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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, audio-text-to-text, 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

Model Card

Model Description

This model is a fine-tuned version of ChatGLM3-6B, designed for conversational AI applications. It uses a BERT-based embedding model for text representation.

[rest of the model card content remains the same...]

Model Card Model Description This model is a fine-tuned version of ChatGLM3-6B, designed for conversational AI applications. It uses a BERT-based embedding model for text representation. Model Architecture

Base Model: ChatGLM3-6B Embedding Model: BERT-based architecture (BertForMaskedLM) Type: Conversational AI Language: Chinese (presumably, based on ChatGLM3's primary language support)

Input & Output

Input: Text (conversation/dialogue format) Output: Text (conversational responses)

Uses Primary Intended Uses

Conversational AI applications Text-based dialogue systems

Out-of-Scope Uses

Not intended for production deployment without proper evaluation Not recommended for critical decision-making systems Not suitable for medical, legal, or financial advice

Training Data The model has been trained on custom datasets. Due to the proprietary nature of the training data, specific details are not publicly available. Training Process

Base Model: ChatGLM3-6B Fine-tuning: Custom dataset Embedding: BERT-based model

Performance and Limitations Performance Metrics Performance metrics are not currently available. Users should conduct their own evaluation based on their specific use cases. Limitations

The model's performance characteristics have not been thoroughly evaluated May inherit biases from both ChatGLM3-6B and the custom training data Should be used with appropriate content filtering and safety measures

Recommendations Suggested Uses

Testing and development environments Non-critical conversational applications Research and experimentation

Technical Requirements

Compatible with ChatGLM3-6B system requirements Requires appropriate GPU resources for inference

Ethical Considerations Users should be aware that:

The model may produce unexpected or biased outputs Output should be monitored and filtered for inappropriate content The model should not be used for making critical decisions affecting human welfare

Future Work Suggested areas for improvement:

Comprehensive performance evaluation Documentation of specific use cases and limitations Development of safety guidelines Collection of user feedback for improvement

Citation and License License information is not specified. Users should consult with the model creators regarding usage rights and restrictions.

Note: This model card is based on limited available information and should be updated as more details become available.

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