Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Necraatall/Bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Necraatall/Bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Necraatall/Bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Necraatall/Bert") model = AutoModelForSequenceClassification.from_pretrained("Necraatall/Bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Necraatall/Bert: direct link, hf CLI and curl.
- Browser
- Download file 1.03 kB
-
https://huggingface.co/Necraatall/Bert/resolve/main/README.md
- Command line
-
hf download hf://Necraatall/Bert/README.md
-
curl -L -o README.md https://huggingface.co/Necraatall/Bert/resolve/main/README.md
1.03 kB
metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: Bert
results: []
Bert
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: 3e-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: 10
Framework versions
- Transformers 4.37.1
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1