File size: 3,366 Bytes
edbbaaf 92c050f edbbaaf 92c050f edbbaaf 92c050f edbbaaf 92c050f edbbaaf 92c050f edbbaaf dc85824 edbbaaf 92c050f edbbaaf 7aad16f 92c050f edbbaaf 92c050f edbbaaf 92c050f edbbaaf |
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 99 100 101 102 103 104 105 |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- indian_names
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: indian_names
type: indian_names
config: indian_names
split: train
args: indian_names
metrics:
- name: Precision
type: precision
value: 0.9941348973607038
- name: Recall
type: recall
value: 0.9921951219512195
- name: F1
type: f1
value: 0.9931640625
- name: Accuracy
type: accuracy
value: 0.9998052125131481
---
<!-- 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. -->
# my_awesome_wnut_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on the indian_names dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0012
- Precision: 0.9941
- Recall: 0.9922
- F1: 0.9932
- Accuracy: 0.9998
## 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: 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 63 | 0.1413 | 0.0 | 0.0 | 0.0 | 0.9745 |
| No log | 2.0 | 126 | 0.1211 | 0.0 | 0.0 | 0.0 | 0.9745 |
| No log | 3.0 | 189 | 0.0656 | 0.6231 | 0.2790 | 0.3854 | 0.9811 |
| No log | 4.0 | 252 | 0.0380 | 0.7297 | 0.6059 | 0.6620 | 0.9894 |
| No log | 5.0 | 315 | 0.0259 | 0.8341 | 0.7259 | 0.7762 | 0.9931 |
| No log | 6.0 | 378 | 0.0136 | 0.8842 | 0.8712 | 0.8776 | 0.9963 |
| No log | 7.0 | 441 | 0.0076 | 0.9286 | 0.9268 | 0.9277 | 0.9981 |
| 0.0748 | 8.0 | 504 | 0.0054 | 0.9409 | 0.9473 | 0.9441 | 0.9985 |
| 0.0748 | 9.0 | 567 | 0.0042 | 0.9520 | 0.9678 | 0.9598 | 0.9991 |
| 0.0748 | 10.0 | 630 | 0.0025 | 0.9738 | 0.9795 | 0.9767 | 0.9995 |
| 0.0748 | 11.0 | 693 | 0.0019 | 0.9863 | 0.9863 | 0.9863 | 0.9997 |
| 0.0748 | 12.0 | 756 | 0.0015 | 0.9961 | 0.9912 | 0.9936 | 0.9998 |
| 0.0748 | 13.0 | 819 | 0.0014 | 0.9912 | 0.9912 | 0.9912 | 0.9998 |
| 0.0748 | 14.0 | 882 | 0.0013 | 0.9912 | 0.9912 | 0.9912 | 0.9998 |
| 0.0748 | 15.0 | 945 | 0.0012 | 0.9941 | 0.9922 | 0.9932 | 0.9998 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|