Instructions to use Ojeda01/bert_base_cased_MultiClass_Sentiment_A with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ojeda01/bert_base_cased_MultiClass_Sentiment_A with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ojeda01/bert_base_cased_MultiClass_Sentiment_A")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ojeda01/bert_base_cased_MultiClass_Sentiment_A") model = AutoModelForSequenceClassification.from_pretrained("Ojeda01/bert_base_cased_MultiClass_Sentiment_A", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Ojeda01/bert_base_cased_MultiClass_Sentiment_A: direct link, hf CLI and curl.
- Browser
- Download file 1.5 kB
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https://huggingface.co/Ojeda01/bert_base_cased_MultiClass_Sentiment_A/resolve/main/README.md
- Command line
-
hf download hf://Ojeda01/bert_base_cased_MultiClass_Sentiment_A/README.md
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curl -L -o README.md https://huggingface.co/Ojeda01/bert_base_cased_MultiClass_Sentiment_A/resolve/main/README.md
1.5 kB
metadata
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert_base_cased_MultiClass_Sentiment_A
results: []
bert_base_cased_MultiClass_Sentiment_A
This model is a fine-tuned version of HMEXBI/bert_base_cased_MultiClass on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0470
- Accuracy: 0.8093
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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 357 | 1.0515 | 0.7700 |
| 0.3375 | 2.0 | 714 | 1.0273 | 0.7854 |
| 0.1605 | 3.0 | 1071 | 1.0470 | 0.8093 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2