- 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 | 438 | 0.0725 | 0.9691 | 0.9325 | 0.9505 | 0.9693 |
| 0.0435 | 2.0 | 876 | 0.0635 | 0.9687 | 0.9392 | 0.9537 | 0.9711 |
| 0.039 | 3.0 | 1314 | 0.0569 | 0.9790 | 0.9416 | 0.9599 | 0.9751 |
| 0.0392 | 4.0 | 1752 | 0.0542 | 0.9744 | 0.9490 | 0.9615 | 0.9758 |
| 0.0378 | 5.0 | 2190 | 0.0518 | 0.9779 | 0.9502 | 0.9639 | 0.9773 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
|