Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use cdc-hf/bert-webpage-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cdc-hf/bert-webpage-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cdc-hf/bert-webpage-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cdc-hf/bert-webpage-classifier") model = AutoModelForSequenceClassification.from_pretrained("cdc-hf/bert-webpage-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,083 Bytes
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library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-webpage-classifier
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-webpage-classifier
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0136
- Accuracy: 0.996
## 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: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1497 | 1.0 | 2536 | 0.0185 | 0.996 |
| 0.0308 | 2.0 | 5072 | 0.0151 | 0.996 |
| 0.0263 | 3.0 | 7608 | 0.0143 | 0.996 |
| 0.0245 | 4.0 | 10144 | 0.0140 | 0.996 |
| 0.0213 | 5.0 | 12680 | 0.0140 | 0.996 |
| 0.0199 | 6.0 | 15216 | 0.0139 | 0.998 |
| 0.0184 | 7.0 | 17752 | 0.0133 | 0.996 |
| 0.0187 | 8.0 | 20288 | 0.0141 | 0.996 |
| 0.0181 | 9.0 | 22824 | 0.0139 | 0.996 |
| 0.0174 | 10.0 | 25360 | 0.0136 | 0.996 |
### Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
|