File size: 2,271 Bytes
504ee74 9bd37ec 504ee74 9bd37ec e77bf94 9bd37ec e77bf94 9bd37ec e77bf94 9bd37ec e77bf94 504ee74 9bd37ec e77bf94 504ee74 9bd37ec 504ee74 e77bf94 504ee74 |
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 |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- rotten_tomatoes_movie_review
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: my_distilbert_model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: rotten_tomatoes_movie_review
type: rotten_tomatoes_movie_review
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8395872420262664
- name: F1
type: f1
value: 0.8395554737965957
- name: Precision
type: precision
value: 0.8398564101118846
- name: Recall
type: recall
value: 0.8395872420262664
---
<!-- 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_distilbert_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the rotten_tomatoes_movie_review dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4438
- Accuracy: 0.8396
- F1: 0.8396
- Precision: 0.8399
- Recall: 0.8396
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 267 | 0.4091 | 0.8236 | 0.8232 | 0.8270 | 0.8236 |
| 0.3608 | 2.0 | 534 | 0.3945 | 0.8405 | 0.8405 | 0.8406 | 0.8405 |
| 0.3608 | 3.0 | 801 | 0.4438 | 0.8396 | 0.8396 | 0.8399 | 0.8396 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|