Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("marcolatella/hate_trained_1234567")
model = AutoModelForSequenceClassification.from_pretrained("marcolatella/hate_trained_1234567")Quick Links
hate_trained_1234567
This model is a fine-tuned version of distilbert-base-uncased on the tweet_eval dataset. It achieves the following results on the evaluation set:
- Loss: 0.7927
- F1: 0.7751
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: 2.7272339744854407e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 1234567
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.4835 | 1.0 | 563 | 0.4882 | 0.7534 |
| 0.3236 | 2.0 | 1126 | 0.5286 | 0.7590 |
| 0.2191 | 3.0 | 1689 | 0.6103 | 0.7717 |
| 0.1408 | 4.0 | 2252 | 0.7927 | 0.7751 |
Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
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Dataset used to train marcolatella/hate_trained_1234567
Evaluation results
- F1 on tweet_evalself-reported0.775
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="marcolatella/hate_trained_1234567")