NHS TransformersUpdates
Collection
12 items • Updated
Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("intermezzo672/NHS-pubmedbert-binary")
model = AutoModelForSequenceClassification.from_pretrained("intermezzo672/NHS-pubmedbert-binary")Quick Links
NHS-pubmedbert-binary
This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5861
- Accuracy: 0.8246
- Precision: 0.8180
- Recall: 0.8240
- F1: 0.8202
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: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.0592 | 1.0 | 397 | 0.3980 | 0.8246 | 0.8179 | 0.8220 | 0.8196 |
| 0.0542 | 2.0 | 794 | 0.4714 | 0.7943 | 0.7960 | 0.8064 | 0.7929 |
| 0.6307 | 3.0 | 1191 | 0.5861 | 0.8246 | 0.8180 | 0.8240 | 0.8202 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="intermezzo672/NHS-pubmedbert-binary")