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--- |
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license: apache-2.0 |
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datasets: |
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- webis/tldr-17 |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text-classification |
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widget: |
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- text: "Biden says US is at tipping point on gun control: We will ban assault weapons in this country" |
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example_title: "classification" |
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--- |
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# RedBERT - a Reddit post classifier |
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This model based on distilbert is finetuned to predict the subreddit of a Reddit post. |
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## Usage |
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### Preparations |
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The model uses the transformers library, so make sure to install it. |
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``` |
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pip install transformers[torch] |
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``` |
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After the installation, the model can be loaded from Hugging Face. |
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The model will be sored localy so if you run this lines multiple times the model will be loaded from cache. |
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```py |
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from transformers import pipeline |
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pipe = pipeline("text-classification", model="traberph/RedBERT") |
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``` |
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### Basic |
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For a simple classification task just call the pipeline with the text of your choice |
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```py |
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text = "I (33f) need to explain to my coworker (30m) I don't want his company on the commute back home" |
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pipe(text) |
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``` |
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output: |
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[{'label': 'relationships', 'score': 0.9622366428375244}] |
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### Multiclass with visualization |
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Everyone likes visualizations! Therefore this is an example to output the 5 most probable labels and visualize the result. |
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Make sure that all requirements are satisfied. |
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``` |
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pip install pandas seaborn |
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``` |
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```py |
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import pandas as pd |
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import seaborn as sns |
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# if the model is already loaded this can be skipped |
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from transformers import pipeline |
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pipe = pipeline("text-classification", model="traberph/RedBERT") |
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text = "Today I spilled coffee over my pc. It started to smoke and the screen turned black. I guess I have a problem now." |
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# predict the 5 most probable labels |
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res = pipe(text, top_k=5) |
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# create a pandas dataframe from the result |
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df = pd.DataFrame(res) |
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# use seaborn to create a barplot |
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sns.barplot(df, x='score', y='label', color='steelblue') |
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``` |
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output: |
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## Training |
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The training of the final version of this model took `130h` on a single `Tesla P100 GPU`. |
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90% of the [webis/tldr-17](https://huggingface.co/datasets/webis/tldr-17/) where used for this version. |
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## Bias and Limitations |
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The webis/tldr-17 dataset used to train this model contains 3 848 330 posts from 29 651 subreddits. |
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Those posts however are not equally distributed over the subreddits. 589 947 posts belong to the subreddit `AskReddit`, which is `15%` of the whole dataset. Other subreddits are underrepresented. |
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| top subreddits | distribution | |
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| --- | --- | |
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|  |  | |
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This bias in the subreddit distribution is also represented in the model and can be observed during inference. |
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| class labels for `"Biden says US is at tipping point on gun control: We will ban assault weapons in this country"`, from r/politics | |
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| --- | |
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