# Model Card for GPT2 Eus Euscrawl

<!-- Provide a quick summary of what the model is/does. -->

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](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-language-models/). The team releasing GPT-2 also wrote a
[model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model.

# Model Details

## Model Description

<!-- Provide a longer summary of what this model is. -->

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](https://github.com/juletx)
- **Model type:** GPT2
- **Language(s) (NLP):** Basque (eu)
- **License:** cc

## Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [github.com/juletx/phd](https://github.com/juletx/phd)
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

# Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

## Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

You can use this model directly with a pipeline for text generation.

## Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

You can also fine-tune it to a downstream task. See the
[model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you.

## Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

# Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical 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](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases):

> 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:

```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='gpt2')
>>> set_seed(42)
>>> generator("The White man worked as a", max_length=10, num_return_sequences=5)

[{'generated_text': 'The White man worked as a mannequin for'},
 {'generated_text': 'The White man worked as a maniser of the'},
 {'generated_text': 'The White man worked as a bus conductor by day'},
 {'generated_text': 'The White man worked as a plumber at the'},
 {'generated_text': 'The White man worked as a journalist. He had'}]

>>> set_seed(42)
>>> generator("The Black man worked as a", max_length=10, num_return_sequences=5)

[{'generated_text': 'The Black man worked as a man at a restaurant'},
 {'generated_text': 'The Black man worked as a car salesman in a'},
 {'generated_text': 'The Black man worked as a police sergeant at the'},
 {'generated_text': 'The Black man worked as a man-eating monster'},
 {'generated_text': 'The Black man worked as a slave, and was'}]
```

This bias will also affect all fine-tuned versions of this model.

## Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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:

```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='gpt2')
>>> set_seed(42)
>>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)

[{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."},
 {'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"},
 {'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"},
 {'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"},
 {'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:

```python
from transformers import GPT2Tokenizer, GPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2Model.from_pretrained('gpt2')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```

# Training Details

## Training Data

<!-- 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. -->

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 Card](https://huggingface.co/datasets/HiTZ/euscrawl)

## Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training 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 <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

# Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

## Testing Data, Factors & Metrics

### Testing Data

<!-- This should link to a Data Card if possible. -->

[More Information Needed]

### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

## Results

[More Information Needed]

### Summary



# Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

# Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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).

- **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

[More Information Needed]

### Hardware

[More Information Needed]

### Software

[More Information Needed]

# Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```bibtex
@article{radford2019language,
  title={Language Models are Unsupervised Multitask Learners},
  author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
  year={2019}
}
```

**APA:**

[More Information Needed]

# Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

# More Information [optional]

[More Information Needed]

# Model Card Authors [optional]

[More Information Needed]

# Model Card Contact

[More Information Needed]
{"language": ["eu"], "license": "cc", "library_name": "transformers", "datasets": ["HiTZ/euscrawl"], "metrics": ["perplexity"], "pipeline_tag": "text-generation"}
transformers pytorch safetensors gpt2 text-generation eu dataset:HiTZ/euscrawl arxiv:1910.09700 license:cc autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #eu #dataset-HiTZ/euscrawl #arxiv-1910.09700 #license-cc #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

# 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: URL
- Model type: GPT2
- Language(s) (NLP): Basque (eu)
- License: cc

## Model Sources [optional]



- Repository: URL
- Paper [optional]: 
- Demo [optional]: 

# 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





# 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:



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:



Here is how to use this model to get the features of a given text in PyTorch:



# Training Details

## Training Data



EusCrawl (URL 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 Card

## Training 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]





# Evaluation



## Testing Data, Factors & Metrics

### Testing Data





### Factors





### Metrics





## Results



### Summary



# Model Examination [optional]





# Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: 

# Technical Specifications [optional]

## Model Architecture and Objective



## Compute Infrastructure



### Hardware



### Software



[optional]



BibTeX:



APA:



# Glossary [optional]





# More Information [optional]



# Model Card Authors [optional]



# Model Card Contact


# 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: URL
- Model type: GPT2
- Language(s) (NLP): Basque (eu)
- License: cc ## Model Sources [optional]



- Repository: URL
- Paper [optional]: 
- Demo [optional]: # 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 # 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:



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:



Here is how to use this model to get the features of a given text in PyTorch: # Training Details ## Training Data



EusCrawl (URL 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 Card ## Training 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] # Evaluation ## Testing Data, Factors & Metrics ### Testing Data ### Factors ### Metrics ## Results ### Summary # Model Examination [optional] # Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: # Technical Specifications [optional] ## Model Architecture and Objective ## Compute Infrastructure ### Hardware ### Software



[optional]



BibTeX:



APA: # Glossary [optional] # More Information [optional] # Model Card Authors [optional] # Model Card Contact
TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #eu #dataset-HiTZ/euscrawl #arxiv-1910.09700 #license-cc #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # 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: URL
- Model type: GPT2
- Language(s) (NLP): Basque (eu)
- License: cc ## Model Sources [optional]



- Repository: URL
- Paper [optional]: 
- Demo [optional]: # 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 # 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:



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:



Here is how to use this model to get the features of a given text in PyTorch: # Training Details ## Training Data



EusCrawl (URL 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 Card ## Training 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] # Evaluation ## Testing Data, Factors & Metrics ### Testing Data ### Factors ### Metrics ## Results ### Summary # Model Examination [optional] # Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: # Technical Specifications [optional] ## Model Architecture and Objective ## Compute Infrastructure ### Hardware ### Software



[optional]



BibTeX:



APA: # Glossary [optional] # More Information [optional] # Model Card Authors [optional] # Model Card Contact
TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #eu #dataset-HiTZ/euscrawl #arxiv-1910.09700 #license-cc #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# 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: URL
- Model type: GPT2
- Language(s) (NLP): Basque (eu)
- License: cc## Model Sources [optional]



- Repository: URL
- Paper [optional]: 
- Demo [optional]:# 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# 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:



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:



Here is how to use this model to get the features of a given text in PyTorch:# Training Details## Training Data



EusCrawl (URL 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 Card## Training 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]# Evaluation## Testing Data, Factors & Metrics### Testing Data### Factors### Metrics## Results### Summary# Model Examination [optional]# Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted:# Technical Specifications [optional]## Model Architecture and Objective## Compute Infrastructure### Hardware### Software



[optional]



BibTeX:



APA:# Glossary [optional]# More Information [optional]# Model Card Authors [optional]# Model Card Contact