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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: experience-model-v1 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# experience-model-v1 |
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This model is intended to detect the presence of a present-moment experience a human or animal is experiencing in a sentence. |
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## Usage |
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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. |
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``` |
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model = transformers.AutoModelForSequenceClassification.from_pretrained('edmundmills/experience-model-v1') # type: ignore |
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tokenizer = transformers.AutoTokenizer.from_pretrained('edmundmills/experience-model-v1', use_fast=False) # type: ignore |
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sentence = "I am eating food." |
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tokenized = tokenizer([sentence], return_tensors='pt', return_attention_mask=True) |
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input_ids, masks = tokenized['input_ids'], tokenized['attention_mask'] |
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with torch.inference_mode(): |
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out = model(input_ids, attention_mask=masks) |
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probs = out.logits.sigmoid().squeeze().item() |
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print(probs) # 0.92 |
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``` |
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## Model description |
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This model was fine-tuned from 'microsoft/deberta-v3-large'. |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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This model was trained on 745 training samples, with ~10% of them containing present moment experiences. |
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## Training procedure |
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The model was fine-tuned using https://github.com/AlignmentResearch/experience-model. It used BCE Loss. |
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### Training hyperparameters |
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It used the following hyperparameters: |
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learning_rate: 2.0 e-05 |
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batch_size: 16 |
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epochs: 200 |
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weight_decay: 0.01 |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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