File size: 2,454 Bytes
b30dd4c 702f47f b30dd4c 702f47f b30dd4c 702f47f b30dd4c 702f47f b30dd4c 702f47f b30dd4c 702f47f b30dd4c 702f47f b30dd4c 702f47f b30dd4c |
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/distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
config: wnut_17
split: test
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.5680628272251309
- name: Recall
type: recall
value: 0.40222428174235403
- name: F1
type: f1
value: 0.4709712425393381
- name: Accuracy
type: accuracy
value: 0.9480141934932239
---
<!-- 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/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2966
- Precision: 0.5681
- Recall: 0.4022
- F1: 0.4710
- Accuracy: 0.9480
## 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: 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 | 107 | 0.2496 | 0.5131 | 0.3624 | 0.4248 | 0.9450 |
| No log | 2.0 | 214 | 0.2794 | 0.5829 | 0.3485 | 0.4362 | 0.9456 |
| No log | 3.0 | 321 | 0.2808 | 0.5755 | 0.3781 | 0.4564 | 0.9465 |
| No log | 4.0 | 428 | 0.2935 | 0.5569 | 0.3902 | 0.4589 | 0.9476 |
| 0.059 | 5.0 | 535 | 0.2966 | 0.5681 | 0.4022 | 0.4710 | 0.9480 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.0.1+cu117
- Datasets 2.20.0
- Tokenizers 0.19.1
|