Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use LinusML/my_awesome_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LinusML/my_awesome_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LinusML/my_awesome_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LinusML/my_awesome_model") model = AutoModelForSequenceClassification.from_pretrained("LinusML/my_awesome_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from LinusML/my_awesome_model: direct link, hf CLI and curl.
- Browser
- Download file 1.64 kB
-
https://huggingface.co/LinusML/my_awesome_model/resolve/main/README.md
- Command line
-
hf download hf://LinusML/my_awesome_model/README.md
-
curl -L -o README.md https://huggingface.co/LinusML/my_awesome_model/resolve/main/README.md
1.64 kB
metadata
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_model
results: []
my_awesome_model
This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.1030
- Accuracy: 0.6807
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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 184 | 1.1920 | 0.6454 |
| No log | 2.0 | 368 | 1.1030 | 0.6807 |
Framework versions
- Transformers 5.18.0
- Pytorch 2.14.1+cpu
- Datasets 5.1.0
- Tokenizers 0.23.2