Text Classification
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bert
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Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/bert-base-uncased-mrpc")
model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/bert-base-uncased-mrpc")Quick Links
bert-base-uncased-mrpc
This model is a fine-tuned version of bert-base-uncased on the GLUE MRPC dataset. It achieves the following results on the evaluation set:
- Loss: 0.5572
- Accuracy: 0.8578
- F1: 0.9024
- Combined Score: 0.8801
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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 5.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|---|---|---|---|---|---|---|
| No log | 1.0 | 230 | 0.4111 | 0.8088 | 0.8704 | 0.8396 |
| No log | 2.0 | 460 | 0.3762 | 0.8480 | 0.8942 | 0.8711 |
| 0.4287 | 3.0 | 690 | 0.5572 | 0.8578 | 0.9024 | 0.8801 |
| 0.4287 | 4.0 | 920 | 0.6087 | 0.8554 | 0.8977 | 0.8766 |
| 0.1172 | 5.0 | 1150 | 0.6524 | 0.8456 | 0.8901 | 0.8678 |
Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
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Model tree for JeremiahZ/bert-base-uncased-mrpc
Base model
google-bert/bert-base-uncasedDataset used to train JeremiahZ/bert-base-uncased-mrpc
Evaluation results
- Accuracy on GLUE MRPCself-reported0.858
- F1 on GLUE MRPCself-reported0.902
- Accuracy on gluevalidation set verified0.858
- Precision on gluevalidation set verified0.851
- Recall on gluevalidation set verified0.961
- AUC on gluevalidation set verified0.893
- F1 on gluevalidation set verified0.902
- loss on gluevalidation set verified0.557
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeremiahZ/bert-base-uncased-mrpc")