End of training
Browse files
README.md
ADDED
|
@@ -0,0 +1,91 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
+
base_model: distilbert-base-uncased
|
| 4 |
+
tags:
|
| 5 |
+
- generated_from_trainer
|
| 6 |
+
datasets:
|
| 7 |
+
- wnut_17
|
| 8 |
+
metrics:
|
| 9 |
+
- precision
|
| 10 |
+
- recall
|
| 11 |
+
- f1
|
| 12 |
+
- accuracy
|
| 13 |
+
model-index:
|
| 14 |
+
- name: my_awesome_wnut_model
|
| 15 |
+
results:
|
| 16 |
+
- task:
|
| 17 |
+
name: Token Classification
|
| 18 |
+
type: token-classification
|
| 19 |
+
dataset:
|
| 20 |
+
name: wnut_17
|
| 21 |
+
type: wnut_17
|
| 22 |
+
config: wnut_17
|
| 23 |
+
split: test
|
| 24 |
+
args: wnut_17
|
| 25 |
+
metrics:
|
| 26 |
+
- name: Precision
|
| 27 |
+
type: precision
|
| 28 |
+
value: 0.5442622950819672
|
| 29 |
+
- name: Recall
|
| 30 |
+
type: recall
|
| 31 |
+
value: 0.3076923076923077
|
| 32 |
+
- name: F1
|
| 33 |
+
type: f1
|
| 34 |
+
value: 0.39313203078744824
|
| 35 |
+
- name: Accuracy
|
| 36 |
+
type: accuracy
|
| 37 |
+
value: 0.9407036894532085
|
| 38 |
+
---
|
| 39 |
+
|
| 40 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
| 41 |
+
should probably proofread and complete it, then remove this comment. -->
|
| 42 |
+
|
| 43 |
+
# my_awesome_wnut_model
|
| 44 |
+
|
| 45 |
+
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
|
| 46 |
+
It achieves the following results on the evaluation set:
|
| 47 |
+
- Loss: 0.2772
|
| 48 |
+
- Precision: 0.5443
|
| 49 |
+
- Recall: 0.3077
|
| 50 |
+
- F1: 0.3931
|
| 51 |
+
- Accuracy: 0.9407
|
| 52 |
+
|
| 53 |
+
## Model description
|
| 54 |
+
|
| 55 |
+
More information needed
|
| 56 |
+
|
| 57 |
+
## Intended uses & limitations
|
| 58 |
+
|
| 59 |
+
More information needed
|
| 60 |
+
|
| 61 |
+
## Training and evaluation data
|
| 62 |
+
|
| 63 |
+
More information needed
|
| 64 |
+
|
| 65 |
+
## Training procedure
|
| 66 |
+
|
| 67 |
+
### Training hyperparameters
|
| 68 |
+
|
| 69 |
+
The following hyperparameters were used during training:
|
| 70 |
+
- learning_rate: 2e-05
|
| 71 |
+
- train_batch_size: 16
|
| 72 |
+
- eval_batch_size: 16
|
| 73 |
+
- seed: 42
|
| 74 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
| 75 |
+
- lr_scheduler_type: linear
|
| 76 |
+
- num_epochs: 2
|
| 77 |
+
|
| 78 |
+
### Training results
|
| 79 |
+
|
| 80 |
+
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|
| 81 |
+
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
|
| 82 |
+
| No log | 1.0 | 213 | 0.2940 | 0.4753 | 0.2493 | 0.3271 | 0.9380 |
|
| 83 |
+
| No log | 2.0 | 426 | 0.2772 | 0.5443 | 0.3077 | 0.3931 | 0.9407 |
|
| 84 |
+
|
| 85 |
+
|
| 86 |
+
### Framework versions
|
| 87 |
+
|
| 88 |
+
- Transformers 4.34.1
|
| 89 |
+
- Pytorch 2.1.0+cu118
|
| 90 |
+
- Datasets 2.14.5
|
| 91 |
+
- Tokenizers 0.14.1
|