Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use plphuc017/fintuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use plphuc017/fintuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="plphuc017/fintuning-sentiment-model-3000-samples")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("plphuc017/fintuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("plphuc017/fintuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from plphuc017/fintuning-sentiment-model-3000-samples: direct link, hf CLI and curl.
- Browser
- Download file 1.2 kB
-
https://huggingface.co/plphuc017/fintuning-sentiment-model-3000-samples/resolve/main/README.md
- Command line
-
hf download hf://plphuc017/fintuning-sentiment-model-3000-samples/README.md
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curl -L -o README.md https://huggingface.co/plphuc017/fintuning-sentiment-model-3000-samples/resolve/main/README.md
1.2 kB
metadata
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: fintuning-sentiment-model-3000-samples
results: []
fintuning-sentiment-model-3000-samples
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: 0.3079
- Accuracy: 0.88
- F1: 0.8808
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
Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Tokenizers 0.12.1