# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("everyl12/crisis_sentiment_roberta")
model = AutoModelForSequenceClassification.from_pretrained("everyl12/crisis_sentiment_roberta")Quick Links
crisis_sentiment_roberta
This model is a fine-tuned version of finiteautomata/bertweet-base-sentiment-analysis on an unknown dataset. It achieves the following results on the testing set:
- Accuracy: 0.83
- Macro accuracy: 0.76
- Weighted accuracy: 0.83
Model description
- Negative
- Positive
- Neutral
Sentiment classification using 9,300 tweets of the Flint Water Crisis
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4781 | 1.0 | 349 | 0.4452 | 0.8366 |
| 0.2074 | 2.0 | 698 | 0.5010 | 0.8237 |
| 0.047 | 3.0 | 1047 | 0.5772 | 0.8199 |
| 0.0114 | 4.0 | 1396 | 0.7793 | 0.8226 |
| 0.007 | 5.0 | 1745 | 0.8584 | 0.8188 |
| 0.0144 | 6.0 | 2094 | 0.9517 | 0.8070 |
| 0.0017 | 7.0 | 2443 | 1.0054 | 0.8231 |
| 0.0013 | 8.0 | 2792 | 1.1297 | 0.8172 |
| 0.0008 | 9.0 | 3141 | 1.1622 | 0.8263 |
| 0.001 | 10.0 | 3490 | 1.2313 | 0.8204 |
| 0.0006 | 11.0 | 3839 | 1.2360 | 0.8220 |
| 0.0007 | 12.0 | 4188 | 1.2687 | 0.8161 |
| 0.0004 | 13.0 | 4537 | 1.2940 | 0.8204 |
| 0.0451 | 14.0 | 4886 | 1.3163 | 0.8194 |
| 0.0004 | 15.0 | 5235 | 1.2991 | 0.8242 |
Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.13.0.dev20220917+cu117
- Datasets 2.4.0
- Tokenizers 0.12.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="everyl12/crisis_sentiment_roberta")