Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Simonom/results_nlp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Simonom/results_nlp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Simonom/results_nlp")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Simonom/results_nlp") model = AutoModelForSequenceClassification.from_pretrained("Simonom/results_nlp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Simonom/results_nlp: direct link, hf CLI and curl.
- Browser
- Download file 1.45 kB
-
https://huggingface.co/Simonom/results_nlp/resolve/main/README.md
- Command line
-
hf download hf://Simonom/results_nlp/README.md
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curl -L -o README.md https://huggingface.co/Simonom/results_nlp/resolve/main/README.md
1.45 kB
metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: results_nlp
results: []
results_nlp
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6930
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.6994 | 1.0 | 943 | 0.6930 |
| 0.6968 | 2.0 | 1886 | 0.6931 |
| 0.6941 | 3.0 | 2829 | 0.6930 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Tokenizers 0.21.0