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: