Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("cnut1648/biolinkbert-mnli")
model = AutoModelForSequenceClassification.from_pretrained("cnut1648/biolinkbert-mnli")Quick Links
BioLinkBERT-large-mnli
This model is a fine-tuned version of BioLinkBERT-large on the GLUE MNLI dataset.
The results are
| Model | Dataset | Acc |
|---|---|---|
| Roberta-large-mnli | MNLI dev mm | 90.12 |
| MNLI dev m | 90.59 | |
| SNLI test | 88.25 | |
| BioLinkBERT-large | MNLI dev mm | 33.56 |
| MNLI dev m | 33.18 | |
| SNLI test | 32.66 | |
| BioLinkBERT-large-mnli | MNLI dev mm | 85.19 |
| MNLI dev m | 84.96 | |
| SNLI test | 78.959 |
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.5
- num_epochs: 10.0
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- 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="cnut1648/biolinkbert-mnli")