Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Daniel246/test4_distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Daniel246/test4_distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Daniel246/test4_distilbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Daniel246/test4_distilbert") model = AutoModelForSequenceClassification.from_pretrained("Daniel246/test4_distilbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Daniel246/test4_distilbert: direct link, hf CLI and curl.
- Browser
- Download file 1.08 kB
-
https://huggingface.co/Daniel246/test4_distilbert/resolve/main/README.md
- Command line
-
hf download hf://Daniel246/test4_distilbert/README.md
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curl -L -o README.md https://huggingface.co/Daniel246/test4_distilbert/resolve/main/README.md
1.08 kB
metadata
license: apache-2.0
tags:
- generated_from_trainer
base_model: distilbert-base-uncased
model-index:
- name: test4_distilbert
results: []
test4_distilbert
This model is a fine-tuned version of distilbert-base-uncased 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.1+cpu
- Datasets 2.17.0
- Tokenizers 0.15.2