Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use XvKuoMing/ed_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use XvKuoMing/ed_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="XvKuoMing/ed_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("XvKuoMing/ed_model") model = AutoModelForSequenceClassification.from_pretrained("XvKuoMing/ed_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| base_model: apanc/russian-inappropriate-messages | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: ed_model | |
| 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. --> | |
| # ed_model | |
| This model is a fine-tuned version of [apanc/russian-inappropriate-messages](https://huggingface.co/apanc/russian-inappropriate-messages) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.2974 | |
| - Accuracy: 0.8833 | |
| ## 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: 16 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 2 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | No log | 1.0 | 15 | 0.3543 | 0.8333 | | |
| | No log | 2.0 | 30 | 0.2974 | 0.8833 | | |
| ### Framework versions | |
| - Transformers 4.42.4 | |
| - Pytorch 2.3.1+cu121 | |
| - Datasets 2.13.1 | |
| - Tokenizers 0.19.1 | |