File size: 2,953 Bytes
ce7beb7 9a504c7 ce7beb7 9a504c7 4975b63 9a504c7 a5ddf5c 9a504c7 a5ddf5c 9a504c7 47dc8d7 9a504c7 ce7beb7 9a504c7 ce7beb7 97c6314 4975b63 ce7beb7 9a504c7 |
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 |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: NourhanAbosaeed/Coursera_Reviews_Sentiment_Analysis_DistillBERT
results: []
language:
- en
library_name: transformers
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# NourhanAbosaeed/Coursera_Reviews_Sentiment_Analysis_DistillBERT
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on an a dataset from Cousera courses reviews,
It is publicly available on Kaggle since 2017.
After data preprocessing and model training, It achieves the following results on the evaluation set:
- Train Loss: 0.4934
- Validation Loss: 0.6018
- Train Accuracy: 0.7498
- Epoch: 2
Considering the imbalanced nature of the data, metrics such as recall, precision, and F1 score were employed for evaluation:-
The model achieves these results on the test set:
precision recall f1-score support
0 0.37 0.59 0.46 1928
1 0.71 0.74 0.72 1022
2 0.91 0.79 0.85 8712
accuracy 0.75 11662
macro avg 0.67 0.71 0.68 11662
weighted avg 0.81 0.75 0.77 11662
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
After cleaning the data, It becomes 93291 training size, 11661 for validation and 11662 for test sets.
#### There are 3 lables positive, negative and netural.
### The data have imbalanced nature so I have used class weights during training.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 17490, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.6870 | 0.6382 | 0.7505 | 0 |
| 0.5836 | 0.5976 | 0.7583 | 1 |
| 0.4934 | 0.6018 | 0.7498 | 2 |
### Framework versions
- Transformers 4.35.1
- TensorFlow 2.14.0
- Datasets 2.14.7
- Tokenizers 0.14.1 |