| --- |
| license: mit |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: main_intent_test |
| 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. --> |
|
|
| # main_intent_test |
|
|
| This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. |
|
|
| ## Model description |
|
|
| Custom data generated labeling text according to these five categories. |
| Five categories represent the five essential intents of a user for the ACTS scenario. |
|
|
| - Connect : Greetings and introduction with the student |
| - Pump : Asking the student for information |
| - Inform : Providing information to the student |
| - Feedback : Praising the student (positive feedback) or informing the student they are not on the right path (negative feedback) |
| - None : Not related to scenario |
|
|
| Takes a user input of string text and classifies it according to one of five categories. |
|
|
| ## Intended uses & limitations |
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|
| from transformers import pipeline |
| classifier = pipeline("text-classification",model="mp6kv/main_intent_test") |
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| output = classifier("great job, you're getting it!") |
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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 |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 5 |
|
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| ### Training results |
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| ### Framework versions |
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|
| - Transformers 4.17.0 |
| - Pytorch 1.10.0+cu111 |
| - Datasets 1.18.3 |
| - Tokenizers 0.11.6 |
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|
