Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use ColinCcz/roberta_mental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ColinCcz/roberta_mental with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ColinCcz/roberta_mental")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ColinCcz/roberta_mental") model = AutoModelForSequenceClassification.from_pretrained("ColinCcz/roberta_mental", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from ColinCcz/roberta_mental: direct link, hf CLI and curl.
- Browser
- Download file 945 Bytes
-
https://huggingface.co/ColinCcz/roberta_mental/resolve/main/README.md
- Command line
-
hf download hf://ColinCcz/roberta_mental/README.md
-
curl -L -o README.md https://huggingface.co/ColinCcz/roberta_mental/resolve/main/README.md
945 Bytes
metadata
tags:
- generated_from_trainer
model-index:
- name: roberta_mental
results: []
roberta_mental
This model was trained from scratch on an unknown dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 2
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2