Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Silicon23/BERTForDetectingDepression-Twitter2020 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Silicon23/BERTForDetectingDepression-Twitter2020 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Silicon23/BERTForDetectingDepression-Twitter2020")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Silicon23/BERTForDetectingDepression-Twitter2020") model = AutoModelForSequenceClassification.from_pretrained("Silicon23/BERTForDetectingDepression-Twitter2020") - Notebooks
- Google Colab
- Kaggle
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README.md
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# BERTForDetectingDepression-Twitter2020
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This model is a fine-tuned version of [AIMH/mental-bert-base-cased](https://huggingface.co/AIMH/mental-bert-base-cased) on
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It achieves the following results on the evaluation set:
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- Loss: 0.8966
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- Accuracy: 0.6445
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# BERTForDetectingDepression-Twitter2020
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This model is a fine-tuned version of [AIMH/mental-bert-base-cased](https://huggingface.co/AIMH/mental-bert-base-cased) on data taken from [Safa, R., Bayat, P. & Moghtader, L. Automatic detection of depression symptoms in twitter using multimodal analysis. J Supercomput (2021).](https://doi.org/10.1007/s11227-021-04040-8).
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It achieves the following results on the evaluation set:
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- Loss: 0.8966
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- Accuracy: 0.6445
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