Text Classification
Transformers
PyTorch
Safetensors
bert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("HealthNLP/pubmedbert_tlink")
model = AutoModelForSequenceClassification.from_pretrained("HealthNLP/pubmedbert_tlink")Quick Links
pubmed_thyme_deepphe_no_crc
This model is a fine-tuned version of tlink_shared_task_weights/pubmed_thyme_deepphe_no_crc on an unknown dataset.
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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 62
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
Framework versions
- Transformers 4.24.0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.11.0
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HealthNLP/pubmedbert_tlink")