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BERT-Large-Uncased for Sentiment Analysis

This model is a fine-tuned version of bert-large-uncased originally released in "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" and trained on the Stanford Sentiment Treebank v2 (SST2); part of the General Language Understanding Evaluation (GLUE) benchmark. This model was fine-tuned by the team at AssemblyAI and is released with the corresponding blog post.


To download and utilize this model for sentiment analysis please execute the following:

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)+"%")

For questions about how to use this model feel free to contact the team at AssemblyAI!

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