modelId
stringlengths 4
112
| sha
stringlengths 40
40
| lastModified
stringlengths 24
24
| tags
list | pipeline_tag
stringclasses 29
values | private
bool 1
class | author
stringlengths 2
38
⌀ | config
null | id
stringlengths 4
112
| downloads
float64 0
36.8M
⌀ | likes
float64 0
712
⌀ | library_name
stringclasses 17
values | __index_level_0__
int64 0
38.5k
| readme
stringlengths 0
186k
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SummerChiam/pond_image_classification_2 | 474b45a6cbb6edbba3a09081047477790dea5af7 | 2022-07-29T06:23:30.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"huggingpics",
"model-index"
]
| image-classification | false | SummerChiam | null | SummerChiam/pond_image_classification_2 | 12 | null | transformers | 10,900 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: pond_image_classification_2
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9974489808082581
---
# pond_image_classification_2
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Algae
#### Boiling
#### BoilingNight
#### Normal
#### NormalCement
#### NormalNight
#### NormalRain
 |
Frikallo/out | 31d82a202d500064fbfb87c79140850f705f4652 | 2022-07-29T08:29:57.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
]
| text-generation | false | Frikallo | null | Frikallo/out | 12 | null | transformers | 10,901 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: out
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# out
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001372
- train_batch_size: 1
- eval_batch_size: 8
- seed: 2370848220
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
rufimelo/Legal-BERTimbau-base | 9871473bca33c3e3256761bfda1e565b8ec8c95a | 2022-07-29T16:14:30.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"license:mit",
"autotrain_compatible"
]
| fill-mask | false | rufimelo | null | rufimelo/Legal-BERTimbau-base | 12 | null | transformers | 10,902 | ---
license: mit
---
|
Akjder/DialoGPT-small-harrypotter | b8d3156e5a427a5eb86cc079380ebd89f2879676 | 2021-09-21T06:07:16.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | false | Akjder | null | Akjder/DialoGPT-small-harrypotter | 11 | null | transformers | 10,903 | ---
tags:
- conversational
---
# Harry Potter DialoGPT Model |
Alireza1044/albert-base-v2-mnli | ce8224e1445a916d9d9b9f721bed8dad382f35f0 | 2021-07-27T21:10:33.000Z | [
"pytorch",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
| text-classification | false | Alireza1044 | null | Alireza1044/albert-base-v2-mnli | 11 | null | transformers | 10,904 | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model_index:
- name: mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metric:
name: Accuracy
type: accuracy
value: 0.8500813669650122
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mnli
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5383
- Accuracy: 0.8501
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
|
Alireza1044/albert-base-v2-stsb | 1fb5f36aefaf6c8d4c037b7648c182836497f6a0 | 2021-07-26T10:57:27.000Z | [
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
| text-classification | false | Alireza1044 | null | Alireza1044/albert-base-v2-stsb | 11 | null | transformers | 10,905 | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- spearmanr
model_index:
- name: stsb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE STSB
type: glue
args: stsb
metric:
name: Spearmanr
type: spearmanr
value: 0.9050744778895732
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# stsb
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3978
- Pearson: 0.9090
- Spearmanr: 0.9051
- Combined Score: 0.9071
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.0
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
|
Alstractor/distilbert-base-uncased-finetuned-cola | 186e5e4e33fb7f948b8b9a39e0afe317b08ec5ca | 2021-11-04T21:34:27.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | Alstractor | null | Alstractor/distilbert-base-uncased-finetuned-cola | 11 | null | transformers | 10,906 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5343023846000738
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7272
- Matthews Correlation: 0.5343
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5219 | 1.0 | 535 | 0.5340 | 0.4215 |
| 0.3467 | 2.0 | 1070 | 0.5131 | 0.5181 |
| 0.2331 | 3.0 | 1605 | 0.6406 | 0.5040 |
| 0.1695 | 4.0 | 2140 | 0.7272 | 0.5343 |
| 0.1212 | 5.0 | 2675 | 0.8399 | 0.5230 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
AndrewMcDowell/wav2vec2-xls-r-300m-german-de | e6e17dd3843b4bbcc219a28fc0a8efb655f396ec | 2022-03-23T18:35:11.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | false | AndrewMcDowell | null | AndrewMcDowell/wav2vec2-xls-r-300m-german-de | 11 | 2 | transformers | 10,907 | ---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - German
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: de
metrics:
- name: Test WER
type: wer
value: 20.16
- name: Test CER
type: cer
value: 5.06
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: de
metrics:
- name: Test WER
type: wer
value: 39.79
- name: Test CER
type: cer
value: 15.02
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: de
metrics:
- name: Test WER
type: wer
value: 47.95
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment.
eval results:
WER: 0.20161578657865786
CER: 0.05062357805269733
-->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - DE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1768
- Wer: 0.2016
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 5.7531 | 0.04 | 500 | 5.4564 | 1.0 |
| 2.9882 | 0.08 | 1000 | 3.0041 | 1.0 |
| 2.1953 | 0.13 | 1500 | 1.1723 | 0.7121 |
| 1.2406 | 0.17 | 2000 | 0.3656 | 0.3623 |
| 1.1294 | 0.21 | 2500 | 0.2843 | 0.2926 |
| 1.0731 | 0.25 | 3000 | 0.2554 | 0.2664 |
| 1.051 | 0.3 | 3500 | 0.2387 | 0.2535 |
| 1.0479 | 0.34 | 4000 | 0.2345 | 0.2512 |
| 1.0026 | 0.38 | 4500 | 0.2270 | 0.2452 |
| 0.9921 | 0.42 | 5000 | 0.2212 | 0.2353 |
| 0.9839 | 0.47 | 5500 | 0.2141 | 0.2330 |
| 0.9907 | 0.51 | 6000 | 0.2122 | 0.2334 |
| 0.9788 | 0.55 | 6500 | 0.2114 | 0.2270 |
| 0.9687 | 0.59 | 7000 | 0.2066 | 0.2323 |
| 0.9777 | 0.64 | 7500 | 0.2033 | 0.2237 |
| 0.9476 | 0.68 | 8000 | 0.2020 | 0.2194 |
| 0.9625 | 0.72 | 8500 | 0.1977 | 0.2191 |
| 0.9497 | 0.76 | 9000 | 0.1976 | 0.2175 |
| 0.9781 | 0.81 | 9500 | 0.1956 | 0.2159 |
| 0.9552 | 0.85 | 10000 | 0.1958 | 0.2191 |
| 0.9345 | 0.89 | 10500 | 0.1964 | 0.2158 |
| 0.9528 | 0.93 | 11000 | 0.1926 | 0.2154 |
| 0.9502 | 0.98 | 11500 | 0.1953 | 0.2149 |
| 0.9358 | 1.02 | 12000 | 0.1927 | 0.2167 |
| 0.941 | 1.06 | 12500 | 0.1901 | 0.2115 |
| 0.9287 | 1.1 | 13000 | 0.1936 | 0.2090 |
| 0.9491 | 1.15 | 13500 | 0.1900 | 0.2104 |
| 0.9478 | 1.19 | 14000 | 0.1931 | 0.2120 |
| 0.946 | 1.23 | 14500 | 0.1914 | 0.2134 |
| 0.9499 | 1.27 | 15000 | 0.1931 | 0.2173 |
| 0.9346 | 1.32 | 15500 | 0.1913 | 0.2105 |
| 0.9509 | 1.36 | 16000 | 0.1902 | 0.2137 |
| 0.9294 | 1.4 | 16500 | 0.1895 | 0.2086 |
| 0.9418 | 1.44 | 17000 | 0.1913 | 0.2183 |
| 0.9302 | 1.49 | 17500 | 0.1884 | 0.2114 |
| 0.9418 | 1.53 | 18000 | 0.1894 | 0.2108 |
| 0.9363 | 1.57 | 18500 | 0.1886 | 0.2132 |
| 0.9338 | 1.61 | 19000 | 0.1856 | 0.2078 |
| 0.9185 | 1.66 | 19500 | 0.1852 | 0.2056 |
| 0.9216 | 1.7 | 20000 | 0.1874 | 0.2095 |
| 0.9176 | 1.74 | 20500 | 0.1873 | 0.2078 |
| 0.9288 | 1.78 | 21000 | 0.1865 | 0.2097 |
| 0.9278 | 1.83 | 21500 | 0.1869 | 0.2100 |
| 0.9295 | 1.87 | 22000 | 0.1878 | 0.2095 |
| 0.9221 | 1.91 | 22500 | 0.1852 | 0.2121 |
| 0.924 | 1.95 | 23000 | 0.1855 | 0.2042 |
| 0.9104 | 2.0 | 23500 | 0.1858 | 0.2105 |
| 0.9284 | 2.04 | 24000 | 0.1850 | 0.2080 |
| 0.9162 | 2.08 | 24500 | 0.1839 | 0.2045 |
| 0.9111 | 2.12 | 25000 | 0.1838 | 0.2080 |
| 0.91 | 2.17 | 25500 | 0.1889 | 0.2106 |
| 0.9152 | 2.21 | 26000 | 0.1856 | 0.2026 |
| 0.9209 | 2.25 | 26500 | 0.1891 | 0.2133 |
| 0.9094 | 2.29 | 27000 | 0.1857 | 0.2089 |
| 0.9065 | 2.34 | 27500 | 0.1840 | 0.2052 |
| 0.9156 | 2.38 | 28000 | 0.1833 | 0.2062 |
| 0.8986 | 2.42 | 28500 | 0.1789 | 0.2001 |
| 0.9045 | 2.46 | 29000 | 0.1769 | 0.2022 |
| 0.9039 | 2.51 | 29500 | 0.1819 | 0.2073 |
| 0.9145 | 2.55 | 30000 | 0.1828 | 0.2063 |
| 0.9081 | 2.59 | 30500 | 0.1811 | 0.2049 |
| 0.9252 | 2.63 | 31000 | 0.1833 | 0.2086 |
| 0.8957 | 2.68 | 31500 | 0.1795 | 0.2083 |
| 0.891 | 2.72 | 32000 | 0.1809 | 0.2058 |
| 0.9023 | 2.76 | 32500 | 0.1812 | 0.2061 |
| 0.8918 | 2.8 | 33000 | 0.1775 | 0.1997 |
| 0.8852 | 2.85 | 33500 | 0.1790 | 0.1997 |
| 0.8928 | 2.89 | 34000 | 0.1767 | 0.2013 |
| 0.9079 | 2.93 | 34500 | 0.1735 | 0.1986 |
| 0.9032 | 2.97 | 35000 | 0.1793 | 0.2024 |
| 0.9018 | 3.02 | 35500 | 0.1778 | 0.2027 |
| 0.8846 | 3.06 | 36000 | 0.1776 | 0.2046 |
| 0.8848 | 3.1 | 36500 | 0.1812 | 0.2064 |
| 0.9062 | 3.14 | 37000 | 0.1800 | 0.2018 |
| 0.9011 | 3.19 | 37500 | 0.1783 | 0.2049 |
| 0.8996 | 3.23 | 38000 | 0.1810 | 0.2036 |
| 0.893 | 3.27 | 38500 | 0.1805 | 0.2056 |
| 0.897 | 3.31 | 39000 | 0.1773 | 0.2035 |
| 0.8992 | 3.36 | 39500 | 0.1804 | 0.2054 |
| 0.8987 | 3.4 | 40000 | 0.1768 | 0.2016 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test`
```bash
python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-300m-german-de --dataset mozilla-foundation/common_voice_7_0 --config de --split test --log_outputs
```
2. To evaluate on test dev data
```bash
python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-300m-german-de --dataset speech-recognition-community-v2/dev_data --config de --split validation --chunk_length_s 5.0 --stride_length_s 1.0
``` |
Aron/distilbert-base-uncased-finetuned-emotion | 7e09eaf0dcaea74dcd36ad941fcc93e13f55d5fd | 2022-02-23T10:34:14.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | Aron | null | Aron/distilbert-base-uncased-finetuned-emotion | 11 | null | transformers | 10,908 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.92
- name: F1
type: f1
value: 0.9201604193183255
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2295
- Accuracy: 0.92
- F1: 0.9202
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8187 | 1.0 | 250 | 0.3137 | 0.902 | 0.8983 |
| 0.2514 | 2.0 | 500 | 0.2295 | 0.92 | 0.9202 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Ayran/DialoGPT-medium-harry-potter-1-through-4-plus-6 | cc648b051333929ef52291982bfc852656c51849 | 2021-10-31T18:01:26.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | false | Ayran | null | Ayran/DialoGPT-medium-harry-potter-1-through-4-plus-6 | 11 | null | transformers | 10,909 | ---
tags:
- conversational
---
#DialoGPT medium model (Harry Potter 1 through 4 plus 6) |
BitanBiswas/mbert-bengali-ner-finetuned-ner | aac63e8421628df4ec11db7400406f1e84335572 | 2022-02-14T16:54:04.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | BitanBiswas | null | BitanBiswas/mbert-bengali-ner-finetuned-ner | 11 | null | transformers | 10,910 | Entry not found |
CallumRai/HansardGPT2 | f7d01bb2bafb914a7f315c272ec3b33f228f8372 | 2021-05-21T09:33:25.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | false | CallumRai | null | CallumRai/HansardGPT2 | 11 | null | transformers | 10,911 | A PyTorch GPT-2 model trained on hansard from 2019-01-01 to 2020-06-01
For more information see: https://github.com/CallumRai/Hansard/ |
ClaudeYang/awesome_fb_model | 432124511482ab93d8469a5f7780d82fd10318dc | 2021-11-15T10:29:01.000Z | [
"pytorch",
"bart",
"text-classification",
"dataset:multi_nli",
"transformers",
"zero-shot-classification"
]
| zero-shot-classification | false | ClaudeYang | null | ClaudeYang/awesome_fb_model | 11 | null | transformers | 10,912 | ---
pipeline_tag: zero-shot-classification
datasets:
- multi_nli
widget:
- text: "ETH"
candidate_labels: "Location & Address, Employment, Organizational, Name, Service, Studies, Science"
hypothesis_template: "This is {}."
---
ETH Zeroshot |
Contrastive-Tension/BERT-Large-NLI-CT | 264d3405d54241dfe71ca0d0971aa7e92883941c | 2021-05-18T18:04:22.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | false | Contrastive-Tension | null | Contrastive-Tension/BERT-Large-NLI-CT | 11 | null | transformers | 10,913 | Entry not found |
DCU-NLP/electra-base-irish-cased-generator-v1 | a7fbe12effe2daf8d519d6d2825e10523070dc37 | 2021-11-15T18:03:36.000Z | [
"pytorch",
"electra",
"fill-mask",
"ga",
"arxiv:2107.12930",
"transformers",
"irish",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | false | DCU-NLP | null | DCU-NLP/electra-base-irish-cased-generator-v1 | 11 | null | transformers | 10,914 | ---
language:
- ga
license: apache-2.0
tags:
- irish
- electra
widget:
- text: "Ceoltóir [MASK] ab ea Johnny Cash."
---
# gaELECTRA
[gaELECTRA](https://arxiv.org/abs/2107.12930) is an ELECTRA model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper. For fine-tuning this model on a token classification task, e.g. Named Entity Recognition, use the discriminator model.
### Limitations and bias
Some data used to pretrain gaBERT was scraped from the web which potentially contains ethically problematic text (bias, hate, adult content, etc.). Consequently, downstream tasks/applications using gaBERT should be thoroughly tested with respect to ethical considerations.
### BibTeX entry and citation info
If you use this model in your research, please consider citing our paper:
```
@article{DBLP:journals/corr/abs-2107-12930,
author = {James Barry and
Joachim Wagner and
Lauren Cassidy and
Alan Cowap and
Teresa Lynn and
Abigail Walsh and
M{\'{\i}}che{\'{a}}l J. {\'{O}} Meachair and
Jennifer Foster},
title = {gaBERT - an Irish Language Model},
journal = {CoRR},
volume = {abs/2107.12930},
year = {2021},
url = {https://arxiv.org/abs/2107.12930},
archivePrefix = {arXiv},
eprint = {2107.12930},
timestamp = {Fri, 30 Jul 2021 13:03:06 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2107-12930.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` |
DSI/personal_sentiment | 1f10be4fb1420b1cf0efee0a9dca29ac7d47abdd | 2021-11-13T18:51:22.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | DSI | null | DSI/personal_sentiment | 11 | null | transformers | 10,915 | Entry not found |
Davlan/naija-twitter-sentiment-afriberta-large | 3b9fda62f667a5930d9d2fc73a3ef65ba8564526 | 2022-06-27T11:50:40.000Z | [
"pytorch",
"tf",
"xlm-roberta",
"text-classification",
"hau",
"ibo",
"pcm",
"yor",
"multilingual",
"arxiv:2201.08277",
"transformers"
]
| text-classification | false | Davlan | null | Davlan/naija-twitter-sentiment-afriberta-large | 11 | 1 | transformers | 10,916 | Hugging Face's logo
---
language:
- hau
- ibo
- pcm
- yor
- multilingual
---
# naija-twitter-sentiment-afriberta-large
## Model description
**naija-twitter-sentiment-afriberta-large** is the first multilingual twitter **sentiment classification** model for four (4) Nigerian languages (Hausa, Igbo, Nigerian Pidgin, and Yorùbá) based on a fine-tuned castorini/afriberta_large large model.
It achieves the **state-of-the-art performance** for the twitter sentiment classification task trained on the [NaijaSenti corpus](https://github.com/hausanlp/NaijaSenti).
The model has been trained to classify tweets into 3 sentiment classes: negative, neutral and positive
Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of 4 Nigerian language datasets obtained from [NaijaSenti](https://github.com/hausanlp/NaijaSenti) dataset.
## Intended uses & limitations
#### How to use
You can use this model with Transformers for Sentiment Classification.
```python
from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer
import numpy as np
from scipy.special import softmax
MODEL = "Davlan/naija-twitter-sentiment-afriberta-large"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
# PT
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
text = "I like you"
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
scores = softmax(scores)
id2label = {0:"positive", 1:"neutral", 2:"negative"}
ranking = np.argsort(scores)
ranking = ranking[::-1]
for i in range(scores.shape[0]):
l = id2label[ranking[i]]
s = scores[ranking[i]]
print(f"{i+1}) {l} {np.round(float(s), 4)}")
```
#### Limitations and bias
This model is limited by its training dataset and domain i.e Twitter. This may not generalize well for all use cases in different domains.
## Training procedure
This model was trained on a single Nvidia RTX 2080 GPU with recommended hyperparameters from the [original NaijaSenti paper](https://arxiv.org/abs/2201.08277).
## Eval results on Test set (F-score), average over 5 runs.
language|F1-score
-|-
hau |81.2
ibo |80.8
pcm |74.5
yor |80.4
### BibTeX entry and citation info
```
@inproceedings{Muhammad2022NaijaSentiAN,
title={NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis},
author={Shamsuddeen Hassan Muhammad and David Ifeoluwa Adelani and Sebastian Ruder and Ibrahim Said Ahmad and Idris Abdulmumin and Bello Shehu Bello and Monojit Choudhury and Chris C. Emezue and Saheed Salahudeen Abdullahi and Anuoluwapo Aremu and Alipio Jeorge and Pavel B. Brazdil},
year={2022}
}
```
|
DeadBeast/emoBERTTamil | 60820db97992bedb7055e46570667d3178135467 | 2021-08-22T15:46:05.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"dataset:tamilmixsentiment",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
| text-classification | false | DeadBeast | null | DeadBeast/emoBERTTamil | 11 | 2 | transformers | 10,917 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tamilmixsentiment
metrics:
- accuracy
model_index:
- name: emoBERTTamil
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tamilmixsentiment
type: tamilmixsentiment
args: default
metric:
name: Accuracy
type: accuracy
value: 0.671
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# emoBERTTamil
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the tamilmixsentiment dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9666
- Accuracy: 0.671
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1128 | 1.0 | 250 | 1.0290 | 0.672 |
| 1.0226 | 2.0 | 500 | 1.0172 | 0.686 |
| 0.9137 | 3.0 | 750 | 0.9666 | 0.671 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
Edomonndo/opus-mt-ja-en-finetuned-ja-to-en_test | 3bb793c9a8488ce7fa40dc6baf7d0aa4d895866d | 2021-10-20T06:22:41.000Z | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
]
| text2text-generation | false | Edomonndo | null | Edomonndo/opus-mt-ja-en-finetuned-ja-to-en_test | 11 | null | transformers | 10,918 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model_index:
- name: opus-mt-ja-en-finetuned-ja-to-en_test
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
metric:
name: Bleu
type: bleu
value: 80.2723
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-ja-en-finetuned-ja-to-en_test
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/opus-mt-ja-en) on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4737
- Bleu: 80.2723
- Gen Len: 16.5492
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 1.1237 | 1.0 | 247 | 0.6131 | 60.9383 | 16.4152 |
| 0.5395 | 2.0 | 494 | 0.5274 | 67.5705 | 16.2883 |
| 0.3584 | 3.0 | 741 | 0.5122 | 71.3098 | 16.3777 |
| 0.2563 | 4.0 | 988 | 0.4887 | 73.6639 | 16.401 |
| 0.138 | 5.0 | 1235 | 0.4796 | 76.7942 | 16.4873 |
| 0.0979 | 6.0 | 1482 | 0.4849 | 76.9404 | 16.6162 |
| 0.0792 | 7.0 | 1729 | 0.4806 | 78.9831 | 16.5442 |
| 0.0569 | 8.0 | 1976 | 0.4765 | 79.3461 | 16.4873 |
| 0.0299 | 9.0 | 2223 | 0.4751 | 79.7901 | 16.4863 |
| 0.0204 | 10.0 | 2470 | 0.4737 | 80.2723 | 16.5492 |
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu111
- Datasets 1.10.2
- Tokenizers 0.10.3
|
EhsanYB/distilbert-finetuned-ner | 371f93580b3932f62207c5bf67a1bae9639c033f | 2022-01-14T10:09:06.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | EhsanYB | null | EhsanYB/distilbert-finetuned-ner | 11 | null | transformers | 10,919 | Entry not found |
Evgeneus/distilbert-base-uncased-finetuned-ner | 7e4b1dab0a02decf8bc0e45d8e0c469c888f6a3c | 2021-12-13T11:57:39.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| token-classification | false | Evgeneus | null | Evgeneus/distilbert-base-uncased-finetuned-ner | 11 | null | transformers | 10,920 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.875445994161531
- name: Recall
type: recall
value: 0.9058060185703098
- name: F1
type: f1
value: 0.8903672751264571
- name: Accuracy
type: accuracy
value: 0.9763292928971993
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0845
- Precision: 0.8754
- Recall: 0.9058
- F1: 0.8904
- Accuracy: 0.9763
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2529 | 1.0 | 878 | 0.0845 | 0.8754 | 0.9058 | 0.8904 | 0.9763 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Galuh/id-journal-gpt2 | 66ed8c7923fb9dce2897b78bbc81b07abb1d9ecd | 2021-08-01T14:07:43.000Z | [
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"id",
"transformers"
]
| text-generation | false | Galuh | null | Galuh/id-journal-gpt2 | 11 | 1 | transformers | 10,921 | ---
language: id
widget:
- text: "Penelitian ini bertujuan untuk menentukan identitas invertebrata laut dari Perairan Papua dengan teknik DNA barcoding"
---
# Indonesian GPT-2 finetuned on Indonesian academic journals
This is the [Indonesian gpt2-small model](https://huggingface.co/flax-community/gpt2-small-indonesian) fine-tuned to abstracts of Indonesian academic journals. All training was done on a TPUv2-8 VM sponsored by [TPU Research Cloud](https://sites.research.google/trc/).
The demo can be found [here](https://huggingface.co/spaces/flax-community/gpt2-indonesian).
## How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness,
we set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='Galuh/id-journal-gpt2')
>>> set_seed(42)
>>> generator("Penelitian ini menggunakan teknik DNA barcoding untuk", max_length=30, num_return_sequences=5)
[{'generated_text': 'Penelitian ini menggunakan teknik DNA barcoding untuk mendeteksi perubahan genetik bakteri pada udang windu. Empat tahap telah dilakukan, meliputi preparasi media untuk larva,'},
{'generated_text': 'Penelitian ini menggunakan teknik DNA barcoding untuk identifikasi gen pengasil flavonoid. Data yang diperoleh dari hasil PCR diidentifikasi dengan teknik sekuensing'},
{'generated_text': 'Penelitian ini menggunakan teknik DNA barcoding untuk mengekstraksi fragmen DNA dari sampel kulit buaya dan tulang anjing, di mana proses ini melibatkan karakterisasi enzim yang'},
{'generated_text': 'Penelitian ini menggunakan teknik DNA barcoding untuk melakukan transformasi. Tahapan transformasi meliputi seleksi sel dengan urutan (2, 8, 16,..., 18) dan'},
{'generated_text': 'Penelitian ini menggunakan teknik DNA barcoding untuk amplifikasi genom DNA dengan menggunakan primer TG8226 dan TG806. Metode pol'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import GPT2Tokenizer, GPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('Galuh/id-journal-gpt2')
model = GPT2Model.from_pretrained('Galuh/id-journal-gpt2')
text = "Ubah dengan teks apa saja."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import GPT2Tokenizer, TFGPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('Galuh/id-journal-gpt2')
model = TFGPT2Model.from_pretrained('Galuh/id-journal-gpt2')
text = "Ubah dengan teks apa saja."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Limitations and bias
This model is originally the [Indonesian gpt2-small model](https://huggingface.co/flax-community/gpt2-small-indonesian), thus this model is also subject to the same [limitations and bias as the original model](https://huggingface.co/flax-community/gpt2-small-indonesian#limitations-and-bias). More detailed bias and analysis on this specific model is coming soon.
## Training data
The model was trained on a dataset of Indonesian journals. We only trained this model on the abstracts. We extract the abstract by writing a script to find any text that is located between the word "Abstrak" (abstract) and "Kata kunci" (keywords). The extraction script can be found [here](https://github.com/galuhsahid/id-journal-gpt2/). To separate each abstract, we also add an end of text token (`<|endoftext|>`) between each abstract.
The information of the sub-dataset and the distribution of the training and evaluation dataset are as follows:
| split | count | percentage |
| ---------- | ---------- | -------------- |
| train | 146,248 | 90% |
| validation | 16,250 | 10% |
## Training procedure
The model was trained on a TPUv2-8 VM provided by [TPU Research Cloud](https://sites.research.google/trc/). The training duration was `2h 30m 57s`.
### Evaluation results
The model achieves the following results without any fine-tuning (zero-shot):
| dataset | train loss | eval loss | eval perplexity |
| ---------- | ---------- | -------------- | ---------- |
| Indonesian journals dataset (abstract only) | 2.913 | 2.855 | 17.37 |
### Tracking
The training process was tracked in [TensorBoard](https://huggingface.co/Galuh/id-journal-gpt2/tensorboard). |
Geotrend/bert-base-it-cased | ca5125655bba48f7e5a9ca38bc8e79995440f6bf | 2021-05-18T19:58:28.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"it",
"dataset:wikipedia",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | false | Geotrend | null | Geotrend/bert-base-it-cased | 11 | null | transformers | 10,922 | ---
language: it
datasets: wikipedia
license: apache-2.0
---
# bert-base-it-cased
We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages.
Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the same representations produced by the original model which preserves the original accuracy.
For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf).
## How to use
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-it-cased")
model = AutoModel.from_pretrained("Geotrend/bert-base-it-cased")
```
To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
### How to cite
```bibtex
@inproceedings{smallermbert,
title={Load What You Need: Smaller Versions of Mutlilingual BERT},
author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire},
booktitle={SustaiNLP / EMNLP},
year={2020}
}
```
## Contact
Please contact [email protected] for any question, feedback or request. |
Geotrend/bert-base-pl-cased | a78a67d5438ae11233f1af768f474221e3a1f855 | 2021-05-18T20:05:45.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"pl",
"dataset:wikipedia",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | false | Geotrend | null | Geotrend/bert-base-pl-cased | 11 | null | transformers | 10,923 | ---
language: pl
datasets: wikipedia
license: apache-2.0
---
# bert-base-pl-cased
We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages.
Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the same representations produced by the original model which preserves the original accuracy.
For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf).
## How to use
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-pl-cased")
model = AutoModel.from_pretrained("Geotrend/bert-base-pl-cased")
```
To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
### How to cite
```bibtex
@inproceedings{smallermbert,
title={Load What You Need: Smaller Versions of Mutlilingual BERT},
author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire},
booktitle={SustaiNLP / EMNLP},
year={2020}
}
```
## Contact
Please contact [email protected] for any question, feedback or request. |
Geotrend/distilbert-base-en-es-cased | 0ac1cfe71b07fc80a7b2f18c055da1ece86c5f13 | 2021-08-16T13:58:36.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"multilingual",
"dataset:wikipedia",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | false | Geotrend | null | Geotrend/distilbert-base-en-es-cased | 11 | null | transformers | 10,924 | ---
language: multilingual
datasets: wikipedia
license: apache-2.0
---
# distilbert-base-en-es-cased
We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages.
Our versions give exactly the same representations produced by the original model which preserves the original accuracy.
For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf).
## How to use
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-es-cased")
model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-es-cased")
```
To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
### How to cite
```bibtex
@inproceedings{smallermdistilbert,
title={Load What You Need: Smaller Versions of Mutlilingual BERT},
author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire},
booktitle={SustaiNLP / EMNLP},
year={2020}
}
```
## Contact
Please contact [email protected] for any question, feedback or request. |
Geotrend/distilbert-base-en-nl-cased | 6d0e35d3576d4ac1e3b97f67e0bbb8d2b6fccc5c | 2021-07-27T10:22:30.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"multilingual",
"dataset:wikipedia",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | false | Geotrend | null | Geotrend/distilbert-base-en-nl-cased | 11 | null | transformers | 10,925 | ---
language: multilingual
datasets: wikipedia
license: apache-2.0
---
# distilbert-base-en-nl-cased
We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages.
Our versions give exactly the same representations produced by the original model which preserves the original accuracy.
For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf).
## How to use
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-nl-cased")
model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-nl-cased")
```
To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
### How to cite
```bibtex
@inproceedings{smallermdistilbert,
title={Load What You Need: Smaller Versions of Mutlilingual BERT},
author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire},
booktitle={SustaiNLP / EMNLP},
year={2020}
}
```
## Contact
Please contact [email protected] for any question, feedback or request. |
Helsinki-NLP/opus-mt-ase-de | 8df440a31f6a5af4aa0f9512140373a0ee8eed3d | 2021-09-09T21:26:23.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ase",
"de",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ase-de | 11 | null | transformers | 10,926 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-ase-de
* source languages: ase
* target languages: de
* OPUS readme: [ase-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ase-de/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/ase-de/opus-2020-01-20.zip)
* test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ase-de/opus-2020-01-20.test.txt)
* test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ase-de/opus-2020-01-20.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.ase.de | 27.2 | 0.478 |
|
Helsinki-NLP/opus-mt-bnt-en | daa6f431ef85c1d5923fd6a7e3bcf85dc0ea1dc2 | 2021-01-18T07:52:00.000Z | [
"pytorch",
"marian",
"text2text-generation",
"sn",
"zu",
"rw",
"lg",
"ts",
"ln",
"ny",
"xh",
"rn",
"bnt",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-bnt-en | 11 | null | transformers | 10,927 | ---
language:
- sn
- zu
- rw
- lg
- ts
- ln
- ny
- xh
- rn
- bnt
- en
tags:
- translation
license: apache-2.0
---
### bnt-eng
* source group: Bantu languages
* target group: English
* OPUS readme: [bnt-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bnt-eng/README.md)
* model: transformer
* source language(s): kin lin lug nya run sna swh toi_Latn tso umb xho zul
* target language(s): eng
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus2m-2020-07-31.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/bnt-eng/opus2m-2020-07-31.zip)
* test set translations: [opus2m-2020-07-31.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bnt-eng/opus2m-2020-07-31.test.txt)
* test set scores: [opus2m-2020-07-31.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bnt-eng/opus2m-2020-07-31.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.kin-eng.kin.eng | 31.7 | 0.481 |
| Tatoeba-test.lin-eng.lin.eng | 8.3 | 0.271 |
| Tatoeba-test.lug-eng.lug.eng | 5.3 | 0.128 |
| Tatoeba-test.multi.eng | 23.1 | 0.394 |
| Tatoeba-test.nya-eng.nya.eng | 38.3 | 0.527 |
| Tatoeba-test.run-eng.run.eng | 26.6 | 0.431 |
| Tatoeba-test.sna-eng.sna.eng | 27.5 | 0.440 |
| Tatoeba-test.swa-eng.swa.eng | 4.6 | 0.195 |
| Tatoeba-test.toi-eng.toi.eng | 16.2 | 0.342 |
| Tatoeba-test.tso-eng.tso.eng | 100.0 | 1.000 |
| Tatoeba-test.umb-eng.umb.eng | 8.4 | 0.231 |
| Tatoeba-test.xho-eng.xho.eng | 37.2 | 0.554 |
| Tatoeba-test.zul-eng.zul.eng | 40.9 | 0.576 |
### System Info:
- hf_name: bnt-eng
- source_languages: bnt
- target_languages: eng
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bnt-eng/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['sn', 'zu', 'rw', 'lg', 'ts', 'ln', 'ny', 'xh', 'rn', 'bnt', 'en']
- src_constituents: {'sna', 'zul', 'kin', 'lug', 'tso', 'lin', 'nya', 'xho', 'swh', 'run', 'toi_Latn', 'umb'}
- tgt_constituents: {'eng'}
- src_multilingual: True
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/bnt-eng/opus2m-2020-07-31.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/bnt-eng/opus2m-2020-07-31.test.txt
- src_alpha3: bnt
- tgt_alpha3: eng
- short_pair: bnt-en
- chrF2_score: 0.39399999999999996
- bleu: 23.1
- brevity_penalty: 1.0
- ref_len: 14565.0
- src_name: Bantu languages
- tgt_name: English
- train_date: 2020-07-31
- src_alpha2: bnt
- tgt_alpha2: en
- prefer_old: False
- long_pair: bnt-eng
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Helsinki-NLP/opus-mt-ceb-fi | 8c5cdaa45a8ef959061c6d97a7f118e2714725bc | 2021-09-09T21:28:30.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ceb",
"fi",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ceb-fi | 11 | null | transformers | 10,928 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-ceb-fi
* source languages: ceb
* target languages: fi
* OPUS readme: [ceb-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ceb-fi/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/ceb-fi/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ceb-fi/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ceb-fi/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.ceb.fi | 27.4 | 0.525 |
|
Helsinki-NLP/opus-mt-cs-uk | 358d8385f3eaf83363f2daf7ac81b21a7c9f827a | 2021-01-18T07:56:10.000Z | [
"pytorch",
"marian",
"text2text-generation",
"cs",
"uk",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-cs-uk | 11 | null | transformers | 10,929 | ---
language:
- cs
- uk
tags:
- translation
license: apache-2.0
---
### ces-ukr
* source group: Czech
* target group: Ukrainian
* OPUS readme: [ces-ukr](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ces-ukr/README.md)
* model: transformer-align
* source language(s): ces
* target language(s): ukr
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ces-ukr/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ces-ukr/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ces-ukr/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.ces.ukr | 50.9 | 0.680 |
### System Info:
- hf_name: ces-ukr
- source_languages: ces
- target_languages: ukr
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ces-ukr/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['cs', 'uk']
- src_constituents: {'ces'}
- tgt_constituents: {'ukr'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ces-ukr/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ces-ukr/opus-2020-06-17.test.txt
- src_alpha3: ces
- tgt_alpha3: ukr
- short_pair: cs-uk
- chrF2_score: 0.68
- bleu: 50.9
- brevity_penalty: 0.9940000000000001
- ref_len: 8891.0
- src_name: Czech
- tgt_name: Ukrainian
- train_date: 2020-06-17
- src_alpha2: cs
- tgt_alpha2: uk
- prefer_old: False
- long_pair: ces-ukr
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Helsinki-NLP/opus-mt-de-crs | b9de144126655b973cd8cf74a5651ac999e551a2 | 2021-09-09T21:30:25.000Z | [
"pytorch",
"marian",
"text2text-generation",
"de",
"crs",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-de-crs | 11 | null | transformers | 10,930 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-de-crs
* source languages: de
* target languages: crs
* OPUS readme: [de-crs](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-crs/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-crs/opus-2020-01-20.zip)
* test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-crs/opus-2020-01-20.test.txt)
* test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-crs/opus-2020-01-20.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.de.crs | 24.1 | 0.429 |
|
Helsinki-NLP/opus-mt-de-fj | 596580a8225fb340357d25cd38639fed5d662681 | 2021-09-09T21:31:09.000Z | [
"pytorch",
"marian",
"text2text-generation",
"de",
"fj",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-de-fj | 11 | null | transformers | 10,931 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-de-fj
* source languages: de
* target languages: fj
* OPUS readme: [de-fj](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-fj/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-fj/opus-2020-01-20.zip)
* test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-fj/opus-2020-01-20.test.txt)
* test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-fj/opus-2020-01-20.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.de.fj | 24.6 | 0.470 |
|
Helsinki-NLP/opus-mt-el-fr | b00ba91c42b2f20768228b179f01274048158001 | 2021-09-09T21:33:51.000Z | [
"pytorch",
"marian",
"text2text-generation",
"el",
"fr",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-el-fr | 11 | null | transformers | 10,932 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-el-fr
* source languages: el
* target languages: fr
* OPUS readme: [el-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/el-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/el-fr/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/el-fr/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/el-fr/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.el.fr | 63.0 | 0.741 |
|
Helsinki-NLP/opus-mt-en-bcl | fdda7e146d903da0f4da8895800c52bdcfa07ecc | 2021-09-09T21:34:09.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"bcl",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-bcl | 11 | null | transformers | 10,933 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-bcl
* source languages: en
* target languages: bcl
* OPUS readme: [en-bcl](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-bcl/README.md)
* dataset: opus+bt
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus+bt-2020-02-26.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-bcl/opus+bt-2020-02-26.zip)
* test set translations: [opus+bt-2020-02-26.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-bcl/opus+bt-2020-02-26.test.txt)
* test set scores: [opus+bt-2020-02-26.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-bcl/opus+bt-2020-02-26.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.en.bcl | 54.3 | 0.722 |
|
Helsinki-NLP/opus-mt-en-ho | 12bad640564ae34b349fb0ac28a52995c7e17c2d | 2021-09-09T21:35:57.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"ho",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-ho | 11 | null | transformers | 10,934 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-ho
* source languages: en
* target languages: ho
* OPUS readme: [en-ho](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-ho/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-ho/opus-2020-01-20.zip)
* test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-ho/opus-2020-01-20.test.txt)
* test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-ho/opus-2020-01-20.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.en.ho | 33.9 | 0.563 |
|
Helsinki-NLP/opus-mt-en-kqn | d3adf1c5424a0a362c66279729717f57d76b027e | 2021-09-09T21:36:41.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"kqn",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-kqn | 11 | null | transformers | 10,935 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-kqn
* source languages: en
* target languages: kqn
* OPUS readme: [en-kqn](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-kqn/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-kqn/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-kqn/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-kqn/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.en.kqn | 33.1 | 0.567 |
|
Helsinki-NLP/opus-mt-en-lua | 0164f9af18272b0b05a777f33f0f822fa09af417 | 2021-09-09T21:37:07.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"lua",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-lua | 11 | null | transformers | 10,936 | ---
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 |
|
Helsinki-NLP/opus-mt-en-mfe | dc7d5d1502df1a435d053192fcc0dcfae16f76a5 | 2021-09-09T21:37:27.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"mfe",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-mfe | 11 | null | transformers | 10,937 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-mfe
* source languages: en
* target languages: mfe
* OPUS readme: [en-mfe](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-mfe/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-mfe/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-mfe/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-mfe/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.en.mfe | 32.1 | 0.509 |
|
Helsinki-NLP/opus-mt-en-mos | 302e35c3f1fe631bb0bac15243a8770f6362b7ef | 2021-09-09T21:37:47.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"mos",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-mos | 11 | null | transformers | 10,938 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-mos
* source languages: en
* target languages: mos
* OPUS readme: [en-mos](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-mos/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-mos/opus-2020-01-20.zip)
* test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-mos/opus-2020-01-20.test.txt)
* test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-mos/opus-2020-01-20.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.en.mos | 26.9 | 0.417 |
|
Helsinki-NLP/opus-mt-en-nyk | 2efca81ef2453401aaa06cafe04aa00db56e6eb5 | 2021-09-09T21:38:17.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"nyk",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-nyk | 11 | null | transformers | 10,939 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-nyk
* source languages: en
* target languages: nyk
* OPUS readme: [en-nyk](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-nyk/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-nyk/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-nyk/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-nyk/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.en.nyk | 26.6 | 0.511 |
|
Helsinki-NLP/opus-mt-en-pis | 84d726e58202d97cfa040467e691cb532aee4000 | 2021-09-09T21:38:33.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"pis",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-pis | 11 | null | transformers | 10,940 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-pis
* source languages: en
* target languages: pis
* OPUS readme: [en-pis](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-pis/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-pis/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-pis/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-pis/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.en.pis | 38.3 | 0.571 |
|
Helsinki-NLP/opus-mt-es-guw | a88acc7826825c4732675ed37998fee12b34754c | 2021-09-09T21:42:38.000Z | [
"pytorch",
"marian",
"text2text-generation",
"es",
"guw",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-es-guw | 11 | null | transformers | 10,941 | ---
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 |
|
Helsinki-NLP/opus-mt-es-st | 7b5626bbf76ca489f6a248e7dacdda7f2caa73a9 | 2021-09-09T21:44:53.000Z | [
"pytorch",
"marian",
"text2text-generation",
"es",
"st",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-es-st | 11 | null | transformers | 10,942 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-es-st
* source languages: es
* target languages: st
* OPUS readme: [es-st](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-st/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-st/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-st/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-st/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.es.st | 35.5 | 0.556 |
|
Helsinki-NLP/opus-mt-es-tn | c7ceaa541f5dd1ec57c33e543625c3a201d75d72 | 2021-09-09T21:45:04.000Z | [
"pytorch",
"marian",
"text2text-generation",
"es",
"tn",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-es-tn | 11 | null | transformers | 10,943 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-es-tn
* source languages: es
* target languages: tn
* OPUS readme: [es-tn](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-tn/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-tn/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-tn/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-tn/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.es.tn | 32.2 | 0.528 |
|
Helsinki-NLP/opus-mt-es-to | 1ed93f73b0b2c780c8ab4e1d3495e84ac5bf6886 | 2021-09-09T21:45:08.000Z | [
"pytorch",
"marian",
"text2text-generation",
"es",
"to",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-es-to | 11 | null | transformers | 10,944 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-es-to
* source languages: es
* target languages: to
* OPUS readme: [es-to](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-to/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-to/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-to/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-to/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.es.to | 35.7 | 0.510 |
|
Helsinki-NLP/opus-mt-es-tw | 2b7493bf5c0b2d63dd5043253f11893748c48fdd | 2021-09-09T21:45:19.000Z | [
"pytorch",
"marian",
"text2text-generation",
"es",
"tw",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-es-tw | 11 | null | transformers | 10,945 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-es-tw
* source languages: es
* target languages: tw
* OPUS readme: [es-tw](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-tw/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-tw/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-tw/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-tw/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.es.tw | 26.3 | 0.465 |
|
Helsinki-NLP/opus-mt-fi-es | 1d81789f89a9ada6c9a4b1cacd43bc6faab326a9 | 2021-09-09T21:47:24.000Z | [
"pytorch",
"marian",
"text2text-generation",
"fi",
"es",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-fi-es | 11 | null | transformers | 10,946 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-fi-es
* source languages: fi
* target languages: es
* OPUS readme: [fi-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-04-12.zip](https://object.pouta.csc.fi/OPUS-MT-models/fi-es/opus-2020-04-12.zip)
* test set translations: [opus-2020-04-12.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-es/opus-2020-04-12.test.txt)
* test set scores: [opus-2020-04-12.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-es/opus-2020-04-12.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.fi.es | 51.5 | 0.700 |
|
Helsinki-NLP/opus-mt-fi-ig | 4f565e8da888286ad8d6c9ee976bfa402f5b1e45 | 2021-09-09T21:48:32.000Z | [
"pytorch",
"marian",
"text2text-generation",
"fi",
"ig",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-fi-ig | 11 | null | transformers | 10,947 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-fi-ig
* source languages: fi
* target languages: ig
* OPUS readme: [fi-ig](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-ig/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-ig/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-ig/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-ig/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.fi.ig | 28.5 | 0.456 |
|
Helsinki-NLP/opus-mt-fi-iso | e8ab4b0929ba118babb107935e74ae71f7d8ea36 | 2021-09-09T21:48:44.000Z | [
"pytorch",
"marian",
"text2text-generation",
"fi",
"iso",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-fi-iso | 11 | null | transformers | 10,948 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-fi-iso
* source languages: fi
* target languages: iso
* OPUS readme: [fi-iso](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-iso/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-iso/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-iso/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-iso/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.fi.iso | 26.0 | 0.439 |
|
Helsinki-NLP/opus-mt-fi-mh | 1ee9917dfe5dcb800f1cb71a9494fd5028007a3e | 2021-09-09T21:49:35.000Z | [
"pytorch",
"marian",
"text2text-generation",
"fi",
"mh",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-fi-mh | 11 | null | transformers | 10,949 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-fi-mh
* source languages: fi
* target languages: mh
* OPUS readme: [fi-mh](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-mh/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-24.zip](https://object.pouta.csc.fi/OPUS-MT-models/fi-mh/opus-2020-01-24.zip)
* test set translations: [opus-2020-01-24.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-mh/opus-2020-01-24.test.txt)
* test set scores: [opus-2020-01-24.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-mh/opus-2020-01-24.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.fi.mh | 20.8 | 0.404 |
|
Helsinki-NLP/opus-mt-fi-niu | 89393797459f828fd2ca0a511409ab580a580f84 | 2021-09-09T21:49:51.000Z | [
"pytorch",
"marian",
"text2text-generation",
"fi",
"niu",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-fi-niu | 11 | null | transformers | 10,950 | ---
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 |
|
Helsinki-NLP/opus-mt-fi-pap | 4ead35b0f9dc656671fa9837c8274a0373e0c48f | 2021-09-09T21:50:09.000Z | [
"pytorch",
"marian",
"text2text-generation",
"fi",
"pap",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-fi-pap | 11 | null | transformers | 10,951 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-fi-pap
* source languages: fi
* target languages: pap
* OPUS readme: [fi-pap](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-pap/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-24.zip](https://object.pouta.csc.fi/OPUS-MT-models/fi-pap/opus-2020-01-24.zip)
* test set translations: [opus-2020-01-24.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-pap/opus-2020-01-24.test.txt)
* test set scores: [opus-2020-01-24.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-pap/opus-2020-01-24.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.fi.pap | 27.3 | 0.478 |
|
Helsinki-NLP/opus-mt-fi-sg | 3d3ff9f491d8e9a7362eb30ca278d6e409d3f586 | 2021-09-09T21:50:35.000Z | [
"pytorch",
"marian",
"text2text-generation",
"fi",
"sg",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-fi-sg | 11 | null | transformers | 10,952 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-fi-sg
* source languages: fi
* target languages: sg
* OPUS readme: [fi-sg](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-sg/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-24.zip](https://object.pouta.csc.fi/OPUS-MT-models/fi-sg/opus-2020-01-24.zip)
* test set translations: [opus-2020-01-24.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-sg/opus-2020-01-24.test.txt)
* test set scores: [opus-2020-01-24.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-sg/opus-2020-01-24.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.fi.sg | 29.3 | 0.480 |
|
Helsinki-NLP/opus-mt-fi-wls | 9a78ab2df267ffa3fd7fc634f8f5ea117fb22a86 | 2021-09-09T21:52:13.000Z | [
"pytorch",
"marian",
"text2text-generation",
"fi",
"wls",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-fi-wls | 11 | null | transformers | 10,953 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-fi-wls
* source languages: fi
* target languages: wls
* OPUS readme: [fi-wls](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-wls/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-24.zip](https://object.pouta.csc.fi/OPUS-MT-models/fi-wls/opus-2020-01-24.zip)
* test set translations: [opus-2020-01-24.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-wls/opus-2020-01-24.test.txt)
* test set scores: [opus-2020-01-24.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-wls/opus-2020-01-24.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.fi.wls | 24.7 | 0.466 |
|
Helsinki-NLP/opus-mt-fr-ase | 710d58cad603c9fbb4cad06f79152dc0e5f0243d | 2021-09-09T21:52:48.000Z | [
"pytorch",
"marian",
"text2text-generation",
"fr",
"ase",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-fr-ase | 11 | null | transformers | 10,954 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-fr-ase
* source languages: fr
* target languages: ase
* OPUS readme: [fr-ase](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-ase/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/fr-ase/opus-2020-01-20.zip)
* test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-ase/opus-2020-01-20.test.txt)
* test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-ase/opus-2020-01-20.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.fr.ase | 38.5 | 0.545 |
|
Helsinki-NLP/opus-mt-fr-pon | 1fd95877d97a9b9a5a31c17dc1901f9b275bb184 | 2021-09-09T21:56:15.000Z | [
"pytorch",
"marian",
"text2text-generation",
"fr",
"pon",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-fr-pon | 11 | null | transformers | 10,955 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-fr-pon
* source languages: fr
* target languages: pon
* OPUS readme: [fr-pon](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-pon/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-pon/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-pon/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-pon/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.fr.pon | 23.9 | 0.458 |
|
Helsinki-NLP/opus-mt-fr-sn | d6affd8f83d3a7ddf349cdda4947c666ba110d4a | 2021-09-09T21:56:53.000Z | [
"pytorch",
"marian",
"text2text-generation",
"fr",
"sn",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-fr-sn | 11 | null | transformers | 10,956 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-fr-sn
* source languages: fr
* target languages: sn
* OPUS readme: [fr-sn](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-sn/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-sn/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-sn/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-sn/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.fr.sn | 23.4 | 0.507 |
|
Helsinki-NLP/opus-mt-fr-sv | d1c07247b8c983426342076b3bd3e29776d7723b | 2021-09-09T21:57:06.000Z | [
"pytorch",
"marian",
"text2text-generation",
"fr",
"sv",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-fr-sv | 11 | null | transformers | 10,957 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-fr-sv
* source languages: fr
* target languages: sv
* OPUS readme: [fr-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-sv/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-24.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-sv/opus-2020-01-24.zip)
* test set translations: [opus-2020-01-24.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-sv/opus-2020-01-24.test.txt)
* test set scores: [opus-2020-01-24.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-sv/opus-2020-01-24.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.fr.sv | 60.1 | 0.744 |
|
Helsinki-NLP/opus-mt-fr-tll | 364625c4114eb5592a0b94a948b573d3eda9a71f | 2021-09-09T21:57:19.000Z | [
"pytorch",
"marian",
"text2text-generation",
"fr",
"tll",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-fr-tll | 11 | null | transformers | 10,958 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-fr-tll
* source languages: fr
* target languages: tll
* OPUS readme: [fr-tll](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-tll/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-tll/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-tll/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-tll/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.fr.tll | 24.6 | 0.467 |
|
Helsinki-NLP/opus-mt-fr-uk | e7b16437d9bb57b6510636de109b9c9ef9e2088a | 2021-09-09T21:57:59.000Z | [
"pytorch",
"marian",
"text2text-generation",
"fr",
"uk",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-fr-uk | 11 | null | transformers | 10,959 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-fr-uk
* source languages: fr
* target languages: uk
* OPUS readme: [fr-uk](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-uk/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-uk/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-uk/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-uk/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.fr.uk | 39.4 | 0.581 |
|
Helsinki-NLP/opus-mt-hu-sv | 1153a336b2d0ba262e85298f73c8e906879cbb6e | 2021-09-09T22:11:03.000Z | [
"pytorch",
"marian",
"text2text-generation",
"hu",
"sv",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-hu-sv | 11 | null | transformers | 10,960 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-hu-sv
* source languages: hu
* target languages: sv
* OPUS readme: [hu-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/hu-sv/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-26.zip](https://object.pouta.csc.fi/OPUS-MT-models/hu-sv/opus-2020-01-26.zip)
* test set translations: [opus-2020-01-26.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/hu-sv/opus-2020-01-26.test.txt)
* test set scores: [opus-2020-01-26.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/hu-sv/opus-2020-01-26.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.hu.sv | 52.6 | 0.686 |
|
Helsinki-NLP/opus-mt-inc-inc | e1be60fc72658b90bc708254047be2bb5518abab | 2020-08-21T14:42:46.000Z | [
"pytorch",
"marian",
"text2text-generation",
"bn",
"or",
"gu",
"mr",
"ur",
"hi",
"as",
"si",
"inc",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-inc-inc | 11 | null | transformers | 10,961 | ---
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 |
Helsinki-NLP/opus-mt-it-ca | be40b8f92044c88e5b45840eb706c3196a9da037 | 2020-08-21T14:42:46.000Z | [
"pytorch",
"marian",
"text2text-generation",
"it",
"ca",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-it-ca | 11 | null | transformers | 10,962 | ---
language:
- it
- ca
tags:
- translation
license: apache-2.0
---
### ita-cat
* source group: Italian
* target group: Catalan
* OPUS readme: [ita-cat](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-cat/README.md)
* model: transformer-align
* source language(s): ita
* target language(s): cat
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm12k,spm12k)
* download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ita-cat/opus-2020-06-16.zip)
* test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ita-cat/opus-2020-06-16.test.txt)
* test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ita-cat/opus-2020-06-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.ita.cat | 52.5 | 0.706 |
### System Info:
- hf_name: ita-cat
- source_languages: ita
- target_languages: cat
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-cat/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['it', 'ca']
- src_constituents: {'ita'}
- tgt_constituents: {'cat'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm12k,spm12k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ita-cat/opus-2020-06-16.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ita-cat/opus-2020-06-16.test.txt
- src_alpha3: ita
- tgt_alpha3: cat
- short_pair: it-ca
- chrF2_score: 0.706
- bleu: 52.5
- brevity_penalty: 0.993
- ref_len: 2074.0
- src_name: Italian
- tgt_name: Catalan
- train_date: 2020-06-16
- src_alpha2: it
- tgt_alpha2: ca
- prefer_old: False
- long_pair: ita-cat
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Helsinki-NLP/opus-mt-ja-fi | 7f6dd2d58f7cf578f745e5377569ff9a495651ba | 2021-09-10T13:53:20.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ja",
"fi",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ja-fi | 11 | null | transformers | 10,963 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-ja-fi
* source languages: ja
* target languages: fi
* OPUS readme: [ja-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ja-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/ja-fi/opus-2020-01-09.zip)
* test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ja-fi/opus-2020-01-09.test.txt)
* test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ja-fi/opus-2020-01-09.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.ja.fi | 21.2 | 0.448 |
|
Helsinki-NLP/opus-mt-ja-hu | f75caa085f156b74c435bf097ff363a8bf2ef375 | 2020-08-21T14:42:47.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ja",
"hu",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ja-hu | 11 | null | transformers | 10,964 | ---
language:
- ja
- hu
tags:
- translation
license: apache-2.0
---
### jpn-hun
* source group: Japanese
* target group: Hungarian
* OPUS readme: [jpn-hun](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/jpn-hun/README.md)
* model: transformer-align
* source language(s): jpn_Bopo jpn_Hani jpn_Hira jpn_Kana jpn_Yiii
* target language(s): hun
* 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-hun/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-hun/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-hun/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.jpn.hun | 12.2 | 0.364 |
### System Info:
- hf_name: jpn-hun
- source_languages: jpn
- target_languages: hun
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/jpn-hun/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['ja', 'hu']
- src_constituents: {'jpn_Hang', 'jpn', 'jpn_Yiii', 'jpn_Kana', 'jpn_Hani', 'jpn_Bopo', 'jpn_Latn', 'jpn_Hira'}
- tgt_constituents: {'hun'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-hun/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-hun/opus-2020-06-17.test.txt
- src_alpha3: jpn
- tgt_alpha3: hun
- short_pair: ja-hu
- chrF2_score: 0.364
- bleu: 12.2
- brevity_penalty: 1.0
- ref_len: 18625.0
- src_name: Japanese
- tgt_name: Hungarian
- train_date: 2020-06-17
- src_alpha2: ja
- tgt_alpha2: hu
- prefer_old: False
- long_pair: jpn-hun
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Helsinki-NLP/opus-mt-ka-ru | 549d06a80ecb3c9203d7ecf8eee396daf439daaf | 2020-08-21T14:42:47.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ka",
"ru",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ka-ru | 11 | null | transformers | 10,965 | ---
language:
- ka
- ru
tags:
- translation
license: apache-2.0
---
### kat-rus
* source group: Georgian
* target group: Russian
* OPUS readme: [kat-rus](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/kat-rus/README.md)
* model: transformer-align
* source language(s): kat
* target language(s): rus
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm12k,spm12k)
* download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/kat-rus/opus-2020-06-16.zip)
* test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/kat-rus/opus-2020-06-16.test.txt)
* test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/kat-rus/opus-2020-06-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.kat.rus | 38.2 | 0.604 |
### System Info:
- hf_name: kat-rus
- source_languages: kat
- target_languages: rus
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/kat-rus/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['ka', 'ru']
- src_constituents: {'kat'}
- tgt_constituents: {'rus'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm12k,spm12k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/kat-rus/opus-2020-06-16.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/kat-rus/opus-2020-06-16.test.txt
- src_alpha3: kat
- tgt_alpha3: rus
- short_pair: ka-ru
- chrF2_score: 0.604
- bleu: 38.2
- brevity_penalty: 0.996
- ref_len: 3899.0
- src_name: Georgian
- tgt_name: Russian
- train_date: 2020-06-16
- src_alpha2: ka
- tgt_alpha2: ru
- prefer_old: False
- long_pair: kat-rus
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Helsinki-NLP/opus-mt-ms-fr | 7b7ca29930c9f9b9300a92cdd84e175fbada4865 | 2020-08-21T14:42:48.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ms",
"fr",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ms-fr | 11 | null | transformers | 10,966 | ---
language:
- ms
- fr
tags:
- translation
license: apache-2.0
---
### msa-fra
* source group: Malay (macrolanguage)
* target group: French
* OPUS readme: [msa-fra](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/msa-fra/README.md)
* model: transformer-align
* source language(s): ind zsm_Latn
* target language(s): fra
* 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/msa-fra/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/msa-fra/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/msa-fra/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.msa.fra | 43.7 | 0.609 |
### System Info:
- hf_name: msa-fra
- source_languages: msa
- target_languages: fra
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/msa-fra/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['ms', 'fr']
- src_constituents: {'zsm_Latn', 'ind', 'max_Latn', 'zlm_Latn', 'min'}
- tgt_constituents: {'fra'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/msa-fra/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/msa-fra/opus-2020-06-17.test.txt
- src_alpha3: msa
- tgt_alpha3: fra
- short_pair: ms-fr
- chrF2_score: 0.609
- bleu: 43.7
- brevity_penalty: 0.9740000000000001
- ref_len: 7808.0
- src_name: Malay (macrolanguage)
- tgt_name: French
- train_date: 2020-06-17
- src_alpha2: ms
- tgt_alpha2: fr
- prefer_old: False
- long_pair: msa-fra
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Helsinki-NLP/opus-mt-no-sv | af2f7ad669d1a816ec723ae84376b4ffd2af8c34 | 2020-08-21T14:42:48.000Z | [
"pytorch",
"marian",
"text2text-generation",
"no",
"sv",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-no-sv | 11 | null | transformers | 10,967 | ---
language:
- no
- sv
tags:
- translation
license: apache-2.0
---
### nor-swe
* source group: Norwegian
* target group: Swedish
* OPUS readme: [nor-swe](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-swe/README.md)
* model: transformer-align
* source language(s): nno nob
* target language(s): swe
* 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/nor-swe/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-swe/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-swe/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.nor.swe | 63.7 | 0.773 |
### System Info:
- hf_name: nor-swe
- source_languages: nor
- target_languages: swe
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-swe/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['no', 'sv']
- src_constituents: {'nob', 'nno'}
- tgt_constituents: {'swe'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/nor-swe/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/nor-swe/opus-2020-06-17.test.txt
- src_alpha3: nor
- tgt_alpha3: swe
- short_pair: no-sv
- chrF2_score: 0.773
- bleu: 63.7
- brevity_penalty: 0.9670000000000001
- ref_len: 3672.0
- src_name: Norwegian
- tgt_name: Swedish
- train_date: 2020-06-17
- src_alpha2: no
- tgt_alpha2: sv
- prefer_old: False
- long_pair: nor-swe
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Helsinki-NLP/opus-mt-ro-sv | 7fa2ef5f82b826ec17683cd65d864ffc52d2f9be | 2021-09-10T14:02:14.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ro",
"sv",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ro-sv | 11 | null | transformers | 10,968 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-ro-sv
* source languages: ro
* target languages: sv
* OPUS readme: [ro-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ro-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/ro-sv/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ro-sv/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ro-sv/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.ro.sv | 31.2 | 0.529 |
|
Helsinki-NLP/opus-mt-sl-sv | 3d0cfc54aed676928cca2594bb17d33960e1501b | 2021-09-10T14:03:50.000Z | [
"pytorch",
"marian",
"text2text-generation",
"sl",
"sv",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-sl-sv | 11 | null | transformers | 10,969 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sl-sv
* source languages: sl
* target languages: sv
* OPUS readme: [sl-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sl-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/sl-sv/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sl-sv/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sl-sv/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sl.sv | 27.8 | 0.509 |
|
Helsinki-NLP/opus-mt-sn-es | e8445e4039e78d8a9a27ddcca2342717c4ef57e6 | 2021-09-10T14:04:08.000Z | [
"pytorch",
"marian",
"text2text-generation",
"sn",
"es",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-sn-es | 11 | null | transformers | 10,970 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sn-es
* source languages: sn
* target languages: es
* OPUS readme: [sn-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sn-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/sn-es/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sn-es/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sn-es/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sn.es | 32.5 | 0.509 |
|
Helsinki-NLP/opus-mt-sq-es | 201693b9a3cb4e58e89cc08b2f3cd0179eb5c4c6 | 2021-09-10T14:04:23.000Z | [
"pytorch",
"marian",
"text2text-generation",
"sq",
"es",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-sq-es | 11 | null | transformers | 10,971 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sq-es
* source languages: sq
* target languages: es
* OPUS readme: [sq-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sq-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/sq-es/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sq-es/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sq-es/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| GlobalVoices.sq.es | 23.9 | 0.510 |
|
Helsinki-NLP/opus-mt-sq-sv | 71b15251bc2be502a4d0d14d68ba32caf8bceeb0 | 2021-09-10T14:04:27.000Z | [
"pytorch",
"marian",
"text2text-generation",
"sq",
"sv",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-sq-sv | 11 | null | transformers | 10,972 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sq-sv
* source languages: sq
* target languages: sv
* OPUS readme: [sq-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sq-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/sq-sv/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sq-sv/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sq-sv/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sq.sv | 36.2 | 0.559 |
|
Helsinki-NLP/opus-mt-st-es | d09b48ce08d1187675cac6ecf8146e04876b6111 | 2021-09-10T14:04:58.000Z | [
"pytorch",
"marian",
"text2text-generation",
"st",
"es",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-st-es | 11 | null | transformers | 10,973 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-st-es
* source languages: st
* target languages: es
* OPUS readme: [st-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/st-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/st-es/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/st-es/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/st-es/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.st.es | 31.3 | 0.499 |
|
Helsinki-NLP/opus-mt-st-fi | c4ad55e29b075da7f480d7d4c5d7a4531ea70561 | 2021-09-10T14:05:01.000Z | [
"pytorch",
"marian",
"text2text-generation",
"st",
"fi",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-st-fi | 11 | null | transformers | 10,974 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-st-fi
* source languages: st
* target languages: fi
* OPUS readme: [st-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/st-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/st-fi/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/st-fi/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/st-fi/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.st.fi | 28.8 | 0.520 |
|
Helsinki-NLP/opus-mt-sv-is | 2a99829544782cb3a27594c4121ef5a049a8b1f8 | 2021-09-10T14:07:27.000Z | [
"pytorch",
"marian",
"text2text-generation",
"sv",
"is",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-sv-is | 11 | null | transformers | 10,975 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sv-is
* source languages: sv
* target languages: is
* OPUS readme: [sv-is](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-is/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/sv-is/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-is/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-is/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sv.is | 27.1 | 0.471 |
|
Helsinki-NLP/opus-mt-sv-toi | 6d08c2ea49e26002ae43827819cfef1e8130fa08 | 2021-09-10T14:10:07.000Z | [
"pytorch",
"marian",
"text2text-generation",
"sv",
"toi",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-sv-toi | 11 | null | transformers | 10,976 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sv-toi
* source languages: sv
* target languages: toi
* OPUS readme: [sv-toi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-toi/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/sv-toi/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-toi/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-toi/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sv.toi | 23.2 | 0.512 |
|
Helsinki-NLP/opus-mt-sv-tum | e334319a27a17e358b9002c74a3ddac826d8206a | 2021-09-10T14:10:18.000Z | [
"pytorch",
"marian",
"text2text-generation",
"sv",
"tum",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-sv-tum | 11 | null | transformers | 10,977 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sv-tum
* source languages: sv
* target languages: tum
* OPUS readme: [sv-tum](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-tum/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/sv-tum/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-tum/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-tum/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sv.tum | 22.0 | 0.475 |
|
Helsinki-NLP/opus-mt-to-fr | eb0c363903adb05d20c510e2bae9761310c9d09f | 2021-09-11T10:49:00.000Z | [
"pytorch",
"marian",
"text2text-generation",
"to",
"fr",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-to-fr | 11 | null | transformers | 10,978 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-to-fr
* source languages: to
* target languages: fr
* OPUS readme: [to-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/to-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/to-fr/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/to-fr/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/to-fr/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.to.fr | 27.9 | 0.456 |
|
Helsinki-NLP/opus-mt-tr-az | 07265265c0a05859e0f82e1e04360ccbcbe25fb0 | 2020-08-21T14:42:51.000Z | [
"pytorch",
"marian",
"text2text-generation",
"tr",
"az",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-tr-az | 11 | 1 | transformers | 10,979 | ---
language:
- tr
- az
tags:
- translation
license: apache-2.0
---
### tur-aze
* source group: Turkish
* target group: Azerbaijani
* OPUS readme: [tur-aze](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tur-aze/README.md)
* model: transformer-align
* source language(s): tur
* target language(s): aze_Latn
* 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/tur-aze/opus-2020-06-16.zip)
* test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tur-aze/opus-2020-06-16.test.txt)
* test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tur-aze/opus-2020-06-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.tur.aze | 27.7 | 0.551 |
### System Info:
- hf_name: tur-aze
- source_languages: tur
- target_languages: aze
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tur-aze/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['tr', 'az']
- src_constituents: {'tur'}
- tgt_constituents: {'aze_Latn'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/tur-aze/opus-2020-06-16.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/tur-aze/opus-2020-06-16.test.txt
- src_alpha3: tur
- tgt_alpha3: aze
- short_pair: tr-az
- chrF2_score: 0.551
- bleu: 27.7
- brevity_penalty: 1.0
- ref_len: 5436.0
- src_name: Turkish
- tgt_name: Azerbaijani
- train_date: 2020-06-16
- src_alpha2: tr
- tgt_alpha2: az
- prefer_old: False
- long_pair: tur-aze
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Helsinki-NLP/opus-mt-uk-fi | 61cc07a5eda3c70d48fc7834a8b9d8713a7806ed | 2021-09-11T10:51:22.000Z | [
"pytorch",
"marian",
"text2text-generation",
"uk",
"fi",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-uk-fi | 11 | null | transformers | 10,980 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-uk-fi
* source languages: uk
* target languages: fi
* OPUS readme: [uk-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/uk-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/uk-fi/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/uk-fi/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/uk-fi/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.uk.fi | 24.4 | 0.490 |
|
Helsinki-NLP/opus-mt-wls-fr | 5880ec6be460a23a0793a0db2bff5cf8ce649bc8 | 2021-09-11T10:52:13.000Z | [
"pytorch",
"marian",
"text2text-generation",
"wls",
"fr",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-wls-fr | 11 | null | transformers | 10,981 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-wls-fr
* source languages: wls
* target languages: fr
* OPUS readme: [wls-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/wls-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/wls-fr/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/wls-fr/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/wls-fr/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.wls.fr | 22.6 | 0.389 |
|
LysandreJik/local_dir | a1dc5a26b81c407302cab46144d78fa6a3048e80 | 2021-09-09T15:51:28.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | false | LysandreJik | null | LysandreJik/local_dir | 11 | null | transformers | 10,982 | Entry not found |
Jorgeutd/bert-base-uncased-ade-Ade-corpus-v2 | 65b3b51f511e1b215984ea641b2959ac8d8c774a | 2021-11-30T14:17:19.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:adecorpusv2",
"transformers",
"sagemaker",
"bert-base-uncased",
"text classification",
"license:apache-2.0",
"model-index"
]
| text-classification | false | Jorgeutd | null | Jorgeutd/bert-base-uncased-ade-Ade-corpus-v2 | 11 | null | transformers | 10,983 | ---
language: en
widget:
- text: "I got a rash from taking acetaminophen"
tags:
- sagemaker
- bert-base-uncased
- text classification
license: apache-2.0
datasets:
- adecorpusv2
model-index:
- name: BERT-ade_corpus
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: "ade_corpus_v2Ade_corpus_v2_classification"
type: ade_corpus
metrics:
- name: Validation Accuracy
type: accuracy
value: 92.98
- name: Validation F1
type: f1
value: 82.73
---
## bert-base-uncased
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.
- Problem type: Text Classification(adverse drug effects detection).
## Hyperparameters
```json
{
"do_eval": true,
"do_train": true,
"fp16": true,
"load_best_model_at_end": true,
"model_name": "bert-base-uncased",
"num_train_epochs": 10,
"per_device_eval_batch_size": 16,
"per_device_train_batch_size": 16,
"learning_rate":5e-5
}
```
## Validation Metrics
| key | value |
| --- | ----- |
| eval_accuracy | 0.9298021697511167 |
| eval_auc | 0.8902672664394546 |
| eval_f1 | 0.827315541601256 |
| eval_loss | 0.17835010588169098 |
| eval_recall | 0.8234375 |
| eval_precision | 0.831230283911672 |
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I got a rash from taking acetaminophen"}' https://api-inference.huggingface.co/models/Jorgeutd/bert-base-uncased-ade-Ade-corpus-v2
```
""" |
KBLab/electra-small-swedish-cased-generator | 8a4906f83401fe2b1f454aa855ec85a30df61e6b | 2020-10-21T08:17:40.000Z | [
"pytorch",
"tf",
"electra",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | false | KBLab | null | KBLab/electra-small-swedish-cased-generator | 11 | null | transformers | 10,984 | Entry not found |
LeBenchmark/wav2vec2-FR-1K-large | f766b6c8eb44300bc4a66d4896ef782416912d1f | 2021-11-30T04:21:31.000Z | [
"pytorch",
"jax",
"wav2vec2",
"feature-extraction",
"fr",
"transformers",
"license:apache-2.0"
]
| feature-extraction | false | LeBenchmark | null | LeBenchmark/wav2vec2-FR-1K-large | 11 | null | transformers | 10,985 | ---
language: "fr"
thumbnail:
tags:
- wav2vec2
license: "apache-2.0"
---
# LeBenchmark: wav2vec2 large model trained on 1K hours of French speech
LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. For more information on the different benchmarks that can be used to evaluate the wav2vec2 models, please refer to our paper at: [Task Agnostic and Task Specific Self-Supervised Learning from Speech with LeBenchmark](https://openreview.net/pdf?id=TSvj5dmuSd)
## Model and data descriptions
We release four different models that can be found under our HuggingFace organization. Two different wav2vec2 architectures *Base* and *Large* are coupled with our small (1K), medium (3K), and large (7K) corpus. A larger one should come later. In short:
- [wav2vec2-FR-7K-large](https://huggingface.co/LeBenchmark/wav2vec2-FR-7K-large): Large wav2vec2 trained on 7.6K hours of French speech (1.8K Males / 1.0K Females / 4.8K unknown).
- [wav2vec2-FR-7K-base](https://huggingface.co/LeBenchmark/wav2vec2-FR-7K-base): Base wav2vec2 trained on 7.6K hours of French speech (1.8K Males / 1.0K Females / 4.8K unknown).
- [wav2vec2-FR-3K-large](https://huggingface.co/LeBenchmark/wav2vec2-FR-3K-large): Large wav2vec2 trained on 2.9K hours of French speech (1.8K Males / 1.0K Females / 0.1K unknown).
- [wav2vec2-FR-3K-base](https://huggingface.co/LeBenchmark/wav2vec2-FR-3K-base): Base wav2vec2 trained on 2.9K hours of French speech (1.8K Males / 1.0K Females / 0.1K unknown).
- [wav2vec2-FR-2.6K-base](https://huggingface.co/LeBenchmark/wav2vec2-FR-2.6K-base): Base wav2vec2 trained on 2.6K hours of French speech (**no spontaneous speech**).
- [wav2vec2-FR-1K-large](https://huggingface.co/LeBenchmark/wav2vec2-FR-1K-large): Large wav2vec2 trained on 1K hours of French speech (0.5K Males / 0.5K Females).
- [wav2vec2-FR-1K-base](https://huggingface.co/LeBenchmark/wav2vec2-FR-1K-base): Base wav2vec2 trained on 1K hours of French speech (0.5K Males / 0.5K Females).
## Intended uses & limitations
Pretrained wav2vec2 models are distributed under the Apache-2.0 license. Hence, they can be reused extensively without strict limitations. However, benchmarks and data may be linked to corpora that are not completely open-sourced.
## Fine-tune with Fairseq for ASR with CTC
As our wav2vec2 models were trained with Fairseq, then can be used in the different tools that they provide to fine-tune the model for ASR with CTC. The full procedure has been nicely summarized in [this blogpost](https://huggingface.co/blog/fine-tune-wav2vec2-english).
Please note that due to the nature of CTC, speech-to-text results aren't expected to be state-of-the-art. Moreover, future features might appear depending on the involvement of Fairseq and HuggingFace on this part.
## Integrate to SpeechBrain for ASR, Speaker, Source Separation ...
Pretrained wav2vec models recently gained in popularity. At the same time, [SpeechBrain toolkit](https://speechbrain.github.io) came out, proposing a new and simpler way of dealing with state-of-the-art speech & deep-learning technologies.
While it currently is in beta, SpeechBrain offers two different ways of nicely integrating wav2vec2 models that were trained with Fairseq i.e our LeBenchmark models!
1. Extract wav2vec2 features on-the-fly (with a frozen wav2vec2 encoder) to be combined with any speech-related architecture. Examples are: E2E ASR with CTC+Att+Language Models; Speaker Recognition or Verification, Source Separation ...
2. *Experimental:* To fully benefit from wav2vec2, the best solution remains to fine-tune the model while you train your downstream task. This is very simply allowed within SpeechBrain as just a flag needs to be turned on. Thus, our wav2vec2 models can be fine-tuned while training your favorite ASR pipeline or Speaker Recognizer.
**If interested, simply follow this [tutorial](https://colab.research.google.com/drive/17Hu1pxqhfMisjkSgmM2CnZxfqDyn2hSY?usp=sharing)**
## Referencing LeBenchmark
```
@article{Evain2021LeBenchmarkAR,
title={LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech},
author={Sol{\`e}ne Evain and Ha Nguyen and Hang Le and Marcely Zanon Boito and Salima Mdhaffar and Sina Alisamir and Ziyi Tong and N. Tomashenko and Marco Dinarelli and Titouan Parcollet and A. Allauzen and Y. Est{\`e}ve and B. Lecouteux and F. Portet and S. Rossato and F. Ringeval and D. Schwab and L. Besacier},
journal={ArXiv},
year={2021},
volume={abs/2104.11462}
}
```
|
Maaly/host | 18da8d2a8d19bf16e8b7cbe1463c637a0cbc3639 | 2022-05-28T15:33:10.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | Maaly | null | Maaly/host | 11 | null | transformers | 10,986 | host model is a Named Entity Recognition (NER) model that identifies and annotates the host (living organism) of microbiome samples in texts.
The model is a fine-tuned BioBERT model and the training dataset is available in https://gitlab.com/maaly7/emerald_metagenomics_annotations
Testing examples:
1. Turkestan cockroach nymphs (Finke, 2013) were fed to the treefrogs at a quantity of 10% of treefrog biomass twice a week.
2. Samples were collected from clinically healthy giant pandas (five females and four males) at the China Conservation and Research Center for Giant Pandas (Ya'an, China).
3. Field-collected bee samples were dissected on dry ice and separated into head, thorax (excluding legs and wings), and abdomens.
|
Marc/pegasus_xsum_gigaword | e7c9ae792ba42b2a6735c85472a986348d8b4e78 | 2021-03-26T22:49:11.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"dataset:XSUM",
"dataset:Gigaword",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | Marc | null | Marc/pegasus_xsum_gigaword | 11 | null | transformers | 10,987 | ---
language:
- English
-
thumbnail:
tags:
-
-
-
license:
datasets:
- XSUM
- Gigaword
metrics:
- Rouge
-
---
# Pegasus XSUM Gigaword
## Model description
Pegasus XSUM model finetuned to Gigaword Summarization task, significantly better performance than pegasus gigaword, but still doesn't match model paper performance.
## Intended uses & limitations
Produces short summaries with the coherence of the XSUM Model
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Still has all the biases of any of the abstractive models, but seems a little less prone to hallucination.
## Training data
Initialized with pegasus-XSUM
## Training procedure
Trained for 11500 iterations on Gigaword corpus using OOB seq2seq (from hugging face using the default parameters)
## Eval results
Evaluated on Gigaword test set (from hugging face using the default parameters)
run_summarization.py --model_name_or_path pegasus-xsum/checkpoint-11500/ --do_predict --dataset_name gigaword --dataset_config "3.0.0" --source_prefix "summarize: " --output_dir pegasus-xsum --per_device_train_batch_size=8 --per_device_eval_batch_size=8 --overwrite_output_dir --predict_with_generate
| Metric | Score |
| ----------- | ----------- |
| eval_rouge1 | 34.1958 |
| eval_rouge2 | 15.4033 |
| eval_rougeL | 31.4488 |
run_summarization.py --model_name_or_path google/pegasus-gigaword --do_predict --dataset_name gigaword --dataset_config "3.0.0" --source_prefix "summarize: " --output_dir pegasus-xsum --per_device_train_batch_size=8 --per_device_eval_batch_size=8 --overwrite_output_dir --predict_with_generate
| Metric | Score |
| ----------- | ----------- |
| eval_rouge1 | 20.8111 |
| eval_rouge2 | 8.766 |
| eval_rougeL | 18.4431 |
### BibTeX entry and citation info
```bibtex
@inproceedings{...,
year={2020}
}
```
|
MoritzLaurer/MiniLM-L6-mnli | 6e0917f1a395b7a6c0f054a56b91c45d8e3af92f | 2021-12-13T10:36:43.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"transformers",
"zero-shot-classification"
]
| text-classification | false | MoritzLaurer | null | MoritzLaurer/MiniLM-L6-mnli | 11 | null | transformers | 10,988 | ---
language:
- en
tags:
- text-classification
- zero-shot-classification
metrics:
- accuracy
widget:
- text: "I liked the movie. [SEP] The movie was good."
---
# MiniLM-L6-mnli
## Model description
This model was trained on the [MultiNLI](https://huggingface.co/datasets/multi_nli) dataset.
The base model is MiniLM-L6 from Microsoft, which is very fast, but a bit less accurate than other models.
## Intended uses & limitations
#### How to use the model
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_name = "MoritzLaurer/MiniLM-L6-mnli"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
premise = "I liked the movie"
hypothesis = "The movie was good."
input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")
output = model(input["input_ids"].to(device)) # device = "cuda:0" or "cpu"
prediction = torch.softmax(output["logits"][0], -1).tolist()
label_names = ["entailment", "neutral", "contradiction"]
prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)}
print(prediction)
```
### Training data
[MultiNLI](https://huggingface.co/datasets/multi_nli).
### Training procedure
MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters.
```
training_args = TrainingArguments(
num_train_epochs=5, # total number of training epochs
learning_rate=2e-05,
per_device_train_batch_size=32, # batch size per device during training
per_device_eval_batch_size=32, # batch size for evaluation
warmup_ratio=0.1, # number of warmup steps for learning rate scheduler
weight_decay=0.06, # strength of weight decay
fp16=True # mixed precision training
)
```
### Eval results
The model was evaluated using the (matched) test set from MultiNLI. Accuracy: 0.814
## Limitations and bias
Please consult the original MiniLM paper and literature on different NLI datasets for potential biases.
### BibTeX entry and citation info
If you want to cite this model, please cite the original MiniLM paper, the respective NLI datasets and include a link to this model on the Hugging Face hub. |
MoseliMotsoehli/zuBERTa | 1b62500f041b003632383e96ec790ea6c0d435ce | 2021-05-20T12:14:07.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"zu",
"transformers",
"autotrain_compatible"
]
| fill-mask | false | MoseliMotsoehli | null | MoseliMotsoehli/zuBERTa | 11 | null | transformers | 10,989 | ---
language: zu
---
# zuBERTa
zuBERTa is a RoBERTa style transformer language model trained on zulu text.
## Intended uses & limitations
The model can be used for getting embeddings to use on a down-stream task such as question answering.
#### How to use
```python
>>> from transformers import pipeline
>>> from transformers import AutoTokenizer, AutoModelWithLMHead
>>> tokenizer = AutoTokenizer.from_pretrained("MoseliMotsoehli/zuBERTa")
>>> model = AutoModelWithLMHead.from_pretrained("MoseliMotsoehli/zuBERTa")
>>> unmasker = pipeline('fill-mask', model=model, tokenizer=tokenizer)
>>> unmasker("Abafika eNkandla bafika sebeholwa <mask> uMpongo kaZingelwayo.")
[
{
"sequence": "<s>Abafika eNkandla bafika sebeholwa khona uMpongo kaZingelwayo.</s>",
"score": 0.050459690392017365,
"token": 555,
"token_str": "Ġkhona"
},
{
"sequence": "<s>Abafika eNkandla bafika sebeholwa inkosi uMpongo kaZingelwayo.</s>",
"score": 0.03668094798922539,
"token": 2321,
"token_str": "Ġinkosi"
},
{
"sequence": "<s>Abafika eNkandla bafika sebeholwa ubukhosi uMpongo kaZingelwayo.</s>",
"score": 0.028774697333574295,
"token": 5101,
"token_str": "Ġubukhosi"
}
]
```
## Training data
1. 30k sentences of text, came from the [Leipzig Corpora Collection](https://wortschatz.uni-leipzig.de/en/download) of zulu 2018. These were collected from news articles and creative writtings.
2. ~7500 articles of human generated translations were scraped from the zulu [wikipedia](https://zu.wikipedia.org/wiki/Special:AllPages).
### BibTeX entry and citation info
```bibtex
@inproceedings{author = {Moseli Motsoehli},
title = {Towards transformation of Southern African language models through transformers.},
year={2020}
}
```
|
Muennighoff/SGPT-2.7B-weightedmean-msmarco-specb-bitfit | 41063e375f26777b80416ee80e2b619343fabcbd | 2022-06-18T20:55:55.000Z | [
"pytorch",
"gpt_neo",
"feature-extraction",
"arxiv:2202.08904",
"sentence-transformers",
"sentence-similarity"
]
| sentence-similarity | false | Muennighoff | null | Muennighoff/SGPT-2.7B-weightedmean-msmarco-specb-bitfit | 11 | null | sentence-transformers | 10,990 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# SGPT-2.7B-weightedmean-msmarco-specb-bitfit
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to the eval folder or our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 124796 with parameters:
```
{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 7.5e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: GPTNeoModel
(1): Pooling({'word_embedding_dimension': 2560, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
```bibtex
@article{muennighoff2022sgpt,
title={SGPT: GPT Sentence Embeddings for Semantic Search},
author={Muennighoff, Niklas},
journal={arXiv preprint arXiv:2202.08904},
year={2022}
}
```
|
NDugar/3epoch-3large | 6496d73b67afb79acf989cf4996f218fce9547f1 | 2021-11-30T17:34:56.000Z | [
"pytorch",
"deberta-v2",
"text-classification",
"en",
"arxiv:2006.03654",
"transformers",
"deberta-v3",
"deberta-v2`",
"deberta-mnli",
"license:mit",
"zero-shot-classification"
]
| zero-shot-classification | false | NDugar | null | NDugar/3epoch-3large | 11 | 1 | transformers | 10,991 | ---
language: en
tags:
- deberta-v3
- deberta-v2`
- deberta-mnli
tasks: mnli
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
pipeline_tag: zero-shot-classification
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official repository](https://github.com/microsoft/DeBERTa) for more details and updates.
This is the DeBERTa V2 xxlarge model with 48 layers, 1536 hidden size. The total parameters are 1.5B and it is trained with 160GB raw data.
### Fine-tuning on NLU tasks
We present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.
| Model | SQuAD 1.1 | SQuAD 2.0 | MNLI-m/mm | SST-2 | QNLI | CoLA | RTE | MRPC | QQP |STS-B |
|---------------------------|-----------|-----------|-------------|-------|------|------|--------|-------|-------|------|
| | F1/EM | F1/EM | Acc | Acc | Acc | MCC | Acc |Acc/F1 |Acc/F1 |P/S |
| BERT-Large | 90.9/84.1 | 81.8/79.0 | 86.6/- | 93.2 | 92.3 | 60.6 | 70.4 | 88.0/- | 91.3/- |90.0/- |
| RoBERTa-Large | 94.6/88.9 | 89.4/86.5 | 90.2/- | 96.4 | 93.9 | 68.0 | 86.6 | 90.9/- | 92.2/- |92.4/- |
| XLNet-Large | 95.1/89.7 | 90.6/87.9 | 90.8/- | 97.0 | 94.9 | 69.0 | 85.9 | 90.8/- | 92.3/- |92.5/- |
| [DeBERTa-Large](https://huggingface.co/microsoft/deberta-large)<sup>1</sup> | 95.5/90.1 | 90.7/88.0 | 91.3/91.1| 96.5|95.3| 69.5| 91.0| 92.6/94.6| 92.3/- |92.8/92.5 |
| [DeBERTa-XLarge](https://huggingface.co/microsoft/deberta-xlarge)<sup>1</sup> | -/- | -/- | 91.5/91.2| 97.0 | - | - | 93.1 | 92.1/94.3 | - |92.9/92.7|
| [DeBERTa-V2-XLarge](https://huggingface.co/microsoft/deberta-v2-xlarge)<sup>1</sup>|95.8/90.8| 91.4/88.9|91.7/91.6| **97.5**| 95.8|71.1|**93.9**|92.0/94.2|92.3/89.8|92.9/92.9|
|**[DeBERTa-V2-XXLarge](https://huggingface.co/microsoft/deberta-v2-xxlarge)<sup>1,2</sup>**|**96.1/91.4**|**92.2/89.7**|**91.7/91.9**|97.2|**96.0**|**72.0**| 93.5| **93.1/94.9**|**92.7/90.3** |**93.2/93.1** |
--------
#### Notes.
- <sup>1</sup> Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on [DeBERTa-Large-MNLI](https://huggingface.co/microsoft/deberta-large-mnli), [DeBERTa-XLarge-MNLI](https://huggingface.co/microsoft/deberta-xlarge-mnli), [DeBERTa-V2-XLarge-MNLI](https://huggingface.co/microsoft/deberta-v2-xlarge-mnli), [DeBERTa-V2-XXLarge-MNLI](https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli). The results of SST-2/QQP/QNLI/SQuADv2 will also be slightly improved when start from MNLI fine-tuned models, however, we only report the numbers fine-tuned from pretrained base models for those 4 tasks.
- <sup>2</sup> To try the **XXLarge** model with **[HF transformers](https://huggingface.co/transformers/main_classes/trainer.html)**, we recommand using **deepspeed** as it's faster and saves memory.
Run with `Deepspeed`,
```bash
pip install datasets
pip install deepspeed
# Download the deepspeed config file
wget https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/ds_config.json -O ds_config.json
export TASK_NAME=mnli
output_dir="ds_results"
num_gpus=8
batch_size=8
python -m torch.distributed.launch --nproc_per_node=${num_gpus} \\
run_glue.py \\
--model_name_or_path microsoft/deberta-v2-xxlarge \\
--task_name $TASK_NAME \\
--do_train \\
--do_eval \\
--max_seq_length 256 \\
--per_device_train_batch_size ${batch_size} \\
--learning_rate 3e-6 \\
--num_train_epochs 3 \\
--output_dir $output_dir \\
--overwrite_output_dir \\
--logging_steps 10 \\
--logging_dir $output_dir \\
--deepspeed ds_config.json
```
You can also run with `--sharded_ddp`
```bash
cd transformers/examples/text-classification/
export TASK_NAME=mnli
python -m torch.distributed.launch --nproc_per_node=8 run_glue.py --model_name_or_path microsoft/deberta-v2-xxlarge \\
--task_name $TASK_NAME --do_train --do_eval --max_seq_length 256 --per_device_train_batch_size 8 \\
--learning_rate 3e-6 --num_train_epochs 3 --output_dir /tmp/$TASK_NAME/ --overwrite_output_dir --sharded_ddp --fp16
```
### Citation
If you find DeBERTa useful for your work, please cite the following paper:
``` latex
@inproceedings{
he2021deberta,
title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION},
author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=XPZIaotutsD}
}
``` |
NYTK/summarization-hi-bart-hungarian | 58b5985253f4214f10d07259e515fa8a7530866c | 2022-02-14T13:27:17.000Z | [
"pytorch",
"bart",
"text2text-generation",
"hu",
"transformers",
"summarization",
"license:gpl",
"autotrain_compatible"
]
| summarization | false | NYTK | null | NYTK/summarization-hi-bart-hungarian | 11 | null | transformers | 10,992 | ---
language:
- hu
tags:
- summarization
license: gpl
metrics:
- rouge
widget:
- text: "A Tisza-parti város állatkertjében régóta tartanak szurikátákat ( Suricata suricatta ) , de tavaly tavaszig nem sikerült szaporítani őket , annak ellenére , hogy tágas ház és kifutó épült számukra - közölte Veprik Róbert igazgató . 2010-ben alakult ki az új - három Amszterdamból származó nőstényből és egy budapesti fiatal hímből álló - csapat , amely szaporodni kezdett . 2011-ben három , idén pedig egy utóddal örvendeztették meg a gondozókat és az állatbarátokat . A szurikáták utódai - tizenegy hetes vemhesség után - október és március között vakon és szőrtelenül jönnek a világra . A kicsinyek háromhetesen bújnak elő az üregből , és nevelésükben mindkét szülő részt vesz . A szurikátacsapatokban a család tagjai nagyon szoros kapcsolatban állnak egymással , viszont nagyon harciasan fellépnek az idegenekkel szemben , akár meg is ölhetik azt az állatot , amelyet betolakodónak tekintenek . Bár a Dél-Afrikában , a Kalahári sivatagban őshonos cibetmacskaféle ragadozókat a szegedi állatkertben természetes élőhelyükhöz képest kevesebb veszély fenyegeti , a vadasparki erdőben ragadozó madarak is élnek , amelyek akár zsákmányként is tekinthetnének a szurikátákra . A szegedi csapatnál azonban szigorú őrség van , mindig lesi valaki két lábra állva a veszélyforrásokat . Az őrszemek figyelmét még a sárkányrepülők is felkeltik , és felbukkanásakor valamennyi egyed biztos helyre menekül . A szurikáták a Kalahári sivatag bozótos , sziklás területein csapatokban élnek . A 700 gramm körüli testtömegű ragadozók rovarokkal , lárvákkal , skorpiókkal táplálkoznak , de néha elfogyasztják a kisebb gerinceseket , tojásokat és növényi gumókat is . A nappal aktív állatok földalatti üregrendszert ásnak , amelynek több bejárata is van . Ha a szurikáták idegen csapattal vagy ragadozóval kerülnek szembe , azonnal elkezdenek ásni , nagy porfelhőt kavarva . Az is gyakorta előfordul , hogy szorosan egymáshoz bújnak , felborzolják szőrüket , megnyújtják testüket , hogy minél nagyobbnak látszódjanak . Az előadásuk csúcspontján pedig az egész csapat a levegőbe ugrik , közben pedig morog . A hangadás egyébként is fontos a szurikáták kapcsolatában , az egyedek legalább tízféle jelzést használnak a kolónián belül ."
---
# Hungarian Abstractive Summarization BART model
For further models, scripts and details, see [our repository](https://github.com/nytud/neural-models) or [our demo site](https://juniper.nytud.hu/demo/nlp).
- BART base model (see Results Table - bold):
- Pretrained on Webcorpus 2.0
- Finetuned HI corpus (hvg.hu + index.hu)
- Segments: 559.162
## Limitations
- tokenized input text (tokenizer: [HuSpaCy](https://huggingface.co/huspacy))
- max_source_length = 512
- max_target_length = 256
## Results
| Model | HI | NOL |
| ------------- | ------------- | ------------- |
| BART-base-512 | **30.18/13.86/22.92** | 46.48/32.40/39.45 |
| BART-base-1024| 31.86/14.59/23.79 | 47.01/32.91/39.97 |
## Citation
If you use this model, please cite the following paper:
```
@inproceedings {yang-bart,
title = {{BARTerezzünk! - Messze, messze, messze a világtól, - BART kísérleti modellek magyar nyelvre}},
booktitle = {XVIII. Magyar Számítógépes Nyelvészeti Konferencia},
year = {2022},
publisher = {Szegedi Tudományegyetem, Informatikai Intézet},
address = {Szeged, Magyarország},
author = {{Yang Zijian Győző}},
pages = {15--29}
}
``` |
NYTK/translation-bart-128-en-hu | e7ba362f3067d29ec1fbd38d3b7a7b4557dadb5c | 2022-02-14T13:30:36.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"hu",
"transformers",
"translation",
"license:gpl",
"autotrain_compatible"
]
| translation | false | NYTK | null | NYTK/translation-bart-128-en-hu | 11 | null | transformers | 10,993 | ---
language:
- en
- hu
tags:
- translation
license: gpl
metrics:
- sacrebleu
- chrf
widget:
- text: "This may not make much sense to you, sir, but I'd like to ask your permission to date your daughter."
example_title: "Translation: English-Hungarian"
---
# BART Translation model
For further models, scripts and details, see [our repository](https://github.com/nytud/machine-translation) or [our demo site](https://juniper.nytud.hu/demo/nlp).
- Source language: English
- Target language: Hungarian
- BART base model:
- Pretrained on English WikiText-103 and Hungarian Wikipedia
- Finetuned on subcorpora from OPUS
- Segments: 56.837.602
## Limitations
- tokenized input text (tokenizer: [HuSpaCy](https://huggingface.co/huspacy))
- max_source_length = 128
- max_target_length = 128
## Results
| Model | BLEU | chrF-3 | chrF-6 |
| ------------- | ------------- | ------------- | ------------- |
| Google | 25.30 | 54.09 | 49.0 |
| **BART** | **36.89** | **60.77** | **56.4** |
| mT5 | 27.69 | 53.73 | 48.57 |
## Citation
If you use this model, please cite the following paper:
```
@inproceedings {laki-yang-mt,
title = {{Jobban fordítunk magyarra, mint a Google!}},
booktitle = {XVIII. Magyar Számítógépes Nyelvészeti Konferencia},
year = {2022},
publisher = {Szegedi Tudományegyetem, Informatikai Intézet},
address = {Szeged, Magyarország},
author = {Laki, László and Yang, Zijian Győző},
pages = {357--372}
}
```
|
NYTK/translation-bart-en-hu | 879ebeb975a3226c2336501fbda338b2095ecd9f | 2022-02-14T13:28:40.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"hu",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | false | NYTK | null | NYTK/translation-bart-en-hu | 11 | null | transformers | 10,994 | ---
language:
- en
- hu
tags:
- translation
license: apache-2.0
metrics:
- sacrebleu
- chrf
widget:
- text: "This may not make much sense to you, sir, but I'd like to ask your permission to date your daughter."
example_title: "Translation: English-Hungarian"
---
# BART Translation model
For further models, scripts and details, see [our repository](https://github.com/nytud/machine-translation) or [our demo site](https://juniper.nytud.hu/demo/nlp).
- Source language: English
- Target language: Hungarian
- Pretrained on English WikiText-103 and Hungarian Wikipedia
- Finetuned on subcorpora from OPUS
- Segments: 56.837.602
## Limitations
- tokenized input text (tokenizer: [HuSpaCy](https://huggingface.co/huspacy))
## Results
| Model | BLEU | chrF-3 |
| ------------- | ------------- | ------------- |
| Google en-hu | 25.30 | 54.08 |
| **BART-base-enhu** | **34.38** | **58.88** |
| Google hu-en| 34.48 | 59.59 |
| **BART-base-huen** | **38.03** | **61,37** |
## Citation
If you use this model, please cite the following paper:
```
@inproceedings {yang-bart,
title = {{BARTerezzünk! Messze, messze, messze a világtól, - BART kísérleti modellek magyar nyelvre}},
booktitle = {XVIII. Magyar Számítógépes Nyelvészeti Konferencia},
year = {2022},
publisher = {Szegedi Tudományegyetem, Informatikai Intézet},
address = {Szeged, Magyarország},
author = {{Yang Zijian Győző}},
pages = {15--29}
}
```
|
NbAiLab/XLSR-1B-bokmaal-low | 0527f7470f6f6a352993d4032fa329da459bc2ea | 2022-02-11T17:06:04.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"model-index"
]
| automatic-speech-recognition | false | NbAiLab | null | NbAiLab/XLSR-1B-bokmaal-low | 11 | null | transformers | 10,995 | ---
tags:
- generated_from_trainer
model-index:
- name: XLSR-1B-bokmaal-low
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# XLSR-1B-bokmaal-low
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1579
- Wer: 0.0722
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.7e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 34.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.434 | 0.24 | 500 | 0.1704 | 0.1378 |
| 0.2833 | 0.48 | 1000 | 0.1638 | 0.1324 |
| 0.2478 | 0.72 | 1500 | 0.1606 | 0.1240 |
| 0.2276 | 0.97 | 2000 | 0.1562 | 0.1212 |
| 0.2208 | 1.21 | 2500 | 0.1576 | 0.1172 |
| 0.2148 | 1.45 | 3000 | 0.1502 | 0.1119 |
| 0.1994 | 1.69 | 3500 | 0.1409 | 0.1110 |
| 0.1932 | 1.93 | 4000 | 0.1432 | 0.1112 |
| 0.2122 | 2.17 | 4500 | 0.1443 | 0.1098 |
| 0.2177 | 2.42 | 5000 | 0.1329 | 0.1102 |
| 0.2058 | 2.66 | 5500 | 0.1403 | 0.1070 |
| 0.2216 | 2.9 | 6000 | 0.1342 | 0.1067 |
| 0.1984 | 3.14 | 6500 | 0.1370 | 0.1030 |
| 0.2056 | 3.38 | 7000 | 0.1371 | 0.1041 |
| 0.1735 | 3.62 | 7500 | 0.1296 | 0.1003 |
| 0.203 | 3.87 | 8000 | 0.1301 | 0.1005 |
| 0.1835 | 4.11 | 8500 | 0.1310 | 0.1004 |
| 0.178 | 4.35 | 9000 | 0.1300 | 0.0959 |
| 0.1585 | 4.59 | 9500 | 0.1277 | 0.0966 |
| 0.1848 | 4.83 | 10000 | 0.1260 | 0.0974 |
| 0.169 | 5.07 | 10500 | 0.1281 | 0.0969 |
| 0.1666 | 5.32 | 11000 | 0.1291 | 0.1003 |
| 0.1552 | 5.56 | 11500 | 0.1271 | 0.0959 |
| 0.2736 | 5.8 | 12000 | 0.1320 | 0.0935 |
| 0.2845 | 6.04 | 12500 | 0.1299 | 0.0921 |
| 0.1536 | 6.28 | 13000 | 0.1282 | 0.0927 |
| 0.1491 | 6.52 | 13500 | 0.1240 | 0.0906 |
| 0.1579 | 6.77 | 14000 | 0.1208 | 0.0921 |
| 0.16 | 7.01 | 14500 | 0.1182 | 0.0903 |
| 0.1367 | 7.25 | 15000 | 0.1214 | 0.0922 |
| 0.1499 | 7.49 | 15500 | 0.1232 | 0.0916 |
| 0.148 | 7.73 | 16000 | 0.1184 | 0.0896 |
| 0.1426 | 7.97 | 16500 | 0.1201 | 0.0889 |
| 0.1471 | 8.22 | 17000 | 0.1256 | 0.0882 |
| 0.1358 | 8.46 | 17500 | 0.1265 | 0.0909 |
| 0.1245 | 8.7 | 18000 | 0.1263 | 0.0886 |
| 0.1407 | 8.94 | 18500 | 0.1226 | 0.0885 |
| 0.1289 | 9.18 | 19000 | 0.1315 | 0.0873 |
| 0.1326 | 9.42 | 19500 | 0.1233 | 0.0868 |
| 0.1305 | 9.67 | 20000 | 0.1237 | 0.0870 |
| 0.1432 | 9.91 | 20500 | 0.1234 | 0.0857 |
| 0.1205 | 10.15 | 21000 | 0.1303 | 0.0858 |
| 0.1248 | 10.39 | 21500 | 0.1252 | 0.0858 |
| 0.1251 | 10.63 | 22000 | 0.1253 | 0.0869 |
| 0.1143 | 10.87 | 22500 | 0.1266 | 0.0860 |
| 0.1155 | 11.12 | 23000 | 0.1219 | 0.0862 |
| 0.1227 | 11.36 | 23500 | 0.1329 | 0.0864 |
| 0.1229 | 11.6 | 24000 | 0.1244 | 0.0855 |
| 0.1112 | 11.84 | 24500 | 0.1356 | 0.0851 |
| 0.2163 | 12.08 | 25000 | 0.1252 | 0.0847 |
| 0.1146 | 12.32 | 25500 | 0.1211 | 0.0837 |
| 0.1058 | 12.57 | 26000 | 0.1247 | 0.0843 |
| 0.1099 | 12.81 | 26500 | 0.1189 | 0.0833 |
| 0.1028 | 13.05 | 27000 | 0.1303 | 0.0815 |
| 0.1092 | 13.29 | 27500 | 0.1305 | 0.0838 |
| 0.1076 | 13.53 | 28000 | 0.1276 | 0.0842 |
| 0.1074 | 13.77 | 28500 | 0.1268 | 0.0844 |
| 0.0971 | 14.02 | 29000 | 0.1322 | 0.0839 |
| 0.1109 | 14.26 | 29500 | 0.1287 | 0.0821 |
| 0.0991 | 14.5 | 30000 | 0.1289 | 0.0831 |
| 0.1095 | 14.74 | 30500 | 0.1273 | 0.0822 |
| 0.1015 | 14.98 | 31000 | 0.1326 | 0.0816 |
| 0.1051 | 15.22 | 31500 | 0.1337 | 0.0814 |
| 0.0894 | 15.47 | 32000 | 0.1331 | 0.0802 |
| 0.1 | 15.71 | 32500 | 0.1304 | 0.0798 |
| 0.0957 | 15.95 | 33000 | 0.1293 | 0.0824 |
| 0.0921 | 16.19 | 33500 | 0.1382 | 0.0808 |
| 0.0986 | 16.43 | 34000 | 0.1301 | 0.0788 |
| 0.098 | 16.67 | 34500 | 0.1305 | 0.0795 |
| 0.0974 | 16.92 | 35000 | 0.1325 | 0.0796 |
| 0.0886 | 17.16 | 35500 | 0.1332 | 0.0796 |
| 0.0892 | 17.4 | 36000 | 0.1327 | 0.0785 |
| 0.0917 | 17.64 | 36500 | 0.1304 | 0.0793 |
| 0.0919 | 17.88 | 37000 | 0.1353 | 0.0791 |
| 0.1007 | 18.12 | 37500 | 0.1340 | 0.0791 |
| 0.0831 | 18.37 | 38000 | 0.1327 | 0.0786 |
| 0.0862 | 18.61 | 38500 | 0.1343 | 0.0792 |
| 0.0837 | 18.85 | 39000 | 0.1334 | 0.0777 |
| 0.0771 | 19.09 | 39500 | 0.1456 | 0.0778 |
| 0.0841 | 19.33 | 40000 | 0.1365 | 0.0784 |
| 0.0874 | 19.57 | 40500 | 0.1379 | 0.0779 |
| 0.0773 | 19.82 | 41000 | 0.1359 | 0.0776 |
| 0.0771 | 20.06 | 41500 | 0.1392 | 0.0776 |
| 0.0861 | 20.3 | 42000 | 0.1395 | 0.0774 |
| 0.0773 | 20.54 | 42500 | 0.1356 | 0.0775 |
| 0.069 | 20.78 | 43000 | 0.1399 | 0.0765 |
| 0.0823 | 21.02 | 43500 | 0.1469 | 0.0774 |
| 0.0747 | 21.27 | 44000 | 0.1415 | 0.0768 |
| 0.0703 | 21.51 | 44500 | 0.1405 | 0.0778 |
| 0.0776 | 21.75 | 45000 | 0.1492 | 0.0778 |
| 0.0833 | 21.99 | 45500 | 0.1448 | 0.0767 |
| 0.0796 | 22.23 | 46000 | 0.1434 | 0.0761 |
| 0.0613 | 22.47 | 46500 | 0.1446 | 0.0768 |
| 0.0753 | 22.72 | 47000 | 0.1439 | 0.0757 |
| 0.076 | 22.96 | 47500 | 0.1402 | 0.0759 |
| 0.0619 | 23.2 | 48000 | 0.1473 | 0.0767 |
| 0.1322 | 23.44 | 48500 | 0.1431 | 0.0766 |
| 0.0691 | 23.68 | 49000 | 0.1452 | 0.0753 |
| 0.061 | 23.92 | 49500 | 0.1452 | 0.0752 |
| 0.0716 | 24.17 | 50000 | 0.1429 | 0.0756 |
| 0.074 | 24.41 | 50500 | 0.1440 | 0.0746 |
| 0.0696 | 24.65 | 51000 | 0.1459 | 0.0756 |
| 0.081 | 24.89 | 51500 | 0.1443 | 0.0751 |
| 0.0754 | 25.13 | 52000 | 0.1483 | 0.0755 |
| 0.0864 | 25.37 | 52500 | 0.1467 | 0.0757 |
| 0.0662 | 25.62 | 53000 | 0.1471 | 0.0748 |
| 0.109 | 25.86 | 53500 | 0.1472 | 0.0759 |
| 0.0682 | 26.1 | 54000 | 0.1539 | 0.0748 |
| 0.0655 | 26.34 | 54500 | 0.1469 | 0.0743 |
| 0.0651 | 26.58 | 55000 | 0.1553 | 0.0748 |
| 0.0666 | 26.82 | 55500 | 0.1520 | 0.0744 |
| 0.0724 | 27.07 | 56000 | 0.1526 | 0.0738 |
| 0.067 | 27.31 | 56500 | 0.1489 | 0.0738 |
| 0.0658 | 27.55 | 57000 | 0.1518 | 0.0738 |
| 0.0581 | 27.79 | 57500 | 0.1518 | 0.0739 |
| 0.0639 | 28.03 | 58000 | 0.1495 | 0.0736 |
| 0.0606 | 28.27 | 58500 | 0.1549 | 0.0739 |
| 0.0641 | 28.52 | 59000 | 0.1513 | 0.0735 |
| 0.0612 | 28.76 | 59500 | 0.1524 | 0.0739 |
| 0.0536 | 29.0 | 60000 | 0.1565 | 0.0741 |
| 0.0574 | 29.24 | 60500 | 0.1541 | 0.0741 |
| 0.057 | 29.48 | 61000 | 0.1555 | 0.0741 |
| 0.0624 | 29.72 | 61500 | 0.1590 | 0.0736 |
| 0.0531 | 29.97 | 62000 | 0.1590 | 0.0734 |
| 0.0661 | 30.21 | 62500 | 0.1599 | 0.0732 |
| 0.0641 | 30.45 | 63000 | 0.1576 | 0.0730 |
| 0.0562 | 30.69 | 63500 | 0.1593 | 0.0734 |
| 0.0527 | 30.93 | 64000 | 0.1604 | 0.0730 |
| 0.0579 | 31.17 | 64500 | 0.1571 | 0.0734 |
| 0.0508 | 31.42 | 65000 | 0.1603 | 0.0733 |
| 0.0524 | 31.66 | 65500 | 0.1588 | 0.0726 |
| 0.0564 | 31.9 | 66000 | 0.1571 | 0.0727 |
| 0.0551 | 32.14 | 66500 | 0.1584 | 0.0728 |
| 0.0564 | 32.38 | 67000 | 0.1565 | 0.0726 |
| 0.0628 | 32.62 | 67500 | 0.1558 | 0.0725 |
| 0.0561 | 32.87 | 68000 | 0.1582 | 0.0727 |
| 0.0553 | 33.11 | 68500 | 0.1591 | 0.0726 |
| 0.0504 | 33.35 | 69000 | 0.1590 | 0.0725 |
| 0.0539 | 33.59 | 69500 | 0.1582 | 0.0723 |
| 0.0576 | 33.83 | 70000 | 0.1579 | 0.0722 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
NbAiLab/test_w5_long | 09a805d260186524481aa1028efaf9b1303bd7ce | 2021-12-16T12:46:14.000Z | [
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | false | NbAiLab | null | NbAiLab/test_w5_long | 11 | null | transformers | 10,996 | Just for performing some experiments. Do not use. |
Nokia/nlgp-natural | b874c088fcfafda664d8e3ef47e94aecbf7f86b2 | 2022-02-18T14:16:33.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"python",
"arxiv:2108.05198",
"transformers",
"code completion",
"code generation",
"license:apache-2.0"
]
| text-generation | false | Nokia | null | Nokia/nlgp-natural | 11 | null | transformers | 10,997 | ---
language:
- en
- python
tags:
- code completion
- code generation
license: "apache-2.0"
---
# NLGP natural model
The NLGP natural model was introduced in the paper [Natural Language-Guided Programming](https://arxiv.org/abs/2108.05198). The model was trained on a collection of Jupyter notebooks and can be used to synthesize Python code that addresses a natural language **intent** in a certain code **context** (see the example below). This work was carried out by a research team in Nokia Bell Labs.
**Context**
```py
import matplotlib.pyplot as plt
values = [1, 2, 3, 4]
labels = ["a", "b", "c", "d"]
```
**Intent**
```py
# plot a bar chart
```
**Prediction**
```py
plt.bar(labels, values)
plt.show()
```
## Usage
```py
import re
from transformers import GPT2LMHeadModel, GPT2TokenizerFast
# load the model
tok = GPT2TokenizerFast.from_pretrained("Nokia/nlgp-natural")
model = GPT2LMHeadModel.from_pretrained("Nokia/nlgp-natural")
# preprocessing functions
num_spaces = [2, 4, 6, 8, 10, 12, 14, 16, 18]
def preprocess(context, query):
"""
Encodes context + query as a single string and
replaces whitespace with special tokens <|2space|>, <|4space|>, ...
"""
input_str = f"{context}\n{query} <|endofcomment|>\n"
indentation_symbols = {n: f"<|{n}space|>" for n in num_spaces}
m = re.match("^[ ]+", input_str)
if not m:
return input_str
leading_whitespace = m.group(0)
N = len(leading_whitespace)
for n in self.num_spaces:
leading_whitespace = leading_whitespace.replace(n * " ", self.indentation_symbols[n])
return leading_whitespace + input_str[N:]
detokenize_pattern = re.compile(fr"<\|(\d+)space\|>")
def postprocess(output):
output = output.split("<|cell|>")[0]
def insert_space(m):
num_spaces = int(m.group(1))
return num_spaces * " "
return detokenize_pattern.sub(insert_space, output)
# inference
code_context = """
import matplotlib.pyplot as plt
values = [1, 2, 3, 4]
labels = ["a", "b", "c", "d"]
"""
query = "# plot a bar chart"
input_str = preprocess(code_context, query)
input_ids = tok(input_str, return_tensors="pt").input_ids
max_length = 150 # don't generate output longer than this length
total_max_length = min(1024 - input_ids.shape[-1], input_ids.shape[-1] + 150) # total = input + output
input_and_output = model.generate(
input_ids=input_ids,
max_length=total_max_length,
min_length=10,
do_sample=False,
num_beams=4,
early_stopping=True,
eos_token_id=tok.encode("<|cell|>")[0]
)
output = input_and_output[:, input_ids.shape[-1]:] # remove the tokens that correspond to the input_str
output_str = tok.decode(output[0])
postprocess(output_str)
```
## License and copyright
Copyright 2021 Nokia
Licensed under the Apache License 2.0
SPDX-License-Identifier: Apache-2.0 |
PhilSad/gpt-scp-neo-125M | fc566b64d003ca29bbaf856bbb6d3ac990ddd5d7 | 2022-02-23T22:41:55.000Z | [
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-generation | false | PhilSad | null | PhilSad/gpt-scp-neo-125M | 11 | null | transformers | 10,998 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: output_gptneo125-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# output_gptneo125-2
This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: tpu
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
RJ3vans/CLNspanTagger | e1f68e7537552b5686c3829240bf30e98f6d190a | 2021-09-07T13:24:46.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | RJ3vans | null | RJ3vans/CLNspanTagger | 11 | null | transformers | 10,999 | This model identifies compound nouns in input sentences.
Try the test sentence:
I love apples [and] potatoes.
Accuracy is best when you place square brackets around the coordinating conjunction.
The model was derived using code adapted from an original program written by Dr. Le An Ha at the University of Wolverhampton. |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.