1. For your specific purpose, a Transformer-based language model such as BERT, GPT-3, or RoBERTa might be best suited. These models have achieved state-of-the-art results on various natural language processing tasks, including reading comprehension and text classification, and have been pre-trained on large amounts of text data. You should also consider the size of the model and its fine-tuning capabilities.

2. A good place to start looking for pre-trained language models is Hugging Face's model hub (https://huggingface.co/models). You can also find models on the websites of major AI research organizations such as OpenAI and Google AI. A search query such as "pre-trained language models for NLP" or "pre-trained transformer models" can also provide useful results.

3. Some options for you to consider include:

BERT: BERT (Bidirectional Encoder Representations from Transformers) is a popular pre-trained language model developed by Google. It has been fine-tuned on a wide range of NLP tasks and has achieved state-of-the-art results on many of them. BERT's advantages include its bidirectional nature and its ability to handle a wide range of NLP tasks, but its size may make fine-tuning on smaller datasets more challenging.

GPT-3: GPT-3 (Generative Pre-trained Transformer 2) is another pre-trained language model developed by OpenAI. It is one of the largest language models available, with over 1.5 billion parameters. GPT-2 has been fine-tuned on a variety of NLP tasks, but its large size may make fine-tuning on smaller datasets more challenging.

RoBERTa: RoBERTa (Robustly Optimized BERT Pretraining Approach) is a variant of BERT developed by Facebook AI. It is designed to outperform BERT on a wide range of NLP tasks and has been pre-trained on a much larger corpus of text data. RoBERTa's advantages include its optimized pre-training procedure and its ability to handle a wide range of NLP tasks, but its large size may make fine-tuning on smaller datasets more challenging.

These are just a few options to consider, and you may find other models that are better suited to your specific needs. It's important to thoroughly evaluate the models' capabilities and limitations, as well as the availability of pre-trained models and fine-tuning resources, before making a final decision.
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