Bert Large Uncased Sst2

🚀 BERT-Large-Uncased for Sentiment Analysis

This model is a fine - tuned version of bert - large - uncased, designed for sentiment analysis, offering high - performance solutions for modern developers.

🚀 Quick Start

This model is a fine - tuned version of [bert - large - uncased](https://huggingface.co/bert - large - uncased), which was originally introduced in the paper "BERT: Pre - training of Deep Bidirectional Transformers for Language Understanding". It has been trained on the Stanford Sentiment Treebank v2 (SST2), a part of the General Language Understanding Evaluation (GLUE) benchmark. The fine - tuning was carried out by the team at AssemblyAI, and it is accompanied by a corresponding blog post.

💻 Usage Examples

Basic Usage

import torch.nn.functional as F 
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("assemblyai/bert-large-uncased-sst2") 
model = AutoModelForSequenceClassification.from_pretrained("assemblyai/bert-large-uncased-sst2")

tokenized_segments = tokenizer(["AssemblyAI is the best speech-to-text API for modern developers with performance being second to none!"], return_tensors="pt", padding=True, truncation=True)
tokenized_segments_input_ids, tokenized_segments_attention_mask = tokenized_segments.input_ids, tokenized_segments.attention_mask
model_predictions = F.softmax(model(input_ids=tokenized_segments_input_ids, attention_mask=tokenized_segments_attention_mask)['logits'], dim=1)

print("Positive probability: "+str(model_predictions[0][1].item()*100)+"%")
print("Negative probability: "+str(model_predictions[0][0].item()*100)+"%")

If you have any questions about how to use this model, feel free to contact the team at AssemblyAI!