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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: pump_intent_test |
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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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# pump_intent_test |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. |
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## Model description |
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Custom data generated labeling text according to these three categories. |
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These three categories are the subcategories of Pump - essentially when a user asks a question and expects an answer in response |
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- Value: a slot value or a calculation |
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- Clarification: Asking for further information on a previous answer |
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- Testing: Testing for knowledge of facts and definitions |
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Takes a user input of string text and classifies it according to one of three categories. |
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## Intended uses & limitations |
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from transformers import pipeline |
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classifier = pipeline("text-classification",model="mp6kv/pump_intent_test") |
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output = classifier("What is the value of the length of the blue object?") |
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score = output[0]['score'] |
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label = output[0]['label'] |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.18.3 |
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- Tokenizers 0.11.6 |
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