Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use jonaskoenig/topic_classification_03 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonaskoenig/topic_classification_03 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jonaskoenig/topic_classification_03")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jonaskoenig/topic_classification_03") model = AutoModelForSequenceClassification.from_pretrained("jonaskoenig/topic_classification_03", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| tags: | |
| - generated_from_keras_callback | |
| model-index: | |
| - name: topic_classification_03 | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information Keras had access to. You should | |
| probably proofread and complete it, then remove this comment. --> | |
| # topic_classification_03 | |
| This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Train Loss: 1.0459 | |
| - Train Sparse Categorical Accuracy: 0.6535 | |
| - Validation Loss: 1.1181 | |
| - Validation Sparse Categorical Accuracy: 0.6354 | |
| - Epoch: 5 | |
| ## 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: | |
| - optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} | |
| - training_precision: float32 | |
| ### Training results | |
| | Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch | | |
| |:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:| | |
| | 1.2710 | 0.5838 | 1.1683 | 0.6156 | 0 | | |
| | 1.1546 | 0.6193 | 1.1376 | 0.6259 | 1 | | |
| | 1.1163 | 0.6314 | 1.1247 | 0.6292 | 2 | | |
| | 1.0888 | 0.6400 | 1.1253 | 0.6323 | 3 | | |
| | 1.0662 | 0.6473 | 1.1182 | 0.6344 | 4 | | |
| | 1.0459 | 0.6535 | 1.1181 | 0.6354 | 5 | | |
| ### Framework versions | |
| - Transformers 4.20.1 | |
| - TensorFlow 2.9.1 | |
| - Datasets 2.3.2 | |
| - Tokenizers 0.12.1 | |