Edit model card
YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/models-cards#model-card-metadata)

DistilBERT-Base-Uncased for Sentiment Analysis

This model is a fine-tuned version of distilbert-base-uncased originally released in "DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter" 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/distilbert-base-uncased-sst2")
model = AutoModelForSequenceClassification.from_pretrained("assemblyai/distilbert-base-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!

Downloads last month
Hosted inference API
Text Classification
This model can be loaded on the Inference API on-demand.