File size: 1,291 Bytes
a613d9d 8060aea a613d9d 8060aea a613d9d 8060aea a613d9d 8060aea a613d9d 8060aea a613d9d 8060aea a613d9d 8060aea a613d9d 8060aea |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 |
---
tags:
- bert
- transformer
- text-classification
license: apache-2.0
---
# Model Card for BERT
## Model Description
This is a BERT model fine-tuned for sentiment analysis. BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based model designed to understand the context of words in search queries.
## Intended Use
- **Primary use case:** Sentiment analysis on social media posts.
- **Limitations:** The model may exhibit biases present in the training data and may not perform well on out-of-domain data.
## Training Data
This model was trained on the [Stanford Sentiment Treebank]. The dataset consists of 11,855 labeled sentences for sentiment classification.
## Evaluation Results
The model achieves the following results on the Stanford Sentiment Treebank:
- Accuracy: 92%
- F1 Score: 0.91
## How to Use
Here’s how to load and use the model in Python:
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "FoundationsofInformationRetrieval/my_model_repo"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Example usage
inputs = tokenizer("I love using Hugging Face!", return_tensors="pt")
outputs = model(**inputs)
|