Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use windshield-viper/discord-twitter-distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use windshield-viper/discord-twitter-distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="windshield-viper/discord-twitter-distilbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("windshield-viper/discord-twitter-distilbert") model = AutoModelForSequenceClassification.from_pretrained("windshield-viper/discord-twitter-distilbert") - Notebooks
- Google Colab
- Kaggle
my_awesome_model
This model is a fine-tuned version of windshield-viper/discord-distilbert on the Twitter sentiment dataset. It achieves the following results on the evaluation set:
- Loss: 0.0714
- Accuracy: 0.9796
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1045 | 1.0 | 4075 | 0.0929 | 0.9712 |
| 0.0626 | 2.0 | 8150 | 0.0714 | 0.9796 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2
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Model tree for windshield-viper/discord-twitter-distilbert
Base model
distilbert/distilbert-base-uncased Finetuned
windshield-viper/discord-distilbert