# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("bllin001/bert-contextual")
model = AutoModelForSequenceClassification.from_pretrained("bllin001/bert-contextual")Quick Links
model
This model is a fine-tuned version of google-bert/bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- eval_loss: 2.1211
- eval_accuracy: 0.6112
- eval_precision: 0.5704
- eval_recall: 0.5609
- eval_f1: 0.5599
- eval_runtime: 16.4845
- eval_samples_per_second: 26.995
- eval_steps_per_second: 3.397
- step: 0
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Framework versions
- Transformers 4.40.1
- Pytorch 2.4.0.dev20240427
- Datasets 2.19.0
- Tokenizers 0.19.1
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Model tree for bllin001/bert-contextual
Base model
google-bert/bert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bllin001/bert-contextual")