Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use fernandals/sentiment_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fernandals/sentiment_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fernandals/sentiment_v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fernandals/sentiment_v1") model = AutoModelForSequenceClassification.from_pretrained("fernandals/sentiment_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from fernandals/sentiment_v1: direct link, hf CLI and curl.
- Browser
- Download file 1.43 kB
-
https://huggingface.co/fernandals/sentiment_v1/resolve/main/README.md
- Command line
-
hf download hf://fernandals/sentiment_v1/README.md
-
curl -L -o README.md https://huggingface.co/fernandals/sentiment_v1/resolve/main/README.md
1.43 kB
metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sentiment_v1
results: []
sentiment_v1
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4863
- Accuracy: 0.8312
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5858 | 1.0 | 3410 | 0.5747 | 0.7928 |
| 0.4237 | 2.0 | 6820 | 0.4863 | 0.8312 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3