Instructions to use Jedida/sentence_sentiments_analysis_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jedida/sentence_sentiments_analysis_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jedida/sentence_sentiments_analysis_bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jedida/sentence_sentiments_analysis_bert") model = AutoModelForSequenceClassification.from_pretrained("Jedida/sentence_sentiments_analysis_bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Jedida/sentence_sentiments_analysis_bert: direct link, hf CLI and curl.
- Browser
- Download file 1.62 kB
-
https://huggingface.co/Jedida/sentence_sentiments_analysis_bert/resolve/main/README.md
- Command line
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hf download hf://Jedida/sentence_sentiments_analysis_bert/README.md
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curl -L -o README.md https://huggingface.co/Jedida/sentence_sentiments_analysis_bert/resolve/main/README.md
1.62 kB
metadata
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: sentence_sentiments_analysis_bert
results: []
sentence_sentiments_analysis_bert
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2690
- F1-score: 0.9132
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1-score |
|---|---|---|---|---|
| 0.3365 | 1.0 | 2500 | 0.3218 | 0.9066 |
| 0.2477 | 2.0 | 5000 | 0.2690 | 0.9132 |
| 0.1417 | 3.0 | 7500 | 0.3876 | 0.9178 |
| 0.0645 | 4.0 | 10000 | 0.4436 | 0.9216 |
| 0.0304 | 5.0 | 12500 | 0.5194 | 0.9208 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3