Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("maayansharon/climate_sequence_classification_model")
model = AutoModelForSequenceClassification.from_pretrained("maayansharon/climate_sequence_classification_model")Quick Links
climate_sequence_classification_model
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5600
- F1: 0.8852
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: 0.0002
- 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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| No log | 1.0 | 10 | 0.4769 | 0.8852 |
| No log | 2.0 | 20 | 0.5600 | 0.8852 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.7.0a0
- Datasets 2.9.0
- Tokenizers 0.13.2
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="maayansharon/climate_sequence_classification_model")