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Model: HiTZ/gpt2-eus-euscrawl Source: Original Platform
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README.md
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README.md
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---
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license: cc
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datasets:
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- HiTZ/euscrawl
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language:
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- eu
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metrics:
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- perplexity
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library_name: transformers
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pipeline_tag: text-generation
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---
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# Model Card for GPT2 Eus Euscrawl
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<!-- Provide a quick summary of what the model is/does. -->
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Pretrained GPT2 small model (124M parameters) on Basque language using a causal language modeling (CLM) objective. The English version of GPT2 was introduced in
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[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
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and first released at [this page](https://openai.com/blog/better-language-models/). The team releasing GPT-2 also wrote a
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[model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model.
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# Model Details
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## Model Description
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<!-- Provide a longer summary of what this model is. -->
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GPT-2 is a transformers model pretrained on a very large corpus of Basque data in a self-supervised fashion. This
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means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots
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of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
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it was trained to guess the next word in sentences.
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More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
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shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
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predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
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This way, the model learns an inner representation of the English language that can then be used to extract features
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useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
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prompt.
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This is the **smallest** version of GPT-2, with 124M parameters.
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- **Developed by:** [github.com/juletx](https://github.com/juletx)
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- **Model type:** GPT2
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- **Language(s) (NLP):** Basque (eu)
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- **License:** cc
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## Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [github.com/juletx/phd](https://github.com/juletx/phd)
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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# Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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## Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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You can use this model directly with a pipeline for text generation.
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## Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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You can also fine-tune it to a downstream task. See the
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[model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you.
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## Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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# Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of
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unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their
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[model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases):
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> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases
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> that require the generated text to be true.
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>
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> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do
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> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a
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> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,
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> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar
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> levels of caution around use cases that are sensitive to biases around human attributes.
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Here's an example of how the model can have biased predictions:
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```python
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>>> from transformers import pipeline, set_seed
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>>> generator = pipeline('text-generation', model='gpt2')
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>>> set_seed(42)
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>>> generator("The White man worked as a", max_length=10, num_return_sequences=5)
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[{'generated_text': 'The White man worked as a mannequin for'},
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{'generated_text': 'The White man worked as a maniser of the'},
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{'generated_text': 'The White man worked as a bus conductor by day'},
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{'generated_text': 'The White man worked as a plumber at the'},
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{'generated_text': 'The White man worked as a journalist. He had'}]
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>>> set_seed(42)
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>>> generator("The Black man worked as a", max_length=10, num_return_sequences=5)
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[{'generated_text': 'The Black man worked as a man at a restaurant'},
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{'generated_text': 'The Black man worked as a car salesman in a'},
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{'generated_text': 'The Black man worked as a police sergeant at the'},
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{'generated_text': 'The Black man worked as a man-eating monster'},
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{'generated_text': 'The Black man worked as a slave, and was'}]
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```
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This bias will also affect all fine-tuned versions of this model.
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## Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
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set a seed for reproducibility:
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```python
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>>> from transformers import pipeline, set_seed
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>>> generator = pipeline('text-generation', model='gpt2')
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>>> set_seed(42)
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>>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)
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[{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."},
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{'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"},
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{'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"},
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{'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"},
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{'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}]
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```
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Here is how to use this model to get the features of a given text in PyTorch:
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```python
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from transformers import GPT2Tokenizer, GPT2Model
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tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
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model = GPT2Model.from_pretrained('gpt2')
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text = "Replace me by any text you'd like."
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encoded_input = tokenizer(text, return_tensors='pt')
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output = model(**encoded_input)
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```
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# Training Details
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## Training Data
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<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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EusCrawl (http://www.ixa.eus/euscrawl/) is a high-quality corpus for Basque comprising 12.5 million documents
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and 423 million tokens, totalling 2.1 GiB of uncompressed text. EusCrawl was built using ad-hoc scrapers to
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extract text from 33 Basque websites with high-quality content, resulting in cleaner text compared to
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general purpose approaches. [Dataset Card](https://huggingface.co/datasets/HiTZ/euscrawl)
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## Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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### Preprocessing [optional]
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The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
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vocabulary size of 50,304. The inputs are sequences of 1024 consecutive tokens.
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### Training Hyperparameters
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- **Training regime:** bf16 mixed precission <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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# Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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## Testing Data, Factors & Metrics
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### Testing Data
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<!-- This should link to a Data Card if possible. -->
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[More Information Needed]
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### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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## Results
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[More Information Needed]
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### Summary
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# Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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# Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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# Technical Specifications [optional]
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## Model Architecture and Objective
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[More Information Needed]
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## Compute Infrastructure
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[More Information Needed]
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### Hardware
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[More Information Needed]
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### Software
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[More Information Needed]
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# Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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```bibtex
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@article{radford2019language,
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title={Language Models are Unsupervised Multitask Learners},
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author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
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year={2019}
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}
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```
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**APA:**
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[More Information Needed]
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# Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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# More Information [optional]
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[More Information Needed]
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# Model Card Authors [optional]
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[More Information Needed]
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# Model Card Contact
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[More Information Needed]
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