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--- |
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pipeline_tag: text-generation |
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language: |
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- multilingual |
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inference: false |
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license: cc-by-nc-4.0 |
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library_name: transformers |
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--- |
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<br><br> |
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<p align="center"> |
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<img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px"> |
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</p> |
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<p align="center"> |
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<b>Trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b> |
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</p> |
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# Intro |
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Jina Reader-LM is a series of models that convert HTML content to Markdown content, which is useful for content conversion tasks. The model is trained on a curated collection of HTML content and its corresponding Markdown content. |
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# Models |
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| Name | Context Length | Download | |
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|-----------------|-------------------|-----------------------------------------------------------------------| |
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| reader-lm-0.5b | 256K | [🤗 Hugging Face](https://huggingface.co/jinaai/reader-lm-0.5b) | |
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| reader-lm-1.5b | 256K | [🤗 Hugging Face](https://huggingface.co/jinaai/reader-lm-1.5b) | |
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# Get Started |
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## On Google Colab |
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The easiest way to experience reader-lm is by running [our Colab notebook](https://colab.research.google.com/drive/1wXWyj5hOxEHY6WeHbOwEzYAC0WB1I5uA), |
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where we demonstrate how to use reader-lm-1.5b to convert the HackerNews website into markdown. The notebook is optimized to run smoothly on Google Colab’s free T4 GPU tier. You can also load reader-lm-0.5b or change the URL to any website and explore the output. Note that the input (i.e., the prompt) to the model is the raw HTML—no prefix instruction is required. |
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## Local |
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To use this model, you need to install `transformers`: |
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```bash |
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pip install transformers<=4.43.4 |
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``` |
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Then, you can use the model as follows: |
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```python |
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# pip install transformers |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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checkpoint = "jinaai/reader-lm-1.5b" |
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device = "cuda" # for GPU usage or "cpu" for CPU usage |
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tokenizer = AutoTokenizer.from_pretrained(checkpoint) |
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model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) |
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# example html content |
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html_content = "<html><body><h1>Hello, world!</h1></body></html>" |
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messages = [{"role": "user", "content": html_content}] |
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input_text=tokenizer.apply_chat_template(messages, tokenize=False) |
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print(input_text) |
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inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) |
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outputs = model.generate(inputs, max_new_tokens=1024, temperature=0, do_sample=False, repetition_penalty=1.08) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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