File size: 6,967 Bytes
6922b1b 649d283 6922b1b 649d283 6922b1b 649d283 6922b1b 649d283 6922b1b 649d283 ec03228 6922b1b ec03228 6922b1b ec03228 6922b1b ec03228 6922b1b ec03228 6922b1b ec03228 6922b1b 649d283 6922b1b 649d283 6922b1b |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 |
---
base_model: readerbench/RoBERT-base
language:
- ro
tags:
- sentiment
- classification
- nlp
- bert
datasets:
- decathlon_reviews
- cinemagia_reviews
metrics:
- accuracy
- precision
- recall
- f1
- f1 weighted
model-index:
- name: ro-sentiment-03
results:
- task:
type: text-classification # Required. Example: automatic-speech-recognition
name: Text Classification # Optional. Example: Speech Recognition
dataset:
type: ro_sent # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: Rommanian Sentiment Dataset # Required. A pretty name for the dataset. Example: Common Voice (French)
config: default # Optional. The name of the dataset configuration used in `load_dataset()`. Example: fr in `load_dataset("common_voice", "fr")`. See the `datasets` docs for more info: https://huggingface.co/docs/datasets/package_reference/loading_methods#datasets.load_dataset.name
split: all # Optional. Example: test
metrics:
- type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 0.85 # Required. Example: 20.90
name: Accuracy # Optional. Example: Test WER
- type: precision # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 0.85 # Required. Example: 20.90
name: Precision # Optional. Example: Test WER
- type: recall # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 0.85 # Required. Example: 20.90
name: Recall # Optional. Example: Test WER
- type: f1_weighted # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 0.85 # Required. Example: 20.90
name: Weighted F1 # Optional. Example: Test WER
- type: f1_macro # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 0.84 # Required. Example: 20.90
name: Weighted F1 # Optional. Example: Test WER
- task:
type: text-classification # Required. Example: automatic-speech-recognition
name: Text Classification # Optional. Example: Speech Recognition
dataset:
type: laroseda # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: A Large Romanian Sentiment Data Set # Required. A pretty name for the dataset. Example: Common Voice (French)
config: default # Optional. The name of the dataset configuration used in `load_dataset()`. Example: fr in `load_dataset("common_voice", "fr")`. See the `datasets` docs for more info: https://huggingface.co/docs/datasets/package_reference/loading_methods#datasets.load_dataset.name
split: all # Optional. Example: test
metrics:
- type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 0.85 # Required. Example: 20.90
name: Accuracy # Optional. Example: Test WER
- type: precision # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 0.86 # Required. Example: 20.90
name: Precision # Optional. Example: Test WER
- type: recall # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 0.85 # Required. Example: 20.90
name: Recall # Optional. Example: Test WER
- type: f1_weighted # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 0.84 # Required. Example: 20.90
name: Weighted F1 # Optional. Example: Test WER
- type: f1_macro # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 0.84 # Required. Example: 20.90
name: Weighted F1 # Optional. Example: Test WER
---
<!-- 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. -->
# ro-sentiment-03
This model is a fine-tuned version of [readerbench/RoBERT-base](https://huggingface.co/readerbench/RoBERT-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3923
- Accuracy: 0.8307
- Precision: 0.8366
- Recall: 0.8959
- F1: 0.8652
- F1 Weighted: 0.8287
### Evaluation on other datasets
**SENT_RO**
| |precision | recall | f1-score | support |
|:-------------:|:-----:|:----:|:------:|:--------:|
| Negative (0) | 0.79 | 0.83 | 0.81 | 11,675 |
| Positive (1) | 0.88 | 0.85 | 0.87 | 17,271 |
| | | | | |
| Accuracy | | | 0.85 | 28,946 |
| Macro Avg | 0.84 | 0.84 | 0.84 | 28,946 |
| Weighted Avg | 0.85 | 0.85 | 0.85 | 28,946 |
**LaRoSeDa**
| |precision | recall | f1-score | support |
|:-------------:|:-----:|:----:|:------:|:--------:|
| Negative (0) | 0.79 | 0.94 | 0.86 | 7,500 |
| Positive (1) | 0.93 | 0.75 | 0.83 | 7,500 |
| | | | | |
| Accuracy | | | 0.85 | 15,000 |
| Macro Avg | 0.86 | 0.85 | 0.84 | 15,000 |
| Weighted Avg | 0.86 | 0.85 | 0.84 | 15,000 |
## Model description
Finetuned Romanian BERT model for sentiment classification.
Trained on a mix of product reviews from Decathlon retailer website and movie reviews from cinemagia.
## Intended uses & limitations
Sentiment classification for Romanian Language.
Biased towards Product reviews.
## Training and evaluation data
**Trained on:**
- Decathlon Dataset available on request
- Cinemagia Movie reviews public on kaggle [Link](https://www.kaggle.com/datasets/gringoandy/romanian-sentiment-movie-reviews)
**Evaluated on**
- Holdout data from training dataset
- RO_SENT Dataset
- LaROSeDa Dataset
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 64
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 10 (Early stop epoch 3, best epoch 2)
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | F1 Weighted |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------:|
| 0.4198 | 1.0 | 1629 | 0.3983 | 0.8377 | 0.8791 | 0.8721 | 0.8756 | 0.8380 |
| 0.3861 | **2.0** | 3258 | 0.4312 | 0.8429 | 0.8963 | 0.8665 | 0.8812 | **0.8442** |
| 0.3189 | 3.0 | 4887 | 0.3923 | 0.8307 | 0.8366 | 0.8959 | 0.8652 | 0.8287 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|