---
pipeline_tag: sentence-similarity
language: "multilingual"
tags:
- feature-extraction
- sentence-similarity
- transformers
- multilingual
---

# mstsb-paraphrase-multilingual-mpnet-base-v2

This is a fine-tuned version of `paraphrase-multilingual-mpnet-base-v2` from [sentence-transformers](https://www.SBERT.net) model with [Semantic Textual Similarity Benchmark](http://ixa2.si.ehu.eus/stswiki/index.php/Main_Page) extended to 15 languages: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering, semantic search and measuring the similarity between two sentences.

This model is fine-tuned version of `paraphrase-multilingual-mpnet-base-v2` for semantic textual similarity with multilingual data. The dataset used for this fine-tuning is STSb extended to 15 languages with Google Translator. For mantaining data quality the sentence pairs with a confidence value below 0.7 were dropped. The extended dataset is available at [GitHub](https://github.com/Huertas97/Multilingual-STSB). The languages included in the extended version are: ar, cs, de, en, es, fr, hi, it, ja, nl, pl, pt, ru, tr, zh-CN, zh-TW. The pooling operation used to condense the word embeddings into a sentence embedding is mean pooling (more info below).

## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

```python
from transformers import AutoTokenizer, AutoModel
import torch

# We should define the proper pooling function: Mean pooling
# Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

# Sentences we want sentence embeddings for
sentences = ["This is an example sentence", "Esta es otra frase de ejemplo", "最後の例文"]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('AIDA-UPM/mstsb-paraphrase-multilingual-mpnet-base-v2')
model = AutoModel.from_pretrained('AIDA-UPM/mstsb-paraphrase-multilingual-mpnet-base-v2')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)
```

## Evaluation Results

Check the test results in the Semantic Textual Similarity Tasks. The 15 languages available at the [Multilingual STSB](https://github.com/Huertas97/Multilingual-STSB) have been combined into monolingual and cross-lingual tasks, giving a total of 31 tasks. Monolingual tasks have both sentences from the same language source (e.g., Ar-Ar, Es-Es), while cross-lingual tasks have two sentences, each in a different language being one of them English (e.g., en-ar, en-es).

Here we compare the average multilingual semantic textual similairty capabilities between the  `paraphrase-multilingual-mpnet-base-v2` based model and the `mstsb-paraphrase-multilingual-mpnet-base-v2` fine-tuned model across the 31 tasks. It is worth noting that both models are multilingual, but the second model is adjusted with multilingual data for semantic similarity. The average of correlation coefficients is computed by transforming each correlation coefficient to a Fisher's z value, averaging them, and then back-transforming to a correlation coefficient.

| Model                                       | Average Spearman Cosine Test |
|:---------------------------------------------:|:------------------------------:|
| mstsb-paraphrase-multilingual-mpnet-base-v2 | 0.835890                     |
| paraphrase-multilingual-mpnet-base-v2       | 0.818896                     |

The following tables breakdown the performance of `mstsb-paraphrase-multilingual-mpnet-base-v2` according to the different tasks. For the sake of readability tasks have been splitted into monolingual and cross-lingual tasks.

| Monolingual Task | Pearson Cosine test | Spearman Cosine  test |
|:------------------:|:---------------------:|:-----------------------:|
| en;en            | 0.868048310692506   | 0.8740170943535747    |
| ar;ar            | 0.8267139454193487  | 0.8284459741532022    |
| cs;cs            | 0.8466821720942157  | 0.8485417688803879    |
| de;de            | 0.8517285961812183  | 0.8557680051557893    |
| es;es            | 0.8519185309064691  | 0.8552243211580456    |
| fr;fr            | 0.8430951067985064  | 0.8466614534379704    |
| hi;hi            | 0.8178258630578092  | 0.8176462079184331    |
| it;it            | 0.8475909574305637  | 0.8494216064459076    |
| ja;ja            | 0.8435588859386477  | 0.8456031494178619    |
| nl;nl            | 0.8486765104527032  | 0.8520856765262531    |
| pl;pl            | 0.8407840177883407  | 0.8443070467300299    |
| pt;pt            | 0.8534880178249296  | 0.8578544068829622    |
| ru;ru            | 0.8390897585455678  | 0.8423041443534423    |
| tr;tr            | 0.8382125451820572  | 0.8421587450058385    |
| zh-CN;zh-CN      | 0.826233678946644   | 0.8248515460782744    |
| zh-TW;zh-TW      | 0.8242683809675422  | 0.8235506799952028    |

| Cross-lingual Task | Pearson Cosine test | Spearman Cosine  test |
|:--------------------:|:---------------------:|:-----------------------:|
| en;ar              | 0.7990830340462535  | 0.7956792016468148    |
| en;cs              | 0.8381274879061265  | 0.8388713450024455    |
| en;de              | 0.8414439600928739  | 0.8441971698649943    |
| en;es              | 0.8442337511356952  | 0.8445035292903559    |
| en;fr              | 0.8378437644605063  | 0.8387903367907733    |
| en;hi              | 0.7951955086055527  | 0.7905052217683244    |
| en;it              | 0.8415686372978766  | 0.8419480899107785    |
| en;ja              | 0.8094306665283388  | 0.8032512280936449    |
| en;nl              | 0.8389526140129767  | 0.8409310421803277    |
| en;pl              | 0.8261309163979578  | 0.825976253023656     |
| en;pt              | 0.8475546209070765  | 0.8506606391790897    |
| en;ru              | 0.8248514914263723  | 0.8224871183202255    |
| en;tr              | 0.8191803661207868  | 0.8194200775744044    |
| en;zh-CN           | 0.8147678083378249  | 0.8102089470690433    |
| en;zh-TW           | 0.8107272160374955  | 0.8056129680510944    |

## Training
The model was trained with the parameters:

**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 687 with parameters:
```
{'batch_size': 132, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```

**Loss**:

`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`

Parameters of the fit()-Method:
```
{
    "callback": null,
    "epochs": 2,
    "evaluation_steps": 1000,
    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 140,
    "weight_decay": 0.01
}
```

## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```

## Citing & Authors


---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---

# {MODEL_NAME}

This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.

<!--- Describe your model here -->

## Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```

## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

```python
from transformers import AutoTokenizer, AutoModel
import torch

#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)
```

## Evaluation Results

<!--- Describe how your model was evaluated -->

For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})

## Training
The model was trained with the parameters:

**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 1438 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```

**Loss**:

`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`

Parameters of the fit()-Method:
```
{
    "callback": null,
    "epochs": 2,
    "evaluation_steps": 1000,
    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 288,
    "weight_decay": 0.05
}
```

## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```

## Citing & Authors

<!--- Describe where people can find more information -->


---
pipeline_tag: text-classification
tags:
- text-classification
- misogyny
language: en
license: apache-2.0
widget:
- text: "Women wear yoga pants because men don't stare at their personality"
  example_title: "Misogyny detection"
---

# bertweet-base-multi-mami
This is a Bertweet model: It maps sentences & paragraphs to a 768 dimensional dense vector space and classifies them into 5 multi labels.

# Multilabels
    label2id={
        "misogynous": 0,
        "shaming": 1,
        "stereotype": 2,
        "objectification": 3,
        "violence": 4,
    },


---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---

# {MODEL_NAME}

This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.

<!--- Describe your model here -->

## Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```

## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

```python
from transformers import AutoTokenizer, AutoModel
import torch

#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)
```

## Evaluation Results

<!--- Describe how your model was evaluated -->

For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})

## Training
The model was trained with the parameters:

**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 1438 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```

**Loss**:

`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`

Parameters of the fit()-Method:
```
{
    "callback": null,
    "epochs": 1,
    "evaluation_steps": 1000,
    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
    "optimizer_params": {
        "lr": 4e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 144,
    "weight_decay": 0.01
}
```

## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```

## Citing & Authors

<!--- Describe where people can find more information -->


---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---

# AIDA-UPM/MSTSb_paraphrase-xlm-r-multilingual-v1

This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.

<!--- Describe your model here -->

## Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```

## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

```python
from transformers import AutoTokenizer, AutoModel
import torch

#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)
```

## Evaluation Results

<!--- Describe how your model was evaluated -->

For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})

## Training
The model was trained with the parameters:

**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 1438 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```

**Loss**:

`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`

Parameters of the fit()-Method:
```
{
    "callback": null,
    "epochs": 2,
    "evaluation_steps": 1000,
    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
    "optimizer_params": {
        "lr": 4e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 288,
    "weight_decay": 0.1
}
```

## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```

## Citing & Authors

<!--- Describe where people can find more information -->


---
datasets:
- squad_v2
---

# RoBERTa-base for QA

## Overview
**Language model:** 'roberta-base'

**Language:** English

**Downstream-task:** Extractive QA

**Training data:** SQuAD 2.0

**Eval data:** SQuAD 2.0

**Code:** <TBD>

## Env Information
`transformers` version: 4.9.1

Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic

Python version: 3.7.11

PyTorch version (GPU?): 1.9.0+cu102 (False)

Tensorflow version (GPU?): 2.5.0 (False)

## Hyperparameters
```
max_seq_len=386
doc_stride=128
n_best_size=20
max_answer_length=30
min_null_score=7.0
batch_size=8

n_epochs=6
base_LM_model = "roberta-base"
learning_rate=1.5e-5
adam_epsilon=1e-5
adam_beta1=0.95
adam_beta2=0.999
warmup_steps=100
weight_decay=0.01
optimizer=AdamW
lr_scheduler="polynomial"
```
##### There is a special threshold value CLS_threshold=-3 used to more accurately identify no answers [Logic will be available in GitHub Repo [TBD]

## Performance
```
"exact": 81.192622
"f1":    83.95408
"total": 11873
"HasAns_exact": 74.190283
"HasAns_f1":    79.721119
"HasAns_total": 5928
"NoAns_exact":  88.174937
"NoAns_f1":     88.174937
"NoAns_total":  5945
```

## Usage
### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

model_name = "PremalMatalia/roberta-base-best-squad2"

# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
    'question': 'Which name is also used to describe the Amazon rainforest in English?',
    'context': 'The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain "Amazonas" in their names. The Amazon represents over half of the planet\'s remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species.'
}
res = nlp(QA_input)
print(res)

# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```

## Authors
Premal Matalia


---
datasets:
- squad_v2
---

# ELECTRA-base for QA

## Overview
**Language model:** electra-base

**Language:** English

**Downstream-task:** Extractive QA

**Training data:** SQuAD 2.0

**Eval data:** SQuAD 2.0

**Code:** <TBD>

## Env Information
`transformers` version: 4.9.1

Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic

Python version: 3.7.11

PyTorch version (GPU?): 1.9.0+cu102 (False)

Tensorflow version (GPU?): 2.5.0 (False)

## Hyperparameters
```
max_seq_len=386
doc_stride=128
n_best_size=20
max_answer_length=30
min_null_score=7.0
batch_size=8

n_epochs=2
base_LM_model = "google/electra-base-discriminator"
learning_rate=1.5e-5
adam_epsilon=1e-5
adam_beta1=0.95
adam_beta2=0.999
warmup_steps=100
weight_decay=0.01
optimizer=AdamW
lr_scheduler="polynomial"
```
##### There is a special threshold value CLS_threshold=-3 used to more accurately identify no answers [Logic will be available in GitHub Repo [TBD]

## Performance
```
"exact": 79.331256
"f1":    83.232347\t
"total": 11873
"HasAns_exact": 76.501350
"HasAns_f1":    84.314719
"HasAns_total": 5928
"NoAns_exact":  82.153070
"NoAns_f1":     82.153070
"NoAns_total":  5945
```

## Usage
### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

model_name = "PremalMatalia/electra-base-best-squad2"

# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
    'question': 'Which name is also used to describe the Amazon rainforest in English?',
    'context': 'The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain "Amazonas" in their names. The Amazon represents over half of the planet\'s remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species.'
}
res = nlp(QA_input)
print(res)

# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```

## Authors
Premal Matalia


---
datasets:
- squad_v2
---

# ALBERT-base for QA

## Overview
**Language model:** albert-base

**Language:** English

**Downstream-task:** Extractive QA

**Training data:** SQuAD 2.0

**Eval data:** SQuAD 2.0

**Code:** <TBD>

## Env Information
`transformers` version: 4.9.1

Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic

Python version: 3.7.11

PyTorch version (GPU?): 1.9.0+cu102 (False)

Tensorflow version (GPU?): 2.5.0 (False)

## Hyperparameters
```
max_seq_len=386
doc_stride=128
n_best_size=20
max_answer_length=30
min_null_score=7.0
batch_size=32

n_epochs=3
base_LM_model = "albert-base-v2"
learning_rate=3e-5
adam_epsilon=1e-5
adam_beta1=0.95
adam_beta2=0.999
warmup_steps=300
weight_decay=0.01
optimizer=AdamW
lr_scheduler="polynomial"
```

## Performance
```
"exact": 78.253
"f1":    81.523
"total": 11873
"HasAns_exact": 73.616
"HasAns_f1":    80.165
"HasAns_total": 5928
"NoAns_exact":  82.876
"NoAns_f1":     82.876
"NoAns_total":  5945
```

## Usage
### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

model_name = "PremalMatalia/albert-base-best-squad2"

# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
    'question': 'Which name is also used to describe the Amazon rainforest in English?',
    'context': 'The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain "Amazonas" in their names. The Amazon represents over half of the planet\'s remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species.'
}
res = nlp(QA_input)
print(res)

# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```

## Authors
Premal Matalia


---
tags:
- translation
license: apache-2.0
---

### opus-mt-en-nl

* source languages: en
* target languages: nl
*  OPUS readme: [en-nl](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-nl/README.md)

*  dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2019-12-04.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-nl/opus-2019-12-04.zip)
* test set translations: [opus-2019-12-04.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-nl/opus-2019-12-04.test.txt)
* test set scores: [opus-2019-12-04.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-nl/opus-2019-12-04.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| Tatoeba.en.nl 	| 57.1 	| 0.730 |


---
tags:
- translation
license: apache-2.0
---

### opus-mt-de-lt

* source languages: de
* target languages: lt
*  OPUS readme: [de-lt](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-lt/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/de-lt/opus-2020-01-20.zip)
* test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-lt/opus-2020-01-20.test.txt)
* test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-lt/opus-2020-01-20.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| Tatoeba.de.lt 	| 37.9 	| 0.633 |


---
language:
- uk
- sl

tags:
- translation

license: apache-2.0
---

### ukr-slv

* source group: Ukrainian
* target group: Slovenian
*  OPUS readme: [ukr-slv](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-slv/README.md)

*  model: transformer-align
* source language(s): ukr
* target language(s): slv
* 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/ukr-slv/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-slv/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-slv/opus-2020-06-17.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.ukr.slv 	| 11.8 	| 0.280 |

### System Info:
- hf_name: ukr-slv

- source_languages: ukr

- target_languages: slv

- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-slv/README.md

- original_repo: Tatoeba-Challenge

- tags: ['translation']

- languages: ['uk', 'sl']

- src_constituents: {'ukr'}

- tgt_constituents: {'slv'}

- src_multilingual: False

- tgt_multilingual: False

- prepro:  normalization + SentencePiece (spm32k,spm32k)

- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-slv/opus-2020-06-17.zip

- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-slv/opus-2020-06-17.test.txt

- src_alpha3: ukr

- tgt_alpha3: slv

- short_pair: uk-sl

- chrF2_score: 0.28

- bleu: 11.8

- brevity_penalty: 1.0

- ref_len: 3823.0

- src_name: Ukrainian

- tgt_name: Slovenian

- train_date: 2020-06-17

- src_alpha2: uk

- tgt_alpha2: sl

- prefer_old: False

- long_pair: ukr-slv

- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535

- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b

- port_machine: brutasse

- port_time: 2020-08-21-14:41


---
tags:
- translation
license: apache-2.0
---

### opus-mt-sl-es

* source languages: sl
* target languages: es
*  OPUS readme: [sl-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sl-es/README.md)

*  dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-21.zip](https://object.pouta.csc.fi/OPUS-MT-models/sl-es/opus-2020-01-21.zip)
* test set translations: [opus-2020-01-21.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sl-es/opus-2020-01-21.test.txt)
* test set scores: [opus-2020-01-21.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sl-es/opus-2020-01-21.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.sl.es 	| 26.3 	| 0.483 |


---
language:
- en
- sal

tags:
- translation

license: apache-2.0
---

### eng-sal

* source group: English
* target group: Salishan languages
*  OPUS readme: [eng-sal](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-sal/README.md)

*  model: transformer
* source language(s): eng
* target language(s): shs_Latn
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-07-14.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-sal/opus-2020-07-14.zip)
* test set translations: [opus-2020-07-14.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-sal/opus-2020-07-14.test.txt)
* test set scores: [opus-2020-07-14.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-sal/opus-2020-07-14.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.eng.multi 	| 32.6 	| 0.585 |
| Tatoeba-test.eng.shs 	| 1.1 	| 0.072 |
| Tatoeba-test.eng-shs.eng.shs 	| 1.2 	| 0.065 |

### System Info:
- hf_name: eng-sal

- source_languages: eng

- target_languages: sal

- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-sal/README.md

- original_repo: Tatoeba-Challenge

- tags: ['translation']

- languages: ['en', 'sal']

- src_constituents: {'eng'}

- tgt_constituents: {'shs_Latn'}

- src_multilingual: False

- tgt_multilingual: True

- prepro:  normalization + SentencePiece (spm32k,spm32k)

- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-sal/opus-2020-07-14.zip

- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-sal/opus-2020-07-14.test.txt

- src_alpha3: eng

- tgt_alpha3: sal

- short_pair: en-sal

- chrF2_score: 0.07200000000000001

- bleu: 1.1

- brevity_penalty: 1.0

- ref_len: 199.0

- src_name: English

- tgt_name: Salishan languages

- train_date: 2020-07-14

- src_alpha2: en

- tgt_alpha2: sal

- prefer_old: False

- long_pair: eng-sal

- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535

- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b

- port_machine: brutasse

- port_time: 2020-08-21-14:41


---
tags:
- translation
license: apache-2.0
---

### opus-mt-guw-de

* source languages: guw
* target languages: de
*  OPUS readme: [guw-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/guw-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/guw-de/opus-2020-01-20.zip)
* test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/guw-de/opus-2020-01-20.test.txt)
* test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/guw-de/opus-2020-01-20.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.guw.de 	| 22.7 	| 0.434 |


---
language:
- ja
- da

tags:
- translation

license: apache-2.0
---

### jpn-dan

* source group: Japanese
* target group: Danish
*  OPUS readme: [jpn-dan](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/jpn-dan/README.md)

*  model: transformer-align
* source language(s): jpn_Hani jpn_Hira jpn_Kana jpn_Latn jpn_Yiii
* target language(s): dan
* 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/jpn-dan/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-dan/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-dan/opus-2020-06-17.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.jpn.dan 	| 43.2 	| 0.590 |

### System Info:
- hf_name: jpn-dan

- source_languages: jpn

- target_languages: dan

- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/jpn-dan/README.md

- original_repo: Tatoeba-Challenge

- tags: ['translation']

- languages: ['ja', 'da']

- src_constituents: {'jpn_Hang', 'jpn', 'jpn_Yiii', 'jpn_Kana', 'jpn_Hani', 'jpn_Bopo', 'jpn_Latn', 'jpn_Hira'}

- tgt_constituents: {'dan'}

- src_multilingual: False

- tgt_multilingual: False

- prepro:  normalization + SentencePiece (spm32k,spm32k)

- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-dan/opus-2020-06-17.zip

- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-dan/opus-2020-06-17.test.txt

- src_alpha3: jpn

- tgt_alpha3: dan

- short_pair: ja-da

- chrF2_score: 0.59

- bleu: 43.2

- brevity_penalty: 0.972

- ref_len: 5823.0

- src_name: Japanese

- tgt_name: Danish

- train_date: 2020-06-17

- src_alpha2: ja

- tgt_alpha2: da

- prefer_old: False

- long_pair: jpn-dan

- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535

- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b

- port_machine: brutasse

- port_time: 2020-08-21-14:41


---
tags:
- translation
license: apache-2.0
---

### opus-mt-fi-niu

* source languages: fi
* target languages: niu
*  OPUS readme: [fi-niu](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-niu/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/fi-niu/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-niu/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-niu/opus-2020-01-08.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.fi.niu 	| 35.3 	| 0.565 |


---
language:
- rn
- de

tags:
- translation

license: apache-2.0
---

### run-deu

* source group: Rundi
* target group: German
*  OPUS readme: [run-deu](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/run-deu/README.md)

*  model: transformer-align
* source language(s): run
* target language(s): deu
* 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/run-deu/opus-2020-06-16.zip)
* test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/run-deu/opus-2020-06-16.test.txt)
* test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/run-deu/opus-2020-06-16.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.run.deu 	| 17.1 	| 0.344 |

### System Info:
- hf_name: run-deu

- source_languages: run

- target_languages: deu

- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/run-deu/README.md

- original_repo: Tatoeba-Challenge

- tags: ['translation']

- languages: ['rn', 'de']

- src_constituents: {'run'}

- tgt_constituents: {'deu'}

- src_multilingual: False

- tgt_multilingual: False

- prepro:  normalization + SentencePiece (spm4k,spm4k)

- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/run-deu/opus-2020-06-16.zip

- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/run-deu/opus-2020-06-16.test.txt

- src_alpha3: run

- tgt_alpha3: deu

- short_pair: rn-de

- chrF2_score: 0.344

- bleu: 17.1

- brevity_penalty: 0.961

- ref_len: 10562.0

- src_name: Rundi

- tgt_name: German

- train_date: 2020-06-16

- src_alpha2: rn

- tgt_alpha2: de

- prefer_old: False

- long_pair: run-deu

- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535

- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b

- port_machine: brutasse

- port_time: 2020-08-21-14:41


---
tags:
- translation
license: apache-2.0
---

### opus-mt-ss-en

* source languages: ss
* target languages: en
*  OPUS readme: [ss-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ss-en/README.md)

*  dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/ss-en/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ss-en/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ss-en/opus-2020-01-16.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.ss.en 	| 30.9 	| 0.478 |


---
tags:
- translation
license: apache-2.0
---

### opus-mt-zne-sv

* source languages: zne
* target languages: sv
*  OPUS readme: [zne-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/zne-sv/README.md)

*  dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/zne-sv/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/zne-sv/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/zne-sv/opus-2020-01-16.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.zne.sv 	| 25.2 	| 0.425 |


---
language:
- sh
- eo

tags:
- translation

license: apache-2.0
---

### hbs-epo

* source group: Serbo-Croatian
* target group: Esperanto
*  OPUS readme: [hbs-epo](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/hbs-epo/README.md)

*  model: transformer-align
* source language(s): bos_Latn hrv srp_Cyrl srp_Latn
* 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/hbs-epo/opus-2020-06-16.zip)
* test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/hbs-epo/opus-2020-06-16.test.txt)
* test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/hbs-epo/opus-2020-06-16.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.hbs.epo 	| 18.7 	| 0.383 |

### System Info:
- hf_name: hbs-epo

- source_languages: hbs

- target_languages: epo

- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/hbs-epo/README.md

- original_repo: Tatoeba-Challenge

- tags: ['translation']

- languages: ['sh', 'eo']

- src_constituents: {'hrv', 'srp_Cyrl', 'bos_Latn', 'srp_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/hbs-epo/opus-2020-06-16.zip

- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/hbs-epo/opus-2020-06-16.test.txt

- src_alpha3: hbs

- tgt_alpha3: epo

- short_pair: sh-eo

- chrF2_score: 0.38299999999999995

- bleu: 18.7

- brevity_penalty: 0.9990000000000001

- ref_len: 18457.0

- src_name: Serbo-Croatian

- tgt_name: Esperanto

- train_date: 2020-06-16

- src_alpha2: sh

- tgt_alpha2: eo

- prefer_old: False

- long_pair: hbs-epo

- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535

- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b

- port_machine: brutasse

- port_time: 2020-08-21-14:41


---
tags:
- translation
license: apache-2.0
---

### opus-mt-is-fi

* source languages: is
* target languages: fi
*  OPUS readme: [is-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/is-fi/README.md)

*  dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/is-fi/opus-2020-01-09.zip)
* test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/is-fi/opus-2020-01-09.test.txt)
* test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/is-fi/opus-2020-01-09.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.is.fi 	| 25.0 	| 0.489 |


---
tags:
- translation
license: apache-2.0
---

### opus-mt-es-NORWAY

* source languages: es
* target languages: nb_NO,nb,nn_NO,nn,nog,no_nb,no
*  OPUS readme: [es-nb_NO+nb+nn_NO+nn+nog+no_nb+no](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-nb_NO+nb+nn_NO+nn+nog+no_nb+no/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-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/es-nb_NO+nb+nn_NO+nn+nog+no_nb+no/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-nb_NO+nb+nn_NO+nn+nog+no_nb+no/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-nb_NO+nb+nn_NO+nn+nog+no_nb+no/opus-2020-01-16.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.es.no 	| 31.6 	| 0.523 |


---
tags:
- translation
license: apache-2.0
---

### opus-mt-ty-fi

* source languages: ty
* target languages: fi
*  OPUS readme: [ty-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ty-fi/README.md)

*  dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/ty-fi/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ty-fi/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ty-fi/opus-2020-01-16.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.ty.fi 	| 21.7 	| 0.451 |


---
tags:
- translation
license: apache-2.0
---

### opus-mt-en-uk

* source languages: en
* target languages: uk
*  OPUS readme: [en-uk](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-uk/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/en-uk/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-uk/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-uk/opus-2020-01-08.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| Tatoeba.en.uk 	| 50.2 	| 0.674 |


---
tags:
- translation
license: apache-2.0
---

### opus-mt-lg-en

* source languages: lg
* target languages: en
*  OPUS readme: [lg-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lg-en/README.md)

*  dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/lg-en/opus-2020-01-09.zip)
* test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/lg-en/opus-2020-01-09.test.txt)
* test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/lg-en/opus-2020-01-09.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.lg.en 	| 32.6 	| 0.480 |
| Tatoeba.lg.en 	| 5.4 	| 0.243 |


---
tags:
- translation
license: apache-2.0
---

### opus-mt-es-guw

* source languages: es
* target languages: guw
*  OPUS readme: [es-guw](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-guw/README.md)

*  dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/es-guw/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-guw/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-guw/opus-2020-01-16.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.es.guw 	| 28.6 	| 0.480 |


---
tags:
- translation
license: apache-2.0
---

### opus-mt-ru-en

* source languages: ru
* target languages: en
*  OPUS readme: [ru-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ru-en/README.md)

*  dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-02-26.zip](https://object.pouta.csc.fi/OPUS-MT-models/ru-en/opus-2020-02-26.zip)
* test set translations: [opus-2020-02-26.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ru-en/opus-2020-02-26.test.txt)
* test set scores: [opus-2020-02-26.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ru-en/opus-2020-02-26.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| newstest2012.ru.en 	| 34.8 	| 0.603 |
| newstest2013.ru.en 	| 27.9 	| 0.545 |
| newstest2014-ruen.ru.en 	| 31.9 	| 0.591 |
| newstest2015-enru.ru.en 	| 30.4 	| 0.568 |
| newstest2016-enru.ru.en 	| 30.1 	| 0.565 |
| newstest2017-enru.ru.en 	| 33.4 	| 0.593 |
| newstest2018-enru.ru.en 	| 29.6 	| 0.565 |
| newstest2019-ruen.ru.en 	| 31.4 	| 0.576 |
| Tatoeba.ru.en 	| 61.1 	| 0.736 |


---
language:
- ru
- ar

tags:
- translation

license: apache-2.0
---

### rus-ara

* source group: Russian
* target group: Arabic
*  OPUS readme: [rus-ara](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/rus-ara/README.md)

*  model: transformer
* source language(s): rus
* target language(s): apc ara arz
* 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-2020-07-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/rus-ara/opus-2020-07-03.zip)
* test set translations: [opus-2020-07-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/rus-ara/opus-2020-07-03.test.txt)
* test set scores: [opus-2020-07-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/rus-ara/opus-2020-07-03.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.rus.ara 	| 16.6 	| 0.486 |

### System Info:
- hf_name: rus-ara

- source_languages: rus

- target_languages: ara

- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/rus-ara/README.md

- original_repo: Tatoeba-Challenge

- tags: ['translation']

- languages: ['ru', 'ar']

- src_constituents: {'rus'}

- tgt_constituents: {'apc', 'ara', 'arq_Latn', 'arq', 'afb', 'ara_Latn', 'apc_Latn', 'arz'}

- src_multilingual: False

- tgt_multilingual: False

- prepro:  normalization + SentencePiece (spm32k,spm32k)

- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/rus-ara/opus-2020-07-03.zip

- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/rus-ara/opus-2020-07-03.test.txt

- src_alpha3: rus

- tgt_alpha3: ara

- short_pair: ru-ar

- chrF2_score: 0.486

- bleu: 16.6

- brevity_penalty: 0.9690000000000001

- ref_len: 18878.0

- src_name: Russian

- tgt_name: Arabic

- train_date: 2020-07-03

- src_alpha2: ru

- tgt_alpha2: ar

- prefer_old: False

- long_pair: rus-ara

- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535

- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b

- port_machine: brutasse

- port_time: 2020-08-21-14:41


---
tags:
- translation
license: apache-2.0
---

### opus-mt-fr-ho

* source languages: fr
* target languages: ho
*  OPUS readme: [fr-ho](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-ho/README.md)

*  dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-ho/opus-2020-01-09.zip)
* test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-ho/opus-2020-01-09.test.txt)
* test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-ho/opus-2020-01-09.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.fr.ho 	| 25.4 	| 0.480 |


---
tags:
- translation
license: apache-2.0
---

### opus-mt-sv-sv

* source languages: sv
* target languages: sv
*  OPUS readme: [sv-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-sv/README.md)

*  dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-21.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-sv/opus-2020-01-21.zip)
* test set translations: [opus-2020-01-21.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-sv/opus-2020-01-21.test.txt)
* test set scores: [opus-2020-01-21.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-sv/opus-2020-01-21.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| Tatoeba.sv.sv 	| 49.2 	| 0.741 |


---
language:
- af
- eo

tags:
- translation

license: apache-2.0
---

### 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


---
tags:
- translation
license: apache-2.0
---

### opus-mt-rw-sv

* source languages: rw
* target languages: sv
*  OPUS readme: [rw-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/rw-sv/README.md)

*  dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/rw-sv/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/rw-sv/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/rw-sv/opus-2020-01-16.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.rw.sv 	| 29.1 	| 0.476 |


---
tags:
- translation
license: apache-2.0
---

### opus-mt-ho-en

* source languages: ho
* target languages: en
*  OPUS readme: [ho-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ho-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/ho-en/opus-2020-01-20.zip)
* test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ho-en/opus-2020-01-20.test.txt)
* test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ho-en/opus-2020-01-20.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.ho.en 	| 26.8 	| 0.428 |


---
tags:
- translation
license: apache-2.0
---

### opus-mt-en-lun

* source languages: en
* target languages: lun
*  OPUS readme: [en-lun](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-lun/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/en-lun/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-lun/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-lun/opus-2020-01-08.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.en.lun 	| 28.9 	| 0.552 |


---
tags:
- translation
license: apache-2.0
---

### opus-mt-nl-sv

* source languages: nl
* target languages: sv
*  OPUS readme: [nl-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/nl-sv/README.md)

*  dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/nl-sv/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/nl-sv/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/nl-sv/opus-2020-01-16.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| GlobalVoices.nl.sv 	| 25.0 	| 0.518 |


---
tags:
- translation
license: apache-2.0
---

### opus-mt-xh-fr

* source languages: xh
* target languages: fr
*  OPUS readme: [xh-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/xh-fr/README.md)

*  dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/xh-fr/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/xh-fr/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/xh-fr/opus-2020-01-16.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.xh.fr 	| 30.6 	| 0.487 |


---
tags:
- translation
license: apache-2.0
---

### 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 |


---
tags:
- translation
license: apache-2.0
---

### opus-mt-tvl-es

* source languages: tvl
* target languages: es
*  OPUS readme: [tvl-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/tvl-es/README.md)

*  dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/tvl-es/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/tvl-es/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/tvl-es/opus-2020-01-16.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.tvl.es 	| 21.0 	| 0.388 |


---
tags:
- translation
license: apache-2.0
---

### opus-mt-en-lua

* source languages: en
* target languages: lua
*  OPUS readme: [en-lua](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-lua/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/en-lua/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-lua/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-lua/opus-2020-01-08.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.en.lua 	| 35.3 	| 0.578 |


---
tags:
- translation
license: apache-2.0
---

### opus-mt-tw-fr

* source languages: tw
* target languages: fr
*  OPUS readme: [tw-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/tw-fr/README.md)

*  dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/tw-fr/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/tw-fr/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/tw-fr/opus-2020-01-16.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.tw.fr 	| 26.7 	| 0.442 |


---
language:
- ru
- af

tags:
- translation

license: apache-2.0
---

### rus-afr

* source group: Russian
* target group: Afrikaans
*  OPUS readme: [rus-afr](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/rus-afr/README.md)

*  model: transformer-align
* source language(s): rus
* target language(s): afr
* 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/rus-afr/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/rus-afr/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/rus-afr/opus-2020-06-17.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.rus.afr 	| 48.1 	| 0.669 |

### System Info:
- hf_name: rus-afr

- source_languages: rus

- target_languages: afr

- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/rus-afr/README.md

- original_repo: Tatoeba-Challenge

- tags: ['translation']

- languages: ['ru', 'af']

- src_constituents: {'rus'}

- tgt_constituents: {'afr'}

- src_multilingual: False

- tgt_multilingual: False

- prepro:  normalization + SentencePiece (spm32k,spm32k)

- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/rus-afr/opus-2020-06-17.zip

- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/rus-afr/opus-2020-06-17.test.txt

- src_alpha3: rus

- tgt_alpha3: afr

- short_pair: ru-af

- chrF2_score: 0.669

- bleu: 48.1

- brevity_penalty: 1.0

- ref_len: 1390.0

- src_name: Russian

- tgt_name: Afrikaans

- train_date: 2020-06-17

- src_alpha2: ru

- tgt_alpha2: af

- prefer_old: False

- long_pair: rus-afr

- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535

- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b

- port_machine: brutasse

- port_time: 2020-08-21-14:41


---
tags:
- translation
license: apache-2.0
---

### opus-mt-fr-lus

* source languages: fr
* target languages: lus
*  OPUS readme: [fr-lus](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-lus/README.md)

*  dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-lus/opus-2020-01-09.zip)
* test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-lus/opus-2020-01-09.test.txt)
* test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-lus/opus-2020-01-09.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.fr.lus 	| 25.5 	| 0.455 |


---
tags:
- translation
license: apache-2.0
---

### opus-mt-fi-cs

* source languages: fi
* target languages: cs
*  OPUS readme: [fi-cs](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-cs/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/fi-cs/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-cs/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-cs/opus-2020-01-08.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.fi.cs 	| 25.0 	| 0.470 |


---
tags:
- translation
license: apache-2.0
---

### opus-mt-ha-sv

* source languages: ha
* target languages: sv
*  OPUS readme: [ha-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ha-sv/README.md)

*  dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/ha-sv/opus-2020-01-09.zip)
* test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ha-sv/opus-2020-01-09.test.txt)
* test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ha-sv/opus-2020-01-09.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.ha.sv 	| 25.8 	| 0.438 |


---
language:
- he
- it

tags:
- translation

license: apache-2.0
---
### he-it

* source group: Hebrew
* target group: Italian
*  OPUS readme: [heb-ita](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/heb-ita/README.md)

*  model: transformer
* source language(s): heb
* target language(s): ita
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-12-10.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/heb-ita/opus-2020-12-10.zip)
* test set translations: [opus-2020-12-10.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/heb-ita/opus-2020-12-10.test.txt)
* test set scores: [opus-2020-12-10.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/heb-ita/opus-2020-12-10.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.heb.ita 	| 41.1 	| 0.643 |

### System Info:
- hf_name: he-it

- source_languages: heb

- target_languages: ita

- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/heb-ita/README.md

- original_repo: Tatoeba-Challenge

- tags: ['translation']

- languages: ['he', 'it']

- src_constituents: ('Hebrew', {'heb'})

- tgt_constituents: ('Italian', {'ita'})

- src_multilingual: False

- tgt_multilingual: False

- long_pair: heb-ita

- prepro:  normalization + SentencePiece (spm32k,spm32k)

- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/heb-ita/opus-2020-12-10.zip

- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/heb-ita/opus-2020-12-10.test.txt

- src_alpha3: heb

- tgt_alpha3: ita

- chrF2_score: 0.643

- bleu: 41.1

- brevity_penalty: 0.997

- ref_len: 11464.0

- src_name: Hebrew

- tgt_name: Italian

- train_date: 2020-12-10 00:00:00

- src_alpha2: he

- tgt_alpha2: it

- prefer_old: False

- short_pair: he-it

- helsinki_git_sha: b317f78a3ec8a556a481b6a53dc70dc11769ca96

- transformers_git_sha: 1310e1a758edc8e89ec363db76863c771fbeb1de

- port_machine: LM0-400-22516.local

- port_time: 2020-12-11-16:01


---
tags:
- translation
license: apache-2.0
---

### opus-mt-fi-srn

* source languages: fi
* target languages: srn
*  OPUS readme: [fi-srn](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-srn/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/fi-srn/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-srn/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-srn/opus-2020-01-08.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.fi.srn 	| 29.2 	| 0.491 |


---
language:
- bn
- or
- gu
- mr
- ur
- hi
- as
- si
- inc

tags:
- translation

license: apache-2.0
---

### inc-inc

* source group: Indic languages
* target group: Indic languages
*  OPUS readme: [inc-inc](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/inc-inc/README.md)

*  model: transformer
* source language(s): asm hin mar urd
* target language(s): asm hin mar urd
* 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-2020-07-27.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/inc-inc/opus-2020-07-27.zip)
* test set translations: [opus-2020-07-27.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/inc-inc/opus-2020-07-27.test.txt)
* test set scores: [opus-2020-07-27.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/inc-inc/opus-2020-07-27.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.asm-hin.asm.hin 	| 2.6 	| 0.231 |
| Tatoeba-test.hin-asm.hin.asm 	| 9.1 	| 0.262 |
| Tatoeba-test.hin-mar.hin.mar 	| 28.1 	| 0.548 |
| Tatoeba-test.hin-urd.hin.urd 	| 19.9 	| 0.508 |
| Tatoeba-test.mar-hin.mar.hin 	| 11.6 	| 0.466 |
| Tatoeba-test.multi.multi 	| 17.1 	| 0.464 |
| Tatoeba-test.urd-hin.urd.hin 	| 13.5 	| 0.377 |

### System Info:
- hf_name: inc-inc

- source_languages: inc

- target_languages: inc

- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/inc-inc/README.md

- original_repo: Tatoeba-Challenge

- tags: ['translation']

- languages: ['bn', 'or', 'gu', 'mr', 'ur', 'hi', 'as', 'si', 'inc']

- src_constituents: {'pnb', 'gom', 'ben', 'hif_Latn', 'ori', 'guj', 'pan_Guru', 'snd_Arab', 'npi', 'mar', 'urd', 'bho', 'hin', 'san_Deva', 'asm', 'rom', 'mai', 'awa', 'sin'}

- tgt_constituents: {'pnb', 'gom', 'ben', 'hif_Latn', 'ori', 'guj', 'pan_Guru', 'snd_Arab', 'npi', 'mar', 'urd', 'bho', 'hin', 'san_Deva', 'asm', 'rom', 'mai', 'awa', 'sin'}

- src_multilingual: True

- tgt_multilingual: True

- prepro:  normalization + SentencePiece (spm32k,spm32k)

- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/inc-inc/opus-2020-07-27.zip

- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/inc-inc/opus-2020-07-27.test.txt

- src_alpha3: inc

- tgt_alpha3: inc

- short_pair: inc-inc

- chrF2_score: 0.46399999999999997

- bleu: 17.1

- brevity_penalty: 1.0

- ref_len: 4985.0

- src_name: Indic languages

- tgt_name: Indic languages

- train_date: 2020-07-27

- src_alpha2: inc

- tgt_alpha2: inc

- prefer_old: False

- long_pair: inc-inc

- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535

- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b

- port_machine: brutasse

- port_time: 2020-08-21-14:41


---
tags:
- translation
license: apache-2.0
---

### opus-mt-en-el

* source languages: en
* target languages: el
*  OPUS readme: [en-el](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-el/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/en-el/opus-2019-12-18.zip)
* test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-el/opus-2019-12-18.test.txt)
* test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-el/opus-2019-12-18.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| Tatoeba.en.el 	| 56.4 	| 0.745 |


---
tags:
- translation
license: apache-2.0
---

### opus-mt-lu-en

* source languages: lu
* target languages: en
*  OPUS readme: [lu-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lu-en/README.md)

*  dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/lu-en/opus-2020-01-09.zip)
* test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/lu-en/opus-2020-01-09.test.txt)
* test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/lu-en/opus-2020-01-09.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.lu.en 	| 35.7 	| 0.517 |


---
tags:
- translation
license: apache-2.0
---

### opus-mt-fi-sn

* source languages: fi
* target languages: sn
*  OPUS readme: [fi-sn](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-sn/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/fi-sn/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-sn/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-sn/opus-2020-01-08.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.fi.sn 	| 25.3 	| 0.547 |


---
language:
- uk
- no

tags:
- translation

license: apache-2.0
---

### ukr-nor

* source group: Ukrainian
* target group: Norwegian
*  OPUS readme: [ukr-nor](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-nor/README.md)

*  model: transformer-align
* source language(s): ukr
* target language(s): nob
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-nor/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-nor/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-nor/opus-2020-06-17.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.ukr.nor 	| 21.3 	| 0.397 |

### System Info:
- hf_name: ukr-nor

- source_languages: ukr

- target_languages: nor

- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-nor/README.md

- original_repo: Tatoeba-Challenge

- tags: ['translation']

- languages: ['uk', 'no']

- src_constituents: {'ukr'}

- tgt_constituents: {'nob', 'nno'}

- src_multilingual: False

- tgt_multilingual: False

- prepro:  normalization + SentencePiece (spm4k,spm4k)

- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-nor/opus-2020-06-17.zip

- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-nor/opus-2020-06-17.test.txt

- src_alpha3: ukr

- tgt_alpha3: nor

- short_pair: uk-no

- chrF2_score: 0.397

- bleu: 21.3

- brevity_penalty: 0.966

- ref_len: 4378.0

- src_name: Ukrainian

- tgt_name: Norwegian

- train_date: 2020-06-17

- src_alpha2: uk

- tgt_alpha2: no

- prefer_old: False

- long_pair: ukr-nor

- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535

- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b

- port_machine: brutasse

- port_time: 2020-08-21-14:41


---
tags:
- translation
license: apache-2.0
---

### 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 |


---
tags:
- translation
license: apache-2.0
---

### opus-mt-en-luo

* source languages: en
* target languages: luo
*  OPUS readme: [en-luo](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-luo/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/en-luo/opus-2020-01-20.zip)
* test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-luo/opus-2020-01-20.test.txt)
* test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-luo/opus-2020-01-20.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.en.luo 	| 27.6 	| 0.495 |


---
tags:
- translation
license: apache-2.0
---

### opus-mt-en-chk

* source languages: en
* target languages: chk
*  OPUS readme: [en-chk](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-chk/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/en-chk/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-chk/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-chk/opus-2020-01-08.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.en.chk 	| 26.1 	| 0.468 |


---
language:
- no
- es

tags:
- translation

license: apache-2.0
---

### nor-spa

* source group: Norwegian
* target group: Spanish
*  OPUS readme: [nor-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-spa/README.md)

*  model: transformer-align
* source language(s): nno nob
* target language(s): spa
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm12k,spm12k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-spa/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-spa/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-spa/opus-2020-06-17.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.nor.spa 	| 34.2 	| 0.565 |

### System Info:
- hf_name: nor-spa

- source_languages: nor

- target_languages: spa

- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-spa/README.md

- original_repo: Tatoeba-Challenge

- tags: ['translation']

- languages: ['no', 'es']

- src_constituents: {'nob', 'nno'}

- tgt_constituents: {'spa'}

- src_multilingual: False

- tgt_multilingual: False

- prepro:  normalization + SentencePiece (spm12k,spm12k)

- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/nor-spa/opus-2020-06-17.zip

- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/nor-spa/opus-2020-06-17.test.txt

- src_alpha3: nor

- tgt_alpha3: spa

- short_pair: no-es

- chrF2_score: 0.565

- bleu: 34.2

- brevity_penalty: 0.997

- ref_len: 7311.0

- src_name: Norwegian

- tgt_name: Spanish

- train_date: 2020-06-17

- src_alpha2: no

- tgt_alpha2: es

- prefer_old: False

- long_pair: nor-spa

- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535

- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b

- port_machine: brutasse

- port_time: 2020-08-21-14:41


---
tags:
- translation
license: apache-2.0
---

### opus-mt-hu-de

* source languages: hu
* target languages: de
*  OPUS readme: [hu-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/hu-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/hu-de/opus-2020-01-20.zip)
* test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/hu-de/opus-2020-01-20.test.txt)
* test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/hu-de/opus-2020-01-20.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| Tatoeba.hu.de 	| 44.1 	| 0.637 |


---
tags:
- translation
license: apache-2.0
---

### opus-mt-es-csg

* source languages: es
* target languages: csg
*  OPUS readme: [es-csg](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-csg/README.md)

*  dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/es-csg/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-csg/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-csg/opus-2020-01-16.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.es.csg 	| 91.2 	| 0.937 |


---
language:
- bn
- or
- gu
- mr
- ur
- hi
- ps
- os
- as
- si
- iir

tags:
- translation

license: apache-2.0
---

### iir-iir

* source group: Indo-Iranian languages
* target group: Indo-Iranian languages
*  OPUS readme: [iir-iir](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/iir-iir/README.md)

*  model: transformer
* source language(s): asm hin mar urd zza
* target language(s): asm hin mar urd zza
* 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-2020-07-27.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/iir-iir/opus-2020-07-27.zip)
* test set translations: [opus-2020-07-27.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/iir-iir/opus-2020-07-27.test.txt)
* test set scores: [opus-2020-07-27.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/iir-iir/opus-2020-07-27.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.asm-hin.asm.hin 	| 3.5 	| 0.202 |
| Tatoeba-test.asm-zza.asm.zza 	| 12.4 	| 0.014 |
| Tatoeba-test.hin-asm.hin.asm 	| 6.2 	| 0.238 |
| Tatoeba-test.hin-mar.hin.mar 	| 27.0 	| 0.560 |
| Tatoeba-test.hin-urd.hin.urd 	| 21.4 	| 0.507 |
| Tatoeba-test.mar-hin.mar.hin 	| 13.4 	| 0.463 |
| Tatoeba-test.multi.multi 	| 17.7 	| 0.460 |
| Tatoeba-test.urd-hin.urd.hin 	| 13.4 	| 0.363 |
| Tatoeba-test.zza-asm.zza.asm 	| 5.3 	| 0.000 |

### System Info:
- hf_name: iir-iir

- source_languages: iir

- target_languages: iir

- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/iir-iir/README.md

- original_repo: Tatoeba-Challenge

- tags: ['translation']

- languages: ['bn', 'or', 'gu', 'mr', 'ur', 'hi', 'ps', 'os', 'as', 'si', 'iir']

- src_constituents: {'pnb', 'gom', 'ben', 'hif_Latn', 'ori', 'guj', 'pan_Guru', 'snd_Arab', 'npi', 'mar', 'urd', 'pes', 'bho', 'kur_Arab', 'tgk_Cyrl', 'hin', 'kur_Latn', 'pes_Thaa', 'pus', 'san_Deva', 'oss', 'tly_Latn', 'jdt_Cyrl', 'asm', 'zza', 'rom', 'mai', 'pes_Latn', 'awa', 'sin'}

- tgt_constituents: {'pnb', 'gom', 'ben', 'hif_Latn', 'ori', 'guj', 'pan_Guru', 'snd_Arab', 'npi', 'mar', 'urd', 'pes', 'bho', 'kur_Arab', 'tgk_Cyrl', 'hin', 'kur_Latn', 'pes_Thaa', 'pus', 'san_Deva', 'oss', 'tly_Latn', 'jdt_Cyrl', 'asm', 'zza', 'rom', 'mai', 'pes_Latn', 'awa', 'sin'}

- src_multilingual: True

- tgt_multilingual: True

- prepro:  normalization + SentencePiece (spm32k,spm32k)

- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/iir-iir/opus-2020-07-27.zip

- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/iir-iir/opus-2020-07-27.test.txt

- src_alpha3: iir

- tgt_alpha3: iir

- short_pair: iir-iir

- chrF2_score: 0.46

- bleu: 17.7

- brevity_penalty: 1.0

- ref_len: 4992.0

- src_name: Indo-Iranian languages

- tgt_name: Indo-Iranian languages

- train_date: 2020-07-27

- src_alpha2: iir

- tgt_alpha2: iir

- prefer_old: False

- long_pair: iir-iir

- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535

- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b

- port_machine: brutasse

- port_time: 2020-08-21-14:41


---
language:
- ja
- vi

tags:
- translation

license: apache-2.0
---

### jpn-vie

* source group: Japanese
* target group: Vietnamese
*  OPUS readme: [jpn-vie](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/jpn-vie/README.md)

*  model: transformer-align
* source language(s): jpn jpn_Bopo jpn_Hang jpn_Hani jpn_Hira jpn_Kana jpn_Latn jpn_Yiii
* target language(s): vie
* 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/jpn-vie/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-vie/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-vie/opus-2020-06-17.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.jpn.vie 	| 20.3 	| 0.380 |

### System Info:
- hf_name: jpn-vie

- source_languages: jpn

- target_languages: vie

- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/jpn-vie/README.md

- original_repo: Tatoeba-Challenge

- tags: ['translation']

- languages: ['ja', 'vi']

- src_constituents: {'jpn_Hang', 'jpn', 'jpn_Yiii', 'jpn_Kana', 'jpn_Hani', 'jpn_Bopo', 'jpn_Latn', 'jpn_Hira'}

- tgt_constituents: {'vie', 'vie_Hani'}

- src_multilingual: False

- tgt_multilingual: False

- prepro:  normalization + SentencePiece (spm32k,spm32k)

- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-vie/opus-2020-06-17.zip

- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-vie/opus-2020-06-17.test.txt

- src_alpha3: jpn

- tgt_alpha3: vie

- short_pair: ja-vi

- chrF2_score: 0.38

- bleu: 20.3

- brevity_penalty: 0.909

- ref_len: 10779.0

- src_name: Japanese

- tgt_name: Vietnamese

- train_date: 2020-06-17

- src_alpha2: ja

- tgt_alpha2: vi

- prefer_old: False

- long_pair: jpn-vie

- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535

- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b

- port_machine: brutasse

- port_time: 2020-08-21-14:41


---
language:
- en
- nl
- lb
- af
- de
- fy
- yi
- gmw

tags:
- translation

license: apache-2.0
---

### eng-gmw

* source group: English
* target group: West Germanic languages
*  OPUS readme: [eng-gmw](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-gmw/README.md)

*  model: transformer
* source language(s): eng
* target language(s): afr ang_Latn deu enm_Latn frr fry gos gsw ksh ltz nds nld pdc sco stq swg yid
* 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: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-gmw/opus2m-2020-08-01.zip)
* test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-gmw/opus2m-2020-08-01.test.txt)
* test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-gmw/opus2m-2020-08-01.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| newssyscomb2009-engdeu.eng.deu 	| 21.4 	| 0.518 |
| news-test2008-engdeu.eng.deu 	| 21.0 	| 0.510 |
| newstest2009-engdeu.eng.deu 	| 20.4 	| 0.513 |
| newstest2010-engdeu.eng.deu 	| 22.9 	| 0.528 |
| newstest2011-engdeu.eng.deu 	| 20.5 	| 0.508 |
| newstest2012-engdeu.eng.deu 	| 21.0 	| 0.507 |
| newstest2013-engdeu.eng.deu 	| 24.7 	| 0.533 |
| newstest2015-ende-engdeu.eng.deu 	| 28.2 	| 0.568 |
| newstest2016-ende-engdeu.eng.deu 	| 33.3 	| 0.605 |
| newstest2017-ende-engdeu.eng.deu 	| 26.5 	| 0.559 |
| newstest2018-ende-engdeu.eng.deu 	| 39.9 	| 0.649 |
| newstest2019-ende-engdeu.eng.deu 	| 35.9 	| 0.616 |
| Tatoeba-test.eng-afr.eng.afr 	| 55.7 	| 0.740 |
| Tatoeba-test.eng-ang.eng.ang 	| 6.5 	| 0.164 |
| Tatoeba-test.eng-deu.eng.deu 	| 40.4 	| 0.614 |
| Tatoeba-test.eng-enm.eng.enm 	| 2.3 	| 0.254 |
| Tatoeba-test.eng-frr.eng.frr 	| 8.4 	| 0.248 |
| Tatoeba-test.eng-fry.eng.fry 	| 17.9 	| 0.424 |
| Tatoeba-test.eng-gos.eng.gos 	| 2.2 	| 0.309 |
| Tatoeba-test.eng-gsw.eng.gsw 	| 1.6 	| 0.186 |
| Tatoeba-test.eng-ksh.eng.ksh 	| 1.5 	| 0.189 |
| Tatoeba-test.eng-ltz.eng.ltz 	| 20.2 	| 0.383 |
| Tatoeba-test.eng.multi 	| 41.6 	| 0.609 |
| Tatoeba-test.eng-nds.eng.nds 	| 18.9 	| 0.437 |
| Tatoeba-test.eng-nld.eng.nld 	| 53.1 	| 0.699 |
| Tatoeba-test.eng-pdc.eng.pdc 	| 7.7 	| 0.262 |
| Tatoeba-test.eng-sco.eng.sco 	| 37.7 	| 0.557 |
| Tatoeba-test.eng-stq.eng.stq 	| 5.9 	| 0.380 |
| Tatoeba-test.eng-swg.eng.swg 	| 6.2 	| 0.236 |
| Tatoeba-test.eng-yid.eng.yid 	| 6.8 	| 0.296 |

### System Info:
- hf_name: eng-gmw

- source_languages: eng

- target_languages: gmw

- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-gmw/README.md

- original_repo: Tatoeba-Challenge

- tags: ['translation']

- languages: ['en', 'nl', 'lb', 'af', 'de', 'fy', 'yi', 'gmw']

- src_constituents: {'eng'}

- tgt_constituents: {'ksh', 'nld', 'eng', 'enm_Latn', 'ltz', 'stq', 'afr', 'pdc', 'deu', 'gos', 'ang_Latn', 'fry', 'gsw', 'frr', 'nds', 'yid', 'swg', 'sco'}

- src_multilingual: False

- tgt_multilingual: True

- prepro:  normalization + SentencePiece (spm32k,spm32k)

- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-gmw/opus2m-2020-08-01.zip

- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-gmw/opus2m-2020-08-01.test.txt

- src_alpha3: eng

- tgt_alpha3: gmw

- short_pair: en-gmw

- chrF2_score: 0.609

- bleu: 41.6

- brevity_penalty: 0.9890000000000001

- ref_len: 74922.0

- src_name: English

- tgt_name: West Germanic languages

- train_date: 2020-08-01

- src_alpha2: en

- tgt_alpha2: gmw

- prefer_old: False

- long_pair: eng-gmw

- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535

- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b

- port_machine: brutasse

- port_time: 2020-08-21-14:41


---
tags:
- translation
license: apache-2.0
---

### opus-mt-srn-es

* source languages: srn
* target languages: es
*  OPUS readme: [srn-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/srn-es/README.md)

*  dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/srn-es/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/srn-es/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/srn-es/opus-2020-01-16.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.srn.es 	| 30.4 	| 0.481 |


---
tags:
- translation
license: apache-2.0
---

### opus-mt-fr-ty

* source languages: fr
* target languages: ty
*  OPUS readme: [fr-ty](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-ty/README.md)

*  dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-ty/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-ty/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-ty/opus-2020-01-16.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.fr.ty 	| 39.6 	| 0.561 |


---
tags:
- translation
license: apache-2.0
---

### opus-mt-toi-en

* source languages: toi
* target languages: en
*  OPUS readme: [toi-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/toi-en/README.md)

*  dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/toi-en/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/toi-en/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/toi-en/opus-2020-01-16.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.toi.en 	| 39.0 	| 0.539 |


---
tags:
- translation
license: apache-2.0
---

### opus-mt-de-loz

* source languages: de
* target languages: loz
*  OPUS readme: [de-loz](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-loz/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/de-loz/opus-2020-01-20.zip)
* test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-loz/opus-2020-01-20.test.txt)
* test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-loz/opus-2020-01-20.eval.txt)

## Benchmarks

| testset               | BLEU  | chr-F |
|-----------------------|-------|-------|
| JW300.de.loz 	| 27.7 	| 0.480 |