dibsondivya
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README.md
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---
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tags:
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- distilbert
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- phm
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datasets:
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- custom-phm-tweets
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metrics:
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- accuracy
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model-index:
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- name: distilbert-phmtweets-sutd
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results:
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- task:
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name: Text Classification
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type: text-classification
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dataset:
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name: custom-phm-tweets
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type: labelled
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.87
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---
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# finetuning-sentiment-model-3000-samples-5pm
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on a custom tweet dataset that references public health mentions.
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It achieves the following results on the evaluation set:
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- Accuracy: 0.87
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## Usage
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`from transformers import AutoTokenizer, AutoModelForSequenceClassification`
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`tokenizer = AutoTokenizer.from_pretrained("dibsondivya/distilbert-phmtweets-sutd")`
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`model = AutoModelForSequenceClassification.from_pretrained("dibsondivya/distilbert-phmtweets-sutd")`
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