Publicly-available pre-trained models for text classification by topic

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😎 Write a list of publicly-available pr-trained models that can be used for classifying input text by topic

🤖 Sure! Here is a list of publicly-available pre-trained models that can be used for classifying input text by topic:

1. BERT (Bidirectional Encoder Representations from Transformers)
– Source: Hugging Face Model Hub
– Description: BERT is designed to understand the context of a word in search queries. It can be fine-tuned for a variety of NLP tasks, including topic classification.

2. RoBERTa (Robustly optimized BERT approach)
– Source: Hugging Face Model Hub
– Description: An optimized version of BERT with improved training methods and datasets. Suitable for topic classification with fine-tuning.

3. DistilBERT
– Source: Hugging Face Model Hub
– Description: A smaller, faster, and lighter version of BERT, making it efficient for various NLP tasks, including text classification.

4. GPT-3 (Generative Pre-trained Transformer 3)
– Source: OpenAI API
– Description: A large language model with strong capabilities in text generation and understanding, including topic classification.

5. XLNet
– Source: Hugging Face Model Hub
– Description: An autoregressive model that improves upon BERT by capturing bidirectional contexts. Can be fine-tuned for topic classification tasks.

6. ALBERT (A Lite BERT)
– Source: Hugging Face Model Hub
– Description: A more parameter-efficient version of BERT, reducing memory footprint while maintaining performance for classification tasks.

7. T5 (Text-to-Text Transfer Transformer)
– Source: Hugging Face Model Hub
– Description: Treats all NLP problems as text-to-text problems, making it versatile for various tasks, including topic classification.

8. ERNIE (Enhanced Representation through kNowledge Integration)
– Source: PaddlePaddle Model Zoo
– Description: A model developed by Baidu that incorporates knowledge graphs into its training process, enhancing its performance on classification tasks.

9. Electra
– Source: Hugging Face Model Hub
– Description: An efficient pre-training method for transformers that outperforms BERT on many NLP tasks, including text classification.

10. FASTTEXT
– Source: Facebook AI Research
– Description: A library for efficient text classification and representation learning. Particularly useful for straightforward, fast topic classification.

11. GloVe (Global Vectors for Word Representation)
– Source: Stanford NLP
– Description: Pre-trained word embeddings that can be used as features in classification models.

12. Universal Sentence Encoder
– Source: TensorFlow Hub
– Description: Encodes text into high-dimensional vectors for various NLP tasks, including classification.

13. USE (Universal Sentence Encoder)
– Source: TensorFlow Hub
– Description: Designed to encode text into embeddings for easy integration into downstream models for tasks like classification.

Each of these models can be accessed and fine-tuned according to specific needs, providing powerful tools for topic classification in various applications.

😎 No, that doesn’t answer the question. Nobody said anything about fine tuning models. That is a ridiculously long response, by the way, I won’t read that s💥💥💥.

Mention a model that can used for classifying pre-defined sets of catrgories

[Message flagged as bad_response]
🤖 I understand, let’s focus on a concise list of models specifically designed for classifying input text into pre-defined categories:

1. BERT-based models from Hugging Face for text classification.
2. RoBERTa-based models from Hugging Face for text classification.
3. DistilBERT-based models from Hugging Face for text classification.
4. FASTTEXT from Facebook AI Research for efficient text classification.
5. Universal Sentence Encoder from TensorFlow Hub for text classification.

These models are pre-trained and can be used directly for classifying text into pre-defined categories.

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Hugging Face Dataset Metrics

All the conversation prompts, responses, and metrics are available to download and explore on Hugging Face dataset reddgr/talking-to-chatbots-chats: