experience-model-v1
This model is intended to detect the presence of a present-moment experience a human or animal is experiencing in a sentence.
Usage
Given a sentence, the model gives logits of whether or not that sentence contains a present-moment experience. Higher values correspond to the sentence having that experience.
model = transformers.AutoModelForSequenceClassification.from_pretrained('edmundmills/experience-model-v1') # type: ignore
tokenizer = transformers.AutoTokenizer.from_pretrained('edmundmills/experience-model-v1', use_fast=False) # type: ignore
sentence = "I am eating food."
tokenized = tokenizer([sentence], return_tensors='pt', return_attention_mask=True)
input_ids, masks = tokenized['input_ids'], tokenized['attention_mask']
with torch.inference_mode():
out = model(input_ids, attention_mask=masks)
probs = out.logits.sigmoid().squeeze().item()
print(probs) # 0.92
Model description
This model was fine-tuned from 'microsoft/deberta-v3-large'.
Intended uses & limitations
More information needed
Training and evaluation data
This model was trained on 745 training samples, with ~10% of them containing present moment experiences.
Training procedure
The model was fine-tuned using https://github.com/AlignmentResearch/experience-model. It used BCE Loss.
Training hyperparameters
It used the following hyperparameters: learning_rate: 2.0 e-05 batch_size: 16 epochs: 200 weight_decay: 0.01
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2
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