Model Card for GPT2 Eus Euscrawl
Pretrained GPT2 small model (124M parameters) on Basque language using a causal language modeling (CLM) objective. The English version of GPT2 was introduced in
this paper
and first released at
this page. The team releasing GPT-2 also wrote a
model card for their model.
Model Details
Model Description
GPT-2 is a transformers model pretrained on a very large corpus of Basque data in a self-supervised fashion. This
means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots
of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
it was trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
predictions for the token i only uses the inputs from 1 to i but not the future tokens.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
prompt.
This is the smallest version of GPT-2, with 124M parameters.
- Developed by: github.com/juletx
- Model type: GPT2
- Language(s) (NLP): Basque (eu)
- License: cc
Model Sources [optional]
- Repository: github.com/juletx/phd
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
You can use this model directly with a pipeline for text generation.
Downstream Use [optional]
You can also fine-tune it to a downstream task. See the
model hub to look for fine-tuned versions on a task that interests you.
Out-of-Scope Use
[More Information Needed]
Bias, Risks, and Limitations
The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of
unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their
model card:
Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases
that require the generated text to be true.
Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do
not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a
study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,
and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar
levels of caution around use cases that are sensitive to biases around human attributes.
Here's an example of how the model can have biased predictions:
1>>> from transformers import pipeline, set_seed
2>>> generator = pipeline('text-generation', model='gpt2')
3>>> set_seed(42)
4>>> generator("The White man worked as a", max_length=10, num_return_sequences=5)
5
6[{'generated_text': 'The White man worked as a mannequin for'},
7 {'generated_text': 'The White man worked as a maniser of the'},
8 {'generated_text': 'The White man worked as a bus conductor by day'},
9 {'generated_text': 'The White man worked as a plumber at the'},
10 {'generated_text': 'The White man worked as a journalist. He had'}]
11
12>>> set_seed(42)
13>>> generator("The Black man worked as a", max_length=10, num_return_sequences=5)
14
15[{'generated_text': 'The Black man worked as a man at a restaurant'},
16 {'generated_text': 'The Black man worked as a car salesman in a'},
17 {'generated_text': 'The Black man worked as a police sergeant at the'},
18 {'generated_text': 'The Black man worked as a man-eating monster'},
19 {'generated_text': 'The Black man worked as a slave, and was'}]
This bias will also affect all fine-tuned versions of this model.
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
set a seed for reproducibility:
1>>> from transformers import pipeline, set_seed
2>>> generator = pipeline('text-generation', model='gpt2')
3>>> set_seed(42)
4>>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)
5
6[{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."},
7 {'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"},
8 {'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"},
9 {'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"},
10 {'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}]
Here is how to use this model to get the features of a given text in PyTorch:
1from transformers import GPT2Tokenizer, GPT2Model
2tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
3model = GPT2Model.from_pretrained('gpt2')
4text = "Replace me by any text you'd like."
5encoded_input = tokenizer(text, return_tensors='pt')
6output = model(**encoded_input)
Training Details
Training Data
EusCrawl (
http://www.ixa.eus/euscrawl/) is a high-quality corpus for Basque comprising 12.5 million documents
and 423 million tokens, totalling 2.1 GiB of uncompressed text. EusCrawl was built using ad-hoc scrapers to
extract text from 33 Basque websites with high-quality content, resulting in cleaner text compared to
general purpose approaches.
Dataset CardTraining Procedure
Preprocessing [optional]
The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50,304. The inputs are sequences of 1024 consecutive tokens.
Training Hyperparameters
- Training regime: bf16 mixed precission
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
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Metrics
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Results
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Summary
Model Examination [optional]
[More Information Needed]
Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
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Hardware
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Software
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Citation [optional]
BibTeX:
1@article{radford2019language,
2 title={Language Models are Unsupervised Multitask Learners},
3 author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
4 year={2019}
5}
APA:
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Glossary [optional]
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Model Card Authors [optional]
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Model Card Contact
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