---
tags:
- generated_from_trainer
model-index:
- name: experience-model-v1
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# 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