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YAML Metadata Warning: The pipeline tag "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, any-to-any, other

falcon-7b-sharded-bf16-finetuned-mental-health-conversational

This model is a fine-tuned version of ybelkada/falcon-7b-sharded-bf16 on a custom heliosbrahma/mental_health_chatbot_dataset dataset.

Model description

This model is fine-tuned on custom mental health conversational dataset. The rationale behind this is to answer mental health related queries that can be factually verified without responding gibberish words.

Intended uses & limitations

The model was trained on the dataset which may contain sensitive information related to mental health. It is important to note that while mental health chatbots built using this model can be helpful, they are not a replacement for professional mental health care.

Training and evaluation data

This model was trained on custom heliosbrahma/mental_health_chatbot_dataset dataset which 172 rows of conversational pair of questions and answers.

Training procedure

This model was trained using QLoRA technique to fine-tune on a custom dataset on free-tier GPU available in Google Colab.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.03
training_steps: 320

Training results

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.14.2
Tokenizers 0.13.3

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Inference Examples

Text Generation

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Model tree for Sanrove/falcon-7b-sharded-bf16-finetuned-mental-health-conversational

Base model

ybelkada/falcon-7b-sharded-bf16

Finetuned

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this model

Dataset used to train Sanrove/falcon-7b-sharded-bf16-finetuned-mental-health-conversational

Evaluation results

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