Instructions to use assemblyai/bert-large-uncased-sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use assemblyai/bert-large-uncased-sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="assemblyai/bert-large-uncased-sst2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("assemblyai/bert-large-uncased-sst2") model = AutoModelForSequenceClassification.from_pretrained("assemblyai/bert-large-uncased-sst2") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
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# BERT-Large-Uncased for Sentiment Analysis
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This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) originally released in ["BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding"](https://arxiv.org/abs/1810.04805) and trained on the [Stanford Sentiment Treebank v2 (SST2)](https://nlp.stanford.edu/sentiment/); part of the [General Language Understanding Evaluation (GLUE)](https://gluebenchmark.com) benchmark. This model was fine-tuned by the team at [AssemblyAI](https://www.assemblyai.com) and is released with the [corresponding blog post]().
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base_model: bert-large-uncased
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# BERT-Large-Uncased for Sentiment Analysis
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This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) originally released in ["BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding"](https://arxiv.org/abs/1810.04805) and trained on the [Stanford Sentiment Treebank v2 (SST2)](https://nlp.stanford.edu/sentiment/); part of the [General Language Understanding Evaluation (GLUE)](https://gluebenchmark.com) benchmark. This model was fine-tuned by the team at [AssemblyAI](https://www.assemblyai.com) and is released with the [corresponding blog post]().
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