# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("poooj/MPNetForClassification")
model = AutoModelForSequenceClassification.from_pretrained("poooj/MPNetForClassification")Quick Links
MPNetForClassification
This model is a fine-tuned version of sentence-transformers/all-mpnet-base-v2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3608
- Accuracy: 0.84
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: 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 | Accuracy |
|---|---|---|---|---|
| 0.5352 | 1.0 | 625 | 0.3941 | 0.835 |
| 0.4063 | 2.0 | 1250 | 0.3608 | 0.84 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.2.1+cpu
- Datasets 2.12.0
- Tokenizers 0.13.2
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Model tree for poooj/MPNetForClassification
Base model
sentence-transformers/all-mpnet-base-v2
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="poooj/MPNetForClassification")