Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use toasterboy/TESDFEEEE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use toasterboy/TESDFEEEE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="toasterboy/TESDFEEEE")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("toasterboy/TESDFEEEE") model = AutoModelForSequenceClassification.from_pretrained("toasterboy/TESDFEEEE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from toasterboy/TESDFEEEE: direct link, hf CLI and curl.
- Browser
- Download file 1.21 kB
-
https://huggingface.co/toasterboy/TESDFEEEE/resolve/main/README.md
- Command line
-
hf download hf://toasterboy/TESDFEEEE/README.md
-
curl -L -o README.md https://huggingface.co/toasterboy/TESDFEEEE/resolve/main/README.md
1.21 kB
metadata
license: mit
tags:
- generated_from_trainer
model-index:
- name: TESDFEEEE
results: []
TESDFEEEE
This model is a fine-tuned version of bert-base-german-cased on an unknown dataset.
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: 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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 421 | 0.3940 | 0.8306 |
Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3