transformers text-classification sentiment-analysis NLP transformer en es fr dataset:mteb/imdb dataset:lmqg/qg_squad dataset:commoncrawl/statistics base_model:google-bert/bert-base-uncased base_model:finetune:google-bert/bert-base-uncased license:apache-2.0 endpoints_compatible region:us
---
base_model:
- google-bert/bert-base-uncased
datasets:
- mteb/imdb
- lmqg/qg_squad
- commoncrawl/statistics
language:
- en
- es
- fr
library_name: transformers
license: apache-2.0
metrics:
- accuracy
- f1
- perplexity
- bleu
pipeline_tag: text-classification
tags:
- text-classification
- sentiment-analysis
- NLP
- transformer
new_version: mradermacher/Slm-4B-Instruct-v1.0.1-GGUF
---

# BasePlate

## Model Description
The **BasePlate** model is a [brief description of what the model does, e.g., "a transformer-based model fine-tuned for text classification tasks"].

It can be used for [list the tasks it can perform, e.g., text generation, sentiment analysis, etc.]. The model is based on [mention the underlying architecture or base model, e.g., BERT, GPT-2, etc.].

### Model Features:
- Task: [e.g., Text Classification, Question Answering, Summarization]
- Languages: [List supported languages, e.g., English, French, Spanish, etc.]
- Dataset: [Name of the dataset(s) used to train the model, e.g., "Fine-tuned on the IMDB reviews dataset."]
- Performance: [Optional: Describe the model's performance metrics, e.g., "Achieved an F1 score of 92% on the test set."]

## Intended Use
This model is intended for [intended use cases, e.g., text classification tasks, content moderation, etc.].

### How to Use:
Here’s a simple usage example in Python using the `transformers` library:
```python
from transformers import pipeline

# Load the pre-trained model
model = pipeline('text-classification', model='huggingface/BasePlate')

# Example usage
text = "This is an example sentence."
result = model(text)
print(result)