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null | null | transformers | ## Overview
We present a CLSRIL-23 (Cross Lingual Speech Representations on Indic Languages), a self supervised learning based audio pre-trained model which learns cross
lingual speech representations from raw audio across **23 Indic languages**. It is built on top of wav2vec
2.0 which is solved by training a contrastive task over masked latent speech representations and
jointly learns the quantization of latents shared across all languages.
[Arxiv Link](https://arxiv.org/pdf/2107.07402.pdf)
[Original Repo](https://github.com/Open-Speech-EkStep/vakyansh-models) contains models in fairseq format.
## Languages in the pretraining dataset
| Language | Data (In Hrs) |
|-----------|---------------|
| Assamese | 254.9 |
| Bengali | 331.3 |
| Bodo | 26.9 |
| Dogri | 17.1 |
| English | 819.7 |
| Gujarati | 336.7 |
| Hindi | 4563.7 |
| Kannada | 451.8 |
| Kashmiri | 67.8 |
| Konkani | 36.8 |
| Maithili | 113.8 |
| Malayalam | 297.7 |
| Manipuri | 171.9 |
| Marathi | 458.2 |
| Nepali | 31.6 |
| Odia | 131.4 |
| Punjabi | 486.05 |
| Sanskrit | 58.8 |
| Santali | 6.56 |
| Sindhi | 16 |
| Tamil | 542.6 |
| Telugu | 302.8 |
| Urdu | 259.68 |
## Repo for training:
[Experimentation](https://github.com/Open-Speech-EkStep/vakyansh-wav2vec2-experimentation) platform built on top of fairseq.
| {} | feature-extraction | Harveenchadha/wav2vec2-pretrained-clsril-23-10k | [
"transformers",
"pytorch",
"wav2vec2",
"feature-extraction",
"arxiv:2107.07402",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2107.07402"
] | [] | TAGS
#transformers #pytorch #wav2vec2 #feature-extraction #arxiv-2107.07402 #endpoints_compatible #region-us
| Overview
--------
We present a CLSRIL-23 (Cross Lingual Speech Representations on Indic Languages), a self supervised learning based audio pre-trained model which learns cross
lingual speech representations from raw audio across 23 Indic languages. It is built on top of wav2vec
2.0 which is solved by training a contrastive task over masked latent speech representations and
jointly learns the quantization of latents shared across all languages.
Arxiv Link
Original Repo contains models in fairseq format.
Languages in the pretraining dataset
------------------------------------
Repo for training:
------------------
Experimentation platform built on top of fairseq.
| [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #feature-extraction #arxiv-2107.07402 #endpoints_compatible #region-us \n"
] | [
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null | null | transformers |
## Table of Contents
- [Model Details](#model-details)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Technical Specifications](#technical-specifications)
- [Citation Information](#citation-information)
## Model Details
**Model Description:**
The model is used for classifying a text as Abusive (Hatespeech and Offensive) or Normal. The model is trained using data from Gab and Twitter and Human Rationales were included as part of the training data to boost the performance. The model also has a rationale predictor head that can predict the rationales given an abusive sentence
- **Developed by:** Binny Mathew, Punyajoy Saha, Seid Muhie Yimam, Chris Biemann, Pawan Goyal, and Animesh Mukherjee
- **Model Type:** Text Classification
- **Language(s):** English
- **License:** Apache-2.0
- **Parent Model:** See the [BERT base uncased model](https://huggingface.co/bert-base-uncased) for more information about the BERT base model.
- **Resources for more information:**
- [Research Paper](https://arxiv.org/abs/2012.10289) Accepted at AAAI 2021.
- [GitHub Repo with datatsets and models](https://github.com/punyajoy/HateXplain)
## How to Get Started with the Model
**Details of usage**
Please use the **Model_Rational_Label** class inside [models.py](models.py) to load the models. The default prediction in this hosted inference API may be wrong due to the use of different class initialisations.
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
### from models.py
from models import *
tokenizer = AutoTokenizer.from_pretrained("Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two")
model = Model_Rational_Label.from_pretrained("Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two")
inputs = tokenizer('He is a great guy", return_tensors="pt")
prediction_logits, _ = model(input_ids=inputs['input_ids'],attention_mask=inputs['attention_mask'])
```
## Uses
#### Direct Use
This model can be used for Text Classification
#### Downstream Use
[More information needed]
#### Misuse and Out-of-scope Use
The model should not be used to intentionally create hostile or alienating environments for people. In addition, the model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
(and if you can generate an example of a biased prediction, also something like this):
Predictions generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. For 
The model author's also note in their HateXplain paper that they
> *have not considered any external context such as profile bio, user gender, history of posts etc., which might be helpful in the classification task. Also, in this work we have focused on the English language. It does not consider multilingual hate speech into account.*
#### Training Procedure
##### Preprocessing
The authors detail their preprocessing procedure in the [Github repository](https://github.com/hate-alert/HateXplain/tree/master/Preprocess)
## Evaluation
The mode authors detail the Hidden layer size and attention for the HateXplain fien tuned models in the [associated paper](https://arxiv.org/pdf/2012.10289.pdf)
#### Results
The model authors both in their paper and in the git repository provide the illustrative output of the BERT - HateXplain in comparison to BERT and and other HateXplain fine tuned 
## Citation Information
```bibtex
@article{mathew2020hatexplain,
title={HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection},
author={Mathew, Binny and Saha, Punyajoy and Yimam, Seid Muhie and Biemann, Chris and Goyal, Pawan and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2012.10289},
year={2020}
}
```
| {"language": "en", "license": "apache-2.0", "datasets": ["hatexplain"]} | text-classification | Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two | [
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"dataset:hatexplain",
"arxiv:2012.10289",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2012.10289"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #en #dataset-hatexplain #arxiv-2012.10289 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Table of Contents
- Model Details
- How to Get Started With the Model
- Uses
- Risks, Limitations and Biases
- Training
- Evaluation
- Technical Specifications
- Citation Information
## Model Details
Model Description:
The model is used for classifying a text as Abusive (Hatespeech and Offensive) or Normal. The model is trained using data from Gab and Twitter and Human Rationales were included as part of the training data to boost the performance. The model also has a rationale predictor head that can predict the rationales given an abusive sentence
- Developed by: Binny Mathew, Punyajoy Saha, Seid Muhie Yimam, Chris Biemann, Pawan Goyal, and Animesh Mukherjee
- Model Type: Text Classification
- Language(s): English
- License: Apache-2.0
- Parent Model: See the BERT base uncased model for more information about the BERT base model.
- Resources for more information:
- Research Paper Accepted at AAAI 2021.
- GitHub Repo with datatsets and models
## How to Get Started with the Model
Details of usage
Please use the Model_Rational_Label class inside URL to load the models. The default prediction in this hosted inference API may be wrong due to the use of different class initialisations.
## Uses
#### Direct Use
This model can be used for Text Classification
#### Downstream Use
[More information needed]
#### Misuse and Out-of-scope Use
The model should not be used to intentionally create hostile or alienating environments for people. In addition, the model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
## Risks, Limitations and Biases
CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.
Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)).
(and if you can generate an example of a biased prediction, also something like this):
Predictions generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. For !example:
The model author's also note in their HateXplain paper that they
> *have not considered any external context such as profile bio, user gender, history of posts etc., which might be helpful in the classification task. Also, in this work we have focused on the English language. It does not consider multilingual hate speech into account.*
#### Training Procedure
##### Preprocessing
The authors detail their preprocessing procedure in the Github repository
## Evaluation
The mode authors detail the Hidden layer size and attention for the HateXplain fien tuned models in the associated paper
#### Results
The model authors both in their paper and in the git repository provide the illustrative output of the BERT - HateXplain in comparison to BERT and and other HateXplain fine tuned !models
| [
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"## Model Details\nModel Description: \nThe model is used for classifying a text as Abusive (Hatespeech and Offensive) or Normal. The model is trained using data from Gab and Twitter and Human Rationales were included as part of the training data to boost the performance. The model also has a rationale predictor head that can predict the rationales given an abusive sentence\n\n\n- Developed by: Binny Mathew, Punyajoy Saha, Seid Muhie Yimam, Chris Biemann, Pawan Goyal, and Animesh Mukherjee \n- Model Type: Text Classification\n- Language(s): English\n- License: Apache-2.0\n- Parent Model: See the BERT base uncased model for more information about the BERT base model.\n- Resources for more information:\n - Research Paper Accepted at AAAI 2021.\n - GitHub Repo with datatsets and models",
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"## Risks, Limitations and Biases\n\nCONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.\n\nSignificant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)).\n\n(and if you can generate an example of a biased prediction, also something like this): \n\nPredictions generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. For !example: \n\nThe model author's also note in their HateXplain paper that they \n> *have not considered any external context such as profile bio, user gender, history of posts etc., which might be helpful in the classification task. Also, in this work we have focused on the English language. It does not consider multilingual hate speech into account.*",
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"## Table of Contents\n- Model Details\n- How to Get Started With the Model\n- Uses\n- Risks, Limitations and Biases\n- Training\n- Evaluation\n- Technical Specifications\n- Citation Information",
"## Model Details\nModel Description: \nThe model is used for classifying a text as Abusive (Hatespeech and Offensive) or Normal. The model is trained using data from Gab and Twitter and Human Rationales were included as part of the training data to boost the performance. The model also has a rationale predictor head that can predict the rationales given an abusive sentence\n\n\n- Developed by: Binny Mathew, Punyajoy Saha, Seid Muhie Yimam, Chris Biemann, Pawan Goyal, and Animesh Mukherjee \n- Model Type: Text Classification\n- Language(s): English\n- License: Apache-2.0\n- Parent Model: See the BERT base uncased model for more information about the BERT base model.\n- Resources for more information:\n - Research Paper Accepted at AAAI 2021.\n - GitHub Repo with datatsets and models",
"## How to Get Started with the Model\n\nDetails of usage\n\nPlease use the Model_Rational_Label class inside URL to load the models. The default prediction in this hosted inference API may be wrong due to the use of different class initialisations.",
"## Uses",
"#### Direct Use\n\nThis model can be used for Text Classification",
"#### Downstream Use\n\n[More information needed]",
"#### Misuse and Out-of-scope Use\n\nThe model should not be used to intentionally create hostile or alienating environments for people. In addition, the model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.",
"## Risks, Limitations and Biases\n\nCONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.\n\nSignificant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)).\n\n(and if you can generate an example of a biased prediction, also something like this): \n\nPredictions generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. For !example: \n\nThe model author's also note in their HateXplain paper that they \n> *have not considered any external context such as profile bio, user gender, history of posts etc., which might be helpful in the classification task. Also, in this work we have focused on the English language. It does not consider multilingual hate speech into account.*",
"#### Training Procedure",
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"## Evaluation\nThe mode authors detail the Hidden layer size and attention for the HateXplain fien tuned models in the associated paper",
"#### Results \n\nThe model authors both in their paper and in the git repository provide the illustrative output of the BERT - HateXplain in comparison to BERT and and other HateXplain fine tuned !models"
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null | null | transformers | The model is used for classifying a text as **Hatespeech**, **Offensive**, or **Normal**. The model is trained using data from Gab and Twitter and *Human Rationales* were included as part of the training data to boost the performance.
The dataset and models are available here: https://github.com/punyajoy/HateXplain
**For more details about our paper**
Binny Mathew, Punyajoy Saha, Seid Muhie Yimam, Chris Biemann, Pawan Goyal, and Animesh Mukherjee "[HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection)". Accepted at AAAI 2021.
***Please cite our paper in any published work that uses any of these resources.***
~~~
@article{mathew2020hatexplain,
title={HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection},
author={Mathew, Binny and Saha, Punyajoy and Yimam, Seid Muhie and Biemann, Chris and Goyal, Pawan and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2012.10289},
year={2020}
}
~~~
| {"language": "en", "license": "apache-2.0", "datasets": ["hatexplain"]} | text-classification | Hate-speech-CNERG/bert-base-uncased-hatexplain | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"en",
"dataset:hatexplain",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #en #dataset-hatexplain #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| The model is used for classifying a text as Hatespeech, Offensive, or Normal. The model is trained using data from Gab and Twitter and *Human Rationales* were included as part of the training data to boost the performance.
The dataset and models are available here: URL
For more details about our paper
Binny Mathew, Punyajoy Saha, Seid Muhie Yimam, Chris Biemann, Pawan Goyal, and Animesh Mukherjee "[HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection)". Accepted at AAAI 2021.
*Please cite our paper in any published work that uses any of these resources.*
~~~
@article{mathew2020hatexplain,
title={HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection},
author={Mathew, Binny and Saha, Punyajoy and Yimam, Seid Muhie and Biemann, Chris and Goyal, Pawan and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2012.10289},
year={2020}
}
~~~
| [] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #en #dataset-hatexplain #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] | [
60
] | [
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #en #dataset-hatexplain #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
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null | null | transformers |
This model is used detecting **hatespeech** in **Arabic language**. The mono in the name refers to the monolingual setting, where the model is trained using only Arabic language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieved is 0.877609 for a learning rate of 2e-5. Training code can be found at this [url](https://github.com/punyajoy/DE-LIMIT)
### For more details about our paper
Sai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. "[Deep Learning Models for Multilingual Hate Speech Detection](https://arxiv.org/abs/2004.06465)". Accepted at ECML-PKDD 2020.
***Please cite our paper in any published work that uses any of these resources.***
~~~
@article{aluru2020deep,
title={Deep Learning Models for Multilingual Hate Speech Detection},
author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2004.06465},
year={2020}
}
~~~
| {"language": "ar", "license": "apache-2.0"} | text-classification | Hate-speech-CNERG/dehatebert-mono-arabic | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"ar",
"arxiv:2004.06465",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2004.06465"
] | [
"ar"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #ar #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is used detecting hatespeech in Arabic language. The mono in the name refers to the monolingual setting, where the model is trained using only Arabic language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieved is 0.877609 for a learning rate of 2e-5. Training code can be found at this url
### For more details about our paper
Sai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. "Deep Learning Models for Multilingual Hate Speech Detection". Accepted at ECML-PKDD 2020.
*Please cite our paper in any published work that uses any of these resources.*
~~~
@article{aluru2020deep,
title={Deep Learning Models for Multilingual Hate Speech Detection},
author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2004.06465},
year={2020}
}
~~~
| [
"### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~"
] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #ar #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~"
] | [
58,
154
] | [
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #ar #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~"
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null | null | transformers | This model is used detecting **hatespeech** in **English language**. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieved is 0.726030 for a learning rate of 2e-5. Training code can be found here https://github.com/punyajoy/DE-LIMIT
### For more details about our paper
Sai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. "[Deep Learning Models for Multilingual Hate Speech Detection](https://arxiv.org/abs/2004.06465)". Accepted at ECML-PKDD 2020.
***Please cite our paper in any published work that uses any of these resources.***
~~~
@article{aluru2020deep,
title={Deep Learning Models for Multilingual Hate Speech Detection},
author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2004.06465},
year={2020}
}
~~~
| {"language": "en", "license": "apache-2.0"} | text-classification | Hate-speech-CNERG/dehatebert-mono-english | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"en",
"arxiv:2004.06465",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2004.06465"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #en #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| This model is used detecting hatespeech in English language. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieved is 0.726030 for a learning rate of 2e-5. Training code can be found here URL
### For more details about our paper
Sai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. "Deep Learning Models for Multilingual Hate Speech Detection". Accepted at ECML-PKDD 2020.
*Please cite our paper in any published work that uses any of these resources.*
~~~
@article{aluru2020deep,
title={Deep Learning Models for Multilingual Hate Speech Detection},
author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2004.06465},
year={2020}
}
~~~
| [
"### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n~~~"
] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #en #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n~~~"
] | [
62,
154
] | [
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #en #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n~~~"
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] |
null | null | transformers |
This model is used detecting **hatespeech** in **French language**. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieved is 0.692094 for a learning rate of 3e-5. Training code can be found at this [url](https://github.com/punyajoy/DE-LIMIT)
### For more details about our paper
Sai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. "[Deep Learning Models for Multilingual Hate Speech Detection](https://arxiv.org/abs/2004.06465)". Accepted at ECML-PKDD 2020.
***Please cite our paper in any published work that uses any of these resources.***
~~~
@article{aluru2020deep,
title={Deep Learning Models for Multilingual Hate Speech Detection},
author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2004.06465},
year={2020}
}
~~~
| {"language": "fr", "license": "apache-2.0"} | text-classification | Hate-speech-CNERG/dehatebert-mono-french | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"fr",
"arxiv:2004.06465",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2004.06465"
] | [
"fr"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #fr #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is used detecting hatespeech in French language. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieved is 0.692094 for a learning rate of 3e-5. Training code can be found at this url
### For more details about our paper
Sai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. "Deep Learning Models for Multilingual Hate Speech Detection". Accepted at ECML-PKDD 2020.
*Please cite our paper in any published work that uses any of these resources.*
~~~
@article{aluru2020deep,
title={Deep Learning Models for Multilingual Hate Speech Detection},
author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2004.06465},
year={2020}
}
~~~
| [
"### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~"
] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #fr #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~"
] | [
58,
154
] | [
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #fr #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~"
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null | null | transformers |
This model is used detecting **hatespeech** in **German language**. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieved is 0.649794 for a learning rate of 3e-5. Training code can be found at this [url](https://github.com/punyajoy/DE-LIMIT)
### For more details about our paper
Sai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. "[Deep Learning Models for Multilingual Hate Speech Detection](https://arxiv.org/abs/2004.06465)". Accepted at ECML-PKDD 2020.
***Please cite our paper in any published work that uses any of these resources.***
~~~
@article{aluru2020deep,
title={Deep Learning Models for Multilingual Hate Speech Detection},
author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2004.06465},
year={2020}
}
~~~
| {"language": "de", "license": "apache-2.0"} | text-classification | Hate-speech-CNERG/dehatebert-mono-german | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"de",
"arxiv:2004.06465",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2004.06465"
] | [
"de"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #de #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is used detecting hatespeech in German language. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieved is 0.649794 for a learning rate of 3e-5. Training code can be found at this url
### For more details about our paper
Sai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. "Deep Learning Models for Multilingual Hate Speech Detection". Accepted at ECML-PKDD 2020.
*Please cite our paper in any published work that uses any of these resources.*
~~~
@article{aluru2020deep,
title={Deep Learning Models for Multilingual Hate Speech Detection},
author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2004.06465},
year={2020}
}
~~~
| [
"### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~"
] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #de #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~"
] | [
58,
154
] | [
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #de #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~"
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null | null | transformers | This model is used detecting **hatespeech** in **Indonesian language**. The mono in the name refers to the monolingual setting, where the model is trained using only Arabic language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieved is 0.844494 for a learning rate of 2e-5. Training code can be found at this [url](https://github.com/punyajoy/DE-LIMIT)
### For more details about our paper
Sai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. "[Deep Learning Models for Multilingual Hate Speech Detection](https://arxiv.org/abs/2004.06465)". Accepted at ECML-PKDD 2020.
***Please cite our paper in any published work that uses any of these resources.***
~~~
@article{aluru2020deep,
title={Deep Learning Models for Multilingual Hate Speech Detection},
author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2004.06465},
year={2020}
}
~~~
| {} | text-classification | Hate-speech-CNERG/dehatebert-mono-indonesian | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"arxiv:2004.06465",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2004.06465"
] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #arxiv-2004.06465 #autotrain_compatible #endpoints_compatible #region-us
| This model is used detecting hatespeech in Indonesian language. The mono in the name refers to the monolingual setting, where the model is trained using only Arabic language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieved is 0.844494 for a learning rate of 2e-5. Training code can be found at this url
### For more details about our paper
Sai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. "Deep Learning Models for Multilingual Hate Speech Detection". Accepted at ECML-PKDD 2020.
*Please cite our paper in any published work that uses any of these resources.*
~~~
@article{aluru2020deep,
title={Deep Learning Models for Multilingual Hate Speech Detection},
author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2004.06465},
year={2020}
}
~~~
| [
"### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~"
] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #arxiv-2004.06465 #autotrain_compatible #endpoints_compatible #region-us \n",
"### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~"
] | [
48,
154
] | [
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #arxiv-2004.06465 #autotrain_compatible #endpoints_compatible #region-us \n### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~"
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null | null | transformers | This model is used detecting **hatespeech** in **Italian language**. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieved is 0.837288 for a learning rate of 3e-5. Training code can be found at this [url](https://github.com/punyajoy/DE-LIMIT)
### For more details about our paper
Sai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. "[Deep Learning Models for Multilingual Hate Speech Detection](https://arxiv.org/abs/2004.06465)". Accepted at ECML-PKDD 2020.
***Please cite our paper in any published work that uses any of these resources.***
~~~
@article{aluru2020deep,
title={Deep Learning Models for Multilingual Hate Speech Detection},
author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2004.06465},
year={2020}
}
~~~
| {"language": "it", "license": "apache-2.0"} | text-classification | Hate-speech-CNERG/dehatebert-mono-italian | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"it",
"arxiv:2004.06465",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2004.06465"
] | [
"it"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #it #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| This model is used detecting hatespeech in Italian language. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieved is 0.837288 for a learning rate of 3e-5. Training code can be found at this url
### For more details about our paper
Sai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. "Deep Learning Models for Multilingual Hate Speech Detection". Accepted at ECML-PKDD 2020.
*Please cite our paper in any published work that uses any of these resources.*
~~~
@article{aluru2020deep,
title={Deep Learning Models for Multilingual Hate Speech Detection},
author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2004.06465},
year={2020}
}
~~~
| [
"### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~"
] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #it #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~"
] | [
58,
154
] | [
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #it #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~"
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null | null | transformers | This model is used detecting **hatespeech** in **Polish language**. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieved is 0.723254 for a learning rate of 2e-5. Training code can be found at this [url](https://github.com/punyajoy/DE-LIMIT)
### For more details about our paper
Sai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. "[Deep Learning Models for Multilingual Hate Speech Detection](https://arxiv.org/abs/2004.06465)". Accepted at ECML-PKDD 2020.
***Please cite our paper in any published work that uses any of these resources.***
~~~
@article{aluru2020deep,
title={Deep Learning Models for Multilingual Hate Speech Detection},
author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2004.06465},
year={2020}
}
~~~
| {"language": "pl", "license": "apache-2.0"} | text-classification | Hate-speech-CNERG/dehatebert-mono-polish | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"pl",
"arxiv:2004.06465",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2004.06465"
] | [
"pl"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #pl #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| This model is used detecting hatespeech in Polish language. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieved is 0.723254 for a learning rate of 2e-5. Training code can be found at this url
### For more details about our paper
Sai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. "Deep Learning Models for Multilingual Hate Speech Detection". Accepted at ECML-PKDD 2020.
*Please cite our paper in any published work that uses any of these resources.*
~~~
@article{aluru2020deep,
title={Deep Learning Models for Multilingual Hate Speech Detection},
author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2004.06465},
year={2020}
}
~~~
| [
"### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~"
] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #pl #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~"
] | [
58,
154
] | [
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #pl #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~"
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null | null | transformers | This model is used detecting **hatespeech** in **Portuguese language**. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieved is 0.716119 for a learning rate of 3e-5. Training code can be found at this [url](https://github.com/punyajoy/DE-LIMIT)
### For more details about our paper
Sai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. "[Deep Learning Models for Multilingual Hate Speech Detection](https://arxiv.org/abs/2004.06465)". Accepted at ECML-PKDD 2020.
***Please cite our paper in any published work that uses any of these resources.***
~~~
@article{aluru2020deep,
title={Deep Learning Models for Multilingual Hate Speech Detection},
author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2004.06465},
year={2020}
}
~~~
| {"language": "pt", "license": "apache-2.0"} | text-classification | Hate-speech-CNERG/dehatebert-mono-portugese | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"pt",
"arxiv:2004.06465",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2004.06465"
] | [
"pt"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #pt #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| This model is used detecting hatespeech in Portuguese language. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieved is 0.716119 for a learning rate of 3e-5. Training code can be found at this url
### For more details about our paper
Sai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. "Deep Learning Models for Multilingual Hate Speech Detection". Accepted at ECML-PKDD 2020.
*Please cite our paper in any published work that uses any of these resources.*
~~~
@article{aluru2020deep,
title={Deep Learning Models for Multilingual Hate Speech Detection},
author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2004.06465},
year={2020}
}
~~~
| [
"### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~"
] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #pt #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~"
] | [
58,
154
] | [
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #pt #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~"
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null | null | transformers | This model is used detecting **hatespeech** in **Spanish language**. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieved is 0.740287 for a learning rate of 3e-5. Training code can be found at this [url](https://github.com/punyajoy/DE-LIMIT)
### For more details about our paper
Sai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. "[Deep Learning Models for Multilingual Hate Speech Detection](https://arxiv.org/abs/2004.06465)". Accepted at ECML-PKDD 2020.
***Please cite our paper in any published work that uses any of these resources.***
~~~
@article{aluru2020deep,
title={Deep Learning Models for Multilingual Hate Speech Detection},
author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2004.06465},
year={2020}
}
~~~
| {"language": "es", "license": "apache-2.0"} | text-classification | Hate-speech-CNERG/dehatebert-mono-spanish | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"es",
"arxiv:2004.06465",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2004.06465"
] | [
"es"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #es #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| This model is used detecting hatespeech in Spanish language. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieved is 0.740287 for a learning rate of 3e-5. Training code can be found at this url
### For more details about our paper
Sai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. "Deep Learning Models for Multilingual Hate Speech Detection". Accepted at ECML-PKDD 2020.
*Please cite our paper in any published work that uses any of these resources.*
~~~
@article{aluru2020deep,
title={Deep Learning Models for Multilingual Hate Speech Detection},
author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2004.06465},
year={2020}
}
~~~
| [
"### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~"
] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #es #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~"
] | [
62,
154
] | [
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #es #arxiv-2004.06465 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### For more details about our paper\n\nSai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. \"Deep Learning Models for Multilingual Hate Speech Detection\". Accepted at ECML-PKDD 2020.\n\n*Please cite our paper in any published work that uses any of these resources.*\n\n~~~\n@article{aluru2020deep,\n title={Deep Learning Models for Multilingual Hate Speech Detection},\n author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n journal={arXiv preprint arXiv:2004.06465},\n year={2020}\n}\n\n~~~"
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null | null | transformers | This model is used to detect **Offensive Content** in **Kannada Code-Mixed language**. The mono in the name refers to the monolingual setting, where the model is trained using only Kannada(pure and code-mixed) data. The weights are initialized from pretrained XLM-Roberta-Base and pretrained using Masked Language Modelling on the target dataset before fine-tuning using Cross-Entropy Loss.
This model is the best of multiple trained for **EACL 2021 Shared Task on Offensive Language Identification in Dravidian Languages**. Genetic-Algorithm based ensembled test predictions got the second-highest weighted F1 score at the leaderboard (Weighted F1 score on hold out test set: This model - 0.73, Ensemble - 0.74)
### For more details about our paper
Debjoy Saha, Naman Paharia, Debajit Chakraborty, Punyajoy Saha, Animesh Mukherjee. "[Hate-Alert@DravidianLangTech-EACL2021: Ensembling strategies for Transformer-based Offensive language Detection](https://www.aclweb.org/anthology/2021.dravidianlangtech-1.38/)".
***Please cite our paper in any published work that uses any of these resources.***
~~~
@inproceedings{saha-etal-2021-hate,
title = "Hate-Alert@{D}ravidian{L}ang{T}ech-{EACL}2021: Ensembling strategies for Transformer-based Offensive language Detection",
author = "Saha, Debjoy and Paharia, Naman and Chakraborty, Debajit and Saha, Punyajoy and Mukherjee, Animesh",
booktitle = "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages",
month = apr,
year = "2021",
address = "Kyiv",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.dravidianlangtech-1.38",
pages = "270--276",
abstract = "Social media often acts as breeding grounds for different forms of offensive content. For low resource languages like Tamil, the situation is more complex due to the poor performance of multilingual or language-specific models and lack of proper benchmark datasets. Based on this shared task {``}Offensive Language Identification in Dravidian Languages{''} at EACL 2021; we present an exhaustive exploration of different transformer models, We also provide a genetic algorithm technique for ensembling different models. Our ensembled models trained separately for each language secured the first position in Tamil, the second position in Kannada, and the first position in Malayalam sub-tasks. The models and codes are provided.",
}
~~~ | {"language": "kn", "license": "apache-2.0"} | text-classification | Hate-speech-CNERG/deoffxlmr-mono-kannada | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"kn",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"kn"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #kn #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| This model is used to detect Offensive Content in Kannada Code-Mixed language. The mono in the name refers to the monolingual setting, where the model is trained using only Kannada(pure and code-mixed) data. The weights are initialized from pretrained XLM-Roberta-Base and pretrained using Masked Language Modelling on the target dataset before fine-tuning using Cross-Entropy Loss.
This model is the best of multiple trained for EACL 2021 Shared Task on Offensive Language Identification in Dravidian Languages. Genetic-Algorithm based ensembled test predictions got the second-highest weighted F1 score at the leaderboard (Weighted F1 score on hold out test set: This model - 0.73, Ensemble - 0.74)
### For more details about our paper
Debjoy Saha, Naman Paharia, Debajit Chakraborty, Punyajoy Saha, Animesh Mukherjee. "Hate-Alert@DravidianLangTech-EACL2021: Ensembling strategies for Transformer-based Offensive language Detection".
*Please cite our paper in any published work that uses any of these resources.*
~~~
@inproceedings{saha-etal-2021-hate,
title = "Hate-Alert@{D}ravidian{L}ang{T}ech-{EACL}2021: Ensembling strategies for Transformer-based Offensive language Detection",
author = "Saha, Debjoy and Paharia, Naman and Chakraborty, Debajit and Saha, Punyajoy and Mukherjee, Animesh",
booktitle = "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages",
month = apr,
year = "2021",
address = "Kyiv",
publisher = "Association for Computational Linguistics",
url = "URL
pages = "270--276",
abstract = "Social media often acts as breeding grounds for different forms of offensive content. For low resource languages like Tamil, the situation is more complex due to the poor performance of multilingual or language-specific models and lack of proper benchmark datasets. Based on this shared task {''}Offensive Language Identification in Dravidian Languages{''} at EACL 2021; we present an exhaustive exploration of different transformer models, We also provide a genetic algorithm technique for ensembling different models. Our ensembled models trained separately for each language secured the first position in Tamil, the second position in Kannada, and the first position in Malayalam sub-tasks. The models and codes are provided.",
}
~~~ | [
"### For more details about our paper\n\nDebjoy Saha, Naman Paharia, Debajit Chakraborty, Punyajoy Saha, Animesh Mukherjee. \"Hate-Alert@DravidianLangTech-EACL2021: Ensembling strategies for Transformer-based Offensive language Detection\".\n\n*Please cite our paper in any published work that uses any of these resources.*\n~~~\n@inproceedings{saha-etal-2021-hate,\n title = \"Hate-Alert@{D}ravidian{L}ang{T}ech-{EACL}2021: Ensembling strategies for Transformer-based Offensive language Detection\",\n author = \"Saha, Debjoy and Paharia, Naman and Chakraborty, Debajit and Saha, Punyajoy and Mukherjee, Animesh\",\n booktitle = \"Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages\",\n month = apr,\n year = \"2021\",\n address = \"Kyiv\",\n publisher = \"Association for Computational Linguistics\",\n url = \"URL\n pages = \"270--276\",\n abstract = \"Social media often acts as breeding grounds for different forms of offensive content. For low resource languages like Tamil, the situation is more complex due to the poor performance of multilingual or language-specific models and lack of proper benchmark datasets. Based on this shared task {''}Offensive Language Identification in Dravidian Languages{''} at EACL 2021; we present an exhaustive exploration of different transformer models, We also provide a genetic algorithm technique for ensembling different models. Our ensembled models trained separately for each language secured the first position in Tamil, the second position in Kannada, and the first position in Malayalam sub-tasks. The models and codes are provided.\",\n}\n~~~"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #kn #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### For more details about our paper\n\nDebjoy Saha, Naman Paharia, Debajit Chakraborty, Punyajoy Saha, Animesh Mukherjee. \"Hate-Alert@DravidianLangTech-EACL2021: Ensembling strategies for Transformer-based Offensive language Detection\".\n\n*Please cite our paper in any published work that uses any of these resources.*\n~~~\n@inproceedings{saha-etal-2021-hate,\n title = \"Hate-Alert@{D}ravidian{L}ang{T}ech-{EACL}2021: Ensembling strategies for Transformer-based Offensive language Detection\",\n author = \"Saha, Debjoy and Paharia, Naman and Chakraborty, Debajit and Saha, Punyajoy and Mukherjee, Animesh\",\n booktitle = \"Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages\",\n month = apr,\n year = \"2021\",\n address = \"Kyiv\",\n publisher = \"Association for Computational Linguistics\",\n url = \"URL\n pages = \"270--276\",\n abstract = \"Social media often acts as breeding grounds for different forms of offensive content. For low resource languages like Tamil, the situation is more complex due to the poor performance of multilingual or language-specific models and lack of proper benchmark datasets. Based on this shared task {''}Offensive Language Identification in Dravidian Languages{''} at EACL 2021; we present an exhaustive exploration of different transformer models, We also provide a genetic algorithm technique for ensembling different models. Our ensembled models trained separately for each language secured the first position in Tamil, the second position in Kannada, and the first position in Malayalam sub-tasks. The models and codes are provided.\",\n}\n~~~"
] | [
50,
420
] | [
"passage: TAGS\n#transformers #pytorch #xlm-roberta #text-classification #kn #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### For more details about our paper\n\nDebjoy Saha, Naman Paharia, Debajit Chakraborty, Punyajoy Saha, Animesh Mukherjee. \"Hate-Alert@DravidianLangTech-EACL2021: Ensembling strategies for Transformer-based Offensive language Detection\".\n\n*Please cite our paper in any published work that uses any of these resources.*\n~~~\n@inproceedings{saha-etal-2021-hate,\n title = \"Hate-Alert@{D}ravidian{L}ang{T}ech-{EACL}2021: Ensembling strategies for Transformer-based Offensive language Detection\",\n author = \"Saha, Debjoy and Paharia, Naman and Chakraborty, Debajit and Saha, Punyajoy and Mukherjee, Animesh\",\n booktitle = \"Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages\",\n month = apr,\n year = \"2021\",\n address = \"Kyiv\",\n publisher = \"Association for Computational Linguistics\",\n url = \"URL\n pages = \"270--276\",\n abstract = \"Social media often acts as breeding grounds for different forms of offensive content. For low resource languages like Tamil, the situation is more complex due to the poor performance of multilingual or language-specific models and lack of proper benchmark datasets. Based on this shared task {''}Offensive Language Identification in Dravidian Languages{''} at EACL 2021; we present an exhaustive exploration of different transformer models, We also provide a genetic algorithm technique for ensembling different models. Our ensembled models trained separately for each language secured the first position in Tamil, the second position in Kannada, and the first position in Malayalam sub-tasks. The models and codes are provided.\",\n}\n~~~"
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null | null | transformers | This model is used to detect **Offensive Content** in **Malayalam Code-Mixed language**. The mono in the name refers to the monolingual setting, where the model is trained using only Malayalam(pure and code-mixed) data. The weights are initialized from pretrained XLM-Roberta-Base and pretrained using Masked Language Modelling on the target dataset before fine-tuning using Cross-Entropy Loss.
This model is the best of multiple trained for **EACL 2021 Shared Task on Offensive Language Identification in Dravidian Languages**. Genetic-Algorithm based ensembled test predictions got the highest weighted F1 score at the leaderboard (Weighted F1 score on hold out test set: This model - 0.97, Ensemble - 0.97)
### For more details about our paper
Debjoy Saha, Naman Paharia, Debajit Chakraborty, Punyajoy Saha, Animesh Mukherjee. "[Hate-Alert@DravidianLangTech-EACL2021: Ensembling strategies for Transformer-based Offensive language Detection](https://www.aclweb.org/anthology/2021.dravidianlangtech-1.38/)".
***Please cite our paper in any published work that uses any of these resources.***
~~~
@inproceedings{saha-etal-2021-hate,
title = "Hate-Alert@{D}ravidian{L}ang{T}ech-{EACL}2021: Ensembling strategies for Transformer-based Offensive language Detection",
author = "Saha, Debjoy and Paharia, Naman and Chakraborty, Debajit and Saha, Punyajoy and Mukherjee, Animesh",
booktitle = "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages",
month = apr,
year = "2021",
address = "Kyiv",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.dravidianlangtech-1.38",
pages = "270--276",
abstract = "Social media often acts as breeding grounds for different forms of offensive content. For low resource languages like Tamil, the situation is more complex due to the poor performance of multilingual or language-specific models and lack of proper benchmark datasets. Based on this shared task {``}Offensive Language Identification in Dravidian Languages{''} at EACL 2021; we present an exhaustive exploration of different transformer models, We also provide a genetic algorithm technique for ensembling different models. Our ensembled models trained separately for each language secured the first position in Tamil, the second position in Kannada, and the first position in Malayalam sub-tasks. The models and codes are provided.",
}
~~~ | {"language": "ml", "license": "apache-2.0"} | text-classification | Hate-speech-CNERG/deoffxlmr-mono-malyalam | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"ml",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ml"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #ml #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| This model is used to detect Offensive Content in Malayalam Code-Mixed language. The mono in the name refers to the monolingual setting, where the model is trained using only Malayalam(pure and code-mixed) data. The weights are initialized from pretrained XLM-Roberta-Base and pretrained using Masked Language Modelling on the target dataset before fine-tuning using Cross-Entropy Loss.
This model is the best of multiple trained for EACL 2021 Shared Task on Offensive Language Identification in Dravidian Languages. Genetic-Algorithm based ensembled test predictions got the highest weighted F1 score at the leaderboard (Weighted F1 score on hold out test set: This model - 0.97, Ensemble - 0.97)
### For more details about our paper
Debjoy Saha, Naman Paharia, Debajit Chakraborty, Punyajoy Saha, Animesh Mukherjee. "Hate-Alert@DravidianLangTech-EACL2021: Ensembling strategies for Transformer-based Offensive language Detection".
*Please cite our paper in any published work that uses any of these resources.*
~~~
@inproceedings{saha-etal-2021-hate,
title = "Hate-Alert@{D}ravidian{L}ang{T}ech-{EACL}2021: Ensembling strategies for Transformer-based Offensive language Detection",
author = "Saha, Debjoy and Paharia, Naman and Chakraborty, Debajit and Saha, Punyajoy and Mukherjee, Animesh",
booktitle = "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages",
month = apr,
year = "2021",
address = "Kyiv",
publisher = "Association for Computational Linguistics",
url = "URL
pages = "270--276",
abstract = "Social media often acts as breeding grounds for different forms of offensive content. For low resource languages like Tamil, the situation is more complex due to the poor performance of multilingual or language-specific models and lack of proper benchmark datasets. Based on this shared task {''}Offensive Language Identification in Dravidian Languages{''} at EACL 2021; we present an exhaustive exploration of different transformer models, We also provide a genetic algorithm technique for ensembling different models. Our ensembled models trained separately for each language secured the first position in Tamil, the second position in Kannada, and the first position in Malayalam sub-tasks. The models and codes are provided.",
}
~~~ | [
"### For more details about our paper\n\nDebjoy Saha, Naman Paharia, Debajit Chakraborty, Punyajoy Saha, Animesh Mukherjee. \"Hate-Alert@DravidianLangTech-EACL2021: Ensembling strategies for Transformer-based Offensive language Detection\".\n\n*Please cite our paper in any published work that uses any of these resources.*\n~~~\n@inproceedings{saha-etal-2021-hate,\n title = \"Hate-Alert@{D}ravidian{L}ang{T}ech-{EACL}2021: Ensembling strategies for Transformer-based Offensive language Detection\",\n author = \"Saha, Debjoy and Paharia, Naman and Chakraborty, Debajit and Saha, Punyajoy and Mukherjee, Animesh\",\n booktitle = \"Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages\",\n month = apr,\n year = \"2021\",\n address = \"Kyiv\",\n publisher = \"Association for Computational Linguistics\",\n url = \"URL\n pages = \"270--276\",\n abstract = \"Social media often acts as breeding grounds for different forms of offensive content. For low resource languages like Tamil, the situation is more complex due to the poor performance of multilingual or language-specific models and lack of proper benchmark datasets. Based on this shared task {''}Offensive Language Identification in Dravidian Languages{''} at EACL 2021; we present an exhaustive exploration of different transformer models, We also provide a genetic algorithm technique for ensembling different models. Our ensembled models trained separately for each language secured the first position in Tamil, the second position in Kannada, and the first position in Malayalam sub-tasks. The models and codes are provided.\",\n}\n~~~"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #ml #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### For more details about our paper\n\nDebjoy Saha, Naman Paharia, Debajit Chakraborty, Punyajoy Saha, Animesh Mukherjee. \"Hate-Alert@DravidianLangTech-EACL2021: Ensembling strategies for Transformer-based Offensive language Detection\".\n\n*Please cite our paper in any published work that uses any of these resources.*\n~~~\n@inproceedings{saha-etal-2021-hate,\n title = \"Hate-Alert@{D}ravidian{L}ang{T}ech-{EACL}2021: Ensembling strategies for Transformer-based Offensive language Detection\",\n author = \"Saha, Debjoy and Paharia, Naman and Chakraborty, Debajit and Saha, Punyajoy and Mukherjee, Animesh\",\n booktitle = \"Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages\",\n month = apr,\n year = \"2021\",\n address = \"Kyiv\",\n publisher = \"Association for Computational Linguistics\",\n url = \"URL\n pages = \"270--276\",\n abstract = \"Social media often acts as breeding grounds for different forms of offensive content. For low resource languages like Tamil, the situation is more complex due to the poor performance of multilingual or language-specific models and lack of proper benchmark datasets. Based on this shared task {''}Offensive Language Identification in Dravidian Languages{''} at EACL 2021; we present an exhaustive exploration of different transformer models, We also provide a genetic algorithm technique for ensembling different models. Our ensembled models trained separately for each language secured the first position in Tamil, the second position in Kannada, and the first position in Malayalam sub-tasks. The models and codes are provided.\",\n}\n~~~"
] | [
50,
420
] | [
"passage: TAGS\n#transformers #pytorch #xlm-roberta #text-classification #ml #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### For more details about our paper\n\nDebjoy Saha, Naman Paharia, Debajit Chakraborty, Punyajoy Saha, Animesh Mukherjee. \"Hate-Alert@DravidianLangTech-EACL2021: Ensembling strategies for Transformer-based Offensive language Detection\".\n\n*Please cite our paper in any published work that uses any of these resources.*\n~~~\n@inproceedings{saha-etal-2021-hate,\n title = \"Hate-Alert@{D}ravidian{L}ang{T}ech-{EACL}2021: Ensembling strategies for Transformer-based Offensive language Detection\",\n author = \"Saha, Debjoy and Paharia, Naman and Chakraborty, Debajit and Saha, Punyajoy and Mukherjee, Animesh\",\n booktitle = \"Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages\",\n month = apr,\n year = \"2021\",\n address = \"Kyiv\",\n publisher = \"Association for Computational Linguistics\",\n url = \"URL\n pages = \"270--276\",\n abstract = \"Social media often acts as breeding grounds for different forms of offensive content. For low resource languages like Tamil, the situation is more complex due to the poor performance of multilingual or language-specific models and lack of proper benchmark datasets. Based on this shared task {''}Offensive Language Identification in Dravidian Languages{''} at EACL 2021; we present an exhaustive exploration of different transformer models, We also provide a genetic algorithm technique for ensembling different models. Our ensembled models trained separately for each language secured the first position in Tamil, the second position in Kannada, and the first position in Malayalam sub-tasks. The models and codes are provided.\",\n}\n~~~"
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null | null | transformers | This model is used to detect **Offensive Content** in **Tamil Code-Mixed language**. The mono in the name refers to the monolingual setting, where the model is trained using only Tamil(pure and code-mixed) data. The weights are initialized from pretrained XLM-Roberta-Base and pretrained using Masked Language Modelling on the target dataset before fine-tuning using Cross-Entropy Loss.
This model is the best of multiple trained for **EACL 2021 Shared Task on Offensive Language Identification in Dravidian Languages**. Genetic-Algorithm based ensembled test predictions got the highest weighted F1 score at the leaderboard (Weighted F1 score on hold out test set: This model - 0.76, Ensemble - 0.78)
### For more details about our paper
Debjoy Saha, Naman Paharia, Debajit Chakraborty, Punyajoy Saha, Animesh Mukherjee. "[Hate-Alert@DravidianLangTech-EACL2021: Ensembling strategies for Transformer-based Offensive language Detection](https://www.aclweb.org/anthology/2021.dravidianlangtech-1.38/)".
***Please cite our paper in any published work that uses any of these resources.***
~~~
@inproceedings{saha-etal-2021-hate,
title = "Hate-Alert@{D}ravidian{L}ang{T}ech-{EACL}2021: Ensembling strategies for Transformer-based Offensive language Detection",
author = "Saha, Debjoy and Paharia, Naman and Chakraborty, Debajit and Saha, Punyajoy and Mukherjee, Animesh",
booktitle = "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages",
month = apr,
year = "2021",
address = "Kyiv",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.dravidianlangtech-1.38",
pages = "270--276",
abstract = "Social media often acts as breeding grounds for different forms of offensive content. For low resource languages like Tamil, the situation is more complex due to the poor performance of multilingual or language-specific models and lack of proper benchmark datasets. Based on this shared task {``}Offensive Language Identification in Dravidian Languages{''} at EACL 2021; we present an exhaustive exploration of different transformer models, We also provide a genetic algorithm technique for ensembling different models. Our ensembled models trained separately for each language secured the first position in Tamil, the second position in Kannada, and the first position in Malayalam sub-tasks. The models and codes are provided.",
}
~~~ | {"language": "ta", "license": "apache-2.0"} | text-classification | Hate-speech-CNERG/deoffxlmr-mono-tamil | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"ta",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ta"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #ta #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| This model is used to detect Offensive Content in Tamil Code-Mixed language. The mono in the name refers to the monolingual setting, where the model is trained using only Tamil(pure and code-mixed) data. The weights are initialized from pretrained XLM-Roberta-Base and pretrained using Masked Language Modelling on the target dataset before fine-tuning using Cross-Entropy Loss.
This model is the best of multiple trained for EACL 2021 Shared Task on Offensive Language Identification in Dravidian Languages. Genetic-Algorithm based ensembled test predictions got the highest weighted F1 score at the leaderboard (Weighted F1 score on hold out test set: This model - 0.76, Ensemble - 0.78)
### For more details about our paper
Debjoy Saha, Naman Paharia, Debajit Chakraborty, Punyajoy Saha, Animesh Mukherjee. "Hate-Alert@DravidianLangTech-EACL2021: Ensembling strategies for Transformer-based Offensive language Detection".
*Please cite our paper in any published work that uses any of these resources.*
~~~
@inproceedings{saha-etal-2021-hate,
title = "Hate-Alert@{D}ravidian{L}ang{T}ech-{EACL}2021: Ensembling strategies for Transformer-based Offensive language Detection",
author = "Saha, Debjoy and Paharia, Naman and Chakraborty, Debajit and Saha, Punyajoy and Mukherjee, Animesh",
booktitle = "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages",
month = apr,
year = "2021",
address = "Kyiv",
publisher = "Association for Computational Linguistics",
url = "URL
pages = "270--276",
abstract = "Social media often acts as breeding grounds for different forms of offensive content. For low resource languages like Tamil, the situation is more complex due to the poor performance of multilingual or language-specific models and lack of proper benchmark datasets. Based on this shared task {''}Offensive Language Identification in Dravidian Languages{''} at EACL 2021; we present an exhaustive exploration of different transformer models, We also provide a genetic algorithm technique for ensembling different models. Our ensembled models trained separately for each language secured the first position in Tamil, the second position in Kannada, and the first position in Malayalam sub-tasks. The models and codes are provided.",
}
~~~ | [
"### For more details about our paper\n\nDebjoy Saha, Naman Paharia, Debajit Chakraborty, Punyajoy Saha, Animesh Mukherjee. \"Hate-Alert@DravidianLangTech-EACL2021: Ensembling strategies for Transformer-based Offensive language Detection\".\n\n*Please cite our paper in any published work that uses any of these resources.*\n~~~\n@inproceedings{saha-etal-2021-hate,\n title = \"Hate-Alert@{D}ravidian{L}ang{T}ech-{EACL}2021: Ensembling strategies for Transformer-based Offensive language Detection\",\n author = \"Saha, Debjoy and Paharia, Naman and Chakraborty, Debajit and Saha, Punyajoy and Mukherjee, Animesh\",\n booktitle = \"Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages\",\n month = apr,\n year = \"2021\",\n address = \"Kyiv\",\n publisher = \"Association for Computational Linguistics\",\n url = \"URL\n pages = \"270--276\",\n abstract = \"Social media often acts as breeding grounds for different forms of offensive content. For low resource languages like Tamil, the situation is more complex due to the poor performance of multilingual or language-specific models and lack of proper benchmark datasets. Based on this shared task {''}Offensive Language Identification in Dravidian Languages{''} at EACL 2021; we present an exhaustive exploration of different transformer models, We also provide a genetic algorithm technique for ensembling different models. Our ensembled models trained separately for each language secured the first position in Tamil, the second position in Kannada, and the first position in Malayalam sub-tasks. The models and codes are provided.\",\n}\n~~~"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #ta #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### For more details about our paper\n\nDebjoy Saha, Naman Paharia, Debajit Chakraborty, Punyajoy Saha, Animesh Mukherjee. \"Hate-Alert@DravidianLangTech-EACL2021: Ensembling strategies for Transformer-based Offensive language Detection\".\n\n*Please cite our paper in any published work that uses any of these resources.*\n~~~\n@inproceedings{saha-etal-2021-hate,\n title = \"Hate-Alert@{D}ravidian{L}ang{T}ech-{EACL}2021: Ensembling strategies for Transformer-based Offensive language Detection\",\n author = \"Saha, Debjoy and Paharia, Naman and Chakraborty, Debajit and Saha, Punyajoy and Mukherjee, Animesh\",\n booktitle = \"Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages\",\n month = apr,\n year = \"2021\",\n address = \"Kyiv\",\n publisher = \"Association for Computational Linguistics\",\n url = \"URL\n pages = \"270--276\",\n abstract = \"Social media often acts as breeding grounds for different forms of offensive content. For low resource languages like Tamil, the situation is more complex due to the poor performance of multilingual or language-specific models and lack of proper benchmark datasets. Based on this shared task {''}Offensive Language Identification in Dravidian Languages{''} at EACL 2021; we present an exhaustive exploration of different transformer models, We also provide a genetic algorithm technique for ensembling different models. Our ensembled models trained separately for each language secured the first position in Tamil, the second position in Kannada, and the first position in Malayalam sub-tasks. The models and codes are provided.\",\n}\n~~~"
] | [
50,
420
] | [
"passage: TAGS\n#transformers #pytorch #xlm-roberta #text-classification #ta #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### For more details about our paper\n\nDebjoy Saha, Naman Paharia, Debajit Chakraborty, Punyajoy Saha, Animesh Mukherjee. \"Hate-Alert@DravidianLangTech-EACL2021: Ensembling strategies for Transformer-based Offensive language Detection\".\n\n*Please cite our paper in any published work that uses any of these resources.*\n~~~\n@inproceedings{saha-etal-2021-hate,\n title = \"Hate-Alert@{D}ravidian{L}ang{T}ech-{EACL}2021: Ensembling strategies for Transformer-based Offensive language Detection\",\n author = \"Saha, Debjoy and Paharia, Naman and Chakraborty, Debajit and Saha, Punyajoy and Mukherjee, Animesh\",\n booktitle = \"Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages\",\n month = apr,\n year = \"2021\",\n address = \"Kyiv\",\n publisher = \"Association for Computational Linguistics\",\n url = \"URL\n pages = \"270--276\",\n abstract = \"Social media often acts as breeding grounds for different forms of offensive content. For low resource languages like Tamil, the situation is more complex due to the poor performance of multilingual or language-specific models and lack of proper benchmark datasets. Based on this shared task {''}Offensive Language Identification in Dravidian Languages{''} at EACL 2021; we present an exhaustive exploration of different transformer models, We also provide a genetic algorithm technique for ensembling different models. Our ensembled models trained separately for each language secured the first position in Tamil, the second position in Kannada, and the first position in Malayalam sub-tasks. The models and codes are provided.\",\n}\n~~~"
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null | null | transformers |
# Rick Sanchez DialoGPT Model
| {"tags": ["conversational"]} | text-generation | Havokx/DialoGPT-small-Rick | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick Sanchez DialoGPT Model
| [
"# Rick Sanchez DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick Sanchez DialoGPT Model"
] | [
51,
8
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Rick Sanchez DialoGPT Model"
] | [
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null | null | null |
# My Awesome Model | {"tags": ["conversational"]} | text-generation | Heldhy/DialoGPT-small-tony | [
"conversational",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#conversational #region-us
|
# My Awesome Model | [
"# My Awesome Model"
] | [
"TAGS\n#conversational #region-us \n",
"# My Awesome Model"
] | [
10,
4
] | [
"passage: TAGS\n#conversational #region-us \n# My Awesome Model"
] | [
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null | null | transformers | # My Awesome Model | {"tags": ["conversational"]} | text-generation | Heldhy/testingAgain | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # My Awesome Model | [
"# My Awesome Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model"
] | [
51,
4
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# My Awesome Model"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4568
- Wer: 0.3422
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.3896 | 4.0 | 500 | 1.1573 | 0.8886 |
| 0.5667 | 8.0 | 1000 | 0.4841 | 0.4470 |
| 0.2126 | 12.0 | 1500 | 0.4201 | 0.3852 |
| 0.1235 | 16.0 | 2000 | 0.4381 | 0.3623 |
| 0.0909 | 20.0 | 2500 | 0.4784 | 0.3748 |
| 0.0611 | 24.0 | 3000 | 0.4390 | 0.3577 |
| 0.0454 | 28.0 | 3500 | 0.4568 | 0.3422 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | automatic-speech-recognition | Heldhy/wav2vec2-base-timit-demo-colab | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4568
* Wer: 0.3422
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 30
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.13.3
* Tokenizers 0.10.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] | [
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130,
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"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
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null | null | transformers | https://imgs.xkcd.com/comics/reassuring.png
| {} | text2text-generation | Hellisotherpeople/T5_Reassuring_Parables | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| URL
| [] | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] | [
57
] | [
"passage: TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
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] |
null | null | fasttext |
# debate2vec
Word-vectors created from a large corpus of competitive debate evidence, and data extraction / processing scripts
#usage
```
import fasttext.util
ft = fasttext.load_model('debate2vec.bin')
ft.get_word_vector('dialectics')
```
# Download Link
Github won't let me store large files in their repos.
* [FastText Vectors Here](https://drive.google.com/file/d/1m-CwPcaIUun4qvg69Hx2gom9dMScuQwS/view?usp=sharing) (~260mb)
# About
Created from all publically available Cross Examination Competitive debate evidence posted by the community on [Open Evidence](https://openev.debatecoaches.org/) (From 2013-2020)
Search through the original evidence by going to [debate.cards](http://debate.cards/)
Stats about this corpus:
* 222485 unique documents larger than 200 words (DebateSum plus some additional debate docs that weren't well-formed enough for inclusion into DebateSum)
* 107555 unique words (showing up more than 10 times in the corpus)
* 101 million total words
Stats about debate2vec vectors:
* 300 dimensions, minimum number of appearances of a word was 10, trained for 100 epochs with lr set to 0.10 using FastText
* lowercased (will release cased)
* No subword information
The corpus includes the following topics
* 2013-2014 Cuba/Mexico/Venezuela Economic Engagement
* 2014-2015 Oceans
* 2015-2016 Domestic Surveillance
* 2016-2017 China
* 2017-2018 Education
* 2018-2019 Immigration
* 2019-2020 Reducing Arms Sales
Other topics that this word vector model will handle extremely well
* Philosophy (Especially Left-Wing / Post-modernist)
* Law
* Government
* Politics
Initial release is of fasttext vectors without subword information. Future releases will include fine-tuned GPT-2 and other high end models as my GPU compute allows.
# Screenshots
| {"library_name": "fasttext", "tags": ["text-classification"], "widget": [{"text": "dialectics", "example_title": "dialectics"}, {"text": "schizoanalysis", "example_title": "schizoanalysis"}, {"text": "praxis", "example_title": "praxis"}, {"text": "topicality", "example_title": "topicality"}]} | text-classification | Hellisotherpeople/debate2vec | [
"fasttext",
"text-classification",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#fasttext #text-classification #region-us
|
# debate2vec
Word-vectors created from a large corpus of competitive debate evidence, and data extraction / processing scripts
#usage
# Download Link
Github won't let me store large files in their repos.
* FastText Vectors Here (~260mb)
# About
Created from all publically available Cross Examination Competitive debate evidence posted by the community on Open Evidence (From 2013-2020)
Search through the original evidence by going to URL
Stats about this corpus:
* 222485 unique documents larger than 200 words (DebateSum plus some additional debate docs that weren't well-formed enough for inclusion into DebateSum)
* 107555 unique words (showing up more than 10 times in the corpus)
* 101 million total words
Stats about debate2vec vectors:
* 300 dimensions, minimum number of appearances of a word was 10, trained for 100 epochs with lr set to 0.10 using FastText
* lowercased (will release cased)
* No subword information
The corpus includes the following topics
* 2013-2014 Cuba/Mexico/Venezuela Economic Engagement
* 2014-2015 Oceans
* 2015-2016 Domestic Surveillance
* 2016-2017 China
* 2017-2018 Education
* 2018-2019 Immigration
* 2019-2020 Reducing Arms Sales
Other topics that this word vector model will handle extremely well
* Philosophy (Especially Left-Wing / Post-modernist)
* Law
* Government
* Politics
Initial release is of fasttext vectors without subword information. Future releases will include fine-tuned GPT-2 and other high end models as my GPU compute allows.
# Screenshots
![](URL
![](URL
![](URL
| [
"# debate2vec\nWord-vectors created from a large corpus of competitive debate evidence, and data extraction / processing scripts",
"# Download Link\nGithub won't let me store large files in their repos. \n* FastText Vectors Here (~260mb)",
"# About \n\nCreated from all publically available Cross Examination Competitive debate evidence posted by the community on Open Evidence (From 2013-2020)\n\nSearch through the original evidence by going to URL\n\nStats about this corpus: \n* 222485 unique documents larger than 200 words (DebateSum plus some additional debate docs that weren't well-formed enough for inclusion into DebateSum)\n* 107555 unique words (showing up more than 10 times in the corpus)\n* 101 million total words\n\nStats about debate2vec vectors: \n* 300 dimensions, minimum number of appearances of a word was 10, trained for 100 epochs with lr set to 0.10 using FastText\n* lowercased (will release cased)\n* No subword information\n\nThe corpus includes the following topics \n\n* 2013-2014 Cuba/Mexico/Venezuela Economic Engagement\n* 2014-2015 Oceans\n* 2015-2016 Domestic Surveillance\n* 2016-2017 China\n* 2017-2018 Education\n* 2018-2019 Immigration\n* 2019-2020 Reducing Arms Sales\n\nOther topics that this word vector model will handle extremely well\n\n* Philosophy (Especially Left-Wing / Post-modernist)\n* Law\n* Government \n* Politics\n\n\nInitial release is of fasttext vectors without subword information. Future releases will include fine-tuned GPT-2 and other high end models as my GPU compute allows.",
"# Screenshots\n![](URL\n![](URL\n![](URL"
] | [
"TAGS\n#fasttext #text-classification #region-us \n",
"# debate2vec\nWord-vectors created from a large corpus of competitive debate evidence, and data extraction / processing scripts",
"# Download Link\nGithub won't let me store large files in their repos. \n* FastText Vectors Here (~260mb)",
"# About \n\nCreated from all publically available Cross Examination Competitive debate evidence posted by the community on Open Evidence (From 2013-2020)\n\nSearch through the original evidence by going to URL\n\nStats about this corpus: \n* 222485 unique documents larger than 200 words (DebateSum plus some additional debate docs that weren't well-formed enough for inclusion into DebateSum)\n* 107555 unique words (showing up more than 10 times in the corpus)\n* 101 million total words\n\nStats about debate2vec vectors: \n* 300 dimensions, minimum number of appearances of a word was 10, trained for 100 epochs with lr set to 0.10 using FastText\n* lowercased (will release cased)\n* No subword information\n\nThe corpus includes the following topics \n\n* 2013-2014 Cuba/Mexico/Venezuela Economic Engagement\n* 2014-2015 Oceans\n* 2015-2016 Domestic Surveillance\n* 2016-2017 China\n* 2017-2018 Education\n* 2018-2019 Immigration\n* 2019-2020 Reducing Arms Sales\n\nOther topics that this word vector model will handle extremely well\n\n* Philosophy (Especially Left-Wing / Post-modernist)\n* Law\n* Government \n* Politics\n\n\nInitial release is of fasttext vectors without subword information. Future releases will include fine-tuned GPT-2 and other high end models as my GPU compute allows.",
"# Screenshots\n![](URL\n![](URL\n![](URL"
] | [
14,
27,
29,
295,
19
] | [
"passage: TAGS\n#fasttext #text-classification #region-us \n# debate2vec\nWord-vectors created from a large corpus of competitive debate evidence, and data extraction / processing scripts# Download Link\nGithub won't let me store large files in their repos. \n* FastText Vectors Here (~260mb)# About \n\nCreated from all publically available Cross Examination Competitive debate evidence posted by the community on Open Evidence (From 2013-2020)\n\nSearch through the original evidence by going to URL\n\nStats about this corpus: \n* 222485 unique documents larger than 200 words (DebateSum plus some additional debate docs that weren't well-formed enough for inclusion into DebateSum)\n* 107555 unique words (showing up more than 10 times in the corpus)\n* 101 million total words\n\nStats about debate2vec vectors: \n* 300 dimensions, minimum number of appearances of a word was 10, trained for 100 epochs with lr set to 0.10 using FastText\n* lowercased (will release cased)\n* No subword information\n\nThe corpus includes the following topics \n\n* 2013-2014 Cuba/Mexico/Venezuela Economic Engagement\n* 2014-2015 Oceans\n* 2015-2016 Domestic Surveillance\n* 2016-2017 China\n* 2017-2018 Education\n* 2018-2019 Immigration\n* 2019-2020 Reducing Arms Sales\n\nOther topics that this word vector model will handle extremely well\n\n* Philosophy (Especially Left-Wing / Post-modernist)\n* Law\n* Government \n* Politics\n\n\nInitial release is of fasttext vectors without subword information. Future releases will include fine-tuned GPT-2 and other high end models as my GPU compute allows.# Screenshots\n![](URL\n![](URL\n![](URL"
] | [
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null | null | transformers |
### opus-mt-NORTH_EU-NORTH_EU
* source languages: de,nl,fy,af,da,fo,is,no,nb,nn,sv
* target languages: de,nl,fy,af,da,fo,is,no,nb,nn,sv
* OPUS readme: [de+nl+fy+af+da+fo+is+no+nb+nn+sv-de+nl+fy+af+da+fo+is+no+nb+nn+sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de+nl+fy+af+da+fo+is+no+nb+nn+sv-de+nl+fy+af+da+fo+is+no+nb+nn+sv/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID)
* download original weights: [opus-2020-01-15.zip](https://object.pouta.csc.fi/OPUS-MT-models/de+nl+fy+af+da+fo+is+no+nb+nn+sv-de+nl+fy+af+da+fo+is+no+nb+nn+sv/opus-2020-01-15.zip)
* test set translations: [opus-2020-01-15.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/de+nl+fy+af+da+fo+is+no+nb+nn+sv-de+nl+fy+af+da+fo+is+no+nb+nn+sv/opus-2020-01-15.test.txt)
* test set scores: [opus-2020-01-15.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/de+nl+fy+af+da+fo+is+no+nb+nn+sv-de+nl+fy+af+da+fo+is+no+nb+nn+sv/opus-2020-01-15.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.de.sv | 48.1 | 0.663 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-NORTH_EU-NORTH_EU | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"multilingual",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #multilingual #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-NORTH\_EU-NORTH\_EU
* source languages: de,nl,fy,af,da,fo,is,no,nb,nn,sv
* target languages: de,nl,fy,af,da,fo,is,no,nb,nn,sv
* OPUS readme: de+nl+fy+af+da+fo+is+no+nb+nn+sv-de+nl+fy+af+da+fo+is+no+nb+nn+sv
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 48.1, chr-F: 0.663
| [
"### opus-mt-NORTH\\_EU-NORTH\\_EU\n\n\n* source languages: de,nl,fy,af,da,fo,is,no,nb,nn,sv\n* target languages: de,nl,fy,af,da,fo,is,no,nb,nn,sv\n* OPUS readme: de+nl+fy+af+da+fo+is+no+nb+nn+sv-de+nl+fy+af+da+fo+is+no+nb+nn+sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 48.1, chr-F: 0.663"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #multilingual #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-NORTH\\_EU-NORTH\\_EU\n\n\n* source languages: de,nl,fy,af,da,fo,is,no,nb,nn,sv\n* target languages: de,nl,fy,af,da,fo,is,no,nb,nn,sv\n* OPUS readme: de+nl+fy+af+da+fo+is+no+nb+nn+sv-de+nl+fy+af+da+fo+is+no+nb+nn+sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 48.1, chr-F: 0.663"
] | [
61,
219
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #multilingual #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-NORTH\\_EU-NORTH\\_EU\n\n\n* source languages: de,nl,fy,af,da,fo,is,no,nb,nn,sv\n* target languages: de,nl,fy,af,da,fo,is,no,nb,nn,sv\n* OPUS readme: de+nl+fy+af+da+fo+is+no+nb+nn+sv-de+nl+fy+af+da+fo+is+no+nb+nn+sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 48.1, chr-F: 0.663"
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] |
null | null | transformers |
### opus-mt-ROMANCE-en
* source languages: fr,fr_BE,fr_CA,fr_FR,wa,frp,oc,ca,rm,lld,fur,lij,lmo,es,es_AR,es_CL,es_CO,es_CR,es_DO,es_EC,es_ES,es_GT,es_HN,es_MX,es_NI,es_PA,es_PE,es_PR,es_SV,es_UY,es_VE,pt,pt_br,pt_BR,pt_PT,gl,lad,an,mwl,it,it_IT,co,nap,scn,vec,sc,ro,la
* target languages: en
* OPUS readme: [fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO+es_EC+es_ES+es_GT+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR+pt_PT+gl+lad+an+mwl+it+it_IT+co+nap+scn+vec+sc+ro+la-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO+es_EC+es_ES+es_GT+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR+pt_PT+gl+lad+an+mwl+it+it_IT+co+nap+scn+vec+sc+ro+la-en/README.md)
* dataset: opus
* model: transformer
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-04-01.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO+es_EC+es_ES+es_GT+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR+pt_PT+gl+lad+an+mwl+it+it_IT+co+nap+scn+vec+sc+ro+la-en/opus-2020-04-01.zip)
* test set translations: [opus-2020-04-01.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO+es_EC+es_ES+es_GT+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR+pt_PT+gl+lad+an+mwl+it+it_IT+co+nap+scn+vec+sc+ro+la-en/opus-2020-04-01.test.txt)
* test set scores: [opus-2020-04-01.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO+es_EC+es_ES+es_GT+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR+pt_PT+gl+lad+an+mwl+it+it_IT+co+nap+scn+vec+sc+ro+la-en/opus-2020-04-01.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.fr.en | 62.2 | 0.750 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-ROMANCE-en | [
"transformers",
"pytorch",
"tf",
"rust",
"marian",
"text2text-generation",
"translation",
"roa",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #rust #marian #text2text-generation #translation #roa #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-ROMANCE-en
* source languages: fr,fr\_BE,fr\_CA,fr\_FR,wa,frp,oc,ca,rm,lld,fur,lij,lmo,es,es\_AR,es\_CL,es\_CO,es\_CR,es\_DO,es\_EC,es\_ES,es\_GT,es\_HN,es\_MX,es\_NI,es\_PA,es\_PE,es\_PR,es\_SV,es\_UY,es\_VE,pt,pt\_br,pt\_BR,pt\_PT,gl,lad,an,mwl,it,it\_IT,co,nap,scn,vec,sc,ro,la
* target languages: en
* OPUS readme: fr+fr\_BE+fr\_CA+fr\_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es\_AR+es\_CL+es\_CO+es\_CR+es\_DO+es\_EC+es\_ES+es\_GT+es\_HN+es\_MX+es\_NI+es\_PA+es\_PE+es\_PR+es\_SV+es\_UY+es\_VE+pt+pt\_br+pt\_BR+pt\_PT+gl+lad+an+mwl+it+it\_IT+co+nap+scn+vec+sc+ro+la-en
* dataset: opus
* model: transformer
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 62.2, chr-F: 0.750
| [
"### opus-mt-ROMANCE-en\n\n\n* source languages: fr,fr\\_BE,fr\\_CA,fr\\_FR,wa,frp,oc,ca,rm,lld,fur,lij,lmo,es,es\\_AR,es\\_CL,es\\_CO,es\\_CR,es\\_DO,es\\_EC,es\\_ES,es\\_GT,es\\_HN,es\\_MX,es\\_NI,es\\_PA,es\\_PE,es\\_PR,es\\_SV,es\\_UY,es\\_VE,pt,pt\\_br,pt\\_BR,pt\\_PT,gl,lad,an,mwl,it,it\\_IT,co,nap,scn,vec,sc,ro,la\n* target languages: en\n* OPUS readme: fr+fr\\_BE+fr\\_CA+fr\\_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es\\_AR+es\\_CL+es\\_CO+es\\_CR+es\\_DO+es\\_EC+es\\_ES+es\\_GT+es\\_HN+es\\_MX+es\\_NI+es\\_PA+es\\_PE+es\\_PR+es\\_SV+es\\_UY+es\\_VE+pt+pt\\_br+pt\\_BR+pt\\_PT+gl+lad+an+mwl+it+it\\_IT+co+nap+scn+vec+sc+ro+la-en\n* dataset: opus\n* model: transformer\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 62.2, chr-F: 0.750"
] | [
"TAGS\n#transformers #pytorch #tf #rust #marian #text2text-generation #translation #roa #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-ROMANCE-en\n\n\n* source languages: fr,fr\\_BE,fr\\_CA,fr\\_FR,wa,frp,oc,ca,rm,lld,fur,lij,lmo,es,es\\_AR,es\\_CL,es\\_CO,es\\_CR,es\\_DO,es\\_EC,es\\_ES,es\\_GT,es\\_HN,es\\_MX,es\\_NI,es\\_PA,es\\_PE,es\\_PR,es\\_SV,es\\_UY,es\\_VE,pt,pt\\_br,pt\\_BR,pt\\_PT,gl,lad,an,mwl,it,it\\_IT,co,nap,scn,vec,sc,ro,la\n* target languages: en\n* OPUS readme: fr+fr\\_BE+fr\\_CA+fr\\_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es\\_AR+es\\_CL+es\\_CO+es\\_CR+es\\_DO+es\\_EC+es\\_ES+es\\_GT+es\\_HN+es\\_MX+es\\_NI+es\\_PA+es\\_PE+es\\_PR+es\\_SV+es\\_UY+es\\_VE+pt+pt\\_br+pt\\_BR+pt\\_PT+gl+lad+an+mwl+it+it\\_IT+co+nap+scn+vec+sc+ro+la-en\n* dataset: opus\n* model: transformer\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 62.2, chr-F: 0.750"
] | [
64,
447
] | [
"passage: TAGS\n#transformers #pytorch #tf #rust #marian #text2text-generation #translation #roa #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
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null | null | transformers |
### opus-mt-SCANDINAVIA-SCANDINAVIA
* source languages: da,fo,is,no,nb,nn,sv
* target languages: da,fo,is,no,nb,nn,sv
* OPUS readme: [da+fo+is+no+nb+nn+sv-da+fo+is+no+nb+nn+sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/da+fo+is+no+nb+nn+sv-da+fo+is+no+nb+nn+sv/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID)
* download original weights: [opus-2019-12-18.zip](https://object.pouta.csc.fi/OPUS-MT-models/da+fo+is+no+nb+nn+sv-da+fo+is+no+nb+nn+sv/opus-2019-12-18.zip)
* test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/da+fo+is+no+nb+nn+sv-da+fo+is+no+nb+nn+sv/opus-2019-12-18.test.txt)
* test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/da+fo+is+no+nb+nn+sv-da+fo+is+no+nb+nn+sv/opus-2019-12-18.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.da.sv | 69.2 | 0.811 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-SCANDINAVIA-SCANDINAVIA | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"scandinavia",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #scandinavia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-SCANDINAVIA-SCANDINAVIA
* source languages: da,fo,is,no,nb,nn,sv
* target languages: da,fo,is,no,nb,nn,sv
* OPUS readme: da+fo+is+no+nb+nn+sv-da+fo+is+no+nb+nn+sv
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 69.2, chr-F: 0.811
| [
"### opus-mt-SCANDINAVIA-SCANDINAVIA\n\n\n* source languages: da,fo,is,no,nb,nn,sv\n* target languages: da,fo,is,no,nb,nn,sv\n* OPUS readme: da+fo+is+no+nb+nn+sv-da+fo+is+no+nb+nn+sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 69.2, chr-F: 0.811"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #scandinavia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-SCANDINAVIA-SCANDINAVIA\n\n\n* source languages: da,fo,is,no,nb,nn,sv\n* target languages: da,fo,is,no,nb,nn,sv\n* OPUS readme: da+fo+is+no+nb+nn+sv-da+fo+is+no+nb+nn+sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 69.2, chr-F: 0.811"
] | [
61,
185
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #scandinavia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-SCANDINAVIA-SCANDINAVIA\n\n\n* source languages: da,fo,is,no,nb,nn,sv\n* target languages: da,fo,is,no,nb,nn,sv\n* OPUS readme: da+fo+is+no+nb+nn+sv-da+fo+is+no+nb+nn+sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 69.2, chr-F: 0.811"
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null | null | transformers |
### aav-eng
* source group: Austro-Asiatic languages
* target group: English
* OPUS readme: [aav-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/aav-eng/README.md)
* model: transformer
* source language(s): hoc hoc_Latn kha khm khm_Latn mnw vie vie_Hani
* target language(s): eng
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus2m-2020-07-31.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/aav-eng/opus2m-2020-07-31.zip)
* test set translations: [opus2m-2020-07-31.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/aav-eng/opus2m-2020-07-31.test.txt)
* test set scores: [opus2m-2020-07-31.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/aav-eng/opus2m-2020-07-31.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.hoc-eng.hoc.eng | 0.3 | 0.095 |
| Tatoeba-test.kha-eng.kha.eng | 1.0 | 0.115 |
| Tatoeba-test.khm-eng.khm.eng | 8.9 | 0.271 |
| Tatoeba-test.mnw-eng.mnw.eng | 0.8 | 0.118 |
| Tatoeba-test.multi.eng | 24.8 | 0.391 |
| Tatoeba-test.vie-eng.vie.eng | 38.7 | 0.567 |
### System Info:
- hf_name: aav-eng
- source_languages: aav
- target_languages: eng
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/aav-eng/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['vi', 'km', 'aav', 'en']
- src_constituents: {'mnw', 'vie', 'kha', 'khm', 'vie_Hani', 'khm_Latn', 'hoc_Latn', 'hoc'}
- tgt_constituents: {'eng'}
- src_multilingual: True
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/aav-eng/opus2m-2020-07-31.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/aav-eng/opus2m-2020-07-31.test.txt
- src_alpha3: aav
- tgt_alpha3: eng
- short_pair: aav-en
- chrF2_score: 0.391
- bleu: 24.8
- brevity_penalty: 0.968
- ref_len: 36693.0
- src_name: Austro-Asiatic languages
- tgt_name: English
- train_date: 2020-07-31
- src_alpha2: aav
- tgt_alpha2: en
- prefer_old: False
- long_pair: aav-eng
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["vi", "km", "aav", "en"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-aav-en | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"vi",
"km",
"aav",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"vi",
"km",
"aav",
"en"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #vi #km #aav #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### aav-eng
* source group: Austro-Asiatic languages
* target group: English
* OPUS readme: aav-eng
* model: transformer
* source language(s): hoc hoc\_Latn kha khm khm\_Latn mnw vie vie\_Hani
* target language(s): eng
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 0.3, chr-F: 0.095
testset: URL, BLEU: 1.0, chr-F: 0.115
testset: URL, BLEU: 8.9, chr-F: 0.271
testset: URL, BLEU: 0.8, chr-F: 0.118
testset: URL, BLEU: 24.8, chr-F: 0.391
testset: URL, BLEU: 38.7, chr-F: 0.567
### System Info:
* hf\_name: aav-eng
* source\_languages: aav
* target\_languages: eng
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['vi', 'km', 'aav', 'en']
* src\_constituents: {'mnw', 'vie', 'kha', 'khm', 'vie\_Hani', 'khm\_Latn', 'hoc\_Latn', 'hoc'}
* tgt\_constituents: {'eng'}
* src\_multilingual: True
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: aav
* tgt\_alpha3: eng
* short\_pair: aav-en
* chrF2\_score: 0.391
* bleu: 24.8
* brevity\_penalty: 0.968
* ref\_len: 36693.0
* src\_name: Austro-Asiatic languages
* tgt\_name: English
* train\_date: 2020-07-31
* src\_alpha2: aav
* tgt\_alpha2: en
* prefer\_old: False
* long\_pair: aav-eng
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### aav-eng\n\n\n* source group: Austro-Asiatic languages\n* target group: English\n* OPUS readme: aav-eng\n* model: transformer\n* source language(s): hoc hoc\\_Latn kha khm khm\\_Latn mnw vie vie\\_Hani\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 0.3, chr-F: 0.095\ntestset: URL, BLEU: 1.0, chr-F: 0.115\ntestset: URL, BLEU: 8.9, chr-F: 0.271\ntestset: URL, BLEU: 0.8, chr-F: 0.118\ntestset: URL, BLEU: 24.8, chr-F: 0.391\ntestset: URL, BLEU: 38.7, chr-F: 0.567",
"### System Info:\n\n\n* hf\\_name: aav-eng\n* source\\_languages: aav\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['vi', 'km', 'aav', 'en']\n* src\\_constituents: {'mnw', 'vie', 'kha', 'khm', 'vie\\_Hani', 'khm\\_Latn', 'hoc\\_Latn', 'hoc'}\n* tgt\\_constituents: {'eng'}\n* src\\_multilingual: True\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: aav\n* tgt\\_alpha3: eng\n* short\\_pair: aav-en\n* chrF2\\_score: 0.391\n* bleu: 24.8\n* brevity\\_penalty: 0.968\n* ref\\_len: 36693.0\n* src\\_name: Austro-Asiatic languages\n* tgt\\_name: English\n* train\\_date: 2020-07-31\n* src\\_alpha2: aav\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* long\\_pair: aav-eng\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #vi #km #aav #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### aav-eng\n\n\n* source group: Austro-Asiatic languages\n* target group: English\n* OPUS readme: aav-eng\n* model: transformer\n* source language(s): hoc hoc\\_Latn kha khm khm\\_Latn mnw vie vie\\_Hani\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 0.3, chr-F: 0.095\ntestset: URL, BLEU: 1.0, chr-F: 0.115\ntestset: URL, BLEU: 8.9, chr-F: 0.271\ntestset: URL, BLEU: 0.8, chr-F: 0.118\ntestset: URL, BLEU: 24.8, chr-F: 0.391\ntestset: URL, BLEU: 38.7, chr-F: 0.567",
"### System Info:\n\n\n* hf\\_name: aav-eng\n* source\\_languages: aav\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['vi', 'km', 'aav', 'en']\n* src\\_constituents: {'mnw', 'vie', 'kha', 'khm', 'vie\\_Hani', 'khm\\_Latn', 'hoc\\_Latn', 'hoc'}\n* tgt\\_constituents: {'eng'}\n* src\\_multilingual: True\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: aav\n* tgt\\_alpha3: eng\n* short\\_pair: aav-en\n* chrF2\\_score: 0.391\n* bleu: 24.8\n* brevity\\_penalty: 0.968\n* ref\\_len: 36693.0\n* src\\_name: Austro-Asiatic languages\n* tgt\\_name: English\n* train\\_date: 2020-07-31\n* src\\_alpha2: aav\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* long\\_pair: aav-eng\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
66,
231,
454
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #vi #km #aav #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### aav-eng\n\n\n* source group: Austro-Asiatic languages\n* target group: English\n* OPUS readme: aav-eng\n* model: transformer\n* source language(s): hoc hoc\\_Latn kha khm khm\\_Latn mnw vie vie\\_Hani\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 0.3, chr-F: 0.095\ntestset: URL, BLEU: 1.0, chr-F: 0.115\ntestset: URL, BLEU: 8.9, chr-F: 0.271\ntestset: URL, BLEU: 0.8, chr-F: 0.118\ntestset: URL, BLEU: 24.8, chr-F: 0.391\ntestset: URL, BLEU: 38.7, chr-F: 0.567"
] | [
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null | null | transformers |
### opus-mt-aed-es
* source languages: aed
* target languages: es
* OPUS readme: [aed-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/aed-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-15.zip](https://object.pouta.csc.fi/OPUS-MT-models/aed-es/opus-2020-01-15.zip)
* test set translations: [opus-2020-01-15.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/aed-es/opus-2020-01-15.test.txt)
* test set scores: [opus-2020-01-15.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/aed-es/opus-2020-01-15.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.aed.es | 89.1 | 0.915 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-aed-es | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"aed",
"es",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #aed #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-aed-es
* source languages: aed
* target languages: es
* OPUS readme: aed-es
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 89.1, chr-F: 0.915
| [
"### opus-mt-aed-es\n\n\n* source languages: aed\n* target languages: es\n* OPUS readme: aed-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 89.1, chr-F: 0.915"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #aed #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-aed-es\n\n\n* source languages: aed\n* target languages: es\n* OPUS readme: aed-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 89.1, chr-F: 0.915"
] | [
62,
104
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #aed #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-aed-es\n\n\n* source languages: aed\n* target languages: es\n* OPUS readme: aed-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 89.1, chr-F: 0.915"
] | [
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] |
null | null | transformers |
### opus-mt-af-de
* source languages: af
* target languages: de
* OPUS readme: [af-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/af-de/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-19.zip](https://object.pouta.csc.fi/OPUS-MT-models/af-de/opus-2020-01-19.zip)
* test set translations: [opus-2020-01-19.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/af-de/opus-2020-01-19.test.txt)
* test set scores: [opus-2020-01-19.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/af-de/opus-2020-01-19.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.af.de | 48.6 | 0.681 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-af-de | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"af",
"de",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #af #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-af-de
* source languages: af
* target languages: de
* OPUS readme: af-de
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 48.6, chr-F: 0.681
| [
"### opus-mt-af-de\n\n\n* source languages: af\n* target languages: de\n* OPUS readme: af-de\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 48.6, chr-F: 0.681"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #af #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-af-de\n\n\n* source languages: af\n* target languages: de\n* OPUS readme: af-de\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 48.6, chr-F: 0.681"
] | [
61,
100
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #af #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-af-de\n\n\n* source languages: af\n* target languages: de\n* OPUS readme: af-de\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 48.6, chr-F: 0.681"
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] |
null | null | transformers |
### opus-mt-af-en
* source languages: af
* target languages: en
* OPUS readme: [af-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/af-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2019-12-18.zip](https://object.pouta.csc.fi/OPUS-MT-models/af-en/opus-2019-12-18.zip)
* test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/af-en/opus-2019-12-18.test.txt)
* test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/af-en/opus-2019-12-18.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.af.en | 60.8 | 0.736 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-af-en | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"af",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #af #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-af-en
* source languages: af
* target languages: en
* OPUS readme: af-en
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 60.8, chr-F: 0.736
| [
"### opus-mt-af-en\n\n\n* source languages: af\n* target languages: en\n* OPUS readme: af-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 60.8, chr-F: 0.736"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #af #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-af-en\n\n\n* source languages: af\n* target languages: en\n* OPUS readme: af-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 60.8, chr-F: 0.736"
] | [
61,
100
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #af #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-af-en\n\n\n* source languages: af\n* target languages: en\n* OPUS readme: af-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 60.8, chr-F: 0.736"
] | [
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null | null | transformers |
### afr-epo
* source group: Afrikaans
* target group: Esperanto
* OPUS readme: [afr-epo](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/afr-epo/README.md)
* model: transformer-align
* source language(s): afr
* target language(s): epo
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/afr-epo/opus-2020-06-16.zip)
* test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/afr-epo/opus-2020-06-16.test.txt)
* test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/afr-epo/opus-2020-06-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.afr.epo | 18.3 | 0.411 |
### System Info:
- hf_name: afr-epo
- source_languages: afr
- target_languages: epo
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/afr-epo/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['af', 'eo']
- src_constituents: {'afr'}
- tgt_constituents: {'epo'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/afr-epo/opus-2020-06-16.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/afr-epo/opus-2020-06-16.test.txt
- src_alpha3: afr
- tgt_alpha3: epo
- short_pair: af-eo
- chrF2_score: 0.41100000000000003
- bleu: 18.3
- brevity_penalty: 0.995
- ref_len: 7517.0
- src_name: Afrikaans
- tgt_name: Esperanto
- train_date: 2020-06-16
- src_alpha2: af
- tgt_alpha2: eo
- prefer_old: False
- long_pair: afr-epo
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["af", "eo"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-af-eo | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"af",
"eo",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"af",
"eo"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #af #eo #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### afr-epo
* source group: Afrikaans
* target group: Esperanto
* OPUS readme: afr-epo
* model: transformer-align
* source language(s): afr
* target language(s): epo
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 18.3, chr-F: 0.411
### System Info:
* hf\_name: afr-epo
* source\_languages: afr
* target\_languages: epo
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['af', 'eo']
* src\_constituents: {'afr'}
* tgt\_constituents: {'epo'}
* src\_multilingual: False
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm4k,spm4k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: afr
* tgt\_alpha3: epo
* short\_pair: af-eo
* chrF2\_score: 0.41100000000000003
* bleu: 18.3
* brevity\_penalty: 0.995
* ref\_len: 7517.0
* src\_name: Afrikaans
* tgt\_name: Esperanto
* train\_date: 2020-06-16
* src\_alpha2: af
* tgt\_alpha2: eo
* prefer\_old: False
* long\_pair: afr-epo
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### afr-epo\n\n\n* source group: Afrikaans\n* target group: Esperanto\n* OPUS readme: afr-epo\n* model: transformer-align\n* source language(s): afr\n* target language(s): epo\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm4k,spm4k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 18.3, chr-F: 0.411",
"### System Info:\n\n\n* hf\\_name: afr-epo\n* source\\_languages: afr\n* target\\_languages: epo\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['af', 'eo']\n* src\\_constituents: {'afr'}\n* tgt\\_constituents: {'epo'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm4k,spm4k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: afr\n* tgt\\_alpha3: epo\n* short\\_pair: af-eo\n* chrF2\\_score: 0.41100000000000003\n* bleu: 18.3\n* brevity\\_penalty: 0.995\n* ref\\_len: 7517.0\n* src\\_name: Afrikaans\n* tgt\\_name: Esperanto\n* train\\_date: 2020-06-16\n* src\\_alpha2: af\n* tgt\\_alpha2: eo\n* prefer\\_old: False\n* long\\_pair: afr-epo\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #af #eo #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### afr-epo\n\n\n* source group: Afrikaans\n* target group: Esperanto\n* OPUS readme: afr-epo\n* model: transformer-align\n* source language(s): afr\n* target language(s): epo\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm4k,spm4k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 18.3, chr-F: 0.411",
"### System Info:\n\n\n* hf\\_name: afr-epo\n* source\\_languages: afr\n* target\\_languages: epo\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['af', 'eo']\n* src\\_constituents: {'afr'}\n* tgt\\_constituents: {'epo'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm4k,spm4k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: afr\n* tgt\\_alpha3: epo\n* short\\_pair: af-eo\n* chrF2\\_score: 0.41100000000000003\n* bleu: 18.3\n* brevity\\_penalty: 0.995\n* ref\\_len: 7517.0\n* src\\_name: Afrikaans\n* tgt\\_name: Esperanto\n* train\\_date: 2020-06-16\n* src\\_alpha2: af\n* tgt\\_alpha2: eo\n* prefer\\_old: False\n* long\\_pair: afr-epo\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
61,
126,
401
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #af #eo #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### afr-epo\n\n\n* source group: Afrikaans\n* target group: Esperanto\n* OPUS readme: afr-epo\n* model: transformer-align\n* source language(s): afr\n* target language(s): epo\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm4k,spm4k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 18.3, chr-F: 0.411"
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null | null | transformers |
### afr-spa
* source group: Afrikaans
* target group: Spanish
* OPUS readme: [afr-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/afr-spa/README.md)
* model: transformer-align
* source language(s): afr
* target language(s): spa
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/afr-spa/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/afr-spa/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/afr-spa/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.afr.spa | 49.9 | 0.680 |
### System Info:
- hf_name: afr-spa
- source_languages: afr
- target_languages: spa
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/afr-spa/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['af', 'es']
- src_constituents: {'afr'}
- tgt_constituents: {'spa'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/afr-spa/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/afr-spa/opus-2020-06-17.test.txt
- src_alpha3: afr
- tgt_alpha3: spa
- short_pair: af-es
- chrF2_score: 0.68
- bleu: 49.9
- brevity_penalty: 1.0
- ref_len: 2783.0
- src_name: Afrikaans
- tgt_name: Spanish
- train_date: 2020-06-17
- src_alpha2: af
- tgt_alpha2: es
- prefer_old: False
- long_pair: afr-spa
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["af", "es"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-af-es | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"af",
"es",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"af",
"es"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #af #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### afr-spa
* source group: Afrikaans
* target group: Spanish
* OPUS readme: afr-spa
* model: transformer-align
* source language(s): afr
* target language(s): spa
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 49.9, chr-F: 0.680
### System Info:
* hf\_name: afr-spa
* source\_languages: afr
* target\_languages: spa
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['af', 'es']
* src\_constituents: {'afr'}
* tgt\_constituents: {'spa'}
* src\_multilingual: False
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: afr
* tgt\_alpha3: spa
* short\_pair: af-es
* chrF2\_score: 0.68
* bleu: 49.9
* brevity\_penalty: 1.0
* ref\_len: 2783.0
* src\_name: Afrikaans
* tgt\_name: Spanish
* train\_date: 2020-06-17
* src\_alpha2: af
* tgt\_alpha2: es
* prefer\_old: False
* long\_pair: afr-spa
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### afr-spa\n\n\n* source group: Afrikaans\n* target group: Spanish\n* OPUS readme: afr-spa\n* model: transformer-align\n* source language(s): afr\n* target language(s): spa\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 49.9, chr-F: 0.680",
"### System Info:\n\n\n* hf\\_name: afr-spa\n* source\\_languages: afr\n* target\\_languages: spa\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['af', 'es']\n* src\\_constituents: {'afr'}\n* tgt\\_constituents: {'spa'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: afr\n* tgt\\_alpha3: spa\n* short\\_pair: af-es\n* chrF2\\_score: 0.68\n* bleu: 49.9\n* brevity\\_penalty: 1.0\n* ref\\_len: 2783.0\n* src\\_name: Afrikaans\n* tgt\\_name: Spanish\n* train\\_date: 2020-06-17\n* src\\_alpha2: af\n* tgt\\_alpha2: es\n* prefer\\_old: False\n* long\\_pair: afr-spa\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #af #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### afr-spa\n\n\n* source group: Afrikaans\n* target group: Spanish\n* OPUS readme: afr-spa\n* model: transformer-align\n* source language(s): afr\n* target language(s): spa\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 49.9, chr-F: 0.680",
"### System Info:\n\n\n* hf\\_name: afr-spa\n* source\\_languages: afr\n* target\\_languages: spa\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['af', 'es']\n* src\\_constituents: {'afr'}\n* tgt\\_constituents: {'spa'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: afr\n* tgt\\_alpha3: spa\n* short\\_pair: af-es\n* chrF2\\_score: 0.68\n* bleu: 49.9\n* brevity\\_penalty: 1.0\n* ref\\_len: 2783.0\n* src\\_name: Afrikaans\n* tgt\\_name: Spanish\n* train\\_date: 2020-06-17\n* src\\_alpha2: af\n* tgt\\_alpha2: es\n* prefer\\_old: False\n* long\\_pair: afr-spa\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
61,
123,
391
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #af #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### afr-spa\n\n\n* source group: Afrikaans\n* target group: Spanish\n* OPUS readme: afr-spa\n* model: transformer-align\n* source language(s): afr\n* target language(s): spa\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 49.9, chr-F: 0.680"
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] |
null | null | transformers |
### opus-mt-af-fi
* source languages: af
* target languages: fi
* OPUS readme: [af-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/af-fi/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/af-fi/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/af-fi/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/af-fi/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.af.fi | 32.3 | 0.576 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-af-fi | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"af",
"fi",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #af #fi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-af-fi
* source languages: af
* target languages: fi
* OPUS readme: af-fi
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 32.3, chr-F: 0.576
| [
"### opus-mt-af-fi\n\n\n* source languages: af\n* target languages: fi\n* OPUS readme: af-fi\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 32.3, chr-F: 0.576"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #af #fi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-af-fi\n\n\n* source languages: af\n* target languages: fi\n* OPUS readme: af-fi\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 32.3, chr-F: 0.576"
] | [
61,
99
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #af #fi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-af-fi\n\n\n* source languages: af\n* target languages: fi\n* OPUS readme: af-fi\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 32.3, chr-F: 0.576"
] | [
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] |
null | null | transformers |
### opus-mt-af-fr
* source languages: af
* target languages: fr
* OPUS readme: [af-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/af-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/af-fr/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/af-fr/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/af-fr/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.af.fr | 35.3 | 0.543 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-af-fr | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"af",
"fr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #af #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-af-fr
* source languages: af
* target languages: fr
* OPUS readme: af-fr
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 35.3, chr-F: 0.543
| [
"### opus-mt-af-fr\n\n\n* source languages: af\n* target languages: fr\n* OPUS readme: af-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 35.3, chr-F: 0.543"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #af #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-af-fr\n\n\n* source languages: af\n* target languages: fr\n* OPUS readme: af-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 35.3, chr-F: 0.543"
] | [
61,
100
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #af #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-af-fr\n\n\n* source languages: af\n* target languages: fr\n* OPUS readme: af-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 35.3, chr-F: 0.543"
] | [
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null | null | transformers |
### afr-nld
* source group: Afrikaans
* target group: Dutch
* OPUS readme: [afr-nld](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/afr-nld/README.md)
* model: transformer-align
* source language(s): afr
* target language(s): nld
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/afr-nld/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/afr-nld/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/afr-nld/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.afr.nld | 55.2 | 0.715 |
### System Info:
- hf_name: afr-nld
- source_languages: afr
- target_languages: nld
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/afr-nld/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['af', 'nl']
- src_constituents: {'afr'}
- tgt_constituents: {'nld'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/afr-nld/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/afr-nld/opus-2020-06-17.test.txt
- src_alpha3: afr
- tgt_alpha3: nld
- short_pair: af-nl
- chrF2_score: 0.715
- bleu: 55.2
- brevity_penalty: 0.995
- ref_len: 6710.0
- src_name: Afrikaans
- tgt_name: Dutch
- train_date: 2020-06-17
- src_alpha2: af
- tgt_alpha2: nl
- prefer_old: False
- long_pair: afr-nld
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["af", "nl"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-af-nl | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"af",
"nl",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"af",
"nl"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #af #nl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### afr-nld
* source group: Afrikaans
* target group: Dutch
* OPUS readme: afr-nld
* model: transformer-align
* source language(s): afr
* target language(s): nld
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 55.2, chr-F: 0.715
### System Info:
* hf\_name: afr-nld
* source\_languages: afr
* target\_languages: nld
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['af', 'nl']
* src\_constituents: {'afr'}
* tgt\_constituents: {'nld'}
* src\_multilingual: False
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: afr
* tgt\_alpha3: nld
* short\_pair: af-nl
* chrF2\_score: 0.715
* bleu: 55.2
* brevity\_penalty: 0.995
* ref\_len: 6710.0
* src\_name: Afrikaans
* tgt\_name: Dutch
* train\_date: 2020-06-17
* src\_alpha2: af
* tgt\_alpha2: nl
* prefer\_old: False
* long\_pair: afr-nld
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### afr-nld\n\n\n* source group: Afrikaans\n* target group: Dutch\n* OPUS readme: afr-nld\n* model: transformer-align\n* source language(s): afr\n* target language(s): nld\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 55.2, chr-F: 0.715",
"### System Info:\n\n\n* hf\\_name: afr-nld\n* source\\_languages: afr\n* target\\_languages: nld\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['af', 'nl']\n* src\\_constituents: {'afr'}\n* tgt\\_constituents: {'nld'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: afr\n* tgt\\_alpha3: nld\n* short\\_pair: af-nl\n* chrF2\\_score: 0.715\n* bleu: 55.2\n* brevity\\_penalty: 0.995\n* ref\\_len: 6710.0\n* src\\_name: Afrikaans\n* tgt\\_name: Dutch\n* train\\_date: 2020-06-17\n* src\\_alpha2: af\n* tgt\\_alpha2: nl\n* prefer\\_old: False\n* long\\_pair: afr-nld\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #af #nl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### afr-nld\n\n\n* source group: Afrikaans\n* target group: Dutch\n* OPUS readme: afr-nld\n* model: transformer-align\n* source language(s): afr\n* target language(s): nld\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 55.2, chr-F: 0.715",
"### System Info:\n\n\n* hf\\_name: afr-nld\n* source\\_languages: afr\n* target\\_languages: nld\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['af', 'nl']\n* src\\_constituents: {'afr'}\n* tgt\\_constituents: {'nld'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: afr\n* tgt\\_alpha3: nld\n* short\\_pair: af-nl\n* chrF2\\_score: 0.715\n* bleu: 55.2\n* brevity\\_penalty: 0.995\n* ref\\_len: 6710.0\n* src\\_name: Afrikaans\n* tgt\\_name: Dutch\n* train\\_date: 2020-06-17\n* src\\_alpha2: af\n* tgt\\_alpha2: nl\n* prefer\\_old: False\n* long\\_pair: afr-nld\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
61,
126,
399
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #af #nl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### afr-nld\n\n\n* source group: Afrikaans\n* target group: Dutch\n* OPUS readme: afr-nld\n* model: transformer-align\n* source language(s): afr\n* target language(s): nld\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 55.2, chr-F: 0.715"
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null | null | transformers |
### afr-rus
* source group: Afrikaans
* target group: Russian
* OPUS readme: [afr-rus](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/afr-rus/README.md)
* model: transformer-align
* source language(s): afr
* target language(s): rus
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/afr-rus/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/afr-rus/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/afr-rus/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.afr.rus | 38.2 | 0.580 |
### System Info:
- hf_name: afr-rus
- source_languages: afr
- target_languages: rus
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/afr-rus/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['af', 'ru']
- src_constituents: {'afr'}
- tgt_constituents: {'rus'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/afr-rus/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/afr-rus/opus-2020-06-17.test.txt
- src_alpha3: afr
- tgt_alpha3: rus
- short_pair: af-ru
- chrF2_score: 0.58
- bleu: 38.2
- brevity_penalty: 0.992
- ref_len: 1213.0
- src_name: Afrikaans
- tgt_name: Russian
- train_date: 2020-06-17
- src_alpha2: af
- tgt_alpha2: ru
- prefer_old: False
- long_pair: afr-rus
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["af", "ru"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-af-ru | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"af",
"ru",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"af",
"ru"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #af #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### afr-rus
* source group: Afrikaans
* target group: Russian
* OPUS readme: afr-rus
* model: transformer-align
* source language(s): afr
* target language(s): rus
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 38.2, chr-F: 0.580
### System Info:
* hf\_name: afr-rus
* source\_languages: afr
* target\_languages: rus
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['af', 'ru']
* src\_constituents: {'afr'}
* tgt\_constituents: {'rus'}
* src\_multilingual: False
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: afr
* tgt\_alpha3: rus
* short\_pair: af-ru
* chrF2\_score: 0.58
* bleu: 38.2
* brevity\_penalty: 0.992
* ref\_len: 1213.0
* src\_name: Afrikaans
* tgt\_name: Russian
* train\_date: 2020-06-17
* src\_alpha2: af
* tgt\_alpha2: ru
* prefer\_old: False
* long\_pair: afr-rus
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### afr-rus\n\n\n* source group: Afrikaans\n* target group: Russian\n* OPUS readme: afr-rus\n* model: transformer-align\n* source language(s): afr\n* target language(s): rus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 38.2, chr-F: 0.580",
"### System Info:\n\n\n* hf\\_name: afr-rus\n* source\\_languages: afr\n* target\\_languages: rus\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['af', 'ru']\n* src\\_constituents: {'afr'}\n* tgt\\_constituents: {'rus'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: afr\n* tgt\\_alpha3: rus\n* short\\_pair: af-ru\n* chrF2\\_score: 0.58\n* bleu: 38.2\n* brevity\\_penalty: 0.992\n* ref\\_len: 1213.0\n* src\\_name: Afrikaans\n* tgt\\_name: Russian\n* train\\_date: 2020-06-17\n* src\\_alpha2: af\n* tgt\\_alpha2: ru\n* prefer\\_old: False\n* long\\_pair: afr-rus\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #af #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### afr-rus\n\n\n* source group: Afrikaans\n* target group: Russian\n* OPUS readme: afr-rus\n* model: transformer-align\n* source language(s): afr\n* target language(s): rus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 38.2, chr-F: 0.580",
"### System Info:\n\n\n* hf\\_name: afr-rus\n* source\\_languages: afr\n* target\\_languages: rus\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['af', 'ru']\n* src\\_constituents: {'afr'}\n* tgt\\_constituents: {'rus'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: afr\n* tgt\\_alpha3: rus\n* short\\_pair: af-ru\n* chrF2\\_score: 0.58\n* bleu: 38.2\n* brevity\\_penalty: 0.992\n* ref\\_len: 1213.0\n* src\\_name: Afrikaans\n* tgt\\_name: Russian\n* train\\_date: 2020-06-17\n* src\\_alpha2: af\n* tgt\\_alpha2: ru\n* prefer\\_old: False\n* long\\_pair: afr-rus\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
61,
122,
391
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #af #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### afr-rus\n\n\n* source group: Afrikaans\n* target group: Russian\n* OPUS readme: afr-rus\n* model: transformer-align\n* source language(s): afr\n* target language(s): rus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 38.2, chr-F: 0.580"
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null | null | transformers |
### opus-mt-af-sv
* source languages: af
* target languages: sv
* OPUS readme: [af-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/af-sv/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/af-sv/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/af-sv/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/af-sv/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.af.sv | 40.4 | 0.599 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-af-sv | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"af",
"sv",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #af #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-af-sv
* source languages: af
* target languages: sv
* OPUS readme: af-sv
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 40.4, chr-F: 0.599
| [
"### opus-mt-af-sv\n\n\n* source languages: af\n* target languages: sv\n* OPUS readme: af-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 40.4, chr-F: 0.599"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #af #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-af-sv\n\n\n* source languages: af\n* target languages: sv\n* OPUS readme: af-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 40.4, chr-F: 0.599"
] | [
61,
99
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #af #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-af-sv\n\n\n* source languages: af\n* target languages: sv\n* OPUS readme: af-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 40.4, chr-F: 0.599"
] | [
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-0.12932179868221283,
-0.08866483718156815
] |
null | null | transformers |
### afa-afa
* source group: Afro-Asiatic languages
* target group: Afro-Asiatic languages
* OPUS readme: [afa-afa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/afa-afa/README.md)
* model: transformer
* source language(s): apc ara arq arz heb kab mlt shy_Latn thv
* target language(s): apc ara arq arz heb kab mlt shy_Latn thv
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID)
* download original weights: [opus-2020-07-26.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/afa-afa/opus-2020-07-26.zip)
* test set translations: [opus-2020-07-26.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/afa-afa/opus-2020-07-26.test.txt)
* test set scores: [opus-2020-07-26.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/afa-afa/opus-2020-07-26.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.ara-ara.ara.ara | 4.3 | 0.148 |
| Tatoeba-test.ara-heb.ara.heb | 31.9 | 0.525 |
| Tatoeba-test.ara-kab.ara.kab | 0.3 | 0.120 |
| Tatoeba-test.ara-mlt.ara.mlt | 14.0 | 0.428 |
| Tatoeba-test.ara-shy.ara.shy | 1.3 | 0.050 |
| Tatoeba-test.heb-ara.heb.ara | 17.0 | 0.464 |
| Tatoeba-test.heb-kab.heb.kab | 1.9 | 0.104 |
| Tatoeba-test.kab-ara.kab.ara | 0.3 | 0.044 |
| Tatoeba-test.kab-heb.kab.heb | 5.1 | 0.099 |
| Tatoeba-test.kab-shy.kab.shy | 2.2 | 0.009 |
| Tatoeba-test.kab-tmh.kab.tmh | 10.7 | 0.007 |
| Tatoeba-test.mlt-ara.mlt.ara | 29.1 | 0.498 |
| Tatoeba-test.multi.multi | 20.8 | 0.434 |
| Tatoeba-test.shy-ara.shy.ara | 1.2 | 0.053 |
| Tatoeba-test.shy-kab.shy.kab | 2.0 | 0.134 |
| Tatoeba-test.tmh-kab.tmh.kab | 0.0 | 0.047 |
### System Info:
- hf_name: afa-afa
- source_languages: afa
- target_languages: afa
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/afa-afa/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['so', 'ti', 'am', 'he', 'mt', 'ar', 'afa']
- src_constituents: {'som', 'rif_Latn', 'tir', 'kab', 'arq', 'afb', 'amh', 'arz', 'heb', 'shy_Latn', 'apc', 'mlt', 'thv', 'ara', 'hau_Latn', 'acm', 'ary'}
- tgt_constituents: {'som', 'rif_Latn', 'tir', 'kab', 'arq', 'afb', 'amh', 'arz', 'heb', 'shy_Latn', 'apc', 'mlt', 'thv', 'ara', 'hau_Latn', 'acm', 'ary'}
- src_multilingual: True
- tgt_multilingual: True
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/afa-afa/opus-2020-07-26.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/afa-afa/opus-2020-07-26.test.txt
- src_alpha3: afa
- tgt_alpha3: afa
- short_pair: afa-afa
- chrF2_score: 0.434
- bleu: 20.8
- brevity_penalty: 1.0
- ref_len: 15215.0
- src_name: Afro-Asiatic languages
- tgt_name: Afro-Asiatic languages
- train_date: 2020-07-26
- src_alpha2: afa
- tgt_alpha2: afa
- prefer_old: False
- long_pair: afa-afa
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["so", "ti", "am", "he", "mt", "ar", "afa"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-afa-afa | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"so",
"ti",
"am",
"he",
"mt",
"ar",
"afa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"so",
"ti",
"am",
"he",
"mt",
"ar",
"afa"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #so #ti #am #he #mt #ar #afa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### afa-afa
* source group: Afro-Asiatic languages
* target group: Afro-Asiatic languages
* OPUS readme: afa-afa
* model: transformer
* source language(s): apc ara arq arz heb kab mlt shy\_Latn thv
* target language(s): apc ara arq arz heb kab mlt shy\_Latn thv
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 4.3, chr-F: 0.148
testset: URL, BLEU: 31.9, chr-F: 0.525
testset: URL, BLEU: 0.3, chr-F: 0.120
testset: URL, BLEU: 14.0, chr-F: 0.428
testset: URL, BLEU: 1.3, chr-F: 0.050
testset: URL, BLEU: 17.0, chr-F: 0.464
testset: URL, BLEU: 1.9, chr-F: 0.104
testset: URL, BLEU: 0.3, chr-F: 0.044
testset: URL, BLEU: 5.1, chr-F: 0.099
testset: URL, BLEU: 2.2, chr-F: 0.009
testset: URL, BLEU: 10.7, chr-F: 0.007
testset: URL, BLEU: 29.1, chr-F: 0.498
testset: URL, BLEU: 20.8, chr-F: 0.434
testset: URL, BLEU: 1.2, chr-F: 0.053
testset: URL, BLEU: 2.0, chr-F: 0.134
testset: URL, BLEU: 0.0, chr-F: 0.047
### System Info:
* hf\_name: afa-afa
* source\_languages: afa
* target\_languages: afa
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['so', 'ti', 'am', 'he', 'mt', 'ar', 'afa']
* src\_constituents: {'som', 'rif\_Latn', 'tir', 'kab', 'arq', 'afb', 'amh', 'arz', 'heb', 'shy\_Latn', 'apc', 'mlt', 'thv', 'ara', 'hau\_Latn', 'acm', 'ary'}
* tgt\_constituents: {'som', 'rif\_Latn', 'tir', 'kab', 'arq', 'afb', 'amh', 'arz', 'heb', 'shy\_Latn', 'apc', 'mlt', 'thv', 'ara', 'hau\_Latn', 'acm', 'ary'}
* src\_multilingual: True
* tgt\_multilingual: True
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: afa
* tgt\_alpha3: afa
* short\_pair: afa-afa
* chrF2\_score: 0.434
* bleu: 20.8
* brevity\_penalty: 1.0
* ref\_len: 15215.0
* src\_name: Afro-Asiatic languages
* tgt\_name: Afro-Asiatic languages
* train\_date: 2020-07-26
* src\_alpha2: afa
* tgt\_alpha2: afa
* prefer\_old: False
* long\_pair: afa-afa
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### afa-afa\n\n\n* source group: Afro-Asiatic languages\n* target group: Afro-Asiatic languages\n* OPUS readme: afa-afa\n* model: transformer\n* source language(s): apc ara arq arz heb kab mlt shy\\_Latn thv\n* target language(s): apc ara arq arz heb kab mlt shy\\_Latn thv\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 4.3, chr-F: 0.148\ntestset: URL, BLEU: 31.9, chr-F: 0.525\ntestset: URL, BLEU: 0.3, chr-F: 0.120\ntestset: URL, BLEU: 14.0, chr-F: 0.428\ntestset: URL, BLEU: 1.3, chr-F: 0.050\ntestset: URL, BLEU: 17.0, chr-F: 0.464\ntestset: URL, BLEU: 1.9, chr-F: 0.104\ntestset: URL, BLEU: 0.3, chr-F: 0.044\ntestset: URL, BLEU: 5.1, chr-F: 0.099\ntestset: URL, BLEU: 2.2, chr-F: 0.009\ntestset: URL, BLEU: 10.7, chr-F: 0.007\ntestset: URL, BLEU: 29.1, chr-F: 0.498\ntestset: URL, BLEU: 20.8, chr-F: 0.434\ntestset: URL, BLEU: 1.2, chr-F: 0.053\ntestset: URL, BLEU: 2.0, chr-F: 0.134\ntestset: URL, BLEU: 0.0, chr-F: 0.047",
"### System Info:\n\n\n* hf\\_name: afa-afa\n* source\\_languages: afa\n* target\\_languages: afa\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['so', 'ti', 'am', 'he', 'mt', 'ar', 'afa']\n* src\\_constituents: {'som', 'rif\\_Latn', 'tir', 'kab', 'arq', 'afb', 'amh', 'arz', 'heb', 'shy\\_Latn', 'apc', 'mlt', 'thv', 'ara', 'hau\\_Latn', 'acm', 'ary'}\n* tgt\\_constituents: {'som', 'rif\\_Latn', 'tir', 'kab', 'arq', 'afb', 'amh', 'arz', 'heb', 'shy\\_Latn', 'apc', 'mlt', 'thv', 'ara', 'hau\\_Latn', 'acm', 'ary'}\n* src\\_multilingual: True\n* tgt\\_multilingual: True\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: afa\n* tgt\\_alpha3: afa\n* short\\_pair: afa-afa\n* chrF2\\_score: 0.434\n* bleu: 20.8\n* brevity\\_penalty: 1.0\n* ref\\_len: 15215.0\n* src\\_name: Afro-Asiatic languages\n* tgt\\_name: Afro-Asiatic languages\n* train\\_date: 2020-07-26\n* src\\_alpha2: afa\n* tgt\\_alpha2: afa\n* prefer\\_old: False\n* long\\_pair: afa-afa\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #so #ti #am #he #mt #ar #afa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### afa-afa\n\n\n* source group: Afro-Asiatic languages\n* target group: Afro-Asiatic languages\n* OPUS readme: afa-afa\n* model: transformer\n* source language(s): apc ara arq arz heb kab mlt shy\\_Latn thv\n* target language(s): apc ara arq arz heb kab mlt shy\\_Latn thv\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 4.3, chr-F: 0.148\ntestset: URL, BLEU: 31.9, chr-F: 0.525\ntestset: URL, BLEU: 0.3, chr-F: 0.120\ntestset: URL, BLEU: 14.0, chr-F: 0.428\ntestset: URL, BLEU: 1.3, chr-F: 0.050\ntestset: URL, BLEU: 17.0, chr-F: 0.464\ntestset: URL, BLEU: 1.9, chr-F: 0.104\ntestset: URL, BLEU: 0.3, chr-F: 0.044\ntestset: URL, BLEU: 5.1, chr-F: 0.099\ntestset: URL, BLEU: 2.2, chr-F: 0.009\ntestset: URL, BLEU: 10.7, chr-F: 0.007\ntestset: URL, BLEU: 29.1, chr-F: 0.498\ntestset: URL, BLEU: 20.8, chr-F: 0.434\ntestset: URL, BLEU: 1.2, chr-F: 0.053\ntestset: URL, BLEU: 2.0, chr-F: 0.134\ntestset: URL, BLEU: 0.0, chr-F: 0.047",
"### System Info:\n\n\n* hf\\_name: afa-afa\n* source\\_languages: afa\n* target\\_languages: afa\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['so', 'ti', 'am', 'he', 'mt', 'ar', 'afa']\n* src\\_constituents: {'som', 'rif\\_Latn', 'tir', 'kab', 'arq', 'afb', 'amh', 'arz', 'heb', 'shy\\_Latn', 'apc', 'mlt', 'thv', 'ara', 'hau\\_Latn', 'acm', 'ary'}\n* tgt\\_constituents: {'som', 'rif\\_Latn', 'tir', 'kab', 'arq', 'afb', 'amh', 'arz', 'heb', 'shy\\_Latn', 'apc', 'mlt', 'thv', 'ara', 'hau\\_Latn', 'acm', 'ary'}\n* src\\_multilingual: True\n* tgt\\_multilingual: True\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: afa\n* tgt\\_alpha3: afa\n* short\\_pair: afa-afa\n* chrF2\\_score: 0.434\n* bleu: 20.8\n* brevity\\_penalty: 1.0\n* ref\\_len: 15215.0\n* src\\_name: Afro-Asiatic languages\n* tgt\\_name: Afro-Asiatic languages\n* train\\_date: 2020-07-26\n* src\\_alpha2: afa\n* tgt\\_alpha2: afa\n* prefer\\_old: False\n* long\\_pair: afa-afa\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
72,
441,
598
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #so #ti #am #he #mt #ar #afa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"passage: ### afa-afa\n\n\n* source group: Afro-Asiatic languages\n* target group: Afro-Asiatic languages\n* OPUS readme: afa-afa\n* model: transformer\n* source language(s): apc ara arq arz heb kab mlt shy\\_Latn thv\n* target language(s): apc ara arq arz heb kab mlt shy\\_Latn thv\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* a sentence initial language token is required in the form of '>>id<<' (id = valid target language ID)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 4.3, chr-F: 0.148\ntestset: URL, BLEU: 31.9, chr-F: 0.525\ntestset: URL, BLEU: 0.3, chr-F: 0.120\ntestset: URL, BLEU: 14.0, chr-F: 0.428\ntestset: URL, BLEU: 1.3, chr-F: 0.050\ntestset: URL, BLEU: 17.0, chr-F: 0.464\ntestset: URL, BLEU: 1.9, chr-F: 0.104\ntestset: URL, BLEU: 0.3, chr-F: 0.044\ntestset: URL, BLEU: 5.1, chr-F: 0.099\ntestset: URL, BLEU: 2.2, chr-F: 0.009\ntestset: URL, BLEU: 10.7, chr-F: 0.007\ntestset: URL, BLEU: 29.1, chr-F: 0.498\ntestset: URL, BLEU: 20.8, chr-F: 0.434\ntestset: URL, BLEU: 1.2, chr-F: 0.053\ntestset: URL, BLEU: 2.0, chr-F: 0.134\ntestset: URL, BLEU: 0.0, chr-F: 0.047"
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] |
null | null | transformers |
### afa-eng
* source group: Afro-Asiatic languages
* target group: English
* OPUS readme: [afa-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/afa-eng/README.md)
* model: transformer
* source language(s): acm afb amh apc ara arq ary arz hau_Latn heb kab mlt rif_Latn shy_Latn som tir
* target language(s): eng
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus2m-2020-07-31.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/afa-eng/opus2m-2020-07-31.zip)
* test set translations: [opus2m-2020-07-31.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/afa-eng/opus2m-2020-07-31.test.txt)
* test set scores: [opus2m-2020-07-31.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/afa-eng/opus2m-2020-07-31.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.amh-eng.amh.eng | 35.9 | 0.550 |
| Tatoeba-test.ara-eng.ara.eng | 36.6 | 0.543 |
| Tatoeba-test.hau-eng.hau.eng | 11.9 | 0.327 |
| Tatoeba-test.heb-eng.heb.eng | 42.7 | 0.591 |
| Tatoeba-test.kab-eng.kab.eng | 4.3 | 0.213 |
| Tatoeba-test.mlt-eng.mlt.eng | 44.3 | 0.618 |
| Tatoeba-test.multi.eng | 27.1 | 0.464 |
| Tatoeba-test.rif-eng.rif.eng | 3.5 | 0.141 |
| Tatoeba-test.shy-eng.shy.eng | 0.6 | 0.125 |
| Tatoeba-test.som-eng.som.eng | 23.6 | 0.472 |
| Tatoeba-test.tir-eng.tir.eng | 13.1 | 0.328 |
### System Info:
- hf_name: afa-eng
- source_languages: afa
- target_languages: eng
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/afa-eng/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['so', 'ti', 'am', 'he', 'mt', 'ar', 'afa', 'en']
- src_constituents: {'som', 'rif_Latn', 'tir', 'kab', 'arq', 'afb', 'amh', 'arz', 'heb', 'shy_Latn', 'apc', 'mlt', 'thv', 'ara', 'hau_Latn', 'acm', 'ary'}
- tgt_constituents: {'eng'}
- src_multilingual: True
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/afa-eng/opus2m-2020-07-31.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/afa-eng/opus2m-2020-07-31.test.txt
- src_alpha3: afa
- tgt_alpha3: eng
- short_pair: afa-en
- chrF2_score: 0.46399999999999997
- bleu: 27.1
- brevity_penalty: 1.0
- ref_len: 69373.0
- src_name: Afro-Asiatic languages
- tgt_name: English
- train_date: 2020-07-31
- src_alpha2: afa
- tgt_alpha2: en
- prefer_old: False
- long_pair: afa-eng
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["so", "ti", "am", "he", "mt", "ar", "afa", "en"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-afa-en | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"so",
"ti",
"am",
"he",
"mt",
"ar",
"afa",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"so",
"ti",
"am",
"he",
"mt",
"ar",
"afa",
"en"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #so #ti #am #he #mt #ar #afa #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### afa-eng
* source group: Afro-Asiatic languages
* target group: English
* OPUS readme: afa-eng
* model: transformer
* source language(s): acm afb amh apc ara arq ary arz hau\_Latn heb kab mlt rif\_Latn shy\_Latn som tir
* target language(s): eng
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 35.9, chr-F: 0.550
testset: URL, BLEU: 36.6, chr-F: 0.543
testset: URL, BLEU: 11.9, chr-F: 0.327
testset: URL, BLEU: 42.7, chr-F: 0.591
testset: URL, BLEU: 4.3, chr-F: 0.213
testset: URL, BLEU: 44.3, chr-F: 0.618
testset: URL, BLEU: 27.1, chr-F: 0.464
testset: URL, BLEU: 3.5, chr-F: 0.141
testset: URL, BLEU: 0.6, chr-F: 0.125
testset: URL, BLEU: 23.6, chr-F: 0.472
testset: URL, BLEU: 13.1, chr-F: 0.328
### System Info:
* hf\_name: afa-eng
* source\_languages: afa
* target\_languages: eng
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['so', 'ti', 'am', 'he', 'mt', 'ar', 'afa', 'en']
* src\_constituents: {'som', 'rif\_Latn', 'tir', 'kab', 'arq', 'afb', 'amh', 'arz', 'heb', 'shy\_Latn', 'apc', 'mlt', 'thv', 'ara', 'hau\_Latn', 'acm', 'ary'}
* tgt\_constituents: {'eng'}
* src\_multilingual: True
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: afa
* tgt\_alpha3: eng
* short\_pair: afa-en
* chrF2\_score: 0.46399999999999997
* bleu: 27.1
* brevity\_penalty: 1.0
* ref\_len: 69373.0
* src\_name: Afro-Asiatic languages
* tgt\_name: English
* train\_date: 2020-07-31
* src\_alpha2: afa
* tgt\_alpha2: en
* prefer\_old: False
* long\_pair: afa-eng
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### afa-eng\n\n\n* source group: Afro-Asiatic languages\n* target group: English\n* OPUS readme: afa-eng\n* model: transformer\n* source language(s): acm afb amh apc ara arq ary arz hau\\_Latn heb kab mlt rif\\_Latn shy\\_Latn som tir\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 35.9, chr-F: 0.550\ntestset: URL, BLEU: 36.6, chr-F: 0.543\ntestset: URL, BLEU: 11.9, chr-F: 0.327\ntestset: URL, BLEU: 42.7, chr-F: 0.591\ntestset: URL, BLEU: 4.3, chr-F: 0.213\ntestset: URL, BLEU: 44.3, chr-F: 0.618\ntestset: URL, BLEU: 27.1, chr-F: 0.464\ntestset: URL, BLEU: 3.5, chr-F: 0.141\ntestset: URL, BLEU: 0.6, chr-F: 0.125\ntestset: URL, BLEU: 23.6, chr-F: 0.472\ntestset: URL, BLEU: 13.1, chr-F: 0.328",
"### System Info:\n\n\n* hf\\_name: afa-eng\n* source\\_languages: afa\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['so', 'ti', 'am', 'he', 'mt', 'ar', 'afa', 'en']\n* src\\_constituents: {'som', 'rif\\_Latn', 'tir', 'kab', 'arq', 'afb', 'amh', 'arz', 'heb', 'shy\\_Latn', 'apc', 'mlt', 'thv', 'ara', 'hau\\_Latn', 'acm', 'ary'}\n* tgt\\_constituents: {'eng'}\n* src\\_multilingual: True\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: afa\n* tgt\\_alpha3: eng\n* short\\_pair: afa-en\n* chrF2\\_score: 0.46399999999999997\n* bleu: 27.1\n* brevity\\_penalty: 1.0\n* ref\\_len: 69373.0\n* src\\_name: Afro-Asiatic languages\n* tgt\\_name: English\n* train\\_date: 2020-07-31\n* src\\_alpha2: afa\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* long\\_pair: afa-eng\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #so #ti #am #he #mt #ar #afa #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### afa-eng\n\n\n* source group: Afro-Asiatic languages\n* target group: English\n* OPUS readme: afa-eng\n* model: transformer\n* source language(s): acm afb amh apc ara arq ary arz hau\\_Latn heb kab mlt rif\\_Latn shy\\_Latn som tir\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 35.9, chr-F: 0.550\ntestset: URL, BLEU: 36.6, chr-F: 0.543\ntestset: URL, BLEU: 11.9, chr-F: 0.327\ntestset: URL, BLEU: 42.7, chr-F: 0.591\ntestset: URL, BLEU: 4.3, chr-F: 0.213\ntestset: URL, BLEU: 44.3, chr-F: 0.618\ntestset: URL, BLEU: 27.1, chr-F: 0.464\ntestset: URL, BLEU: 3.5, chr-F: 0.141\ntestset: URL, BLEU: 0.6, chr-F: 0.125\ntestset: URL, BLEU: 23.6, chr-F: 0.472\ntestset: URL, BLEU: 13.1, chr-F: 0.328",
"### System Info:\n\n\n* hf\\_name: afa-eng\n* source\\_languages: afa\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['so', 'ti', 'am', 'he', 'mt', 'ar', 'afa', 'en']\n* src\\_constituents: {'som', 'rif\\_Latn', 'tir', 'kab', 'arq', 'afb', 'amh', 'arz', 'heb', 'shy\\_Latn', 'apc', 'mlt', 'thv', 'ara', 'hau\\_Latn', 'acm', 'ary'}\n* tgt\\_constituents: {'eng'}\n* src\\_multilingual: True\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: afa\n* tgt\\_alpha3: eng\n* short\\_pair: afa-en\n* chrF2\\_score: 0.46399999999999997\n* bleu: 27.1\n* brevity\\_penalty: 1.0\n* ref\\_len: 69373.0\n* src\\_name: Afro-Asiatic languages\n* tgt\\_name: English\n* train\\_date: 2020-07-31\n* src\\_alpha2: afa\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* long\\_pair: afa-eng\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
74,
332,
513
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #so #ti #am #he #mt #ar #afa #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### afa-eng\n\n\n* source group: Afro-Asiatic languages\n* target group: English\n* OPUS readme: afa-eng\n* model: transformer\n* source language(s): acm afb amh apc ara arq ary arz hau\\_Latn heb kab mlt rif\\_Latn shy\\_Latn som tir\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 35.9, chr-F: 0.550\ntestset: URL, BLEU: 36.6, chr-F: 0.543\ntestset: URL, BLEU: 11.9, chr-F: 0.327\ntestset: URL, BLEU: 42.7, chr-F: 0.591\ntestset: URL, BLEU: 4.3, chr-F: 0.213\ntestset: URL, BLEU: 44.3, chr-F: 0.618\ntestset: URL, BLEU: 27.1, chr-F: 0.464\ntestset: URL, BLEU: 3.5, chr-F: 0.141\ntestset: URL, BLEU: 0.6, chr-F: 0.125\ntestset: URL, BLEU: 23.6, chr-F: 0.472\ntestset: URL, BLEU: 13.1, chr-F: 0.328"
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null | null | transformers |
### alv-eng
* source group: Atlantic-Congo languages
* target group: English
* OPUS readme: [alv-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/alv-eng/README.md)
* model: transformer
* source language(s): ewe fuc fuv ibo kin lin lug nya run sag sna swh toi_Latn tso umb wol xho yor zul
* target language(s): eng
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus2m-2020-07-31.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/alv-eng/opus2m-2020-07-31.zip)
* test set translations: [opus2m-2020-07-31.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/alv-eng/opus2m-2020-07-31.test.txt)
* test set scores: [opus2m-2020-07-31.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/alv-eng/opus2m-2020-07-31.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.ewe-eng.ewe.eng | 6.3 | 0.328 |
| Tatoeba-test.ful-eng.ful.eng | 0.4 | 0.108 |
| Tatoeba-test.ibo-eng.ibo.eng | 4.5 | 0.196 |
| Tatoeba-test.kin-eng.kin.eng | 30.7 | 0.511 |
| Tatoeba-test.lin-eng.lin.eng | 2.8 | 0.213 |
| Tatoeba-test.lug-eng.lug.eng | 3.4 | 0.140 |
| Tatoeba-test.multi.eng | 20.9 | 0.376 |
| Tatoeba-test.nya-eng.nya.eng | 38.7 | 0.492 |
| Tatoeba-test.run-eng.run.eng | 24.5 | 0.417 |
| Tatoeba-test.sag-eng.sag.eng | 5.5 | 0.177 |
| Tatoeba-test.sna-eng.sna.eng | 26.9 | 0.412 |
| Tatoeba-test.swa-eng.swa.eng | 4.9 | 0.196 |
| Tatoeba-test.toi-eng.toi.eng | 3.9 | 0.147 |
| Tatoeba-test.tso-eng.tso.eng | 76.7 | 0.957 |
| Tatoeba-test.umb-eng.umb.eng | 4.0 | 0.195 |
| Tatoeba-test.wol-eng.wol.eng | 3.7 | 0.170 |
| Tatoeba-test.xho-eng.xho.eng | 38.9 | 0.556 |
| Tatoeba-test.yor-eng.yor.eng | 25.1 | 0.412 |
| Tatoeba-test.zul-eng.zul.eng | 46.1 | 0.623 |
### System Info:
- hf_name: alv-eng
- source_languages: alv
- target_languages: eng
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/alv-eng/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['sn', 'rw', 'wo', 'ig', 'sg', 'ee', 'zu', 'lg', 'ts', 'ln', 'ny', 'yo', 'rn', 'xh', 'alv', 'en']
- src_constituents: {'sna', 'kin', 'wol', 'ibo', 'swh', 'sag', 'ewe', 'zul', 'fuc', 'lug', 'tso', 'lin', 'nya', 'yor', 'run', 'xho', 'fuv', 'toi_Latn', 'umb'}
- tgt_constituents: {'eng'}
- src_multilingual: True
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/alv-eng/opus2m-2020-07-31.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/alv-eng/opus2m-2020-07-31.test.txt
- src_alpha3: alv
- tgt_alpha3: eng
- short_pair: alv-en
- chrF2_score: 0.376
- bleu: 20.9
- brevity_penalty: 1.0
- ref_len: 15208.0
- src_name: Atlantic-Congo languages
- tgt_name: English
- train_date: 2020-07-31
- src_alpha2: alv
- tgt_alpha2: en
- prefer_old: False
- long_pair: alv-eng
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["sn", "rw", "wo", "ig", "sg", "ee", "zu", "lg", "ts", "ln", "ny", "yo", "rn", "xh", "alv", "en"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-alv-en | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"sn",
"rw",
"wo",
"ig",
"sg",
"ee",
"zu",
"lg",
"ts",
"ln",
"ny",
"yo",
"rn",
"xh",
"alv",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"sn",
"rw",
"wo",
"ig",
"sg",
"ee",
"zu",
"lg",
"ts",
"ln",
"ny",
"yo",
"rn",
"xh",
"alv",
"en"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #sn #rw #wo #ig #sg #ee #zu #lg #ts #ln #ny #yo #rn #xh #alv #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### alv-eng
* source group: Atlantic-Congo languages
* target group: English
* OPUS readme: alv-eng
* model: transformer
* source language(s): ewe fuc fuv ibo kin lin lug nya run sag sna swh toi\_Latn tso umb wol xho yor zul
* target language(s): eng
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 6.3, chr-F: 0.328
testset: URL, BLEU: 0.4, chr-F: 0.108
testset: URL, BLEU: 4.5, chr-F: 0.196
testset: URL, BLEU: 30.7, chr-F: 0.511
testset: URL, BLEU: 2.8, chr-F: 0.213
testset: URL, BLEU: 3.4, chr-F: 0.140
testset: URL, BLEU: 20.9, chr-F: 0.376
testset: URL, BLEU: 38.7, chr-F: 0.492
testset: URL, BLEU: 24.5, chr-F: 0.417
testset: URL, BLEU: 5.5, chr-F: 0.177
testset: URL, BLEU: 26.9, chr-F: 0.412
testset: URL, BLEU: 4.9, chr-F: 0.196
testset: URL, BLEU: 3.9, chr-F: 0.147
testset: URL, BLEU: 76.7, chr-F: 0.957
testset: URL, BLEU: 4.0, chr-F: 0.195
testset: URL, BLEU: 3.7, chr-F: 0.170
testset: URL, BLEU: 38.9, chr-F: 0.556
testset: URL, BLEU: 25.1, chr-F: 0.412
testset: URL, BLEU: 46.1, chr-F: 0.623
### System Info:
* hf\_name: alv-eng
* source\_languages: alv
* target\_languages: eng
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['sn', 'rw', 'wo', 'ig', 'sg', 'ee', 'zu', 'lg', 'ts', 'ln', 'ny', 'yo', 'rn', 'xh', 'alv', 'en']
* src\_constituents: {'sna', 'kin', 'wol', 'ibo', 'swh', 'sag', 'ewe', 'zul', 'fuc', 'lug', 'tso', 'lin', 'nya', 'yor', 'run', 'xho', 'fuv', 'toi\_Latn', 'umb'}
* tgt\_constituents: {'eng'}
* src\_multilingual: True
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: alv
* tgt\_alpha3: eng
* short\_pair: alv-en
* chrF2\_score: 0.376
* bleu: 20.9
* brevity\_penalty: 1.0
* ref\_len: 15208.0
* src\_name: Atlantic-Congo languages
* tgt\_name: English
* train\_date: 2020-07-31
* src\_alpha2: alv
* tgt\_alpha2: en
* prefer\_old: False
* long\_pair: alv-eng
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### alv-eng\n\n\n* source group: Atlantic-Congo languages\n* target group: English\n* OPUS readme: alv-eng\n* model: transformer\n* source language(s): ewe fuc fuv ibo kin lin lug nya run sag sna swh toi\\_Latn tso umb wol xho yor zul\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 6.3, chr-F: 0.328\ntestset: URL, BLEU: 0.4, chr-F: 0.108\ntestset: URL, BLEU: 4.5, chr-F: 0.196\ntestset: URL, BLEU: 30.7, chr-F: 0.511\ntestset: URL, BLEU: 2.8, chr-F: 0.213\ntestset: URL, BLEU: 3.4, chr-F: 0.140\ntestset: URL, BLEU: 20.9, chr-F: 0.376\ntestset: URL, BLEU: 38.7, chr-F: 0.492\ntestset: URL, BLEU: 24.5, chr-F: 0.417\ntestset: URL, BLEU: 5.5, chr-F: 0.177\ntestset: URL, BLEU: 26.9, chr-F: 0.412\ntestset: URL, BLEU: 4.9, chr-F: 0.196\ntestset: URL, BLEU: 3.9, chr-F: 0.147\ntestset: URL, BLEU: 76.7, chr-F: 0.957\ntestset: URL, BLEU: 4.0, chr-F: 0.195\ntestset: URL, BLEU: 3.7, chr-F: 0.170\ntestset: URL, BLEU: 38.9, chr-F: 0.556\ntestset: URL, BLEU: 25.1, chr-F: 0.412\ntestset: URL, BLEU: 46.1, chr-F: 0.623",
"### System Info:\n\n\n* hf\\_name: alv-eng\n* source\\_languages: alv\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['sn', 'rw', 'wo', 'ig', 'sg', 'ee', 'zu', 'lg', 'ts', 'ln', 'ny', 'yo', 'rn', 'xh', 'alv', 'en']\n* src\\_constituents: {'sna', 'kin', 'wol', 'ibo', 'swh', 'sag', 'ewe', 'zul', 'fuc', 'lug', 'tso', 'lin', 'nya', 'yor', 'run', 'xho', 'fuv', 'toi\\_Latn', 'umb'}\n* tgt\\_constituents: {'eng'}\n* src\\_multilingual: True\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: alv\n* tgt\\_alpha3: eng\n* short\\_pair: alv-en\n* chrF2\\_score: 0.376\n* bleu: 20.9\n* brevity\\_penalty: 1.0\n* ref\\_len: 15208.0\n* src\\_name: Atlantic-Congo languages\n* tgt\\_name: English\n* train\\_date: 2020-07-31\n* src\\_alpha2: alv\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* long\\_pair: alv-eng\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #sn #rw #wo #ig #sg #ee #zu #lg #ts #ln #ny #yo #rn #xh #alv #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### alv-eng\n\n\n* source group: Atlantic-Congo languages\n* target group: English\n* OPUS readme: alv-eng\n* model: transformer\n* source language(s): ewe fuc fuv ibo kin lin lug nya run sag sna swh toi\\_Latn tso umb wol xho yor zul\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 6.3, chr-F: 0.328\ntestset: URL, BLEU: 0.4, chr-F: 0.108\ntestset: URL, BLEU: 4.5, chr-F: 0.196\ntestset: URL, BLEU: 30.7, chr-F: 0.511\ntestset: URL, BLEU: 2.8, chr-F: 0.213\ntestset: URL, BLEU: 3.4, chr-F: 0.140\ntestset: URL, BLEU: 20.9, chr-F: 0.376\ntestset: URL, BLEU: 38.7, chr-F: 0.492\ntestset: URL, BLEU: 24.5, chr-F: 0.417\ntestset: URL, BLEU: 5.5, chr-F: 0.177\ntestset: URL, BLEU: 26.9, chr-F: 0.412\ntestset: URL, BLEU: 4.9, chr-F: 0.196\ntestset: URL, BLEU: 3.9, chr-F: 0.147\ntestset: URL, BLEU: 76.7, chr-F: 0.957\ntestset: URL, BLEU: 4.0, chr-F: 0.195\ntestset: URL, BLEU: 3.7, chr-F: 0.170\ntestset: URL, BLEU: 38.9, chr-F: 0.556\ntestset: URL, BLEU: 25.1, chr-F: 0.412\ntestset: URL, BLEU: 46.1, chr-F: 0.623",
"### System Info:\n\n\n* hf\\_name: alv-eng\n* source\\_languages: alv\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['sn', 'rw', 'wo', 'ig', 'sg', 'ee', 'zu', 'lg', 'ts', 'ln', 'ny', 'yo', 'rn', 'xh', 'alv', 'en']\n* src\\_constituents: {'sna', 'kin', 'wol', 'ibo', 'swh', 'sag', 'ewe', 'zul', 'fuc', 'lug', 'tso', 'lin', 'nya', 'yor', 'run', 'xho', 'fuv', 'toi\\_Latn', 'umb'}\n* tgt\\_constituents: {'eng'}\n* src\\_multilingual: True\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: alv\n* tgt\\_alpha3: eng\n* short\\_pair: alv-en\n* chrF2\\_score: 0.376\n* bleu: 20.9\n* brevity\\_penalty: 1.0\n* ref\\_len: 15208.0\n* src\\_name: Atlantic-Congo languages\n* tgt\\_name: English\n* train\\_date: 2020-07-31\n* src\\_alpha2: alv\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* long\\_pair: alv-eng\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
96,
462,
543
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #sn #rw #wo #ig #sg #ee #zu #lg #ts #ln #ny #yo #rn #xh #alv #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"passage: ### alv-eng\n\n\n* source group: Atlantic-Congo languages\n* target group: English\n* OPUS readme: alv-eng\n* model: transformer\n* source language(s): ewe fuc fuv ibo kin lin lug nya run sag sna swh toi\\_Latn tso umb wol xho yor zul\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 6.3, chr-F: 0.328\ntestset: URL, BLEU: 0.4, chr-F: 0.108\ntestset: URL, BLEU: 4.5, chr-F: 0.196\ntestset: URL, BLEU: 30.7, chr-F: 0.511\ntestset: URL, BLEU: 2.8, chr-F: 0.213\ntestset: URL, BLEU: 3.4, chr-F: 0.140\ntestset: URL, BLEU: 20.9, chr-F: 0.376\ntestset: URL, BLEU: 38.7, chr-F: 0.492\ntestset: URL, BLEU: 24.5, chr-F: 0.417\ntestset: URL, BLEU: 5.5, chr-F: 0.177\ntestset: URL, BLEU: 26.9, chr-F: 0.412\ntestset: URL, BLEU: 4.9, chr-F: 0.196\ntestset: URL, BLEU: 3.9, chr-F: 0.147\ntestset: URL, BLEU: 76.7, chr-F: 0.957\ntestset: URL, BLEU: 4.0, chr-F: 0.195\ntestset: URL, BLEU: 3.7, chr-F: 0.170\ntestset: URL, BLEU: 38.9, chr-F: 0.556\ntestset: URL, BLEU: 25.1, chr-F: 0.412\ntestset: URL, BLEU: 46.1, chr-F: 0.623"
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] |
null | null | transformers |
### opus-mt-am-sv
* source languages: am
* target languages: sv
* OPUS readme: [am-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/am-sv/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/am-sv/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/am-sv/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/am-sv/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.am.sv | 21.0 | 0.377 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-am-sv | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"am",
"sv",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #am #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-am-sv
* source languages: am
* target languages: sv
* OPUS readme: am-sv
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 21.0, chr-F: 0.377
| [
"### opus-mt-am-sv\n\n\n* source languages: am\n* target languages: sv\n* OPUS readme: am-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 21.0, chr-F: 0.377"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #am #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-am-sv\n\n\n* source languages: am\n* target languages: sv\n* OPUS readme: am-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 21.0, chr-F: 0.377"
] | [
61,
99
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #am #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-am-sv\n\n\n* source languages: am\n* target languages: sv\n* OPUS readme: am-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 21.0, chr-F: 0.377"
] | [
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null | null | transformers |
### ara-deu
* source group: Arabic
* target group: German
* OPUS readme: [ara-deu](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-deu/README.md)
* model: transformer-align
* source language(s): afb apc ara ara_Latn arq arz
* target language(s): deu
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-07-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-deu/opus-2020-07-03.zip)
* test set translations: [opus-2020-07-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-deu/opus-2020-07-03.test.txt)
* test set scores: [opus-2020-07-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-deu/opus-2020-07-03.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.ara.deu | 44.7 | 0.629 |
### System Info:
- hf_name: ara-deu
- source_languages: ara
- target_languages: deu
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-deu/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['ar', 'de']
- src_constituents: {'apc', 'ara', 'arq_Latn', 'arq', 'afb', 'ara_Latn', 'apc_Latn', 'arz'}
- tgt_constituents: {'deu'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ara-deu/opus-2020-07-03.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ara-deu/opus-2020-07-03.test.txt
- src_alpha3: ara
- tgt_alpha3: deu
- short_pair: ar-de
- chrF2_score: 0.629
- bleu: 44.7
- brevity_penalty: 0.986
- ref_len: 8371.0
- src_name: Arabic
- tgt_name: German
- train_date: 2020-07-03
- src_alpha2: ar
- tgt_alpha2: de
- prefer_old: False
- long_pair: ara-deu
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["ar", "de"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-ar-de | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"ar",
"de",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ar",
"de"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #ar #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### ara-deu
* source group: Arabic
* target group: German
* OPUS readme: ara-deu
* model: transformer-align
* source language(s): afb apc ara ara\_Latn arq arz
* target language(s): deu
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 44.7, chr-F: 0.629
### System Info:
* hf\_name: ara-deu
* source\_languages: ara
* target\_languages: deu
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['ar', 'de']
* src\_constituents: {'apc', 'ara', 'arq\_Latn', 'arq', 'afb', 'ara\_Latn', 'apc\_Latn', 'arz'}
* tgt\_constituents: {'deu'}
* src\_multilingual: False
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: ara
* tgt\_alpha3: deu
* short\_pair: ar-de
* chrF2\_score: 0.629
* bleu: 44.7
* brevity\_penalty: 0.986
* ref\_len: 8371.0
* src\_name: Arabic
* tgt\_name: German
* train\_date: 2020-07-03
* src\_alpha2: ar
* tgt\_alpha2: de
* prefer\_old: False
* long\_pair: ara-deu
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### ara-deu\n\n\n* source group: Arabic\n* target group: German\n* OPUS readme: ara-deu\n* model: transformer-align\n* source language(s): afb apc ara ara\\_Latn arq arz\n* target language(s): deu\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 44.7, chr-F: 0.629",
"### System Info:\n\n\n* hf\\_name: ara-deu\n* source\\_languages: ara\n* target\\_languages: deu\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['ar', 'de']\n* src\\_constituents: {'apc', 'ara', 'arq\\_Latn', 'arq', 'afb', 'ara\\_Latn', 'apc\\_Latn', 'arz'}\n* tgt\\_constituents: {'deu'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: ara\n* tgt\\_alpha3: deu\n* short\\_pair: ar-de\n* chrF2\\_score: 0.629\n* bleu: 44.7\n* brevity\\_penalty: 0.986\n* ref\\_len: 8371.0\n* src\\_name: Arabic\n* tgt\\_name: German\n* train\\_date: 2020-07-03\n* src\\_alpha2: ar\n* tgt\\_alpha2: de\n* prefer\\_old: False\n* long\\_pair: ara-deu\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ar #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### ara-deu\n\n\n* source group: Arabic\n* target group: German\n* OPUS readme: ara-deu\n* model: transformer-align\n* source language(s): afb apc ara ara\\_Latn arq arz\n* target language(s): deu\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 44.7, chr-F: 0.629",
"### System Info:\n\n\n* hf\\_name: ara-deu\n* source\\_languages: ara\n* target\\_languages: deu\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['ar', 'de']\n* src\\_constituents: {'apc', 'ara', 'arq\\_Latn', 'arq', 'afb', 'ara\\_Latn', 'apc\\_Latn', 'arz'}\n* tgt\\_constituents: {'deu'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: ara\n* tgt\\_alpha3: deu\n* short\\_pair: ar-de\n* chrF2\\_score: 0.629\n* bleu: 44.7\n* brevity\\_penalty: 0.986\n* ref\\_len: 8371.0\n* src\\_name: Arabic\n* tgt\\_name: German\n* train\\_date: 2020-07-03\n* src\\_alpha2: ar\n* tgt\\_alpha2: de\n* prefer\\_old: False\n* long\\_pair: ara-deu\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
61,
138,
443
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ar #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### ara-deu\n\n\n* source group: Arabic\n* target group: German\n* OPUS readme: ara-deu\n* model: transformer-align\n* source language(s): afb apc ara ara\\_Latn arq arz\n* target language(s): deu\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 44.7, chr-F: 0.629"
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null | null | transformers |
### ara-ell
* source group: Arabic
* target group: Modern Greek (1453-)
* OPUS readme: [ara-ell](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-ell/README.md)
* model: transformer-align
* source language(s): ara arz
* target language(s): ell
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-07-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-ell/opus-2020-07-03.zip)
* test set translations: [opus-2020-07-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-ell/opus-2020-07-03.test.txt)
* test set scores: [opus-2020-07-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-ell/opus-2020-07-03.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.ara.ell | 43.9 | 0.636 |
### System Info:
- hf_name: ara-ell
- source_languages: ara
- target_languages: ell
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-ell/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['ar', 'el']
- src_constituents: {'apc', 'ara', 'arq_Latn', 'arq', 'afb', 'ara_Latn', 'apc_Latn', 'arz'}
- tgt_constituents: {'ell'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ara-ell/opus-2020-07-03.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ara-ell/opus-2020-07-03.test.txt
- src_alpha3: ara
- tgt_alpha3: ell
- short_pair: ar-el
- chrF2_score: 0.636
- bleu: 43.9
- brevity_penalty: 0.993
- ref_len: 2009.0
- src_name: Arabic
- tgt_name: Modern Greek (1453-)
- train_date: 2020-07-03
- src_alpha2: ar
- tgt_alpha2: el
- prefer_old: False
- long_pair: ara-ell
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["ar", "el"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-ar-el | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"ar",
"el",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ar",
"el"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #ar #el #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### ara-ell
* source group: Arabic
* target group: Modern Greek (1453-)
* OPUS readme: ara-ell
* model: transformer-align
* source language(s): ara arz
* target language(s): ell
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 43.9, chr-F: 0.636
### System Info:
* hf\_name: ara-ell
* source\_languages: ara
* target\_languages: ell
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['ar', 'el']
* src\_constituents: {'apc', 'ara', 'arq\_Latn', 'arq', 'afb', 'ara\_Latn', 'apc\_Latn', 'arz'}
* tgt\_constituents: {'ell'}
* src\_multilingual: False
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: ara
* tgt\_alpha3: ell
* short\_pair: ar-el
* chrF2\_score: 0.636
* bleu: 43.9
* brevity\_penalty: 0.993
* ref\_len: 2009.0
* src\_name: Arabic
* tgt\_name: Modern Greek (1453-)
* train\_date: 2020-07-03
* src\_alpha2: ar
* tgt\_alpha2: el
* prefer\_old: False
* long\_pair: ara-ell
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### ara-ell\n\n\n* source group: Arabic\n* target group: Modern Greek (1453-)\n* OPUS readme: ara-ell\n* model: transformer-align\n* source language(s): ara arz\n* target language(s): ell\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 43.9, chr-F: 0.636",
"### System Info:\n\n\n* hf\\_name: ara-ell\n* source\\_languages: ara\n* target\\_languages: ell\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['ar', 'el']\n* src\\_constituents: {'apc', 'ara', 'arq\\_Latn', 'arq', 'afb', 'ara\\_Latn', 'apc\\_Latn', 'arz'}\n* tgt\\_constituents: {'ell'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: ara\n* tgt\\_alpha3: ell\n* short\\_pair: ar-el\n* chrF2\\_score: 0.636\n* bleu: 43.9\n* brevity\\_penalty: 0.993\n* ref\\_len: 2009.0\n* src\\_name: Arabic\n* tgt\\_name: Modern Greek (1453-)\n* train\\_date: 2020-07-03\n* src\\_alpha2: ar\n* tgt\\_alpha2: el\n* prefer\\_old: False\n* long\\_pair: ara-ell\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ar #el #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### ara-ell\n\n\n* source group: Arabic\n* target group: Modern Greek (1453-)\n* OPUS readme: ara-ell\n* model: transformer-align\n* source language(s): ara arz\n* target language(s): ell\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 43.9, chr-F: 0.636",
"### System Info:\n\n\n* hf\\_name: ara-ell\n* source\\_languages: ara\n* target\\_languages: ell\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['ar', 'el']\n* src\\_constituents: {'apc', 'ara', 'arq\\_Latn', 'arq', 'afb', 'ara\\_Latn', 'apc\\_Latn', 'arz'}\n* tgt\\_constituents: {'ell'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: ara\n* tgt\\_alpha3: ell\n* short\\_pair: ar-el\n* chrF2\\_score: 0.636\n* bleu: 43.9\n* brevity\\_penalty: 0.993\n* ref\\_len: 2009.0\n* src\\_name: Arabic\n* tgt\\_name: Modern Greek (1453-)\n* train\\_date: 2020-07-03\n* src\\_alpha2: ar\n* tgt\\_alpha2: el\n* prefer\\_old: False\n* long\\_pair: ara-ell\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
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"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ar #el #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### ara-ell\n\n\n* source group: Arabic\n* target group: Modern Greek (1453-)\n* OPUS readme: ara-ell\n* model: transformer-align\n* source language(s): ara arz\n* target language(s): ell\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 43.9, chr-F: 0.636"
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null | null | transformers |
### opus-mt-ar-en
* source languages: ar
* target languages: en
* OPUS readme: [ar-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ar-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2019-12-18.zip](https://object.pouta.csc.fi/OPUS-MT-models/ar-en/opus-2019-12-18.zip)
* test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ar-en/opus-2019-12-18.test.txt)
* test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ar-en/opus-2019-12-18.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.ar.en | 49.4 | 0.661 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-ar-en | [
"transformers",
"pytorch",
"tf",
"rust",
"marian",
"text2text-generation",
"translation",
"ar",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #rust #marian #text2text-generation #translation #ar #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-ar-en
* source languages: ar
* target languages: en
* OPUS readme: ar-en
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 49.4, chr-F: 0.661
| [
"### opus-mt-ar-en\n\n\n* source languages: ar\n* target languages: en\n* OPUS readme: ar-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 49.4, chr-F: 0.661"
] | [
"TAGS\n#transformers #pytorch #tf #rust #marian #text2text-generation #translation #ar #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-ar-en\n\n\n* source languages: ar\n* target languages: en\n* OPUS readme: ar-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 49.4, chr-F: 0.661"
] | [
63,
101
] | [
"passage: TAGS\n#transformers #pytorch #tf #rust #marian #text2text-generation #translation #ar #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-ar-en\n\n\n* source languages: ar\n* target languages: en\n* OPUS readme: ar-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 49.4, chr-F: 0.661"
] | [
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null | null | transformers |
### ara-epo
* source group: Arabic
* target group: Esperanto
* OPUS readme: [ara-epo](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-epo/README.md)
* model: transformer-align
* source language(s): apc apc_Latn ara arq arq_Latn arz
* target language(s): epo
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-epo/opus-2020-06-16.zip)
* test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-epo/opus-2020-06-16.test.txt)
* test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-epo/opus-2020-06-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.ara.epo | 18.9 | 0.376 |
### System Info:
- hf_name: ara-epo
- source_languages: ara
- target_languages: epo
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-epo/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['ar', 'eo']
- src_constituents: {'apc', 'ara', 'arq_Latn', 'arq', 'afb', 'ara_Latn', 'apc_Latn', 'arz'}
- tgt_constituents: {'epo'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ara-epo/opus-2020-06-16.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ara-epo/opus-2020-06-16.test.txt
- src_alpha3: ara
- tgt_alpha3: epo
- short_pair: ar-eo
- chrF2_score: 0.376
- bleu: 18.9
- brevity_penalty: 0.948
- ref_len: 4506.0
- src_name: Arabic
- tgt_name: Esperanto
- train_date: 2020-06-16
- src_alpha2: ar
- tgt_alpha2: eo
- prefer_old: False
- long_pair: ara-epo
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["ar", "eo"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-ar-eo | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"ar",
"eo",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ar",
"eo"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #ar #eo #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### ara-epo
* source group: Arabic
* target group: Esperanto
* OPUS readme: ara-epo
* model: transformer-align
* source language(s): apc apc\_Latn ara arq arq\_Latn arz
* target language(s): epo
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 18.9, chr-F: 0.376
### System Info:
* hf\_name: ara-epo
* source\_languages: ara
* target\_languages: epo
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['ar', 'eo']
* src\_constituents: {'apc', 'ara', 'arq\_Latn', 'arq', 'afb', 'ara\_Latn', 'apc\_Latn', 'arz'}
* tgt\_constituents: {'epo'}
* src\_multilingual: False
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm4k,spm4k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: ara
* tgt\_alpha3: epo
* short\_pair: ar-eo
* chrF2\_score: 0.376
* bleu: 18.9
* brevity\_penalty: 0.948
* ref\_len: 4506.0
* src\_name: Arabic
* tgt\_name: Esperanto
* train\_date: 2020-06-16
* src\_alpha2: ar
* tgt\_alpha2: eo
* prefer\_old: False
* long\_pair: ara-epo
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### ara-epo\n\n\n* source group: Arabic\n* target group: Esperanto\n* OPUS readme: ara-epo\n* model: transformer-align\n* source language(s): apc apc\\_Latn ara arq arq\\_Latn arz\n* target language(s): epo\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm4k,spm4k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 18.9, chr-F: 0.376",
"### System Info:\n\n\n* hf\\_name: ara-epo\n* source\\_languages: ara\n* target\\_languages: epo\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['ar', 'eo']\n* src\\_constituents: {'apc', 'ara', 'arq\\_Latn', 'arq', 'afb', 'ara\\_Latn', 'apc\\_Latn', 'arz'}\n* tgt\\_constituents: {'epo'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm4k,spm4k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: ara\n* tgt\\_alpha3: epo\n* short\\_pair: ar-eo\n* chrF2\\_score: 0.376\n* bleu: 18.9\n* brevity\\_penalty: 0.948\n* ref\\_len: 4506.0\n* src\\_name: Arabic\n* tgt\\_name: Esperanto\n* train\\_date: 2020-06-16\n* src\\_alpha2: ar\n* tgt\\_alpha2: eo\n* prefer\\_old: False\n* long\\_pair: ara-epo\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ar #eo #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### ara-epo\n\n\n* source group: Arabic\n* target group: Esperanto\n* OPUS readme: ara-epo\n* model: transformer-align\n* source language(s): apc apc\\_Latn ara arq arq\\_Latn arz\n* target language(s): epo\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm4k,spm4k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 18.9, chr-F: 0.376",
"### System Info:\n\n\n* hf\\_name: ara-epo\n* source\\_languages: ara\n* target\\_languages: epo\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['ar', 'eo']\n* src\\_constituents: {'apc', 'ara', 'arq\\_Latn', 'arq', 'afb', 'ara\\_Latn', 'apc\\_Latn', 'arz'}\n* tgt\\_constituents: {'epo'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm4k,spm4k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: ara\n* tgt\\_alpha3: epo\n* short\\_pair: ar-eo\n* chrF2\\_score: 0.376\n* bleu: 18.9\n* brevity\\_penalty: 0.948\n* ref\\_len: 4506.0\n* src\\_name: Arabic\n* tgt\\_name: Esperanto\n* train\\_date: 2020-06-16\n* src\\_alpha2: ar\n* tgt\\_alpha2: eo\n* prefer\\_old: False\n* long\\_pair: ara-epo\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
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"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ar #eo #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### ara-epo\n\n\n* source group: Arabic\n* target group: Esperanto\n* OPUS readme: ara-epo\n* model: transformer-align\n* source language(s): apc apc\\_Latn ara arq arq\\_Latn arz\n* target language(s): epo\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm4k,spm4k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 18.9, chr-F: 0.376"
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null | null | transformers |
### ara-spa
* source group: Arabic
* target group: Spanish
* OPUS readme: [ara-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-spa/README.md)
* model: transformer
* source language(s): apc apc_Latn ara arq
* target language(s): spa
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-07-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-spa/opus-2020-07-03.zip)
* test set translations: [opus-2020-07-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-spa/opus-2020-07-03.test.txt)
* test set scores: [opus-2020-07-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-spa/opus-2020-07-03.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.ara.spa | 46.0 | 0.641 |
### System Info:
- hf_name: ara-spa
- source_languages: ara
- target_languages: spa
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-spa/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['ar', 'es']
- src_constituents: {'apc', 'ara', 'arq_Latn', 'arq', 'afb', 'ara_Latn', 'apc_Latn', 'arz'}
- tgt_constituents: {'spa'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ara-spa/opus-2020-07-03.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ara-spa/opus-2020-07-03.test.txt
- src_alpha3: ara
- tgt_alpha3: spa
- short_pair: ar-es
- chrF2_score: 0.6409999999999999
- bleu: 46.0
- brevity_penalty: 0.9620000000000001
- ref_len: 9708.0
- src_name: Arabic
- tgt_name: Spanish
- train_date: 2020-07-03
- src_alpha2: ar
- tgt_alpha2: es
- prefer_old: False
- long_pair: ara-spa
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["ar", "es"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-ar-es | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"ar",
"es",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ar",
"es"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #ar #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### ara-spa
* source group: Arabic
* target group: Spanish
* OPUS readme: ara-spa
* model: transformer
* source language(s): apc apc\_Latn ara arq
* target language(s): spa
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 46.0, chr-F: 0.641
### System Info:
* hf\_name: ara-spa
* source\_languages: ara
* target\_languages: spa
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['ar', 'es']
* src\_constituents: {'apc', 'ara', 'arq\_Latn', 'arq', 'afb', 'ara\_Latn', 'apc\_Latn', 'arz'}
* tgt\_constituents: {'spa'}
* src\_multilingual: False
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: ara
* tgt\_alpha3: spa
* short\_pair: ar-es
* chrF2\_score: 0.6409999999999999
* bleu: 46.0
* brevity\_penalty: 0.9620000000000001
* ref\_len: 9708.0
* src\_name: Arabic
* tgt\_name: Spanish
* train\_date: 2020-07-03
* src\_alpha2: ar
* tgt\_alpha2: es
* prefer\_old: False
* long\_pair: ara-spa
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### ara-spa\n\n\n* source group: Arabic\n* target group: Spanish\n* OPUS readme: ara-spa\n* model: transformer\n* source language(s): apc apc\\_Latn ara arq\n* target language(s): spa\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 46.0, chr-F: 0.641",
"### System Info:\n\n\n* hf\\_name: ara-spa\n* source\\_languages: ara\n* target\\_languages: spa\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['ar', 'es']\n* src\\_constituents: {'apc', 'ara', 'arq\\_Latn', 'arq', 'afb', 'ara\\_Latn', 'apc\\_Latn', 'arz'}\n* tgt\\_constituents: {'spa'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: ara\n* tgt\\_alpha3: spa\n* short\\_pair: ar-es\n* chrF2\\_score: 0.6409999999999999\n* bleu: 46.0\n* brevity\\_penalty: 0.9620000000000001\n* ref\\_len: 9708.0\n* src\\_name: Arabic\n* tgt\\_name: Spanish\n* train\\_date: 2020-07-03\n* src\\_alpha2: ar\n* tgt\\_alpha2: es\n* prefer\\_old: False\n* long\\_pair: ara-spa\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ar #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### ara-spa\n\n\n* source group: Arabic\n* target group: Spanish\n* OPUS readme: ara-spa\n* model: transformer\n* source language(s): apc apc\\_Latn ara arq\n* target language(s): spa\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 46.0, chr-F: 0.641",
"### System Info:\n\n\n* hf\\_name: ara-spa\n* source\\_languages: ara\n* target\\_languages: spa\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['ar', 'es']\n* src\\_constituents: {'apc', 'ara', 'arq\\_Latn', 'arq', 'afb', 'ara\\_Latn', 'apc\\_Latn', 'arz'}\n* tgt\\_constituents: {'spa'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: ara\n* tgt\\_alpha3: spa\n* short\\_pair: ar-es\n* chrF2\\_score: 0.6409999999999999\n* bleu: 46.0\n* brevity\\_penalty: 0.9620000000000001\n* ref\\_len: 9708.0\n* src\\_name: Arabic\n* tgt\\_name: Spanish\n* train\\_date: 2020-07-03\n* src\\_alpha2: ar\n* tgt\\_alpha2: es\n* prefer\\_old: False\n* long\\_pair: ara-spa\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
61,
128,
446
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ar #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### ara-spa\n\n\n* source group: Arabic\n* target group: Spanish\n* OPUS readme: ara-spa\n* model: transformer\n* source language(s): apc apc\\_Latn ara arq\n* target language(s): spa\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 46.0, chr-F: 0.641"
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] |
null | null | transformers |
### opus-mt-ar-fr
* source languages: ar
* target languages: fr
* OPUS readme: [ar-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ar-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-24.zip](https://object.pouta.csc.fi/OPUS-MT-models/ar-fr/opus-2020-01-24.zip)
* test set translations: [opus-2020-01-24.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ar-fr/opus-2020-01-24.test.txt)
* test set scores: [opus-2020-01-24.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ar-fr/opus-2020-01-24.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.ar.fr | 43.5 | 0.602 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-ar-fr | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"ar",
"fr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #ar #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-ar-fr
* source languages: ar
* target languages: fr
* OPUS readme: ar-fr
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 43.5, chr-F: 0.602
| [
"### opus-mt-ar-fr\n\n\n* source languages: ar\n* target languages: fr\n* OPUS readme: ar-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 43.5, chr-F: 0.602"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ar #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-ar-fr\n\n\n* source languages: ar\n* target languages: fr\n* OPUS readme: ar-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 43.5, chr-F: 0.602"
] | [
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] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ar #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-ar-fr\n\n\n* source languages: ar\n* target languages: fr\n* OPUS readme: ar-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 43.5, chr-F: 0.602"
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null | null | transformers |
### ara-heb
* source group: Arabic
* target group: Hebrew
* OPUS readme: [ara-heb](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-heb/README.md)
* model: transformer
* source language(s): apc apc_Latn ara arq arz
* target language(s): heb
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-07-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-heb/opus-2020-07-03.zip)
* test set translations: [opus-2020-07-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-heb/opus-2020-07-03.test.txt)
* test set scores: [opus-2020-07-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-heb/opus-2020-07-03.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.ara.heb | 40.4 | 0.605 |
### System Info:
- hf_name: ara-heb
- source_languages: ara
- target_languages: heb
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-heb/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['ar', 'he']
- src_constituents: {'apc', 'ara', 'arq_Latn', 'arq', 'afb', 'ara_Latn', 'apc_Latn', 'arz'}
- tgt_constituents: {'heb'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ara-heb/opus-2020-07-03.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ara-heb/opus-2020-07-03.test.txt
- src_alpha3: ara
- tgt_alpha3: heb
- short_pair: ar-he
- chrF2_score: 0.605
- bleu: 40.4
- brevity_penalty: 1.0
- ref_len: 6801.0
- src_name: Arabic
- tgt_name: Hebrew
- train_date: 2020-07-03
- src_alpha2: ar
- tgt_alpha2: he
- prefer_old: False
- long_pair: ara-heb
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["ar", "he"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-ar-he | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"ar",
"he",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ar",
"he"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #ar #he #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### ara-heb
* source group: Arabic
* target group: Hebrew
* OPUS readme: ara-heb
* model: transformer
* source language(s): apc apc\_Latn ara arq arz
* target language(s): heb
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 40.4, chr-F: 0.605
### System Info:
* hf\_name: ara-heb
* source\_languages: ara
* target\_languages: heb
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['ar', 'he']
* src\_constituents: {'apc', 'ara', 'arq\_Latn', 'arq', 'afb', 'ara\_Latn', 'apc\_Latn', 'arz'}
* tgt\_constituents: {'heb'}
* src\_multilingual: False
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: ara
* tgt\_alpha3: heb
* short\_pair: ar-he
* chrF2\_score: 0.605
* bleu: 40.4
* brevity\_penalty: 1.0
* ref\_len: 6801.0
* src\_name: Arabic
* tgt\_name: Hebrew
* train\_date: 2020-07-03
* src\_alpha2: ar
* tgt\_alpha2: he
* prefer\_old: False
* long\_pair: ara-heb
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### ara-heb\n\n\n* source group: Arabic\n* target group: Hebrew\n* OPUS readme: ara-heb\n* model: transformer\n* source language(s): apc apc\\_Latn ara arq arz\n* target language(s): heb\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 40.4, chr-F: 0.605",
"### System Info:\n\n\n* hf\\_name: ara-heb\n* source\\_languages: ara\n* target\\_languages: heb\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['ar', 'he']\n* src\\_constituents: {'apc', 'ara', 'arq\\_Latn', 'arq', 'afb', 'ara\\_Latn', 'apc\\_Latn', 'arz'}\n* tgt\\_constituents: {'heb'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: ara\n* tgt\\_alpha3: heb\n* short\\_pair: ar-he\n* chrF2\\_score: 0.605\n* bleu: 40.4\n* brevity\\_penalty: 1.0\n* ref\\_len: 6801.0\n* src\\_name: Arabic\n* tgt\\_name: Hebrew\n* train\\_date: 2020-07-03\n* src\\_alpha2: ar\n* tgt\\_alpha2: he\n* prefer\\_old: False\n* long\\_pair: ara-heb\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ar #he #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### ara-heb\n\n\n* source group: Arabic\n* target group: Hebrew\n* OPUS readme: ara-heb\n* model: transformer\n* source language(s): apc apc\\_Latn ara arq arz\n* target language(s): heb\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 40.4, chr-F: 0.605",
"### System Info:\n\n\n* hf\\_name: ara-heb\n* source\\_languages: ara\n* target\\_languages: heb\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['ar', 'he']\n* src\\_constituents: {'apc', 'ara', 'arq\\_Latn', 'arq', 'afb', 'ara\\_Latn', 'apc\\_Latn', 'arz'}\n* tgt\\_constituents: {'heb'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: ara\n* tgt\\_alpha3: heb\n* short\\_pair: ar-he\n* chrF2\\_score: 0.605\n* bleu: 40.4\n* brevity\\_penalty: 1.0\n* ref\\_len: 6801.0\n* src\\_name: Arabic\n* tgt\\_name: Hebrew\n* train\\_date: 2020-07-03\n* src\\_alpha2: ar\n* tgt\\_alpha2: he\n* prefer\\_old: False\n* long\\_pair: ara-heb\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
61,
130,
436
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ar #he #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### ara-heb\n\n\n* source group: Arabic\n* target group: Hebrew\n* OPUS readme: ara-heb\n* model: transformer\n* source language(s): apc apc\\_Latn ara arq arz\n* target language(s): heb\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 40.4, chr-F: 0.605"
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] |
null | null | transformers |
### ara-ita
* source group: Arabic
* target group: Italian
* OPUS readme: [ara-ita](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-ita/README.md)
* model: transformer
* source language(s): ara
* target language(s): ita
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-07-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-ita/opus-2020-07-03.zip)
* test set translations: [opus-2020-07-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-ita/opus-2020-07-03.test.txt)
* test set scores: [opus-2020-07-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-ita/opus-2020-07-03.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.ara.ita | 44.2 | 0.658 |
### System Info:
- hf_name: ara-ita
- source_languages: ara
- target_languages: ita
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-ita/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['ar', 'it']
- src_constituents: {'apc', 'ara', 'arq_Latn', 'arq', 'afb', 'ara_Latn', 'apc_Latn', 'arz'}
- tgt_constituents: {'ita'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ara-ita/opus-2020-07-03.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ara-ita/opus-2020-07-03.test.txt
- src_alpha3: ara
- tgt_alpha3: ita
- short_pair: ar-it
- chrF2_score: 0.6579999999999999
- bleu: 44.2
- brevity_penalty: 0.9890000000000001
- ref_len: 1495.0
- src_name: Arabic
- tgt_name: Italian
- train_date: 2020-07-03
- src_alpha2: ar
- tgt_alpha2: it
- prefer_old: False
- long_pair: ara-ita
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["ar", "it"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-ar-it | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"ar",
"it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ar",
"it"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #ar #it #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### ara-ita
* source group: Arabic
* target group: Italian
* OPUS readme: ara-ita
* model: transformer
* source language(s): ara
* target language(s): ita
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 44.2, chr-F: 0.658
### System Info:
* hf\_name: ara-ita
* source\_languages: ara
* target\_languages: ita
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['ar', 'it']
* src\_constituents: {'apc', 'ara', 'arq\_Latn', 'arq', 'afb', 'ara\_Latn', 'apc\_Latn', 'arz'}
* tgt\_constituents: {'ita'}
* src\_multilingual: False
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: ara
* tgt\_alpha3: ita
* short\_pair: ar-it
* chrF2\_score: 0.6579999999999999
* bleu: 44.2
* brevity\_penalty: 0.9890000000000001
* ref\_len: 1495.0
* src\_name: Arabic
* tgt\_name: Italian
* train\_date: 2020-07-03
* src\_alpha2: ar
* tgt\_alpha2: it
* prefer\_old: False
* long\_pair: ara-ita
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### ara-ita\n\n\n* source group: Arabic\n* target group: Italian\n* OPUS readme: ara-ita\n* model: transformer\n* source language(s): ara\n* target language(s): ita\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 44.2, chr-F: 0.658",
"### System Info:\n\n\n* hf\\_name: ara-ita\n* source\\_languages: ara\n* target\\_languages: ita\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['ar', 'it']\n* src\\_constituents: {'apc', 'ara', 'arq\\_Latn', 'arq', 'afb', 'ara\\_Latn', 'apc\\_Latn', 'arz'}\n* tgt\\_constituents: {'ita'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: ara\n* tgt\\_alpha3: ita\n* short\\_pair: ar-it\n* chrF2\\_score: 0.6579999999999999\n* bleu: 44.2\n* brevity\\_penalty: 0.9890000000000001\n* ref\\_len: 1495.0\n* src\\_name: Arabic\n* tgt\\_name: Italian\n* train\\_date: 2020-07-03\n* src\\_alpha2: ar\n* tgt\\_alpha2: it\n* prefer\\_old: False\n* long\\_pair: ara-ita\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ar #it #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### ara-ita\n\n\n* source group: Arabic\n* target group: Italian\n* OPUS readme: ara-ita\n* model: transformer\n* source language(s): ara\n* target language(s): ita\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 44.2, chr-F: 0.658",
"### System Info:\n\n\n* hf\\_name: ara-ita\n* source\\_languages: ara\n* target\\_languages: ita\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['ar', 'it']\n* src\\_constituents: {'apc', 'ara', 'arq\\_Latn', 'arq', 'afb', 'ara\\_Latn', 'apc\\_Latn', 'arz'}\n* tgt\\_constituents: {'ita'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: ara\n* tgt\\_alpha3: ita\n* short\\_pair: ar-it\n* chrF2\\_score: 0.6579999999999999\n* bleu: 44.2\n* brevity\\_penalty: 0.9890000000000001\n* ref\\_len: 1495.0\n* src\\_name: Arabic\n* tgt\\_name: Italian\n* train\\_date: 2020-07-03\n* src\\_alpha2: ar\n* tgt\\_alpha2: it\n* prefer\\_old: False\n* long\\_pair: ara-ita\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
61,
116,
443
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ar #it #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### ara-ita\n\n\n* source group: Arabic\n* target group: Italian\n* OPUS readme: ara-ita\n* model: transformer\n* source language(s): ara\n* target language(s): ita\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 44.2, chr-F: 0.658"
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null | null | transformers |
### ara-pol
* source group: Arabic
* target group: Polish
* OPUS readme: [ara-pol](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-pol/README.md)
* model: transformer
* source language(s): ara arz
* target language(s): pol
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-07-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-pol/opus-2020-07-03.zip)
* test set translations: [opus-2020-07-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-pol/opus-2020-07-03.test.txt)
* test set scores: [opus-2020-07-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-pol/opus-2020-07-03.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.ara.pol | 38.0 | 0.623 |
### System Info:
- hf_name: ara-pol
- source_languages: ara
- target_languages: pol
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-pol/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['ar', 'pl']
- src_constituents: {'apc', 'ara', 'arq_Latn', 'arq', 'afb', 'ara_Latn', 'apc_Latn', 'arz'}
- tgt_constituents: {'pol'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ara-pol/opus-2020-07-03.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ara-pol/opus-2020-07-03.test.txt
- src_alpha3: ara
- tgt_alpha3: pol
- short_pair: ar-pl
- chrF2_score: 0.623
- bleu: 38.0
- brevity_penalty: 0.948
- ref_len: 1171.0
- src_name: Arabic
- tgt_name: Polish
- train_date: 2020-07-03
- src_alpha2: ar
- tgt_alpha2: pl
- prefer_old: False
- long_pair: ara-pol
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["ar", "pl"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-ar-pl | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"ar",
"pl",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ar",
"pl"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #ar #pl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### ara-pol
* source group: Arabic
* target group: Polish
* OPUS readme: ara-pol
* model: transformer
* source language(s): ara arz
* target language(s): pol
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 38.0, chr-F: 0.623
### System Info:
* hf\_name: ara-pol
* source\_languages: ara
* target\_languages: pol
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['ar', 'pl']
* src\_constituents: {'apc', 'ara', 'arq\_Latn', 'arq', 'afb', 'ara\_Latn', 'apc\_Latn', 'arz'}
* tgt\_constituents: {'pol'}
* src\_multilingual: False
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: ara
* tgt\_alpha3: pol
* short\_pair: ar-pl
* chrF2\_score: 0.623
* bleu: 38.0
* brevity\_penalty: 0.948
* ref\_len: 1171.0
* src\_name: Arabic
* tgt\_name: Polish
* train\_date: 2020-07-03
* src\_alpha2: ar
* tgt\_alpha2: pl
* prefer\_old: False
* long\_pair: ara-pol
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### ara-pol\n\n\n* source group: Arabic\n* target group: Polish\n* OPUS readme: ara-pol\n* model: transformer\n* source language(s): ara arz\n* target language(s): pol\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 38.0, chr-F: 0.623",
"### System Info:\n\n\n* hf\\_name: ara-pol\n* source\\_languages: ara\n* target\\_languages: pol\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['ar', 'pl']\n* src\\_constituents: {'apc', 'ara', 'arq\\_Latn', 'arq', 'afb', 'ara\\_Latn', 'apc\\_Latn', 'arz'}\n* tgt\\_constituents: {'pol'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: ara\n* tgt\\_alpha3: pol\n* short\\_pair: ar-pl\n* chrF2\\_score: 0.623\n* bleu: 38.0\n* brevity\\_penalty: 0.948\n* ref\\_len: 1171.0\n* src\\_name: Arabic\n* tgt\\_name: Polish\n* train\\_date: 2020-07-03\n* src\\_alpha2: ar\n* tgt\\_alpha2: pl\n* prefer\\_old: False\n* long\\_pair: ara-pol\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ar #pl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### ara-pol\n\n\n* source group: Arabic\n* target group: Polish\n* OPUS readme: ara-pol\n* model: transformer\n* source language(s): ara arz\n* target language(s): pol\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 38.0, chr-F: 0.623",
"### System Info:\n\n\n* hf\\_name: ara-pol\n* source\\_languages: ara\n* target\\_languages: pol\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['ar', 'pl']\n* src\\_constituents: {'apc', 'ara', 'arq\\_Latn', 'arq', 'afb', 'ara\\_Latn', 'apc\\_Latn', 'arz'}\n* tgt\\_constituents: {'pol'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: ara\n* tgt\\_alpha3: pol\n* short\\_pair: ar-pl\n* chrF2\\_score: 0.623\n* bleu: 38.0\n* brevity\\_penalty: 0.948\n* ref\\_len: 1171.0\n* src\\_name: Arabic\n* tgt\\_name: Polish\n* train\\_date: 2020-07-03\n* src\\_alpha2: ar\n* tgt\\_alpha2: pl\n* prefer\\_old: False\n* long\\_pair: ara-pol\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
61,
120,
438
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ar #pl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### ara-pol\n\n\n* source group: Arabic\n* target group: Polish\n* OPUS readme: ara-pol\n* model: transformer\n* source language(s): ara arz\n* target language(s): pol\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 38.0, chr-F: 0.623"
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null | null | transformers |
### ara-rus
* source group: Arabic
* target group: Russian
* OPUS readme: [ara-rus](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-rus/README.md)
* model: transformer
* source language(s): apc ara arz
* target language(s): rus
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-07-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-rus/opus-2020-07-03.zip)
* test set translations: [opus-2020-07-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-rus/opus-2020-07-03.test.txt)
* test set scores: [opus-2020-07-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-rus/opus-2020-07-03.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.ara.rus | 42.5 | 0.605 |
### System Info:
- hf_name: ara-rus
- source_languages: ara
- target_languages: rus
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-rus/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['ar', 'ru']
- src_constituents: {'apc', 'ara', 'arq_Latn', 'arq', 'afb', 'ara_Latn', 'apc_Latn', 'arz'}
- tgt_constituents: {'rus'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ara-rus/opus-2020-07-03.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ara-rus/opus-2020-07-03.test.txt
- src_alpha3: ara
- tgt_alpha3: rus
- short_pair: ar-ru
- chrF2_score: 0.605
- bleu: 42.5
- brevity_penalty: 0.97
- ref_len: 21830.0
- src_name: Arabic
- tgt_name: Russian
- train_date: 2020-07-03
- src_alpha2: ar
- tgt_alpha2: ru
- prefer_old: False
- long_pair: ara-rus
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["ar", "ru"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-ar-ru | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"ar",
"ru",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ar",
"ru"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #ar #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### ara-rus
* source group: Arabic
* target group: Russian
* OPUS readme: ara-rus
* model: transformer
* source language(s): apc ara arz
* target language(s): rus
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 42.5, chr-F: 0.605
### System Info:
* hf\_name: ara-rus
* source\_languages: ara
* target\_languages: rus
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['ar', 'ru']
* src\_constituents: {'apc', 'ara', 'arq\_Latn', 'arq', 'afb', 'ara\_Latn', 'apc\_Latn', 'arz'}
* tgt\_constituents: {'rus'}
* src\_multilingual: False
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: ara
* tgt\_alpha3: rus
* short\_pair: ar-ru
* chrF2\_score: 0.605
* bleu: 42.5
* brevity\_penalty: 0.97
* ref\_len: 21830.0
* src\_name: Arabic
* tgt\_name: Russian
* train\_date: 2020-07-03
* src\_alpha2: ar
* tgt\_alpha2: ru
* prefer\_old: False
* long\_pair: ara-rus
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### ara-rus\n\n\n* source group: Arabic\n* target group: Russian\n* OPUS readme: ara-rus\n* model: transformer\n* source language(s): apc ara arz\n* target language(s): rus\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 42.5, chr-F: 0.605",
"### System Info:\n\n\n* hf\\_name: ara-rus\n* source\\_languages: ara\n* target\\_languages: rus\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['ar', 'ru']\n* src\\_constituents: {'apc', 'ara', 'arq\\_Latn', 'arq', 'afb', 'ara\\_Latn', 'apc\\_Latn', 'arz'}\n* tgt\\_constituents: {'rus'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: ara\n* tgt\\_alpha3: rus\n* short\\_pair: ar-ru\n* chrF2\\_score: 0.605\n* bleu: 42.5\n* brevity\\_penalty: 0.97\n* ref\\_len: 21830.0\n* src\\_name: Arabic\n* tgt\\_name: Russian\n* train\\_date: 2020-07-03\n* src\\_alpha2: ar\n* tgt\\_alpha2: ru\n* prefer\\_old: False\n* long\\_pair: ara-rus\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ar #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### ara-rus\n\n\n* source group: Arabic\n* target group: Russian\n* OPUS readme: ara-rus\n* model: transformer\n* source language(s): apc ara arz\n* target language(s): rus\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 42.5, chr-F: 0.605",
"### System Info:\n\n\n* hf\\_name: ara-rus\n* source\\_languages: ara\n* target\\_languages: rus\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['ar', 'ru']\n* src\\_constituents: {'apc', 'ara', 'arq\\_Latn', 'arq', 'afb', 'ara\\_Latn', 'apc\\_Latn', 'arz'}\n* tgt\\_constituents: {'rus'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: ara\n* tgt\\_alpha3: rus\n* short\\_pair: ar-ru\n* chrF2\\_score: 0.605\n* bleu: 42.5\n* brevity\\_penalty: 0.97\n* ref\\_len: 21830.0\n* src\\_name: Arabic\n* tgt\\_name: Russian\n* train\\_date: 2020-07-03\n* src\\_alpha2: ar\n* tgt\\_alpha2: ru\n* prefer\\_old: False\n* long\\_pair: ara-rus\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
61,
120,
437
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ar #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### ara-rus\n\n\n* source group: Arabic\n* target group: Russian\n* OPUS readme: ara-rus\n* model: transformer\n* source language(s): apc ara arz\n* target language(s): rus\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 42.5, chr-F: 0.605"
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null | null | transformers |
### ara-tur
* source group: Arabic
* target group: Turkish
* OPUS readme: [ara-tur](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-tur/README.md)
* model: transformer
* source language(s): apc_Latn ara ara_Latn arq_Latn
* target language(s): tur
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-07-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-tur/opus-2020-07-03.zip)
* test set translations: [opus-2020-07-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-tur/opus-2020-07-03.test.txt)
* test set scores: [opus-2020-07-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-tur/opus-2020-07-03.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.ara.tur | 33.1 | 0.619 |
### System Info:
- hf_name: ara-tur
- source_languages: ara
- target_languages: tur
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-tur/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['ar', 'tr']
- src_constituents: {'apc', 'ara', 'arq_Latn', 'arq', 'afb', 'ara_Latn', 'apc_Latn', 'arz'}
- tgt_constituents: {'tur'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ara-tur/opus-2020-07-03.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ara-tur/opus-2020-07-03.test.txt
- src_alpha3: ara
- tgt_alpha3: tur
- short_pair: ar-tr
- chrF2_score: 0.619
- bleu: 33.1
- brevity_penalty: 0.9570000000000001
- ref_len: 6949.0
- src_name: Arabic
- tgt_name: Turkish
- train_date: 2020-07-03
- src_alpha2: ar
- tgt_alpha2: tr
- prefer_old: False
- long_pair: ara-tur
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["ar", "tr"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-ar-tr | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"ar",
"tr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ar",
"tr"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #ar #tr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### ara-tur
* source group: Arabic
* target group: Turkish
* OPUS readme: ara-tur
* model: transformer
* source language(s): apc\_Latn ara ara\_Latn arq\_Latn
* target language(s): tur
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 33.1, chr-F: 0.619
### System Info:
* hf\_name: ara-tur
* source\_languages: ara
* target\_languages: tur
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['ar', 'tr']
* src\_constituents: {'apc', 'ara', 'arq\_Latn', 'arq', 'afb', 'ara\_Latn', 'apc\_Latn', 'arz'}
* tgt\_constituents: {'tur'}
* src\_multilingual: False
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: ara
* tgt\_alpha3: tur
* short\_pair: ar-tr
* chrF2\_score: 0.619
* bleu: 33.1
* brevity\_penalty: 0.9570000000000001
* ref\_len: 6949.0
* src\_name: Arabic
* tgt\_name: Turkish
* train\_date: 2020-07-03
* src\_alpha2: ar
* tgt\_alpha2: tr
* prefer\_old: False
* long\_pair: ara-tur
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### ara-tur\n\n\n* source group: Arabic\n* target group: Turkish\n* OPUS readme: ara-tur\n* model: transformer\n* source language(s): apc\\_Latn ara ara\\_Latn arq\\_Latn\n* target language(s): tur\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 33.1, chr-F: 0.619",
"### System Info:\n\n\n* hf\\_name: ara-tur\n* source\\_languages: ara\n* target\\_languages: tur\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['ar', 'tr']\n* src\\_constituents: {'apc', 'ara', 'arq\\_Latn', 'arq', 'afb', 'ara\\_Latn', 'apc\\_Latn', 'arz'}\n* tgt\\_constituents: {'tur'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: ara\n* tgt\\_alpha3: tur\n* short\\_pair: ar-tr\n* chrF2\\_score: 0.619\n* bleu: 33.1\n* brevity\\_penalty: 0.9570000000000001\n* ref\\_len: 6949.0\n* src\\_name: Arabic\n* tgt\\_name: Turkish\n* train\\_date: 2020-07-03\n* src\\_alpha2: ar\n* tgt\\_alpha2: tr\n* prefer\\_old: False\n* long\\_pair: ara-tur\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ar #tr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### ara-tur\n\n\n* source group: Arabic\n* target group: Turkish\n* OPUS readme: ara-tur\n* model: transformer\n* source language(s): apc\\_Latn ara ara\\_Latn arq\\_Latn\n* target language(s): tur\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 33.1, chr-F: 0.619",
"### System Info:\n\n\n* hf\\_name: ara-tur\n* source\\_languages: ara\n* target\\_languages: tur\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['ar', 'tr']\n* src\\_constituents: {'apc', 'ara', 'arq\\_Latn', 'arq', 'afb', 'ara\\_Latn', 'apc\\_Latn', 'arz'}\n* tgt\\_constituents: {'tur'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: ara\n* tgt\\_alpha3: tur\n* short\\_pair: ar-tr\n* chrF2\\_score: 0.619\n* bleu: 33.1\n* brevity\\_penalty: 0.9570000000000001\n* ref\\_len: 6949.0\n* src\\_name: Arabic\n* tgt\\_name: Turkish\n* train\\_date: 2020-07-03\n* src\\_alpha2: ar\n* tgt\\_alpha2: tr\n* prefer\\_old: False\n* long\\_pair: ara-tur\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
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"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ar #tr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### ara-tur\n\n\n* source group: Arabic\n* target group: Turkish\n* OPUS readme: ara-tur\n* model: transformer\n* source language(s): apc\\_Latn ara ara\\_Latn arq\\_Latn\n* target language(s): tur\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 33.1, chr-F: 0.619"
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null | null | transformers |
### art-eng
* source group: Artificial languages
* target group: English
* OPUS readme: [art-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/art-eng/README.md)
* model: transformer
* source language(s): afh_Latn avk_Latn dws_Latn epo ido ido_Latn ile_Latn ina_Latn jbo jbo_Cyrl jbo_Latn ldn_Latn lfn_Cyrl lfn_Latn nov_Latn qya qya_Latn sjn_Latn tlh_Latn tzl tzl_Latn vol_Latn
* target language(s): eng
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus2m-2020-07-31.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/art-eng/opus2m-2020-07-31.zip)
* test set translations: [opus2m-2020-07-31.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/art-eng/opus2m-2020-07-31.test.txt)
* test set scores: [opus2m-2020-07-31.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/art-eng/opus2m-2020-07-31.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.afh-eng.afh.eng | 1.2 | 0.099 |
| Tatoeba-test.avk-eng.avk.eng | 0.4 | 0.105 |
| Tatoeba-test.dws-eng.dws.eng | 1.6 | 0.076 |
| Tatoeba-test.epo-eng.epo.eng | 34.6 | 0.530 |
| Tatoeba-test.ido-eng.ido.eng | 12.7 | 0.310 |
| Tatoeba-test.ile-eng.ile.eng | 4.6 | 0.218 |
| Tatoeba-test.ina-eng.ina.eng | 5.8 | 0.254 |
| Tatoeba-test.jbo-eng.jbo.eng | 0.2 | 0.115 |
| Tatoeba-test.ldn-eng.ldn.eng | 0.7 | 0.083 |
| Tatoeba-test.lfn-eng.lfn.eng | 1.8 | 0.172 |
| Tatoeba-test.multi.eng | 11.6 | 0.287 |
| Tatoeba-test.nov-eng.nov.eng | 5.1 | 0.215 |
| Tatoeba-test.qya-eng.qya.eng | 0.7 | 0.113 |
| Tatoeba-test.sjn-eng.sjn.eng | 0.9 | 0.090 |
| Tatoeba-test.tlh-eng.tlh.eng | 0.2 | 0.124 |
| Tatoeba-test.tzl-eng.tzl.eng | 1.4 | 0.109 |
| Tatoeba-test.vol-eng.vol.eng | 0.5 | 0.115 |
### System Info:
- hf_name: art-eng
- source_languages: art
- target_languages: eng
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/art-eng/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['eo', 'io', 'art', 'en']
- src_constituents: {'sjn_Latn', 'tzl', 'vol_Latn', 'qya', 'tlh_Latn', 'ile_Latn', 'ido_Latn', 'tzl_Latn', 'jbo_Cyrl', 'jbo', 'lfn_Latn', 'nov_Latn', 'dws_Latn', 'ldn_Latn', 'avk_Latn', 'lfn_Cyrl', 'ina_Latn', 'jbo_Latn', 'epo', 'afh_Latn', 'qya_Latn', 'ido'}
- tgt_constituents: {'eng'}
- src_multilingual: True
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/art-eng/opus2m-2020-07-31.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/art-eng/opus2m-2020-07-31.test.txt
- src_alpha3: art
- tgt_alpha3: eng
- short_pair: art-en
- chrF2_score: 0.287
- bleu: 11.6
- brevity_penalty: 1.0
- ref_len: 73037.0
- src_name: Artificial languages
- tgt_name: English
- train_date: 2020-07-31
- src_alpha2: art
- tgt_alpha2: en
- prefer_old: False
- long_pair: art-eng
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["eo", "io", "art", "en"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-art-en | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"eo",
"io",
"art",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"eo",
"io",
"art",
"en"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #eo #io #art #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### art-eng
* source group: Artificial languages
* target group: English
* OPUS readme: art-eng
* model: transformer
* source language(s): afh\_Latn avk\_Latn dws\_Latn epo ido ido\_Latn ile\_Latn ina\_Latn jbo jbo\_Cyrl jbo\_Latn ldn\_Latn lfn\_Cyrl lfn\_Latn nov\_Latn qya qya\_Latn sjn\_Latn tlh\_Latn tzl tzl\_Latn vol\_Latn
* target language(s): eng
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 1.2, chr-F: 0.099
testset: URL, BLEU: 0.4, chr-F: 0.105
testset: URL, BLEU: 1.6, chr-F: 0.076
testset: URL, BLEU: 34.6, chr-F: 0.530
testset: URL, BLEU: 12.7, chr-F: 0.310
testset: URL, BLEU: 4.6, chr-F: 0.218
testset: URL, BLEU: 5.8, chr-F: 0.254
testset: URL, BLEU: 0.2, chr-F: 0.115
testset: URL, BLEU: 0.7, chr-F: 0.083
testset: URL, BLEU: 1.8, chr-F: 0.172
testset: URL, BLEU: 11.6, chr-F: 0.287
testset: URL, BLEU: 5.1, chr-F: 0.215
testset: URL, BLEU: 0.7, chr-F: 0.113
testset: URL, BLEU: 0.9, chr-F: 0.090
testset: URL, BLEU: 0.2, chr-F: 0.124
testset: URL, BLEU: 1.4, chr-F: 0.109
testset: URL, BLEU: 0.5, chr-F: 0.115
### System Info:
* hf\_name: art-eng
* source\_languages: art
* target\_languages: eng
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['eo', 'io', 'art', 'en']
* src\_constituents: {'sjn\_Latn', 'tzl', 'vol\_Latn', 'qya', 'tlh\_Latn', 'ile\_Latn', 'ido\_Latn', 'tzl\_Latn', 'jbo\_Cyrl', 'jbo', 'lfn\_Latn', 'nov\_Latn', 'dws\_Latn', 'ldn\_Latn', 'avk\_Latn', 'lfn\_Cyrl', 'ina\_Latn', 'jbo\_Latn', 'epo', 'afh\_Latn', 'qya\_Latn', 'ido'}
* tgt\_constituents: {'eng'}
* src\_multilingual: True
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: art
* tgt\_alpha3: eng
* short\_pair: art-en
* chrF2\_score: 0.287
* bleu: 11.6
* brevity\_penalty: 1.0
* ref\_len: 73037.0
* src\_name: Artificial languages
* tgt\_name: English
* train\_date: 2020-07-31
* src\_alpha2: art
* tgt\_alpha2: en
* prefer\_old: False
* long\_pair: art-eng
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### art-eng\n\n\n* source group: Artificial languages\n* target group: English\n* OPUS readme: art-eng\n* model: transformer\n* source language(s): afh\\_Latn avk\\_Latn dws\\_Latn epo ido ido\\_Latn ile\\_Latn ina\\_Latn jbo jbo\\_Cyrl jbo\\_Latn ldn\\_Latn lfn\\_Cyrl lfn\\_Latn nov\\_Latn qya qya\\_Latn sjn\\_Latn tlh\\_Latn tzl tzl\\_Latn vol\\_Latn\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 1.2, chr-F: 0.099\ntestset: URL, BLEU: 0.4, chr-F: 0.105\ntestset: URL, BLEU: 1.6, chr-F: 0.076\ntestset: URL, BLEU: 34.6, chr-F: 0.530\ntestset: URL, BLEU: 12.7, chr-F: 0.310\ntestset: URL, BLEU: 4.6, chr-F: 0.218\ntestset: URL, BLEU: 5.8, chr-F: 0.254\ntestset: URL, BLEU: 0.2, chr-F: 0.115\ntestset: URL, BLEU: 0.7, chr-F: 0.083\ntestset: URL, BLEU: 1.8, chr-F: 0.172\ntestset: URL, BLEU: 11.6, chr-F: 0.287\ntestset: URL, BLEU: 5.1, chr-F: 0.215\ntestset: URL, BLEU: 0.7, chr-F: 0.113\ntestset: URL, BLEU: 0.9, chr-F: 0.090\ntestset: URL, BLEU: 0.2, chr-F: 0.124\ntestset: URL, BLEU: 1.4, chr-F: 0.109\ntestset: URL, BLEU: 0.5, chr-F: 0.115",
"### System Info:\n\n\n* hf\\_name: art-eng\n* source\\_languages: art\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['eo', 'io', 'art', 'en']\n* src\\_constituents: {'sjn\\_Latn', 'tzl', 'vol\\_Latn', 'qya', 'tlh\\_Latn', 'ile\\_Latn', 'ido\\_Latn', 'tzl\\_Latn', 'jbo\\_Cyrl', 'jbo', 'lfn\\_Latn', 'nov\\_Latn', 'dws\\_Latn', 'ldn\\_Latn', 'avk\\_Latn', 'lfn\\_Cyrl', 'ina\\_Latn', 'jbo\\_Latn', 'epo', 'afh\\_Latn', 'qya\\_Latn', 'ido'}\n* tgt\\_constituents: {'eng'}\n* src\\_multilingual: True\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: art\n* tgt\\_alpha3: eng\n* short\\_pair: art-en\n* chrF2\\_score: 0.287\n* bleu: 11.6\n* brevity\\_penalty: 1.0\n* ref\\_len: 73037.0\n* src\\_name: Artificial languages\n* tgt\\_name: English\n* train\\_date: 2020-07-31\n* src\\_alpha2: art\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* long\\_pair: art-eng\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #eo #io #art #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### art-eng\n\n\n* source group: Artificial languages\n* target group: English\n* OPUS readme: art-eng\n* model: transformer\n* source language(s): afh\\_Latn avk\\_Latn dws\\_Latn epo ido ido\\_Latn ile\\_Latn ina\\_Latn jbo jbo\\_Cyrl jbo\\_Latn ldn\\_Latn lfn\\_Cyrl lfn\\_Latn nov\\_Latn qya qya\\_Latn sjn\\_Latn tlh\\_Latn tzl tzl\\_Latn vol\\_Latn\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 1.2, chr-F: 0.099\ntestset: URL, BLEU: 0.4, chr-F: 0.105\ntestset: URL, BLEU: 1.6, chr-F: 0.076\ntestset: URL, BLEU: 34.6, chr-F: 0.530\ntestset: URL, BLEU: 12.7, chr-F: 0.310\ntestset: URL, BLEU: 4.6, chr-F: 0.218\ntestset: URL, BLEU: 5.8, chr-F: 0.254\ntestset: URL, BLEU: 0.2, chr-F: 0.115\ntestset: URL, BLEU: 0.7, chr-F: 0.083\ntestset: URL, BLEU: 1.8, chr-F: 0.172\ntestset: URL, BLEU: 11.6, chr-F: 0.287\ntestset: URL, BLEU: 5.1, chr-F: 0.215\ntestset: URL, BLEU: 0.7, chr-F: 0.113\ntestset: URL, BLEU: 0.9, chr-F: 0.090\ntestset: URL, BLEU: 0.2, chr-F: 0.124\ntestset: URL, BLEU: 1.4, chr-F: 0.109\ntestset: URL, BLEU: 0.5, chr-F: 0.115",
"### System Info:\n\n\n* hf\\_name: art-eng\n* source\\_languages: art\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['eo', 'io', 'art', 'en']\n* src\\_constituents: {'sjn\\_Latn', 'tzl', 'vol\\_Latn', 'qya', 'tlh\\_Latn', 'ile\\_Latn', 'ido\\_Latn', 'tzl\\_Latn', 'jbo\\_Cyrl', 'jbo', 'lfn\\_Latn', 'nov\\_Latn', 'dws\\_Latn', 'ldn\\_Latn', 'avk\\_Latn', 'lfn\\_Cyrl', 'ina\\_Latn', 'jbo\\_Latn', 'epo', 'afh\\_Latn', 'qya\\_Latn', 'ido'}\n* tgt\\_constituents: {'eng'}\n* src\\_multilingual: True\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: art\n* tgt\\_alpha3: eng\n* short\\_pair: art-en\n* chrF2\\_score: 0.287\n* bleu: 11.6\n* brevity\\_penalty: 1.0\n* ref\\_len: 73037.0\n* src\\_name: Artificial languages\n* tgt\\_name: English\n* train\\_date: 2020-07-31\n* src\\_alpha2: art\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* long\\_pair: art-eng\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
65,
508,
579
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #eo #io #art #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"passage: ### art-eng\n\n\n* source group: Artificial languages\n* target group: English\n* OPUS readme: art-eng\n* model: transformer\n* source language(s): afh\\_Latn avk\\_Latn dws\\_Latn epo ido ido\\_Latn ile\\_Latn ina\\_Latn jbo jbo\\_Cyrl jbo\\_Latn ldn\\_Latn lfn\\_Cyrl lfn\\_Latn nov\\_Latn qya qya\\_Latn sjn\\_Latn tlh\\_Latn tzl tzl\\_Latn vol\\_Latn\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 1.2, chr-F: 0.099\ntestset: URL, BLEU: 0.4, chr-F: 0.105\ntestset: URL, BLEU: 1.6, chr-F: 0.076\ntestset: URL, BLEU: 34.6, chr-F: 0.530\ntestset: URL, BLEU: 12.7, chr-F: 0.310\ntestset: URL, BLEU: 4.6, chr-F: 0.218\ntestset: URL, BLEU: 5.8, chr-F: 0.254\ntestset: URL, BLEU: 0.2, chr-F: 0.115\ntestset: URL, BLEU: 0.7, chr-F: 0.083\ntestset: URL, BLEU: 1.8, chr-F: 0.172\ntestset: URL, BLEU: 11.6, chr-F: 0.287\ntestset: URL, BLEU: 5.1, chr-F: 0.215\ntestset: URL, BLEU: 0.7, chr-F: 0.113\ntestset: URL, BLEU: 0.9, chr-F: 0.090\ntestset: URL, BLEU: 0.2, chr-F: 0.124\ntestset: URL, BLEU: 1.4, chr-F: 0.109\ntestset: URL, BLEU: 0.5, chr-F: 0.115"
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null | null | transformers |
### opus-mt-ase-de
* source languages: ase
* target languages: de
* OPUS readme: [ase-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ase-de/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/ase-de/opus-2020-01-20.zip)
* test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ase-de/opus-2020-01-20.test.txt)
* test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ase-de/opus-2020-01-20.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.ase.de | 27.2 | 0.478 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-ase-de | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"ase",
"de",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #ase #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-ase-de
* source languages: ase
* target languages: de
* OPUS readme: ase-de
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 27.2, chr-F: 0.478
| [
"### opus-mt-ase-de\n\n\n* source languages: ase\n* target languages: de\n* OPUS readme: ase-de\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 27.2, chr-F: 0.478"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ase #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-ase-de\n\n\n* source languages: ase\n* target languages: de\n* OPUS readme: ase-de\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 27.2, chr-F: 0.478"
] | [
61,
102
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ase #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-ase-de\n\n\n* source languages: ase\n* target languages: de\n* OPUS readme: ase-de\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 27.2, chr-F: 0.478"
] | [
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] |
null | null | transformers |
### opus-mt-ase-en
* source languages: ase
* target languages: en
* OPUS readme: [ase-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ase-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/ase-en/opus-2020-01-20.zip)
* test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ase-en/opus-2020-01-20.test.txt)
* test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ase-en/opus-2020-01-20.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.ase.en | 99.5 | 0.997 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-ase-en | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"ase",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #ase #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-ase-en
* source languages: ase
* target languages: en
* OPUS readme: ase-en
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 99.5, chr-F: 0.997
| [
"### opus-mt-ase-en\n\n\n* source languages: ase\n* target languages: en\n* OPUS readme: ase-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 99.5, chr-F: 0.997"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ase #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-ase-en\n\n\n* source languages: ase\n* target languages: en\n* OPUS readme: ase-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 99.5, chr-F: 0.997"
] | [
61,
102
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ase #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-ase-en\n\n\n* source languages: ase\n* target languages: en\n* OPUS readme: ase-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 99.5, chr-F: 0.997"
] | [
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] |
null | null | transformers |
### opus-mt-ase-es
* source languages: ase
* target languages: es
* OPUS readme: [ase-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ase-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/ase-es/opus-2020-01-20.zip)
* test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ase-es/opus-2020-01-20.test.txt)
* test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ase-es/opus-2020-01-20.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.ase.es | 31.7 | 0.498 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-ase-es | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"ase",
"es",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #ase #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-ase-es
* source languages: ase
* target languages: es
* OPUS readme: ase-es
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 31.7, chr-F: 0.498
| [
"### opus-mt-ase-es\n\n\n* source languages: ase\n* target languages: es\n* OPUS readme: ase-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 31.7, chr-F: 0.498"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ase #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-ase-es\n\n\n* source languages: ase\n* target languages: es\n* OPUS readme: ase-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 31.7, chr-F: 0.498"
] | [
61,
102
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ase #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-ase-es\n\n\n* source languages: ase\n* target languages: es\n* OPUS readme: ase-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 31.7, chr-F: 0.498"
] | [
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null | null | transformers |
### opus-mt-ase-fr
* source languages: ase
* target languages: fr
* OPUS readme: [ase-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ase-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/ase-fr/opus-2020-01-20.zip)
* test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ase-fr/opus-2020-01-20.test.txt)
* test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ase-fr/opus-2020-01-20.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.ase.fr | 37.8 | 0.553 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-ase-fr | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"ase",
"fr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #ase #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-ase-fr
* source languages: ase
* target languages: fr
* OPUS readme: ase-fr
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 37.8, chr-F: 0.553
| [
"### opus-mt-ase-fr\n\n\n* source languages: ase\n* target languages: fr\n* OPUS readme: ase-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 37.8, chr-F: 0.553"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ase #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-ase-fr\n\n\n* source languages: ase\n* target languages: fr\n* OPUS readme: ase-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 37.8, chr-F: 0.553"
] | [
61,
101
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ase #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-ase-fr\n\n\n* source languages: ase\n* target languages: fr\n* OPUS readme: ase-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 37.8, chr-F: 0.553"
] | [
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] |
null | null | transformers |
### opus-mt-ase-sv
* source languages: ase
* target languages: sv
* OPUS readme: [ase-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ase-sv/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/ase-sv/opus-2020-01-20.zip)
* test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ase-sv/opus-2020-01-20.test.txt)
* test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ase-sv/opus-2020-01-20.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.ase.sv | 39.7 | 0.576 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-ase-sv | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"ase",
"sv",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #ase #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-ase-sv
* source languages: ase
* target languages: sv
* OPUS readme: ase-sv
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 39.7, chr-F: 0.576
| [
"### opus-mt-ase-sv\n\n\n* source languages: ase\n* target languages: sv\n* OPUS readme: ase-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 39.7, chr-F: 0.576"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ase #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-ase-sv\n\n\n* source languages: ase\n* target languages: sv\n* OPUS readme: ase-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 39.7, chr-F: 0.576"
] | [
61,
101
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ase #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-ase-sv\n\n\n* source languages: ase\n* target languages: sv\n* OPUS readme: ase-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 39.7, chr-F: 0.576"
] | [
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null | null | transformers |
### aze-eng
* source group: Azerbaijani
* target group: English
* OPUS readme: [aze-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/aze-eng/README.md)
* model: transformer-align
* source language(s): aze_Latn
* target language(s): eng
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm12k,spm12k)
* download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/aze-eng/opus-2020-06-16.zip)
* test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/aze-eng/opus-2020-06-16.test.txt)
* test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/aze-eng/opus-2020-06-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.aze.eng | 31.9 | 0.490 |
### System Info:
- hf_name: aze-eng
- source_languages: aze
- target_languages: eng
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/aze-eng/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['az', 'en']
- src_constituents: {'aze_Latn'}
- tgt_constituents: {'eng'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm12k,spm12k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/aze-eng/opus-2020-06-16.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/aze-eng/opus-2020-06-16.test.txt
- src_alpha3: aze
- tgt_alpha3: eng
- short_pair: az-en
- chrF2_score: 0.49
- bleu: 31.9
- brevity_penalty: 0.997
- ref_len: 16165.0
- src_name: Azerbaijani
- tgt_name: English
- train_date: 2020-06-16
- src_alpha2: az
- tgt_alpha2: en
- prefer_old: False
- long_pair: aze-eng
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["az", "en"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-az-en | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"az",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"az",
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #az #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### aze-eng
* source group: Azerbaijani
* target group: English
* OPUS readme: aze-eng
* model: transformer-align
* source language(s): aze\_Latn
* target language(s): eng
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm12k,spm12k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 31.9, chr-F: 0.490
### System Info:
* hf\_name: aze-eng
* source\_languages: aze
* target\_languages: eng
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['az', 'en']
* src\_constituents: {'aze\_Latn'}
* tgt\_constituents: {'eng'}
* src\_multilingual: False
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm12k,spm12k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: aze
* tgt\_alpha3: eng
* short\_pair: az-en
* chrF2\_score: 0.49
* bleu: 31.9
* brevity\_penalty: 0.997
* ref\_len: 16165.0
* src\_name: Azerbaijani
* tgt\_name: English
* train\_date: 2020-06-16
* src\_alpha2: az
* tgt\_alpha2: en
* prefer\_old: False
* long\_pair: aze-eng
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### aze-eng\n\n\n* source group: Azerbaijani\n* target group: English\n* OPUS readme: aze-eng\n* model: transformer-align\n* source language(s): aze\\_Latn\n* target language(s): eng\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm12k,spm12k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 31.9, chr-F: 0.490",
"### System Info:\n\n\n* hf\\_name: aze-eng\n* source\\_languages: aze\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['az', 'en']\n* src\\_constituents: {'aze\\_Latn'}\n* tgt\\_constituents: {'eng'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm12k,spm12k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: aze\n* tgt\\_alpha3: eng\n* short\\_pair: az-en\n* chrF2\\_score: 0.49\n* bleu: 31.9\n* brevity\\_penalty: 0.997\n* ref\\_len: 16165.0\n* src\\_name: Azerbaijani\n* tgt\\_name: English\n* train\\_date: 2020-06-16\n* src\\_alpha2: az\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* long\\_pair: aze-eng\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #az #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### aze-eng\n\n\n* source group: Azerbaijani\n* target group: English\n* OPUS readme: aze-eng\n* model: transformer-align\n* source language(s): aze\\_Latn\n* target language(s): eng\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm12k,spm12k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 31.9, chr-F: 0.490",
"### System Info:\n\n\n* hf\\_name: aze-eng\n* source\\_languages: aze\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['az', 'en']\n* src\\_constituents: {'aze\\_Latn'}\n* tgt\\_constituents: {'eng'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm12k,spm12k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: aze\n* tgt\\_alpha3: eng\n* short\\_pair: az-en\n* chrF2\\_score: 0.49\n* bleu: 31.9\n* brevity\\_penalty: 0.997\n* ref\\_len: 16165.0\n* src\\_name: Azerbaijani\n* tgt\\_name: English\n* train\\_date: 2020-06-16\n* src\\_alpha2: az\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* long\\_pair: aze-eng\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
66,
129,
399
] | [
"passage: TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #az #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### aze-eng\n\n\n* source group: Azerbaijani\n* target group: English\n* OPUS readme: aze-eng\n* model: transformer-align\n* source language(s): aze\\_Latn\n* target language(s): eng\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm12k,spm12k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 31.9, chr-F: 0.490"
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] |
null | null | transformers |
### aze-spa
* source group: Azerbaijani
* target group: Spanish
* OPUS readme: [aze-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/aze-spa/README.md)
* model: transformer-align
* source language(s): aze_Latn
* target language(s): spa
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/aze-spa/opus-2020-06-16.zip)
* test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/aze-spa/opus-2020-06-16.test.txt)
* test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/aze-spa/opus-2020-06-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.aze.spa | 11.8 | 0.346 |
### System Info:
- hf_name: aze-spa
- source_languages: aze
- target_languages: spa
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/aze-spa/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['az', 'es']
- src_constituents: {'aze_Latn'}
- tgt_constituents: {'spa'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/aze-spa/opus-2020-06-16.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/aze-spa/opus-2020-06-16.test.txt
- src_alpha3: aze
- tgt_alpha3: spa
- short_pair: az-es
- chrF2_score: 0.34600000000000003
- bleu: 11.8
- brevity_penalty: 1.0
- ref_len: 1144.0
- src_name: Azerbaijani
- tgt_name: Spanish
- train_date: 2020-06-16
- src_alpha2: az
- tgt_alpha2: es
- prefer_old: False
- long_pair: aze-spa
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["az", "es"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-az-es | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"az",
"es",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"az",
"es"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #az #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### aze-spa
* source group: Azerbaijani
* target group: Spanish
* OPUS readme: aze-spa
* model: transformer-align
* source language(s): aze\_Latn
* target language(s): spa
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 11.8, chr-F: 0.346
### System Info:
* hf\_name: aze-spa
* source\_languages: aze
* target\_languages: spa
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['az', 'es']
* src\_constituents: {'aze\_Latn'}
* tgt\_constituents: {'spa'}
* src\_multilingual: False
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm4k,spm4k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: aze
* tgt\_alpha3: spa
* short\_pair: az-es
* chrF2\_score: 0.34600000000000003
* bleu: 11.8
* brevity\_penalty: 1.0
* ref\_len: 1144.0
* src\_name: Azerbaijani
* tgt\_name: Spanish
* train\_date: 2020-06-16
* src\_alpha2: az
* tgt\_alpha2: es
* prefer\_old: False
* long\_pair: aze-spa
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### aze-spa\n\n\n* source group: Azerbaijani\n* target group: Spanish\n* OPUS readme: aze-spa\n* model: transformer-align\n* source language(s): aze\\_Latn\n* target language(s): spa\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm4k,spm4k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 11.8, chr-F: 0.346",
"### System Info:\n\n\n* hf\\_name: aze-spa\n* source\\_languages: aze\n* target\\_languages: spa\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['az', 'es']\n* src\\_constituents: {'aze\\_Latn'}\n* tgt\\_constituents: {'spa'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm4k,spm4k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: aze\n* tgt\\_alpha3: spa\n* short\\_pair: az-es\n* chrF2\\_score: 0.34600000000000003\n* bleu: 11.8\n* brevity\\_penalty: 1.0\n* ref\\_len: 1144.0\n* src\\_name: Azerbaijani\n* tgt\\_name: Spanish\n* train\\_date: 2020-06-16\n* src\\_alpha2: az\n* tgt\\_alpha2: es\n* prefer\\_old: False\n* long\\_pair: aze-spa\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #az #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### aze-spa\n\n\n* source group: Azerbaijani\n* target group: Spanish\n* OPUS readme: aze-spa\n* model: transformer-align\n* source language(s): aze\\_Latn\n* target language(s): spa\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm4k,spm4k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 11.8, chr-F: 0.346",
"### System Info:\n\n\n* hf\\_name: aze-spa\n* source\\_languages: aze\n* target\\_languages: spa\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['az', 'es']\n* src\\_constituents: {'aze\\_Latn'}\n* tgt\\_constituents: {'spa'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm4k,spm4k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: aze\n* tgt\\_alpha3: spa\n* short\\_pair: az-es\n* chrF2\\_score: 0.34600000000000003\n* bleu: 11.8\n* brevity\\_penalty: 1.0\n* ref\\_len: 1144.0\n* src\\_name: Azerbaijani\n* tgt\\_name: Spanish\n* train\\_date: 2020-06-16\n* src\\_alpha2: az\n* tgt\\_alpha2: es\n* prefer\\_old: False\n* long\\_pair: aze-spa\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
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128,
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"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #az #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### aze-spa\n\n\n* source group: Azerbaijani\n* target group: Spanish\n* OPUS readme: aze-spa\n* model: transformer-align\n* source language(s): aze\\_Latn\n* target language(s): spa\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm4k,spm4k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 11.8, chr-F: 0.346"
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null | null | transformers |
### aze-tur
* source group: Azerbaijani
* target group: Turkish
* OPUS readme: [aze-tur](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/aze-tur/README.md)
* model: transformer-align
* source language(s): aze_Latn
* target language(s): tur
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/aze-tur/opus-2020-06-16.zip)
* test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/aze-tur/opus-2020-06-16.test.txt)
* test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/aze-tur/opus-2020-06-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.aze.tur | 24.4 | 0.529 |
### System Info:
- hf_name: aze-tur
- source_languages: aze
- target_languages: tur
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/aze-tur/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['az', 'tr']
- src_constituents: {'aze_Latn'}
- tgt_constituents: {'tur'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/aze-tur/opus-2020-06-16.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/aze-tur/opus-2020-06-16.test.txt
- src_alpha3: aze
- tgt_alpha3: tur
- short_pair: az-tr
- chrF2_score: 0.529
- bleu: 24.4
- brevity_penalty: 0.956
- ref_len: 5380.0
- src_name: Azerbaijani
- tgt_name: Turkish
- train_date: 2020-06-16
- src_alpha2: az
- tgt_alpha2: tr
- prefer_old: False
- long_pair: aze-tur
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["az", "tr"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-az-tr | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"az",
"tr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"az",
"tr"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #az #tr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### aze-tur
* source group: Azerbaijani
* target group: Turkish
* OPUS readme: aze-tur
* model: transformer-align
* source language(s): aze\_Latn
* target language(s): tur
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 24.4, chr-F: 0.529
### System Info:
* hf\_name: aze-tur
* source\_languages: aze
* target\_languages: tur
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['az', 'tr']
* src\_constituents: {'aze\_Latn'}
* tgt\_constituents: {'tur'}
* src\_multilingual: False
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm4k,spm4k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: aze
* tgt\_alpha3: tur
* short\_pair: az-tr
* chrF2\_score: 0.529
* bleu: 24.4
* brevity\_penalty: 0.956
* ref\_len: 5380.0
* src\_name: Azerbaijani
* tgt\_name: Turkish
* train\_date: 2020-06-16
* src\_alpha2: az
* tgt\_alpha2: tr
* prefer\_old: False
* long\_pair: aze-tur
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### aze-tur\n\n\n* source group: Azerbaijani\n* target group: Turkish\n* OPUS readme: aze-tur\n* model: transformer-align\n* source language(s): aze\\_Latn\n* target language(s): tur\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm4k,spm4k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 24.4, chr-F: 0.529",
"### System Info:\n\n\n* hf\\_name: aze-tur\n* source\\_languages: aze\n* target\\_languages: tur\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['az', 'tr']\n* src\\_constituents: {'aze\\_Latn'}\n* tgt\\_constituents: {'tur'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm4k,spm4k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: aze\n* tgt\\_alpha3: tur\n* short\\_pair: az-tr\n* chrF2\\_score: 0.529\n* bleu: 24.4\n* brevity\\_penalty: 0.956\n* ref\\_len: 5380.0\n* src\\_name: Azerbaijani\n* tgt\\_name: Turkish\n* train\\_date: 2020-06-16\n* src\\_alpha2: az\n* tgt\\_alpha2: tr\n* prefer\\_old: False\n* long\\_pair: aze-tur\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #az #tr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### aze-tur\n\n\n* source group: Azerbaijani\n* target group: Turkish\n* OPUS readme: aze-tur\n* model: transformer-align\n* source language(s): aze\\_Latn\n* target language(s): tur\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm4k,spm4k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 24.4, chr-F: 0.529",
"### System Info:\n\n\n* hf\\_name: aze-tur\n* source\\_languages: aze\n* target\\_languages: tur\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['az', 'tr']\n* src\\_constituents: {'aze\\_Latn'}\n* tgt\\_constituents: {'tur'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm4k,spm4k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: aze\n* tgt\\_alpha3: tur\n* short\\_pair: az-tr\n* chrF2\\_score: 0.529\n* bleu: 24.4\n* brevity\\_penalty: 0.956\n* ref\\_len: 5380.0\n* src\\_name: Azerbaijani\n* tgt\\_name: Turkish\n* train\\_date: 2020-06-16\n* src\\_alpha2: az\n* tgt\\_alpha2: tr\n* prefer\\_old: False\n* long\\_pair: aze-tur\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
61,
129,
399
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #az #tr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### aze-tur\n\n\n* source group: Azerbaijani\n* target group: Turkish\n* OPUS readme: aze-tur\n* model: transformer-align\n* source language(s): aze\\_Latn\n* target language(s): tur\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm4k,spm4k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 24.4, chr-F: 0.529"
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null | null | transformers |
### bat-eng
* source group: Baltic languages
* target group: English
* OPUS readme: [bat-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bat-eng/README.md)
* model: transformer
* source language(s): lav lit ltg prg_Latn sgs
* target language(s): eng
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus2m-2020-07-31.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/bat-eng/opus2m-2020-07-31.zip)
* test set translations: [opus2m-2020-07-31.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bat-eng/opus2m-2020-07-31.test.txt)
* test set scores: [opus2m-2020-07-31.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bat-eng/opus2m-2020-07-31.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| newsdev2017-enlv-laveng.lav.eng | 27.5 | 0.566 |
| newsdev2019-enlt-liteng.lit.eng | 27.8 | 0.557 |
| newstest2017-enlv-laveng.lav.eng | 21.1 | 0.512 |
| newstest2019-lten-liteng.lit.eng | 30.2 | 0.592 |
| Tatoeba-test.lav-eng.lav.eng | 51.5 | 0.687 |
| Tatoeba-test.lit-eng.lit.eng | 55.1 | 0.703 |
| Tatoeba-test.multi.eng | 50.6 | 0.662 |
| Tatoeba-test.prg-eng.prg.eng | 1.0 | 0.159 |
| Tatoeba-test.sgs-eng.sgs.eng | 16.5 | 0.265 |
### System Info:
- hf_name: bat-eng
- source_languages: bat
- target_languages: eng
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bat-eng/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['lt', 'lv', 'bat', 'en']
- src_constituents: {'lit', 'lav', 'prg_Latn', 'ltg', 'sgs'}
- tgt_constituents: {'eng'}
- src_multilingual: True
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/bat-eng/opus2m-2020-07-31.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/bat-eng/opus2m-2020-07-31.test.txt
- src_alpha3: bat
- tgt_alpha3: eng
- short_pair: bat-en
- chrF2_score: 0.662
- bleu: 50.6
- brevity_penalty: 0.9890000000000001
- ref_len: 30772.0
- src_name: Baltic languages
- tgt_name: English
- train_date: 2020-07-31
- src_alpha2: bat
- tgt_alpha2: en
- prefer_old: False
- long_pair: bat-eng
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["lt", "lv", "bat", "en"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bat-en | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"lt",
"lv",
"bat",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"lt",
"lv",
"bat",
"en"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #lt #lv #bat #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### bat-eng
* source group: Baltic languages
* target group: English
* OPUS readme: bat-eng
* model: transformer
* source language(s): lav lit ltg prg\_Latn sgs
* target language(s): eng
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 27.5, chr-F: 0.566
testset: URL, BLEU: 27.8, chr-F: 0.557
testset: URL, BLEU: 21.1, chr-F: 0.512
testset: URL, BLEU: 30.2, chr-F: 0.592
testset: URL, BLEU: 51.5, chr-F: 0.687
testset: URL, BLEU: 55.1, chr-F: 0.703
testset: URL, BLEU: 50.6, chr-F: 0.662
testset: URL, BLEU: 1.0, chr-F: 0.159
testset: URL, BLEU: 16.5, chr-F: 0.265
### System Info:
* hf\_name: bat-eng
* source\_languages: bat
* target\_languages: eng
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['lt', 'lv', 'bat', 'en']
* src\_constituents: {'lit', 'lav', 'prg\_Latn', 'ltg', 'sgs'}
* tgt\_constituents: {'eng'}
* src\_multilingual: True
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: bat
* tgt\_alpha3: eng
* short\_pair: bat-en
* chrF2\_score: 0.662
* bleu: 50.6
* brevity\_penalty: 0.9890000000000001
* ref\_len: 30772.0
* src\_name: Baltic languages
* tgt\_name: English
* train\_date: 2020-07-31
* src\_alpha2: bat
* tgt\_alpha2: en
* prefer\_old: False
* long\_pair: bat-eng
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### bat-eng\n\n\n* source group: Baltic languages\n* target group: English\n* OPUS readme: bat-eng\n* model: transformer\n* source language(s): lav lit ltg prg\\_Latn sgs\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 27.5, chr-F: 0.566\ntestset: URL, BLEU: 27.8, chr-F: 0.557\ntestset: URL, BLEU: 21.1, chr-F: 0.512\ntestset: URL, BLEU: 30.2, chr-F: 0.592\ntestset: URL, BLEU: 51.5, chr-F: 0.687\ntestset: URL, BLEU: 55.1, chr-F: 0.703\ntestset: URL, BLEU: 50.6, chr-F: 0.662\ntestset: URL, BLEU: 1.0, chr-F: 0.159\ntestset: URL, BLEU: 16.5, chr-F: 0.265",
"### System Info:\n\n\n* hf\\_name: bat-eng\n* source\\_languages: bat\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['lt', 'lv', 'bat', 'en']\n* src\\_constituents: {'lit', 'lav', 'prg\\_Latn', 'ltg', 'sgs'}\n* tgt\\_constituents: {'eng'}\n* src\\_multilingual: True\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: bat\n* tgt\\_alpha3: eng\n* short\\_pair: bat-en\n* chrF2\\_score: 0.662\n* bleu: 50.6\n* brevity\\_penalty: 0.9890000000000001\n* ref\\_len: 30772.0\n* src\\_name: Baltic languages\n* tgt\\_name: English\n* train\\_date: 2020-07-31\n* src\\_alpha2: bat\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* long\\_pair: bat-eng\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #lt #lv #bat #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### bat-eng\n\n\n* source group: Baltic languages\n* target group: English\n* OPUS readme: bat-eng\n* model: transformer\n* source language(s): lav lit ltg prg\\_Latn sgs\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 27.5, chr-F: 0.566\ntestset: URL, BLEU: 27.8, chr-F: 0.557\ntestset: URL, BLEU: 21.1, chr-F: 0.512\ntestset: URL, BLEU: 30.2, chr-F: 0.592\ntestset: URL, BLEU: 51.5, chr-F: 0.687\ntestset: URL, BLEU: 55.1, chr-F: 0.703\ntestset: URL, BLEU: 50.6, chr-F: 0.662\ntestset: URL, BLEU: 1.0, chr-F: 0.159\ntestset: URL, BLEU: 16.5, chr-F: 0.265",
"### System Info:\n\n\n* hf\\_name: bat-eng\n* source\\_languages: bat\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['lt', 'lv', 'bat', 'en']\n* src\\_constituents: {'lit', 'lav', 'prg\\_Latn', 'ltg', 'sgs'}\n* tgt\\_constituents: {'eng'}\n* src\\_multilingual: True\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: bat\n* tgt\\_alpha3: eng\n* short\\_pair: bat-en\n* chrF2\\_score: 0.662\n* bleu: 50.6\n* brevity\\_penalty: 0.9890000000000001\n* ref\\_len: 30772.0\n* src\\_name: Baltic languages\n* tgt\\_name: English\n* train\\_date: 2020-07-31\n* src\\_alpha2: bat\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* long\\_pair: bat-eng\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
65,
269,
424
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #lt #lv #bat #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### bat-eng\n\n\n* source group: Baltic languages\n* target group: English\n* OPUS readme: bat-eng\n* model: transformer\n* source language(s): lav lit ltg prg\\_Latn sgs\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 27.5, chr-F: 0.566\ntestset: URL, BLEU: 27.8, chr-F: 0.557\ntestset: URL, BLEU: 21.1, chr-F: 0.512\ntestset: URL, BLEU: 30.2, chr-F: 0.592\ntestset: URL, BLEU: 51.5, chr-F: 0.687\ntestset: URL, BLEU: 55.1, chr-F: 0.703\ntestset: URL, BLEU: 50.6, chr-F: 0.662\ntestset: URL, BLEU: 1.0, chr-F: 0.159\ntestset: URL, BLEU: 16.5, chr-F: 0.265"
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] |
null | null | transformers |
### opus-mt-bcl-de
* source languages: bcl
* target languages: de
* OPUS readme: [bcl-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bcl-de/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/bcl-de/opus-2020-01-20.zip)
* test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bcl-de/opus-2020-01-20.test.txt)
* test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bcl-de/opus-2020-01-20.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.bcl.de | 30.3 | 0.510 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bcl-de | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bcl",
"de",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bcl #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-bcl-de
* source languages: bcl
* target languages: de
* OPUS readme: bcl-de
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 30.3, chr-F: 0.510
| [
"### opus-mt-bcl-de\n\n\n* source languages: bcl\n* target languages: de\n* OPUS readme: bcl-de\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 30.3, chr-F: 0.510"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bcl #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-bcl-de\n\n\n* source languages: bcl\n* target languages: de\n* OPUS readme: bcl-de\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 30.3, chr-F: 0.510"
] | [
62,
103
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bcl #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-bcl-de\n\n\n* source languages: bcl\n* target languages: de\n* OPUS readme: bcl-de\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 30.3, chr-F: 0.510"
] | [
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null | null | transformers |
### opus-mt-bcl-en
* source languages: bcl
* target languages: en
* OPUS readme: [bcl-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bcl-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-02-11.zip](https://object.pouta.csc.fi/OPUS-MT-models/bcl-en/opus-2020-02-11.zip)
* test set translations: [opus-2020-02-11.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bcl-en/opus-2020-02-11.test.txt)
* test set scores: [opus-2020-02-11.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bcl-en/opus-2020-02-11.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.bcl.en | 56.1 | 0.697 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bcl-en | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bcl",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bcl #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-bcl-en
* source languages: bcl
* target languages: en
* OPUS readme: bcl-en
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 56.1, chr-F: 0.697
| [
"### opus-mt-bcl-en\n\n\n* source languages: bcl\n* target languages: en\n* OPUS readme: bcl-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 56.1, chr-F: 0.697"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bcl #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-bcl-en\n\n\n* source languages: bcl\n* target languages: en\n* OPUS readme: bcl-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 56.1, chr-F: 0.697"
] | [
62,
103
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bcl #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-bcl-en\n\n\n* source languages: bcl\n* target languages: en\n* OPUS readme: bcl-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 56.1, chr-F: 0.697"
] | [
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null | null | transformers |
### opus-mt-bcl-es
* source languages: bcl
* target languages: es
* OPUS readme: [bcl-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bcl-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-15.zip](https://object.pouta.csc.fi/OPUS-MT-models/bcl-es/opus-2020-01-15.zip)
* test set translations: [opus-2020-01-15.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bcl-es/opus-2020-01-15.test.txt)
* test set scores: [opus-2020-01-15.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bcl-es/opus-2020-01-15.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.bcl.es | 37.0 | 0.551 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bcl-es | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bcl",
"es",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bcl #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-bcl-es
* source languages: bcl
* target languages: es
* OPUS readme: bcl-es
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 37.0, chr-F: 0.551
| [
"### opus-mt-bcl-es\n\n\n* source languages: bcl\n* target languages: es\n* OPUS readme: bcl-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 37.0, chr-F: 0.551"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bcl #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-bcl-es\n\n\n* source languages: bcl\n* target languages: es\n* OPUS readme: bcl-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 37.0, chr-F: 0.551"
] | [
62,
102
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bcl #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-bcl-es\n\n\n* source languages: bcl\n* target languages: es\n* OPUS readme: bcl-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 37.0, chr-F: 0.551"
] | [
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] |
null | null | transformers |
### opus-mt-bcl-fi
* source languages: bcl
* target languages: fi
* OPUS readme: [bcl-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bcl-fi/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/bcl-fi/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bcl-fi/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bcl-fi/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.bcl.fi | 33.3 | 0.573 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bcl-fi | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bcl",
"fi",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bcl #fi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-bcl-fi
* source languages: bcl
* target languages: fi
* OPUS readme: bcl-fi
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 33.3, chr-F: 0.573
| [
"### opus-mt-bcl-fi\n\n\n* source languages: bcl\n* target languages: fi\n* OPUS readme: bcl-fi\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 33.3, chr-F: 0.573"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bcl #fi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-bcl-fi\n\n\n* source languages: bcl\n* target languages: fi\n* OPUS readme: bcl-fi\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 33.3, chr-F: 0.573"
] | [
62,
102
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bcl #fi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-bcl-fi\n\n\n* source languages: bcl\n* target languages: fi\n* OPUS readme: bcl-fi\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 33.3, chr-F: 0.573"
] | [
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] |
null | null | transformers |
### opus-mt-bcl-fr
* source languages: bcl
* target languages: fr
* OPUS readme: [bcl-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bcl-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/bcl-fr/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bcl-fr/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bcl-fr/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.bcl.fr | 35.0 | 0.527 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bcl-fr | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bcl",
"fr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bcl #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-bcl-fr
* source languages: bcl
* target languages: fr
* OPUS readme: bcl-fr
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 35.0, chr-F: 0.527
| [
"### opus-mt-bcl-fr\n\n\n* source languages: bcl\n* target languages: fr\n* OPUS readme: bcl-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 35.0, chr-F: 0.527"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bcl #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-bcl-fr\n\n\n* source languages: bcl\n* target languages: fr\n* OPUS readme: bcl-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 35.0, chr-F: 0.527"
] | [
62,
102
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bcl #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-bcl-fr\n\n\n* source languages: bcl\n* target languages: fr\n* OPUS readme: bcl-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 35.0, chr-F: 0.527"
] | [
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null | null | transformers |
### opus-mt-bcl-sv
* source languages: bcl
* target languages: sv
* OPUS readme: [bcl-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bcl-sv/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/bcl-sv/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bcl-sv/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bcl-sv/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.bcl.sv | 38.0 | 0.565 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bcl-sv | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bcl",
"sv",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bcl #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-bcl-sv
* source languages: bcl
* target languages: sv
* OPUS readme: bcl-sv
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 38.0, chr-F: 0.565
| [
"### opus-mt-bcl-sv\n\n\n* source languages: bcl\n* target languages: sv\n* OPUS readme: bcl-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 38.0, chr-F: 0.565"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bcl #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-bcl-sv\n\n\n* source languages: bcl\n* target languages: sv\n* OPUS readme: bcl-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 38.0, chr-F: 0.565"
] | [
62,
103
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bcl #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-bcl-sv\n\n\n* source languages: bcl\n* target languages: sv\n* OPUS readme: bcl-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 38.0, chr-F: 0.565"
] | [
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] |
null | null | transformers |
### bel-spa
* source group: Belarusian
* target group: Spanish
* OPUS readme: [bel-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bel-spa/README.md)
* model: transformer-align
* source language(s): bel bel_Latn
* target language(s): spa
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/bel-spa/opus-2020-06-16.zip)
* test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bel-spa/opus-2020-06-16.test.txt)
* test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bel-spa/opus-2020-06-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.bel.spa | 11.8 | 0.272 |
### System Info:
- hf_name: bel-spa
- source_languages: bel
- target_languages: spa
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bel-spa/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['be', 'es']
- src_constituents: {'bel', 'bel_Latn'}
- tgt_constituents: {'spa'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/bel-spa/opus-2020-06-16.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/bel-spa/opus-2020-06-16.test.txt
- src_alpha3: bel
- tgt_alpha3: spa
- short_pair: be-es
- chrF2_score: 0.272
- bleu: 11.8
- brevity_penalty: 0.892
- ref_len: 1412.0
- src_name: Belarusian
- tgt_name: Spanish
- train_date: 2020-06-16
- src_alpha2: be
- tgt_alpha2: es
- prefer_old: False
- long_pair: bel-spa
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["be", "es"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-be-es | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"be",
"es",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"be",
"es"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #be #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### bel-spa
* source group: Belarusian
* target group: Spanish
* OPUS readme: bel-spa
* model: transformer-align
* source language(s): bel bel\_Latn
* target language(s): spa
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 11.8, chr-F: 0.272
### System Info:
* hf\_name: bel-spa
* source\_languages: bel
* target\_languages: spa
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['be', 'es']
* src\_constituents: {'bel', 'bel\_Latn'}
* tgt\_constituents: {'spa'}
* src\_multilingual: False
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm4k,spm4k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: bel
* tgt\_alpha3: spa
* short\_pair: be-es
* chrF2\_score: 0.272
* bleu: 11.8
* brevity\_penalty: 0.892
* ref\_len: 1412.0
* src\_name: Belarusian
* tgt\_name: Spanish
* train\_date: 2020-06-16
* src\_alpha2: be
* tgt\_alpha2: es
* prefer\_old: False
* long\_pair: bel-spa
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### bel-spa\n\n\n* source group: Belarusian\n* target group: Spanish\n* OPUS readme: bel-spa\n* model: transformer-align\n* source language(s): bel bel\\_Latn\n* target language(s): spa\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm4k,spm4k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 11.8, chr-F: 0.272",
"### System Info:\n\n\n* hf\\_name: bel-spa\n* source\\_languages: bel\n* target\\_languages: spa\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['be', 'es']\n* src\\_constituents: {'bel', 'bel\\_Latn'}\n* tgt\\_constituents: {'spa'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm4k,spm4k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: bel\n* tgt\\_alpha3: spa\n* short\\_pair: be-es\n* chrF2\\_score: 0.272\n* bleu: 11.8\n* brevity\\_penalty: 0.892\n* ref\\_len: 1412.0\n* src\\_name: Belarusian\n* tgt\\_name: Spanish\n* train\\_date: 2020-06-16\n* src\\_alpha2: be\n* tgt\\_alpha2: es\n* prefer\\_old: False\n* long\\_pair: bel-spa\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #be #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### bel-spa\n\n\n* source group: Belarusian\n* target group: Spanish\n* OPUS readme: bel-spa\n* model: transformer-align\n* source language(s): bel bel\\_Latn\n* target language(s): spa\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm4k,spm4k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 11.8, chr-F: 0.272",
"### System Info:\n\n\n* hf\\_name: bel-spa\n* source\\_languages: bel\n* target\\_languages: spa\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['be', 'es']\n* src\\_constituents: {'bel', 'bel\\_Latn'}\n* tgt\\_constituents: {'spa'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm4k,spm4k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: bel\n* tgt\\_alpha3: spa\n* short\\_pair: be-es\n* chrF2\\_score: 0.272\n* bleu: 11.8\n* brevity\\_penalty: 0.892\n* ref\\_len: 1412.0\n* src\\_name: Belarusian\n* tgt\\_name: Spanish\n* train\\_date: 2020-06-16\n* src\\_alpha2: be\n* tgt\\_alpha2: es\n* prefer\\_old: False\n* long\\_pair: bel-spa\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
61,
126,
396
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #be #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### bel-spa\n\n\n* source group: Belarusian\n* target group: Spanish\n* OPUS readme: bel-spa\n* model: transformer-align\n* source language(s): bel bel\\_Latn\n* target language(s): spa\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm4k,spm4k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 11.8, chr-F: 0.272"
] | [
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] |
null | null | transformers |
### opus-mt-bem-en
* source languages: bem
* target languages: en
* OPUS readme: [bem-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bem-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2019-12-18.zip](https://object.pouta.csc.fi/OPUS-MT-models/bem-en/opus-2019-12-18.zip)
* test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bem-en/opus-2019-12-18.test.txt)
* test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bem-en/opus-2019-12-18.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.bem.en | 33.4 | 0.491 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bem-en | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bem",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bem #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-bem-en
* source languages: bem
* target languages: en
* OPUS readme: bem-en
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 33.4, chr-F: 0.491
| [
"### opus-mt-bem-en\n\n\n* source languages: bem\n* target languages: en\n* OPUS readme: bem-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 33.4, chr-F: 0.491"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bem #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-bem-en\n\n\n* source languages: bem\n* target languages: en\n* OPUS readme: bem-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 33.4, chr-F: 0.491"
] | [
61,
100
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bem #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-bem-en\n\n\n* source languages: bem\n* target languages: en\n* OPUS readme: bem-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 33.4, chr-F: 0.491"
] | [
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] |
null | null | transformers |
### opus-mt-bem-es
* source languages: bem
* target languages: es
* OPUS readme: [bem-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bem-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-15.zip](https://object.pouta.csc.fi/OPUS-MT-models/bem-es/opus-2020-01-15.zip)
* test set translations: [opus-2020-01-15.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bem-es/opus-2020-01-15.test.txt)
* test set scores: [opus-2020-01-15.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bem-es/opus-2020-01-15.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.bem.es | 22.8 | 0.403 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bem-es | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bem",
"es",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bem #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-bem-es
* source languages: bem
* target languages: es
* OPUS readme: bem-es
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 22.8, chr-F: 0.403
| [
"### opus-mt-bem-es\n\n\n* source languages: bem\n* target languages: es\n* OPUS readme: bem-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 22.8, chr-F: 0.403"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bem #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-bem-es\n\n\n* source languages: bem\n* target languages: es\n* OPUS readme: bem-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 22.8, chr-F: 0.403"
] | [
61,
100
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bem #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-bem-es\n\n\n* source languages: bem\n* target languages: es\n* OPUS readme: bem-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 22.8, chr-F: 0.403"
] | [
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] |
null | null | transformers |
### opus-mt-bem-fi
* source languages: bem
* target languages: fi
* OPUS readme: [bem-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bem-fi/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/bem-fi/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bem-fi/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bem-fi/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.bem.fi | 22.8 | 0.439 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bem-fi | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bem",
"fi",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bem #fi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-bem-fi
* source languages: bem
* target languages: fi
* OPUS readme: bem-fi
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 22.8, chr-F: 0.439
| [
"### opus-mt-bem-fi\n\n\n* source languages: bem\n* target languages: fi\n* OPUS readme: bem-fi\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 22.8, chr-F: 0.439"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bem #fi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-bem-fi\n\n\n* source languages: bem\n* target languages: fi\n* OPUS readme: bem-fi\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 22.8, chr-F: 0.439"
] | [
61,
100
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bem #fi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-bem-fi\n\n\n* source languages: bem\n* target languages: fi\n* OPUS readme: bem-fi\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 22.8, chr-F: 0.439"
] | [
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] |
null | null | transformers |
### opus-mt-bem-fr
* source languages: bem
* target languages: fr
* OPUS readme: [bem-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bem-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/bem-fr/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bem-fr/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bem-fr/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.bem.fr | 25.0 | 0.417 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bem-fr | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bem",
"fr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bem #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-bem-fr
* source languages: bem
* target languages: fr
* OPUS readme: bem-fr
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 25.0, chr-F: 0.417
| [
"### opus-mt-bem-fr\n\n\n* source languages: bem\n* target languages: fr\n* OPUS readme: bem-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 25.0, chr-F: 0.417"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bem #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-bem-fr\n\n\n* source languages: bem\n* target languages: fr\n* OPUS readme: bem-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 25.0, chr-F: 0.417"
] | [
61,
100
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bem #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-bem-fr\n\n\n* source languages: bem\n* target languages: fr\n* OPUS readme: bem-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 25.0, chr-F: 0.417"
] | [
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] |
null | null | transformers |
### opus-mt-bem-sv
* source languages: bem
* target languages: sv
* OPUS readme: [bem-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bem-sv/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/bem-sv/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bem-sv/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bem-sv/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.bem.sv | 25.6 | 0.434 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bem-sv | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bem",
"sv",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bem #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-bem-sv
* source languages: bem
* target languages: sv
* OPUS readme: bem-sv
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 25.6, chr-F: 0.434
| [
"### opus-mt-bem-sv\n\n\n* source languages: bem\n* target languages: sv\n* OPUS readme: bem-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 25.6, chr-F: 0.434"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bem #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-bem-sv\n\n\n* source languages: bem\n* target languages: sv\n* OPUS readme: bem-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 25.6, chr-F: 0.434"
] | [
61,
100
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bem #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-bem-sv\n\n\n* source languages: bem\n* target languages: sv\n* OPUS readme: bem-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 25.6, chr-F: 0.434"
] | [
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] |
null | null | transformers |
### opus-mt-ber-en
* source languages: ber
* target languages: en
* OPUS readme: [ber-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ber-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2019-12-18.zip](https://object.pouta.csc.fi/OPUS-MT-models/ber-en/opus-2019-12-18.zip)
* test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ber-en/opus-2019-12-18.test.txt)
* test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ber-en/opus-2019-12-18.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.ber.en | 37.3 | 0.566 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-ber-en | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"ber",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #ber #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-ber-en
* source languages: ber
* target languages: en
* OPUS readme: ber-en
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 37.3, chr-F: 0.566
| [
"### opus-mt-ber-en\n\n\n* source languages: ber\n* target languages: en\n* OPUS readme: ber-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 37.3, chr-F: 0.566"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ber #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-ber-en\n\n\n* source languages: ber\n* target languages: en\n* OPUS readme: ber-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 37.3, chr-F: 0.566"
] | [
61,
99
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ber #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-ber-en\n\n\n* source languages: ber\n* target languages: en\n* OPUS readme: ber-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 37.3, chr-F: 0.566"
] | [
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null | null | transformers |
### opus-mt-ber-es
* source languages: ber
* target languages: es
* OPUS readme: [ber-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ber-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-15.zip](https://object.pouta.csc.fi/OPUS-MT-models/ber-es/opus-2020-01-15.zip)
* test set translations: [opus-2020-01-15.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ber-es/opus-2020-01-15.test.txt)
* test set scores: [opus-2020-01-15.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ber-es/opus-2020-01-15.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.ber.es | 33.8 | 0.487 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-ber-es | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"ber",
"es",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #ber #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-ber-es
* source languages: ber
* target languages: es
* OPUS readme: ber-es
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 33.8, chr-F: 0.487
| [
"### opus-mt-ber-es\n\n\n* source languages: ber\n* target languages: es\n* OPUS readme: ber-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 33.8, chr-F: 0.487"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ber #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-ber-es\n\n\n* source languages: ber\n* target languages: es\n* OPUS readme: ber-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 33.8, chr-F: 0.487"
] | [
61,
100
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ber #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-ber-es\n\n\n* source languages: ber\n* target languages: es\n* OPUS readme: ber-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 33.8, chr-F: 0.487"
] | [
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] |
null | null | transformers |
### opus-mt-ber-fr
* source languages: ber
* target languages: fr
* OPUS readme: [ber-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ber-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/ber-fr/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ber-fr/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ber-fr/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.ber.fr | 60.2 | 0.754 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-ber-fr | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"ber",
"fr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #ber #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-ber-fr
* source languages: ber
* target languages: fr
* OPUS readme: ber-fr
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 60.2, chr-F: 0.754
| [
"### opus-mt-ber-fr\n\n\n* source languages: ber\n* target languages: fr\n* OPUS readme: ber-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 60.2, chr-F: 0.754"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ber #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-ber-fr\n\n\n* source languages: ber\n* target languages: fr\n* OPUS readme: ber-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 60.2, chr-F: 0.754"
] | [
61,
101
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ber #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-ber-fr\n\n\n* source languages: ber\n* target languages: fr\n* OPUS readme: ber-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 60.2, chr-F: 0.754"
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null | null | transformers |
### bul-deu
* source group: Bulgarian
* target group: German
* OPUS readme: [bul-deu](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-deu/README.md)
* model: transformer
* source language(s): bul
* target language(s): deu
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-07-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-deu/opus-2020-07-03.zip)
* test set translations: [opus-2020-07-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-deu/opus-2020-07-03.test.txt)
* test set scores: [opus-2020-07-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-deu/opus-2020-07-03.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.bul.deu | 49.3 | 0.676 |
### System Info:
- hf_name: bul-deu
- source_languages: bul
- target_languages: deu
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-deu/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['bg', 'de']
- src_constituents: {'bul', 'bul_Latn'}
- tgt_constituents: {'deu'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/bul-deu/opus-2020-07-03.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/bul-deu/opus-2020-07-03.test.txt
- src_alpha3: bul
- tgt_alpha3: deu
- short_pair: bg-de
- chrF2_score: 0.6759999999999999
- bleu: 49.3
- brevity_penalty: 1.0
- ref_len: 2218.0
- src_name: Bulgarian
- tgt_name: German
- train_date: 2020-07-03
- src_alpha2: bg
- tgt_alpha2: de
- prefer_old: False
- long_pair: bul-deu
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["bg", "de"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bg-de | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bg",
"de",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"bg",
"de"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bg #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### bul-deu
* source group: Bulgarian
* target group: German
* OPUS readme: bul-deu
* model: transformer
* source language(s): bul
* target language(s): deu
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 49.3, chr-F: 0.676
### System Info:
* hf\_name: bul-deu
* source\_languages: bul
* target\_languages: deu
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['bg', 'de']
* src\_constituents: {'bul', 'bul\_Latn'}
* tgt\_constituents: {'deu'}
* src\_multilingual: False
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: bul
* tgt\_alpha3: deu
* short\_pair: bg-de
* chrF2\_score: 0.6759999999999999
* bleu: 49.3
* brevity\_penalty: 1.0
* ref\_len: 2218.0
* src\_name: Bulgarian
* tgt\_name: German
* train\_date: 2020-07-03
* src\_alpha2: bg
* tgt\_alpha2: de
* prefer\_old: False
* long\_pair: bul-deu
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### bul-deu\n\n\n* source group: Bulgarian\n* target group: German\n* OPUS readme: bul-deu\n* model: transformer\n* source language(s): bul\n* target language(s): deu\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 49.3, chr-F: 0.676",
"### System Info:\n\n\n* hf\\_name: bul-deu\n* source\\_languages: bul\n* target\\_languages: deu\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['bg', 'de']\n* src\\_constituents: {'bul', 'bul\\_Latn'}\n* tgt\\_constituents: {'deu'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: bul\n* tgt\\_alpha3: deu\n* short\\_pair: bg-de\n* chrF2\\_score: 0.6759999999999999\n* bleu: 49.3\n* brevity\\_penalty: 1.0\n* ref\\_len: 2218.0\n* src\\_name: Bulgarian\n* tgt\\_name: German\n* train\\_date: 2020-07-03\n* src\\_alpha2: bg\n* tgt\\_alpha2: de\n* prefer\\_old: False\n* long\\_pair: bul-deu\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bg #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### bul-deu\n\n\n* source group: Bulgarian\n* target group: German\n* OPUS readme: bul-deu\n* model: transformer\n* source language(s): bul\n* target language(s): deu\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 49.3, chr-F: 0.676",
"### System Info:\n\n\n* hf\\_name: bul-deu\n* source\\_languages: bul\n* target\\_languages: deu\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['bg', 'de']\n* src\\_constituents: {'bul', 'bul\\_Latn'}\n* tgt\\_constituents: {'deu'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: bul\n* tgt\\_alpha3: deu\n* short\\_pair: bg-de\n* chrF2\\_score: 0.6759999999999999\n* bleu: 49.3\n* brevity\\_penalty: 1.0\n* ref\\_len: 2218.0\n* src\\_name: Bulgarian\n* tgt\\_name: German\n* train\\_date: 2020-07-03\n* src\\_alpha2: bg\n* tgt\\_alpha2: de\n* prefer\\_old: False\n* long\\_pair: bul-deu\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
61,
121,
410
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bg #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### bul-deu\n\n\n* source group: Bulgarian\n* target group: German\n* OPUS readme: bul-deu\n* model: transformer\n* source language(s): bul\n* target language(s): deu\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 49.3, chr-F: 0.676"
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] |
null | null | transformers |
### opus-mt-bg-en
* source languages: bg
* target languages: en
* OPUS readme: [bg-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bg-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2019-12-18.zip](https://object.pouta.csc.fi/OPUS-MT-models/bg-en/opus-2019-12-18.zip)
* test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bg-en/opus-2019-12-18.test.txt)
* test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bg-en/opus-2019-12-18.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.bg.en | 59.4 | 0.727 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bg-en | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bg",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bg #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-bg-en
* source languages: bg
* target languages: en
* OPUS readme: bg-en
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 59.4, chr-F: 0.727
| [
"### opus-mt-bg-en\n\n\n* source languages: bg\n* target languages: en\n* OPUS readme: bg-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 59.4, chr-F: 0.727"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bg #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-bg-en\n\n\n* source languages: bg\n* target languages: en\n* OPUS readme: bg-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 59.4, chr-F: 0.727"
] | [
61,
103
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bg #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-bg-en\n\n\n* source languages: bg\n* target languages: en\n* OPUS readme: bg-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 59.4, chr-F: 0.727"
] | [
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null | null | transformers |
### bul-epo
* source group: Bulgarian
* target group: Esperanto
* OPUS readme: [bul-epo](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-epo/README.md)
* model: transformer-align
* source language(s): bul
* target language(s): epo
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-epo/opus-2020-06-16.zip)
* test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-epo/opus-2020-06-16.test.txt)
* test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-epo/opus-2020-06-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.bul.epo | 24.5 | 0.438 |
### System Info:
- hf_name: bul-epo
- source_languages: bul
- target_languages: epo
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-epo/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['bg', 'eo']
- src_constituents: {'bul', 'bul_Latn'}
- tgt_constituents: {'epo'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/bul-epo/opus-2020-06-16.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/bul-epo/opus-2020-06-16.test.txt
- src_alpha3: bul
- tgt_alpha3: epo
- short_pair: bg-eo
- chrF2_score: 0.43799999999999994
- bleu: 24.5
- brevity_penalty: 0.9670000000000001
- ref_len: 4043.0
- src_name: Bulgarian
- tgt_name: Esperanto
- train_date: 2020-06-16
- src_alpha2: bg
- tgt_alpha2: eo
- prefer_old: False
- long_pair: bul-epo
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["bg", "eo"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bg-eo | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bg",
"eo",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"bg",
"eo"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bg #eo #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### bul-epo
* source group: Bulgarian
* target group: Esperanto
* OPUS readme: bul-epo
* model: transformer-align
* source language(s): bul
* target language(s): epo
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 24.5, chr-F: 0.438
### System Info:
* hf\_name: bul-epo
* source\_languages: bul
* target\_languages: epo
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['bg', 'eo']
* src\_constituents: {'bul', 'bul\_Latn'}
* tgt\_constituents: {'epo'}
* src\_multilingual: False
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm4k,spm4k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: bul
* tgt\_alpha3: epo
* short\_pair: bg-eo
* chrF2\_score: 0.43799999999999994
* bleu: 24.5
* brevity\_penalty: 0.9670000000000001
* ref\_len: 4043.0
* src\_name: Bulgarian
* tgt\_name: Esperanto
* train\_date: 2020-06-16
* src\_alpha2: bg
* tgt\_alpha2: eo
* prefer\_old: False
* long\_pair: bul-epo
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### bul-epo\n\n\n* source group: Bulgarian\n* target group: Esperanto\n* OPUS readme: bul-epo\n* model: transformer-align\n* source language(s): bul\n* target language(s): epo\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm4k,spm4k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 24.5, chr-F: 0.438",
"### System Info:\n\n\n* hf\\_name: bul-epo\n* source\\_languages: bul\n* target\\_languages: epo\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['bg', 'eo']\n* src\\_constituents: {'bul', 'bul\\_Latn'}\n* tgt\\_constituents: {'epo'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm4k,spm4k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: bul\n* tgt\\_alpha3: epo\n* short\\_pair: bg-eo\n* chrF2\\_score: 0.43799999999999994\n* bleu: 24.5\n* brevity\\_penalty: 0.9670000000000001\n* ref\\_len: 4043.0\n* src\\_name: Bulgarian\n* tgt\\_name: Esperanto\n* train\\_date: 2020-06-16\n* src\\_alpha2: bg\n* tgt\\_alpha2: eo\n* prefer\\_old: False\n* long\\_pair: bul-epo\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bg #eo #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### bul-epo\n\n\n* source group: Bulgarian\n* target group: Esperanto\n* OPUS readme: bul-epo\n* model: transformer-align\n* source language(s): bul\n* target language(s): epo\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm4k,spm4k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 24.5, chr-F: 0.438",
"### System Info:\n\n\n* hf\\_name: bul-epo\n* source\\_languages: bul\n* target\\_languages: epo\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['bg', 'eo']\n* src\\_constituents: {'bul', 'bul\\_Latn'}\n* tgt\\_constituents: {'epo'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm4k,spm4k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: bul\n* tgt\\_alpha3: epo\n* short\\_pair: bg-eo\n* chrF2\\_score: 0.43799999999999994\n* bleu: 24.5\n* brevity\\_penalty: 0.9670000000000001\n* ref\\_len: 4043.0\n* src\\_name: Bulgarian\n* tgt\\_name: Esperanto\n* train\\_date: 2020-06-16\n* src\\_alpha2: bg\n* tgt\\_alpha2: eo\n* prefer\\_old: False\n* long\\_pair: bul-epo\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
61,
124,
413
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bg #eo #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### bul-epo\n\n\n* source group: Bulgarian\n* target group: Esperanto\n* OPUS readme: bul-epo\n* model: transformer-align\n* source language(s): bul\n* target language(s): epo\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm4k,spm4k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 24.5, chr-F: 0.438"
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null | null | transformers |
### bul-spa
* source group: Bulgarian
* target group: Spanish
* OPUS readme: [bul-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-spa/README.md)
* model: transformer
* source language(s): bul
* target language(s): spa
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-07-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-spa/opus-2020-07-03.zip)
* test set translations: [opus-2020-07-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-spa/opus-2020-07-03.test.txt)
* test set scores: [opus-2020-07-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-spa/opus-2020-07-03.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.bul.spa | 49.1 | 0.661 |
### System Info:
- hf_name: bul-spa
- source_languages: bul
- target_languages: spa
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-spa/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['bg', 'es']
- src_constituents: {'bul', 'bul_Latn'}
- tgt_constituents: {'spa'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/bul-spa/opus-2020-07-03.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/bul-spa/opus-2020-07-03.test.txt
- src_alpha3: bul
- tgt_alpha3: spa
- short_pair: bg-es
- chrF2_score: 0.6609999999999999
- bleu: 49.1
- brevity_penalty: 0.992
- ref_len: 1783.0
- src_name: Bulgarian
- tgt_name: Spanish
- train_date: 2020-07-03
- src_alpha2: bg
- tgt_alpha2: es
- prefer_old: False
- long_pair: bul-spa
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["bg", "es"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bg-es | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bg",
"es",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"bg",
"es"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bg #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### bul-spa
* source group: Bulgarian
* target group: Spanish
* OPUS readme: bul-spa
* model: transformer
* source language(s): bul
* target language(s): spa
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 49.1, chr-F: 0.661
### System Info:
* hf\_name: bul-spa
* source\_languages: bul
* target\_languages: spa
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['bg', 'es']
* src\_constituents: {'bul', 'bul\_Latn'}
* tgt\_constituents: {'spa'}
* src\_multilingual: False
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: bul
* tgt\_alpha3: spa
* short\_pair: bg-es
* chrF2\_score: 0.6609999999999999
* bleu: 49.1
* brevity\_penalty: 0.992
* ref\_len: 1783.0
* src\_name: Bulgarian
* tgt\_name: Spanish
* train\_date: 2020-07-03
* src\_alpha2: bg
* tgt\_alpha2: es
* prefer\_old: False
* long\_pair: bul-spa
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### bul-spa\n\n\n* source group: Bulgarian\n* target group: Spanish\n* OPUS readme: bul-spa\n* model: transformer\n* source language(s): bul\n* target language(s): spa\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 49.1, chr-F: 0.661",
"### System Info:\n\n\n* hf\\_name: bul-spa\n* source\\_languages: bul\n* target\\_languages: spa\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['bg', 'es']\n* src\\_constituents: {'bul', 'bul\\_Latn'}\n* tgt\\_constituents: {'spa'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: bul\n* tgt\\_alpha3: spa\n* short\\_pair: bg-es\n* chrF2\\_score: 0.6609999999999999\n* bleu: 49.1\n* brevity\\_penalty: 0.992\n* ref\\_len: 1783.0\n* src\\_name: Bulgarian\n* tgt\\_name: Spanish\n* train\\_date: 2020-07-03\n* src\\_alpha2: bg\n* tgt\\_alpha2: es\n* prefer\\_old: False\n* long\\_pair: bul-spa\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bg #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### bul-spa\n\n\n* source group: Bulgarian\n* target group: Spanish\n* OPUS readme: bul-spa\n* model: transformer\n* source language(s): bul\n* target language(s): spa\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 49.1, chr-F: 0.661",
"### System Info:\n\n\n* hf\\_name: bul-spa\n* source\\_languages: bul\n* target\\_languages: spa\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['bg', 'es']\n* src\\_constituents: {'bul', 'bul\\_Latn'}\n* tgt\\_constituents: {'spa'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: bul\n* tgt\\_alpha3: spa\n* short\\_pair: bg-es\n* chrF2\\_score: 0.6609999999999999\n* bleu: 49.1\n* brevity\\_penalty: 0.992\n* ref\\_len: 1783.0\n* src\\_name: Bulgarian\n* tgt\\_name: Spanish\n* train\\_date: 2020-07-03\n* src\\_alpha2: bg\n* tgt\\_alpha2: es\n* prefer\\_old: False\n* long\\_pair: bul-spa\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
61,
118,
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] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bg #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### bul-spa\n\n\n* source group: Bulgarian\n* target group: Spanish\n* OPUS readme: bul-spa\n* model: transformer\n* source language(s): bul\n* target language(s): spa\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 49.1, chr-F: 0.661"
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null | null | transformers |
### opus-mt-bg-fi
* source languages: bg
* target languages: fi
* OPUS readme: [bg-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bg-fi/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/bg-fi/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bg-fi/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bg-fi/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.bg.fi | 23.7 | 0.505 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bg-fi | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bg",
"fi",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bg #fi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-bg-fi
* source languages: bg
* target languages: fi
* OPUS readme: bg-fi
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 23.7, chr-F: 0.505
| [
"### opus-mt-bg-fi\n\n\n* source languages: bg\n* target languages: fi\n* OPUS readme: bg-fi\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 23.7, chr-F: 0.505"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bg #fi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-bg-fi\n\n\n* source languages: bg\n* target languages: fi\n* OPUS readme: bg-fi\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 23.7, chr-F: 0.505"
] | [
61,
101
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bg #fi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-bg-fi\n\n\n* source languages: bg\n* target languages: fi\n* OPUS readme: bg-fi\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 23.7, chr-F: 0.505"
] | [
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null | null | transformers |
### bul-fra
* source group: Bulgarian
* target group: French
* OPUS readme: [bul-fra](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-fra/README.md)
* model: transformer
* source language(s): bul
* target language(s): fra
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-07-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-fra/opus-2020-07-03.zip)
* test set translations: [opus-2020-07-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-fra/opus-2020-07-03.test.txt)
* test set scores: [opus-2020-07-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-fra/opus-2020-07-03.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.bul.fra | 53.7 | 0.693 |
### System Info:
- hf_name: bul-fra
- source_languages: bul
- target_languages: fra
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-fra/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['bg', 'fr']
- src_constituents: {'bul', 'bul_Latn'}
- tgt_constituents: {'fra'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/bul-fra/opus-2020-07-03.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/bul-fra/opus-2020-07-03.test.txt
- src_alpha3: bul
- tgt_alpha3: fra
- short_pair: bg-fr
- chrF2_score: 0.693
- bleu: 53.7
- brevity_penalty: 0.977
- ref_len: 3669.0
- src_name: Bulgarian
- tgt_name: French
- train_date: 2020-07-03
- src_alpha2: bg
- tgt_alpha2: fr
- prefer_old: False
- long_pair: bul-fra
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["bg", "fr"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bg-fr | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bg",
"fr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"bg",
"fr"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bg #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### bul-fra
* source group: Bulgarian
* target group: French
* OPUS readme: bul-fra
* model: transformer
* source language(s): bul
* target language(s): fra
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 53.7, chr-F: 0.693
### System Info:
* hf\_name: bul-fra
* source\_languages: bul
* target\_languages: fra
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['bg', 'fr']
* src\_constituents: {'bul', 'bul\_Latn'}
* tgt\_constituents: {'fra'}
* src\_multilingual: False
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: bul
* tgt\_alpha3: fra
* short\_pair: bg-fr
* chrF2\_score: 0.693
* bleu: 53.7
* brevity\_penalty: 0.977
* ref\_len: 3669.0
* src\_name: Bulgarian
* tgt\_name: French
* train\_date: 2020-07-03
* src\_alpha2: bg
* tgt\_alpha2: fr
* prefer\_old: False
* long\_pair: bul-fra
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### bul-fra\n\n\n* source group: Bulgarian\n* target group: French\n* OPUS readme: bul-fra\n* model: transformer\n* source language(s): bul\n* target language(s): fra\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 53.7, chr-F: 0.693",
"### System Info:\n\n\n* hf\\_name: bul-fra\n* source\\_languages: bul\n* target\\_languages: fra\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['bg', 'fr']\n* src\\_constituents: {'bul', 'bul\\_Latn'}\n* tgt\\_constituents: {'fra'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: bul\n* tgt\\_alpha3: fra\n* short\\_pair: bg-fr\n* chrF2\\_score: 0.693\n* bleu: 53.7\n* brevity\\_penalty: 0.977\n* ref\\_len: 3669.0\n* src\\_name: Bulgarian\n* tgt\\_name: French\n* train\\_date: 2020-07-03\n* src\\_alpha2: bg\n* tgt\\_alpha2: fr\n* prefer\\_old: False\n* long\\_pair: bul-fra\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bg #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### bul-fra\n\n\n* source group: Bulgarian\n* target group: French\n* OPUS readme: bul-fra\n* model: transformer\n* source language(s): bul\n* target language(s): fra\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 53.7, chr-F: 0.693",
"### System Info:\n\n\n* hf\\_name: bul-fra\n* source\\_languages: bul\n* target\\_languages: fra\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['bg', 'fr']\n* src\\_constituents: {'bul', 'bul\\_Latn'}\n* tgt\\_constituents: {'fra'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: bul\n* tgt\\_alpha3: fra\n* short\\_pair: bg-fr\n* chrF2\\_score: 0.693\n* bleu: 53.7\n* brevity\\_penalty: 0.977\n* ref\\_len: 3669.0\n* src\\_name: Bulgarian\n* tgt\\_name: French\n* train\\_date: 2020-07-03\n* src\\_alpha2: bg\n* tgt\\_alpha2: fr\n* prefer\\_old: False\n* long\\_pair: bul-fra\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
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"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bg #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### bul-fra\n\n\n* source group: Bulgarian\n* target group: French\n* OPUS readme: bul-fra\n* model: transformer\n* source language(s): bul\n* target language(s): fra\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 53.7, chr-F: 0.693"
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null | null | transformers |
### bul-ita
* source group: Bulgarian
* target group: Italian
* OPUS readme: [bul-ita](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-ita/README.md)
* model: transformer
* source language(s): bul
* target language(s): ita
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-07-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-ita/opus-2020-07-03.zip)
* test set translations: [opus-2020-07-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-ita/opus-2020-07-03.test.txt)
* test set scores: [opus-2020-07-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-ita/opus-2020-07-03.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.bul.ita | 43.1 | 0.653 |
### System Info:
- hf_name: bul-ita
- source_languages: bul
- target_languages: ita
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-ita/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['bg', 'it']
- src_constituents: {'bul', 'bul_Latn'}
- tgt_constituents: {'ita'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/bul-ita/opus-2020-07-03.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/bul-ita/opus-2020-07-03.test.txt
- src_alpha3: bul
- tgt_alpha3: ita
- short_pair: bg-it
- chrF2_score: 0.653
- bleu: 43.1
- brevity_penalty: 0.987
- ref_len: 16951.0
- src_name: Bulgarian
- tgt_name: Italian
- train_date: 2020-07-03
- src_alpha2: bg
- tgt_alpha2: it
- prefer_old: False
- long_pair: bul-ita
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["bg", "it"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bg-it | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bg",
"it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"bg",
"it"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bg #it #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### bul-ita
* source group: Bulgarian
* target group: Italian
* OPUS readme: bul-ita
* model: transformer
* source language(s): bul
* target language(s): ita
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 43.1, chr-F: 0.653
### System Info:
* hf\_name: bul-ita
* source\_languages: bul
* target\_languages: ita
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['bg', 'it']
* src\_constituents: {'bul', 'bul\_Latn'}
* tgt\_constituents: {'ita'}
* src\_multilingual: False
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: bul
* tgt\_alpha3: ita
* short\_pair: bg-it
* chrF2\_score: 0.653
* bleu: 43.1
* brevity\_penalty: 0.987
* ref\_len: 16951.0
* src\_name: Bulgarian
* tgt\_name: Italian
* train\_date: 2020-07-03
* src\_alpha2: bg
* tgt\_alpha2: it
* prefer\_old: False
* long\_pair: bul-ita
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### bul-ita\n\n\n* source group: Bulgarian\n* target group: Italian\n* OPUS readme: bul-ita\n* model: transformer\n* source language(s): bul\n* target language(s): ita\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 43.1, chr-F: 0.653",
"### System Info:\n\n\n* hf\\_name: bul-ita\n* source\\_languages: bul\n* target\\_languages: ita\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['bg', 'it']\n* src\\_constituents: {'bul', 'bul\\_Latn'}\n* tgt\\_constituents: {'ita'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: bul\n* tgt\\_alpha3: ita\n* short\\_pair: bg-it\n* chrF2\\_score: 0.653\n* bleu: 43.1\n* brevity\\_penalty: 0.987\n* ref\\_len: 16951.0\n* src\\_name: Bulgarian\n* tgt\\_name: Italian\n* train\\_date: 2020-07-03\n* src\\_alpha2: bg\n* tgt\\_alpha2: it\n* prefer\\_old: False\n* long\\_pair: bul-ita\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bg #it #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### bul-ita\n\n\n* source group: Bulgarian\n* target group: Italian\n* OPUS readme: bul-ita\n* model: transformer\n* source language(s): bul\n* target language(s): ita\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 43.1, chr-F: 0.653",
"### System Info:\n\n\n* hf\\_name: bul-ita\n* source\\_languages: bul\n* target\\_languages: ita\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['bg', 'it']\n* src\\_constituents: {'bul', 'bul\\_Latn'}\n* tgt\\_constituents: {'ita'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: bul\n* tgt\\_alpha3: ita\n* short\\_pair: bg-it\n* chrF2\\_score: 0.653\n* bleu: 43.1\n* brevity\\_penalty: 0.987\n* ref\\_len: 16951.0\n* src\\_name: Bulgarian\n* tgt\\_name: Italian\n* train\\_date: 2020-07-03\n* src\\_alpha2: bg\n* tgt\\_alpha2: it\n* prefer\\_old: False\n* long\\_pair: bul-ita\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
61,
117,
400
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bg #it #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### bul-ita\n\n\n* source group: Bulgarian\n* target group: Italian\n* OPUS readme: bul-ita\n* model: transformer\n* source language(s): bul\n* target language(s): ita\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 43.1, chr-F: 0.653"
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null | null | transformers |
### bul-rus
* source group: Bulgarian
* target group: Russian
* OPUS readme: [bul-rus](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-rus/README.md)
* model: transformer
* source language(s): bul bul_Latn
* target language(s): rus
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-07-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-rus/opus-2020-07-03.zip)
* test set translations: [opus-2020-07-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-rus/opus-2020-07-03.test.txt)
* test set scores: [opus-2020-07-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-rus/opus-2020-07-03.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.bul.rus | 48.5 | 0.691 |
### System Info:
- hf_name: bul-rus
- source_languages: bul
- target_languages: rus
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-rus/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['bg', 'ru']
- src_constituents: {'bul', 'bul_Latn'}
- tgt_constituents: {'rus'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/bul-rus/opus-2020-07-03.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/bul-rus/opus-2020-07-03.test.txt
- src_alpha3: bul
- tgt_alpha3: rus
- short_pair: bg-ru
- chrF2_score: 0.691
- bleu: 48.5
- brevity_penalty: 1.0
- ref_len: 7870.0
- src_name: Bulgarian
- tgt_name: Russian
- train_date: 2020-07-03
- src_alpha2: bg
- tgt_alpha2: ru
- prefer_old: False
- long_pair: bul-rus
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["bg", "ru"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bg-ru | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bg",
"ru",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"bg",
"ru"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bg #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### bul-rus
* source group: Bulgarian
* target group: Russian
* OPUS readme: bul-rus
* model: transformer
* source language(s): bul bul\_Latn
* target language(s): rus
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 48.5, chr-F: 0.691
### System Info:
* hf\_name: bul-rus
* source\_languages: bul
* target\_languages: rus
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['bg', 'ru']
* src\_constituents: {'bul', 'bul\_Latn'}
* tgt\_constituents: {'rus'}
* src\_multilingual: False
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: bul
* tgt\_alpha3: rus
* short\_pair: bg-ru
* chrF2\_score: 0.691
* bleu: 48.5
* brevity\_penalty: 1.0
* ref\_len: 7870.0
* src\_name: Bulgarian
* tgt\_name: Russian
* train\_date: 2020-07-03
* src\_alpha2: bg
* tgt\_alpha2: ru
* prefer\_old: False
* long\_pair: bul-rus
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### bul-rus\n\n\n* source group: Bulgarian\n* target group: Russian\n* OPUS readme: bul-rus\n* model: transformer\n* source language(s): bul bul\\_Latn\n* target language(s): rus\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 48.5, chr-F: 0.691",
"### System Info:\n\n\n* hf\\_name: bul-rus\n* source\\_languages: bul\n* target\\_languages: rus\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['bg', 'ru']\n* src\\_constituents: {'bul', 'bul\\_Latn'}\n* tgt\\_constituents: {'rus'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: bul\n* tgt\\_alpha3: rus\n* short\\_pair: bg-ru\n* chrF2\\_score: 0.691\n* bleu: 48.5\n* brevity\\_penalty: 1.0\n* ref\\_len: 7870.0\n* src\\_name: Bulgarian\n* tgt\\_name: Russian\n* train\\_date: 2020-07-03\n* src\\_alpha2: bg\n* tgt\\_alpha2: ru\n* prefer\\_old: False\n* long\\_pair: bul-rus\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bg #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### bul-rus\n\n\n* source group: Bulgarian\n* target group: Russian\n* OPUS readme: bul-rus\n* model: transformer\n* source language(s): bul bul\\_Latn\n* target language(s): rus\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 48.5, chr-F: 0.691",
"### System Info:\n\n\n* hf\\_name: bul-rus\n* source\\_languages: bul\n* target\\_languages: rus\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['bg', 'ru']\n* src\\_constituents: {'bul', 'bul\\_Latn'}\n* tgt\\_constituents: {'rus'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: bul\n* tgt\\_alpha3: rus\n* short\\_pair: bg-ru\n* chrF2\\_score: 0.691\n* bleu: 48.5\n* brevity\\_penalty: 1.0\n* ref\\_len: 7870.0\n* src\\_name: Bulgarian\n* tgt\\_name: Russian\n* train\\_date: 2020-07-03\n* src\\_alpha2: bg\n* tgt\\_alpha2: ru\n* prefer\\_old: False\n* long\\_pair: bul-rus\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
61,
123,
398
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bg #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### bul-rus\n\n\n* source group: Bulgarian\n* target group: Russian\n* OPUS readme: bul-rus\n* model: transformer\n* source language(s): bul bul\\_Latn\n* target language(s): rus\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 48.5, chr-F: 0.691"
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null | null | transformers |
### opus-mt-bg-sv
* source languages: bg
* target languages: sv
* OPUS readme: [bg-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bg-sv/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/bg-sv/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bg-sv/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bg-sv/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.bg.sv | 29.1 | 0.494 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bg-sv | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bg",
"sv",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bg #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-bg-sv
* source languages: bg
* target languages: sv
* OPUS readme: bg-sv
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 29.1, chr-F: 0.494
| [
"### opus-mt-bg-sv\n\n\n* source languages: bg\n* target languages: sv\n* OPUS readme: bg-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 29.1, chr-F: 0.494"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bg #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-bg-sv\n\n\n* source languages: bg\n* target languages: sv\n* OPUS readme: bg-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 29.1, chr-F: 0.494"
] | [
61,
102
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bg #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-bg-sv\n\n\n* source languages: bg\n* target languages: sv\n* OPUS readme: bg-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 29.1, chr-F: 0.494"
] | [
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null | null | transformers |
### bul-tur
* source group: Bulgarian
* target group: Turkish
* OPUS readme: [bul-tur](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-tur/README.md)
* model: transformer
* source language(s): bul bul_Latn
* target language(s): tur
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-07-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-tur/opus-2020-07-03.zip)
* test set translations: [opus-2020-07-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-tur/opus-2020-07-03.test.txt)
* test set scores: [opus-2020-07-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-tur/opus-2020-07-03.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.bul.tur | 40.9 | 0.687 |
### System Info:
- hf_name: bul-tur
- source_languages: bul
- target_languages: tur
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-tur/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['bg', 'tr']
- src_constituents: {'bul', 'bul_Latn'}
- tgt_constituents: {'tur'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/bul-tur/opus-2020-07-03.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/bul-tur/opus-2020-07-03.test.txt
- src_alpha3: bul
- tgt_alpha3: tur
- short_pair: bg-tr
- chrF2_score: 0.687
- bleu: 40.9
- brevity_penalty: 0.946
- ref_len: 4948.0
- src_name: Bulgarian
- tgt_name: Turkish
- train_date: 2020-07-03
- src_alpha2: bg
- tgt_alpha2: tr
- prefer_old: False
- long_pair: bul-tur
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["bg", "tr"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bg-tr | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bg",
"tr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"bg",
"tr"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bg #tr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### bul-tur
* source group: Bulgarian
* target group: Turkish
* OPUS readme: bul-tur
* model: transformer
* source language(s): bul bul\_Latn
* target language(s): tur
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 40.9, chr-F: 0.687
### System Info:
* hf\_name: bul-tur
* source\_languages: bul
* target\_languages: tur
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['bg', 'tr']
* src\_constituents: {'bul', 'bul\_Latn'}
* tgt\_constituents: {'tur'}
* src\_multilingual: False
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: bul
* tgt\_alpha3: tur
* short\_pair: bg-tr
* chrF2\_score: 0.687
* bleu: 40.9
* brevity\_penalty: 0.946
* ref\_len: 4948.0
* src\_name: Bulgarian
* tgt\_name: Turkish
* train\_date: 2020-07-03
* src\_alpha2: bg
* tgt\_alpha2: tr
* prefer\_old: False
* long\_pair: bul-tur
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### bul-tur\n\n\n* source group: Bulgarian\n* target group: Turkish\n* OPUS readme: bul-tur\n* model: transformer\n* source language(s): bul bul\\_Latn\n* target language(s): tur\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 40.9, chr-F: 0.687",
"### System Info:\n\n\n* hf\\_name: bul-tur\n* source\\_languages: bul\n* target\\_languages: tur\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['bg', 'tr']\n* src\\_constituents: {'bul', 'bul\\_Latn'}\n* tgt\\_constituents: {'tur'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: bul\n* tgt\\_alpha3: tur\n* short\\_pair: bg-tr\n* chrF2\\_score: 0.687\n* bleu: 40.9\n* brevity\\_penalty: 0.946\n* ref\\_len: 4948.0\n* src\\_name: Bulgarian\n* tgt\\_name: Turkish\n* train\\_date: 2020-07-03\n* src\\_alpha2: bg\n* tgt\\_alpha2: tr\n* prefer\\_old: False\n* long\\_pair: bul-tur\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bg #tr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### bul-tur\n\n\n* source group: Bulgarian\n* target group: Turkish\n* OPUS readme: bul-tur\n* model: transformer\n* source language(s): bul bul\\_Latn\n* target language(s): tur\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 40.9, chr-F: 0.687",
"### System Info:\n\n\n* hf\\_name: bul-tur\n* source\\_languages: bul\n* target\\_languages: tur\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['bg', 'tr']\n* src\\_constituents: {'bul', 'bul\\_Latn'}\n* tgt\\_constituents: {'tur'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: bul\n* tgt\\_alpha3: tur\n* short\\_pair: bg-tr\n* chrF2\\_score: 0.687\n* bleu: 40.9\n* brevity\\_penalty: 0.946\n* ref\\_len: 4948.0\n* src\\_name: Bulgarian\n* tgt\\_name: Turkish\n* train\\_date: 2020-07-03\n* src\\_alpha2: bg\n* tgt\\_alpha2: tr\n* prefer\\_old: False\n* long\\_pair: bul-tur\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
61,
124,
402
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bg #tr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### bul-tur\n\n\n* source group: Bulgarian\n* target group: Turkish\n* OPUS readme: bul-tur\n* model: transformer\n* source language(s): bul bul\\_Latn\n* target language(s): tur\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 40.9, chr-F: 0.687"
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null | null | transformers |
### bul-ukr
* source group: Bulgarian
* target group: Ukrainian
* OPUS readme: [bul-ukr](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-ukr/README.md)
* model: transformer-align
* source language(s): bul
* target language(s): ukr
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-ukr/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-ukr/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-ukr/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.bul.ukr | 49.2 | 0.683 |
### System Info:
- hf_name: bul-ukr
- source_languages: bul
- target_languages: ukr
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-ukr/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['bg', 'uk']
- src_constituents: {'bul', 'bul_Latn'}
- tgt_constituents: {'ukr'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/bul-ukr/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/bul-ukr/opus-2020-06-17.test.txt
- src_alpha3: bul
- tgt_alpha3: ukr
- short_pair: bg-uk
- chrF2_score: 0.6829999999999999
- bleu: 49.2
- brevity_penalty: 0.983
- ref_len: 4932.0
- src_name: Bulgarian
- tgt_name: Ukrainian
- train_date: 2020-06-17
- src_alpha2: bg
- tgt_alpha2: uk
- prefer_old: False
- long_pair: bul-ukr
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["bg", "uk"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bg-uk | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bg",
"uk",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"bg",
"uk"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bg #uk #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### bul-ukr
* source group: Bulgarian
* target group: Ukrainian
* OPUS readme: bul-ukr
* model: transformer-align
* source language(s): bul
* target language(s): ukr
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 49.2, chr-F: 0.683
### System Info:
* hf\_name: bul-ukr
* source\_languages: bul
* target\_languages: ukr
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['bg', 'uk']
* src\_constituents: {'bul', 'bul\_Latn'}
* tgt\_constituents: {'ukr'}
* src\_multilingual: False
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: bul
* tgt\_alpha3: ukr
* short\_pair: bg-uk
* chrF2\_score: 0.6829999999999999
* bleu: 49.2
* brevity\_penalty: 0.983
* ref\_len: 4932.0
* src\_name: Bulgarian
* tgt\_name: Ukrainian
* train\_date: 2020-06-17
* src\_alpha2: bg
* tgt\_alpha2: uk
* prefer\_old: False
* long\_pair: bul-ukr
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### bul-ukr\n\n\n* source group: Bulgarian\n* target group: Ukrainian\n* OPUS readme: bul-ukr\n* model: transformer-align\n* source language(s): bul\n* target language(s): ukr\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 49.2, chr-F: 0.683",
"### System Info:\n\n\n* hf\\_name: bul-ukr\n* source\\_languages: bul\n* target\\_languages: ukr\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['bg', 'uk']\n* src\\_constituents: {'bul', 'bul\\_Latn'}\n* tgt\\_constituents: {'ukr'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: bul\n* tgt\\_alpha3: ukr\n* short\\_pair: bg-uk\n* chrF2\\_score: 0.6829999999999999\n* bleu: 49.2\n* brevity\\_penalty: 0.983\n* ref\\_len: 4932.0\n* src\\_name: Bulgarian\n* tgt\\_name: Ukrainian\n* train\\_date: 2020-06-17\n* src\\_alpha2: bg\n* tgt\\_alpha2: uk\n* prefer\\_old: False\n* long\\_pair: bul-ukr\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bg #uk #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### bul-ukr\n\n\n* source group: Bulgarian\n* target group: Ukrainian\n* OPUS readme: bul-ukr\n* model: transformer-align\n* source language(s): bul\n* target language(s): ukr\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 49.2, chr-F: 0.683",
"### System Info:\n\n\n* hf\\_name: bul-ukr\n* source\\_languages: bul\n* target\\_languages: ukr\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['bg', 'uk']\n* src\\_constituents: {'bul', 'bul\\_Latn'}\n* tgt\\_constituents: {'ukr'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: bul\n* tgt\\_alpha3: ukr\n* short\\_pair: bg-uk\n* chrF2\\_score: 0.6829999999999999\n* bleu: 49.2\n* brevity\\_penalty: 0.983\n* ref\\_len: 4932.0\n* src\\_name: Bulgarian\n* tgt\\_name: Ukrainian\n* train\\_date: 2020-06-17\n* src\\_alpha2: bg\n* tgt\\_alpha2: uk\n* prefer\\_old: False\n* long\\_pair: bul-ukr\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
61,
124,
409
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bg #uk #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### bul-ukr\n\n\n* source group: Bulgarian\n* target group: Ukrainian\n* OPUS readme: bul-ukr\n* model: transformer-align\n* source language(s): bul\n* target language(s): ukr\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 49.2, chr-F: 0.683"
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] |
null | null | transformers |
### opus-mt-bi-en
* source languages: bi
* target languages: en
* OPUS readme: [bi-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bi-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/bi-en/opus-2020-01-20.zip)
* test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bi-en/opus-2020-01-20.test.txt)
* test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bi-en/opus-2020-01-20.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.bi.en | 30.3 | 0.458 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bi-en | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bi",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bi #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-bi-en
* source languages: bi
* target languages: en
* OPUS readme: bi-en
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 30.3, chr-F: 0.458
| [
"### opus-mt-bi-en\n\n\n* source languages: bi\n* target languages: en\n* OPUS readme: bi-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 30.3, chr-F: 0.458"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bi #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-bi-en\n\n\n* source languages: bi\n* target languages: en\n* OPUS readme: bi-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 30.3, chr-F: 0.458"
] | [
61,
101
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bi #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-bi-en\n\n\n* source languages: bi\n* target languages: en\n* OPUS readme: bi-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 30.3, chr-F: 0.458"
] | [
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null | null | transformers |
### opus-mt-bi-es
* source languages: bi
* target languages: es
* OPUS readme: [bi-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bi-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/bi-es/opus-2020-01-20.zip)
* test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bi-es/opus-2020-01-20.test.txt)
* test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bi-es/opus-2020-01-20.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.bi.es | 21.1 | 0.388 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bi-es | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bi",
"es",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bi #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-bi-es
* source languages: bi
* target languages: es
* OPUS readme: bi-es
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 21.1, chr-F: 0.388
| [
"### opus-mt-bi-es\n\n\n* source languages: bi\n* target languages: es\n* OPUS readme: bi-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 21.1, chr-F: 0.388"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bi #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-bi-es\n\n\n* source languages: bi\n* target languages: es\n* OPUS readme: bi-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 21.1, chr-F: 0.388"
] | [
61,
99
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bi #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-bi-es\n\n\n* source languages: bi\n* target languages: es\n* OPUS readme: bi-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 21.1, chr-F: 0.388"
] | [
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] |
null | null | transformers |
### opus-mt-bi-fr
* source languages: bi
* target languages: fr
* OPUS readme: [bi-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bi-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/bi-fr/opus-2020-01-20.zip)
* test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bi-fr/opus-2020-01-20.test.txt)
* test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bi-fr/opus-2020-01-20.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.bi.fr | 21.5 | 0.382 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bi-fr | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bi",
"fr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bi #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-bi-fr
* source languages: bi
* target languages: fr
* OPUS readme: bi-fr
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 21.5, chr-F: 0.382
| [
"### opus-mt-bi-fr\n\n\n* source languages: bi\n* target languages: fr\n* OPUS readme: bi-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 21.5, chr-F: 0.382"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bi #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-bi-fr\n\n\n* source languages: bi\n* target languages: fr\n* OPUS readme: bi-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 21.5, chr-F: 0.382"
] | [
61,
99
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bi #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-bi-fr\n\n\n* source languages: bi\n* target languages: fr\n* OPUS readme: bi-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 21.5, chr-F: 0.382"
] | [
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] |
null | null | transformers |
### opus-mt-bi-sv
* source languages: bi
* target languages: sv
* OPUS readme: [bi-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bi-sv/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/bi-sv/opus-2020-01-20.zip)
* test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bi-sv/opus-2020-01-20.test.txt)
* test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bi-sv/opus-2020-01-20.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.bi.sv | 22.7 | 0.403 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bi-sv | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bi",
"sv",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bi #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-bi-sv
* source languages: bi
* target languages: sv
* OPUS readme: bi-sv
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 22.7, chr-F: 0.403
| [
"### opus-mt-bi-sv\n\n\n* source languages: bi\n* target languages: sv\n* OPUS readme: bi-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 22.7, chr-F: 0.403"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bi #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-bi-sv\n\n\n* source languages: bi\n* target languages: sv\n* OPUS readme: bi-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 22.7, chr-F: 0.403"
] | [
61,
100
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bi #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-bi-sv\n\n\n* source languages: bi\n* target languages: sv\n* OPUS readme: bi-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 22.7, chr-F: 0.403"
] | [
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null | null | transformers |
### ben-eng
* source group: Bengali
* target group: English
* OPUS readme: [ben-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ben-eng/README.md)
* model: transformer-align
* source language(s): ben
* target language(s): eng
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ben-eng/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ben-eng/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ben-eng/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.ben.eng | 49.7 | 0.641 |
### System Info:
- hf_name: ben-eng
- source_languages: ben
- target_languages: eng
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ben-eng/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['bn', 'en']
- src_constituents: {'ben'}
- tgt_constituents: {'eng'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ben-eng/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ben-eng/opus-2020-06-17.test.txt
- src_alpha3: ben
- tgt_alpha3: eng
- short_pair: bn-en
- chrF2_score: 0.6409999999999999
- bleu: 49.7
- brevity_penalty: 0.976
- ref_len: 13978.0
- src_name: Bengali
- tgt_name: English
- train_date: 2020-06-17
- src_alpha2: bn
- tgt_alpha2: en
- prefer_old: False
- long_pair: ben-eng
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["bn", "en"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bn-en | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bn",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"bn",
"en"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bn #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### ben-eng
* source group: Bengali
* target group: English
* OPUS readme: ben-eng
* model: transformer-align
* source language(s): ben
* target language(s): eng
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 49.7, chr-F: 0.641
### System Info:
* hf\_name: ben-eng
* source\_languages: ben
* target\_languages: eng
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['bn', 'en']
* src\_constituents: {'ben'}
* tgt\_constituents: {'eng'}
* src\_multilingual: False
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: ben
* tgt\_alpha3: eng
* short\_pair: bn-en
* chrF2\_score: 0.6409999999999999
* bleu: 49.7
* brevity\_penalty: 0.976
* ref\_len: 13978.0
* src\_name: Bengali
* tgt\_name: English
* train\_date: 2020-06-17
* src\_alpha2: bn
* tgt\_alpha2: en
* prefer\_old: False
* long\_pair: ben-eng
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### ben-eng\n\n\n* source group: Bengali\n* target group: English\n* OPUS readme: ben-eng\n* model: transformer-align\n* source language(s): ben\n* target language(s): eng\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 49.7, chr-F: 0.641",
"### System Info:\n\n\n* hf\\_name: ben-eng\n* source\\_languages: ben\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['bn', 'en']\n* src\\_constituents: {'ben'}\n* tgt\\_constituents: {'eng'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: ben\n* tgt\\_alpha3: eng\n* short\\_pair: bn-en\n* chrF2\\_score: 0.6409999999999999\n* bleu: 49.7\n* brevity\\_penalty: 0.976\n* ref\\_len: 13978.0\n* src\\_name: Bengali\n* tgt\\_name: English\n* train\\_date: 2020-06-17\n* src\\_alpha2: bn\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* long\\_pair: ben-eng\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bn #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### ben-eng\n\n\n* source group: Bengali\n* target group: English\n* OPUS readme: ben-eng\n* model: transformer-align\n* source language(s): ben\n* target language(s): eng\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 49.7, chr-F: 0.641",
"### System Info:\n\n\n* hf\\_name: ben-eng\n* source\\_languages: ben\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['bn', 'en']\n* src\\_constituents: {'ben'}\n* tgt\\_constituents: {'eng'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: ben\n* tgt\\_alpha3: eng\n* short\\_pair: bn-en\n* chrF2\\_score: 0.6409999999999999\n* bleu: 49.7\n* brevity\\_penalty: 0.976\n* ref\\_len: 13978.0\n* src\\_name: Bengali\n* tgt\\_name: English\n* train\\_date: 2020-06-17\n* src\\_alpha2: bn\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* long\\_pair: ben-eng\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
61,
120,
396
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bn #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### ben-eng\n\n\n* source group: Bengali\n* target group: English\n* OPUS readme: ben-eng\n* model: transformer-align\n* source language(s): ben\n* target language(s): eng\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 49.7, chr-F: 0.641"
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null | null | transformers |
### bnt-eng
* source group: Bantu languages
* target group: English
* OPUS readme: [bnt-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bnt-eng/README.md)
* model: transformer
* source language(s): kin lin lug nya run sna swh toi_Latn tso umb xho zul
* target language(s): eng
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus2m-2020-07-31.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/bnt-eng/opus2m-2020-07-31.zip)
* test set translations: [opus2m-2020-07-31.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bnt-eng/opus2m-2020-07-31.test.txt)
* test set scores: [opus2m-2020-07-31.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bnt-eng/opus2m-2020-07-31.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.kin-eng.kin.eng | 31.7 | 0.481 |
| Tatoeba-test.lin-eng.lin.eng | 8.3 | 0.271 |
| Tatoeba-test.lug-eng.lug.eng | 5.3 | 0.128 |
| Tatoeba-test.multi.eng | 23.1 | 0.394 |
| Tatoeba-test.nya-eng.nya.eng | 38.3 | 0.527 |
| Tatoeba-test.run-eng.run.eng | 26.6 | 0.431 |
| Tatoeba-test.sna-eng.sna.eng | 27.5 | 0.440 |
| Tatoeba-test.swa-eng.swa.eng | 4.6 | 0.195 |
| Tatoeba-test.toi-eng.toi.eng | 16.2 | 0.342 |
| Tatoeba-test.tso-eng.tso.eng | 100.0 | 1.000 |
| Tatoeba-test.umb-eng.umb.eng | 8.4 | 0.231 |
| Tatoeba-test.xho-eng.xho.eng | 37.2 | 0.554 |
| Tatoeba-test.zul-eng.zul.eng | 40.9 | 0.576 |
### System Info:
- hf_name: bnt-eng
- source_languages: bnt
- target_languages: eng
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bnt-eng/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['sn', 'zu', 'rw', 'lg', 'ts', 'ln', 'ny', 'xh', 'rn', 'bnt', 'en']
- src_constituents: {'sna', 'zul', 'kin', 'lug', 'tso', 'lin', 'nya', 'xho', 'swh', 'run', 'toi_Latn', 'umb'}
- tgt_constituents: {'eng'}
- src_multilingual: True
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/bnt-eng/opus2m-2020-07-31.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/bnt-eng/opus2m-2020-07-31.test.txt
- src_alpha3: bnt
- tgt_alpha3: eng
- short_pair: bnt-en
- chrF2_score: 0.39399999999999996
- bleu: 23.1
- brevity_penalty: 1.0
- ref_len: 14565.0
- src_name: Bantu languages
- tgt_name: English
- train_date: 2020-07-31
- src_alpha2: bnt
- tgt_alpha2: en
- prefer_old: False
- long_pair: bnt-eng
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["sn", "zu", "rw", "lg", "ts", "ln", "ny", "xh", "rn", "bnt", "en"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bnt-en | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"sn",
"zu",
"rw",
"lg",
"ts",
"ln",
"ny",
"xh",
"rn",
"bnt",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"sn",
"zu",
"rw",
"lg",
"ts",
"ln",
"ny",
"xh",
"rn",
"bnt",
"en"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #sn #zu #rw #lg #ts #ln #ny #xh #rn #bnt #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### bnt-eng
* source group: Bantu languages
* target group: English
* OPUS readme: bnt-eng
* model: transformer
* source language(s): kin lin lug nya run sna swh toi\_Latn tso umb xho zul
* target language(s): eng
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 31.7, chr-F: 0.481
testset: URL, BLEU: 8.3, chr-F: 0.271
testset: URL, BLEU: 5.3, chr-F: 0.128
testset: URL, BLEU: 23.1, chr-F: 0.394
testset: URL, BLEU: 38.3, chr-F: 0.527
testset: URL, BLEU: 26.6, chr-F: 0.431
testset: URL, BLEU: 27.5, chr-F: 0.440
testset: URL, BLEU: 4.6, chr-F: 0.195
testset: URL, BLEU: 16.2, chr-F: 0.342
testset: URL, BLEU: 100.0, chr-F: 1.000
testset: URL, BLEU: 8.4, chr-F: 0.231
testset: URL, BLEU: 37.2, chr-F: 0.554
testset: URL, BLEU: 40.9, chr-F: 0.576
### System Info:
* hf\_name: bnt-eng
* source\_languages: bnt
* target\_languages: eng
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['sn', 'zu', 'rw', 'lg', 'ts', 'ln', 'ny', 'xh', 'rn', 'bnt', 'en']
* src\_constituents: {'sna', 'zul', 'kin', 'lug', 'tso', 'lin', 'nya', 'xho', 'swh', 'run', 'toi\_Latn', 'umb'}
* tgt\_constituents: {'eng'}
* src\_multilingual: True
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm32k,spm32k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: bnt
* tgt\_alpha3: eng
* short\_pair: bnt-en
* chrF2\_score: 0.39399999999999996
* bleu: 23.1
* brevity\_penalty: 1.0
* ref\_len: 14565.0
* src\_name: Bantu languages
* tgt\_name: English
* train\_date: 2020-07-31
* src\_alpha2: bnt
* tgt\_alpha2: en
* prefer\_old: False
* long\_pair: bnt-eng
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### bnt-eng\n\n\n* source group: Bantu languages\n* target group: English\n* OPUS readme: bnt-eng\n* model: transformer\n* source language(s): kin lin lug nya run sna swh toi\\_Latn tso umb xho zul\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 31.7, chr-F: 0.481\ntestset: URL, BLEU: 8.3, chr-F: 0.271\ntestset: URL, BLEU: 5.3, chr-F: 0.128\ntestset: URL, BLEU: 23.1, chr-F: 0.394\ntestset: URL, BLEU: 38.3, chr-F: 0.527\ntestset: URL, BLEU: 26.6, chr-F: 0.431\ntestset: URL, BLEU: 27.5, chr-F: 0.440\ntestset: URL, BLEU: 4.6, chr-F: 0.195\ntestset: URL, BLEU: 16.2, chr-F: 0.342\ntestset: URL, BLEU: 100.0, chr-F: 1.000\ntestset: URL, BLEU: 8.4, chr-F: 0.231\ntestset: URL, BLEU: 37.2, chr-F: 0.554\ntestset: URL, BLEU: 40.9, chr-F: 0.576",
"### System Info:\n\n\n* hf\\_name: bnt-eng\n* source\\_languages: bnt\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['sn', 'zu', 'rw', 'lg', 'ts', 'ln', 'ny', 'xh', 'rn', 'bnt', 'en']\n* src\\_constituents: {'sna', 'zul', 'kin', 'lug', 'tso', 'lin', 'nya', 'xho', 'swh', 'run', 'toi\\_Latn', 'umb'}\n* tgt\\_constituents: {'eng'}\n* src\\_multilingual: True\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: bnt\n* tgt\\_alpha3: eng\n* short\\_pair: bnt-en\n* chrF2\\_score: 0.39399999999999996\n* bleu: 23.1\n* brevity\\_penalty: 1.0\n* ref\\_len: 14565.0\n* src\\_name: Bantu languages\n* tgt\\_name: English\n* train\\_date: 2020-07-31\n* src\\_alpha2: bnt\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* long\\_pair: bnt-eng\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #sn #zu #rw #lg #ts #ln #ny #xh #rn #bnt #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### bnt-eng\n\n\n* source group: Bantu languages\n* target group: English\n* OPUS readme: bnt-eng\n* model: transformer\n* source language(s): kin lin lug nya run sna swh toi\\_Latn tso umb xho zul\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 31.7, chr-F: 0.481\ntestset: URL, BLEU: 8.3, chr-F: 0.271\ntestset: URL, BLEU: 5.3, chr-F: 0.128\ntestset: URL, BLEU: 23.1, chr-F: 0.394\ntestset: URL, BLEU: 38.3, chr-F: 0.527\ntestset: URL, BLEU: 26.6, chr-F: 0.431\ntestset: URL, BLEU: 27.5, chr-F: 0.440\ntestset: URL, BLEU: 4.6, chr-F: 0.195\ntestset: URL, BLEU: 16.2, chr-F: 0.342\ntestset: URL, BLEU: 100.0, chr-F: 1.000\ntestset: URL, BLEU: 8.4, chr-F: 0.231\ntestset: URL, BLEU: 37.2, chr-F: 0.554\ntestset: URL, BLEU: 40.9, chr-F: 0.576",
"### System Info:\n\n\n* hf\\_name: bnt-eng\n* source\\_languages: bnt\n* target\\_languages: eng\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['sn', 'zu', 'rw', 'lg', 'ts', 'ln', 'ny', 'xh', 'rn', 'bnt', 'en']\n* src\\_constituents: {'sna', 'zul', 'kin', 'lug', 'tso', 'lin', 'nya', 'xho', 'swh', 'run', 'toi\\_Latn', 'umb'}\n* tgt\\_constituents: {'eng'}\n* src\\_multilingual: True\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm32k,spm32k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: bnt\n* tgt\\_alpha3: eng\n* short\\_pair: bnt-en\n* chrF2\\_score: 0.39399999999999996\n* bleu: 23.1\n* brevity\\_penalty: 1.0\n* ref\\_len: 14565.0\n* src\\_name: Bantu languages\n* tgt\\_name: English\n* train\\_date: 2020-07-31\n* src\\_alpha2: bnt\n* tgt\\_alpha2: en\n* prefer\\_old: False\n* long\\_pair: bnt-eng\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
85,
348,
494
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #sn #zu #rw #lg #ts #ln #ny #xh #rn #bnt #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### bnt-eng\n\n\n* source group: Bantu languages\n* target group: English\n* OPUS readme: bnt-eng\n* model: transformer\n* source language(s): kin lin lug nya run sna swh toi\\_Latn tso umb xho zul\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 31.7, chr-F: 0.481\ntestset: URL, BLEU: 8.3, chr-F: 0.271\ntestset: URL, BLEU: 5.3, chr-F: 0.128\ntestset: URL, BLEU: 23.1, chr-F: 0.394\ntestset: URL, BLEU: 38.3, chr-F: 0.527\ntestset: URL, BLEU: 26.6, chr-F: 0.431\ntestset: URL, BLEU: 27.5, chr-F: 0.440\ntestset: URL, BLEU: 4.6, chr-F: 0.195\ntestset: URL, BLEU: 16.2, chr-F: 0.342\ntestset: URL, BLEU: 100.0, chr-F: 1.000\ntestset: URL, BLEU: 8.4, chr-F: 0.231\ntestset: URL, BLEU: 37.2, chr-F: 0.554\ntestset: URL, BLEU: 40.9, chr-F: 0.576"
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] |
null | null | transformers |
### opus-mt-bzs-en
* source languages: bzs
* target languages: en
* OPUS readme: [bzs-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bzs-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2019-12-18.zip](https://object.pouta.csc.fi/OPUS-MT-models/bzs-en/opus-2019-12-18.zip)
* test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bzs-en/opus-2019-12-18.test.txt)
* test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bzs-en/opus-2019-12-18.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.bzs.en | 44.5 | 0.605 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bzs-en | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bzs",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bzs #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-bzs-en
* source languages: bzs
* target languages: en
* OPUS readme: bzs-en
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 44.5, chr-F: 0.605
| [
"### opus-mt-bzs-en\n\n\n* source languages: bzs\n* target languages: en\n* OPUS readme: bzs-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 44.5, chr-F: 0.605"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bzs #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-bzs-en\n\n\n* source languages: bzs\n* target languages: en\n* OPUS readme: bzs-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 44.5, chr-F: 0.605"
] | [
63,
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"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bzs #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-bzs-en\n\n\n* source languages: bzs\n* target languages: en\n* OPUS readme: bzs-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 44.5, chr-F: 0.605"
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] |
null | null | transformers |
### opus-mt-bzs-es
* source languages: bzs
* target languages: es
* OPUS readme: [bzs-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bzs-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-15.zip](https://object.pouta.csc.fi/OPUS-MT-models/bzs-es/opus-2020-01-15.zip)
* test set translations: [opus-2020-01-15.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bzs-es/opus-2020-01-15.test.txt)
* test set scores: [opus-2020-01-15.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bzs-es/opus-2020-01-15.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.bzs.es | 28.1 | 0.464 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bzs-es | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bzs",
"es",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bzs #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-bzs-es
* source languages: bzs
* target languages: es
* OPUS readme: bzs-es
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 28.1, chr-F: 0.464
| [
"### opus-mt-bzs-es\n\n\n* source languages: bzs\n* target languages: es\n* OPUS readme: bzs-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 28.1, chr-F: 0.464"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bzs #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-bzs-es\n\n\n* source languages: bzs\n* target languages: es\n* OPUS readme: bzs-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 28.1, chr-F: 0.464"
] | [
63,
106
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bzs #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-bzs-es\n\n\n* source languages: bzs\n* target languages: es\n* OPUS readme: bzs-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 28.1, chr-F: 0.464"
] | [
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] |
null | null | transformers |
### opus-mt-bzs-fi
* source languages: bzs
* target languages: fi
* OPUS readme: [bzs-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bzs-fi/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/bzs-fi/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bzs-fi/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bzs-fi/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.bzs.fi | 24.7 | 0.464 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bzs-fi | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bzs",
"fi",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bzs #fi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-bzs-fi
* source languages: bzs
* target languages: fi
* OPUS readme: bzs-fi
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 24.7, chr-F: 0.464
| [
"### opus-mt-bzs-fi\n\n\n* source languages: bzs\n* target languages: fi\n* OPUS readme: bzs-fi\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 24.7, chr-F: 0.464"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bzs #fi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-bzs-fi\n\n\n* source languages: bzs\n* target languages: fi\n* OPUS readme: bzs-fi\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 24.7, chr-F: 0.464"
] | [
63,
106
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bzs #fi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-bzs-fi\n\n\n* source languages: bzs\n* target languages: fi\n* OPUS readme: bzs-fi\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 24.7, chr-F: 0.464"
] | [
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] |
null | null | transformers |
### opus-mt-bzs-fr
* source languages: bzs
* target languages: fr
* OPUS readme: [bzs-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bzs-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/bzs-fr/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bzs-fr/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bzs-fr/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.bzs.fr | 30.0 | 0.479 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bzs-fr | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bzs",
"fr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bzs #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-bzs-fr
* source languages: bzs
* target languages: fr
* OPUS readme: bzs-fr
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 30.0, chr-F: 0.479
| [
"### opus-mt-bzs-fr\n\n\n* source languages: bzs\n* target languages: fr\n* OPUS readme: bzs-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 30.0, chr-F: 0.479"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bzs #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-bzs-fr\n\n\n* source languages: bzs\n* target languages: fr\n* OPUS readme: bzs-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 30.0, chr-F: 0.479"
] | [
63,
106
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bzs #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-bzs-fr\n\n\n* source languages: bzs\n* target languages: fr\n* OPUS readme: bzs-fr\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 30.0, chr-F: 0.479"
] | [
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] |
null | null | transformers |
### opus-mt-bzs-sv
* source languages: bzs
* target languages: sv
* OPUS readme: [bzs-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bzs-sv/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/bzs-sv/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bzs-sv/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bzs-sv/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.bzs.sv | 30.7 | 0.489 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-bzs-sv | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bzs",
"sv",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #bzs #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-bzs-sv
* source languages: bzs
* target languages: sv
* OPUS readme: bzs-sv
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 30.7, chr-F: 0.489
| [
"### opus-mt-bzs-sv\n\n\n* source languages: bzs\n* target languages: sv\n* OPUS readme: bzs-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 30.7, chr-F: 0.489"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bzs #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-bzs-sv\n\n\n* source languages: bzs\n* target languages: sv\n* OPUS readme: bzs-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 30.7, chr-F: 0.489"
] | [
63,
106
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #bzs #sv #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-bzs-sv\n\n\n* source languages: bzs\n* target languages: sv\n* OPUS readme: bzs-sv\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 30.7, chr-F: 0.489"
] | [
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null | null | transformers |
### cat-deu
* source group: Catalan
* target group: German
* OPUS readme: [cat-deu](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/cat-deu/README.md)
* model: transformer-align
* source language(s): cat
* target language(s): deu
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm12k,spm12k)
* download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/cat-deu/opus-2020-06-16.zip)
* test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/cat-deu/opus-2020-06-16.test.txt)
* test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/cat-deu/opus-2020-06-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.cat.deu | 39.5 | 0.593 |
### System Info:
- hf_name: cat-deu
- source_languages: cat
- target_languages: deu
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/cat-deu/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['ca', 'de']
- src_constituents: {'cat'}
- tgt_constituents: {'deu'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm12k,spm12k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/cat-deu/opus-2020-06-16.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/cat-deu/opus-2020-06-16.test.txt
- src_alpha3: cat
- tgt_alpha3: deu
- short_pair: ca-de
- chrF2_score: 0.593
- bleu: 39.5
- brevity_penalty: 1.0
- ref_len: 5643.0
- src_name: Catalan
- tgt_name: German
- train_date: 2020-06-16
- src_alpha2: ca
- tgt_alpha2: de
- prefer_old: False
- long_pair: cat-deu
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | {"language": ["ca", "de"], "license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-ca-de | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"ca",
"de",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ca",
"de"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #ca #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### cat-deu
* source group: Catalan
* target group: German
* OPUS readme: cat-deu
* model: transformer-align
* source language(s): cat
* target language(s): deu
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm12k,spm12k)
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 39.5, chr-F: 0.593
### System Info:
* hf\_name: cat-deu
* source\_languages: cat
* target\_languages: deu
* opus\_readme\_url: URL
* original\_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['ca', 'de']
* src\_constituents: {'cat'}
* tgt\_constituents: {'deu'}
* src\_multilingual: False
* tgt\_multilingual: False
* prepro: normalization + SentencePiece (spm12k,spm12k)
* url\_model: URL
* url\_test\_set: URL
* src\_alpha3: cat
* tgt\_alpha3: deu
* short\_pair: ca-de
* chrF2\_score: 0.593
* bleu: 39.5
* brevity\_penalty: 1.0
* ref\_len: 5643.0
* src\_name: Catalan
* tgt\_name: German
* train\_date: 2020-06-16
* src\_alpha2: ca
* tgt\_alpha2: de
* prefer\_old: False
* long\_pair: cat-deu
* helsinki\_git\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
* transformers\_git\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
* port\_machine: brutasse
* port\_time: 2020-08-21-14:41
| [
"### cat-deu\n\n\n* source group: Catalan\n* target group: German\n* OPUS readme: cat-deu\n* model: transformer-align\n* source language(s): cat\n* target language(s): deu\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm12k,spm12k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 39.5, chr-F: 0.593",
"### System Info:\n\n\n* hf\\_name: cat-deu\n* source\\_languages: cat\n* target\\_languages: deu\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['ca', 'de']\n* src\\_constituents: {'cat'}\n* tgt\\_constituents: {'deu'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm12k,spm12k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: cat\n* tgt\\_alpha3: deu\n* short\\_pair: ca-de\n* chrF2\\_score: 0.593\n* bleu: 39.5\n* brevity\\_penalty: 1.0\n* ref\\_len: 5643.0\n* src\\_name: Catalan\n* tgt\\_name: German\n* train\\_date: 2020-06-16\n* src\\_alpha2: ca\n* tgt\\_alpha2: de\n* prefer\\_old: False\n* long\\_pair: cat-deu\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ca #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### cat-deu\n\n\n* source group: Catalan\n* target group: German\n* OPUS readme: cat-deu\n* model: transformer-align\n* source language(s): cat\n* target language(s): deu\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm12k,spm12k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 39.5, chr-F: 0.593",
"### System Info:\n\n\n* hf\\_name: cat-deu\n* source\\_languages: cat\n* target\\_languages: deu\n* opus\\_readme\\_url: URL\n* original\\_repo: Tatoeba-Challenge\n* tags: ['translation']\n* languages: ['ca', 'de']\n* src\\_constituents: {'cat'}\n* tgt\\_constituents: {'deu'}\n* src\\_multilingual: False\n* tgt\\_multilingual: False\n* prepro: normalization + SentencePiece (spm12k,spm12k)\n* url\\_model: URL\n* url\\_test\\_set: URL\n* src\\_alpha3: cat\n* tgt\\_alpha3: deu\n* short\\_pair: ca-de\n* chrF2\\_score: 0.593\n* bleu: 39.5\n* brevity\\_penalty: 1.0\n* ref\\_len: 5643.0\n* src\\_name: Catalan\n* tgt\\_name: German\n* train\\_date: 2020-06-16\n* src\\_alpha2: ca\n* tgt\\_alpha2: de\n* prefer\\_old: False\n* long\\_pair: cat-deu\n* helsinki\\_git\\_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n* transformers\\_git\\_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n* port\\_machine: brutasse\n* port\\_time: 2020-08-21-14:41"
] | [
61,
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"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ca #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### cat-deu\n\n\n* source group: Catalan\n* target group: German\n* OPUS readme: cat-deu\n* model: transformer-align\n* source language(s): cat\n* target language(s): deu\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm12k,spm12k)\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 39.5, chr-F: 0.593"
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null | null | transformers |
### opus-mt-ca-en
* source languages: ca
* target languages: en
* OPUS readme: [ca-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ca-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2019-12-18.zip](https://object.pouta.csc.fi/OPUS-MT-models/ca-en/opus-2019-12-18.zip)
* test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ca-en/opus-2019-12-18.test.txt)
* test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ca-en/opus-2019-12-18.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.ca.en | 51.4 | 0.678 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-ca-en | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"ca",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #ca #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-ca-en
* source languages: ca
* target languages: en
* OPUS readme: ca-en
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 51.4, chr-F: 0.678
| [
"### opus-mt-ca-en\n\n\n* source languages: ca\n* target languages: en\n* OPUS readme: ca-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 51.4, chr-F: 0.678"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ca #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-ca-en\n\n\n* source languages: ca\n* target languages: en\n* OPUS readme: ca-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 51.4, chr-F: 0.678"
] | [
61,
100
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ca #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-ca-en\n\n\n* source languages: ca\n* target languages: en\n* OPUS readme: ca-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 51.4, chr-F: 0.678"
] | [
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null | null | transformers |
### opus-mt-ca-es
* source languages: ca
* target languages: es
* OPUS readme: [ca-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ca-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-15.zip](https://object.pouta.csc.fi/OPUS-MT-models/ca-es/opus-2020-01-15.zip)
* test set translations: [opus-2020-01-15.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ca-es/opus-2020-01-15.test.txt)
* test set scores: [opus-2020-01-15.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ca-es/opus-2020-01-15.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.ca.es | 74.9 | 0.863 |
| {"license": "apache-2.0", "tags": ["translation"]} | translation | Helsinki-NLP/opus-mt-ca-es | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"ca",
"es",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #ca #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### opus-mt-ca-es
* source languages: ca
* target languages: es
* OPUS readme: ca-es
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 74.9, chr-F: 0.863
| [
"### opus-mt-ca-es\n\n\n* source languages: ca\n* target languages: es\n* OPUS readme: ca-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 74.9, chr-F: 0.863"
] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ca #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### opus-mt-ca-es\n\n\n* source languages: ca\n* target languages: es\n* OPUS readme: ca-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 74.9, chr-F: 0.863"
] | [
61,
100
] | [
"passage: TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #ca #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### opus-mt-ca-es\n\n\n* source languages: ca\n* target languages: es\n* OPUS readme: ca-es\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 74.9, chr-F: 0.863"
] | [
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Subsets and Splits