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fill-mask | transformers |
# LitLat BERT
LitLat BERT is a trilingual model, using xlm-roberta-base architecture, trained on Lithuanian, Latvian, and English corpora. Focusing on three languages, the model performs better than [multilingual BERT](https://huggingface.co/bert-base-multilingual-cased), while still offering an option for cross-lingual knowledge transfer, which a monolingual model wouldn't.
### Named entity recognition evaluation
We compare LitLat BERT with multilingual BERT (mBERT), XLM-RoBERTa (XLM-R) and monolingual Latvian BERT (LVBERT) (Znotins and Barzdins, 2020). The report the results as a macro F1 score of 3 named entity classes shared in all three datasets: person, location, organization.
Language | mBERT | XLM-R | LVBERT | LitLat
---|---|---|---|---
Latvian | 0.830 | 0.865 | 0.797 | **0.881**
Lithuanian | 0.797 | 0.817 | / | **0.850**
English | 0.939 | 0.937 | / | **0.943**
| {"language": ["lt", "lv", "en", "multilingual"], "license": "cc-by-sa-4.0"} | EMBEDDIA/litlat-bert | null | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"lt",
"lv",
"en",
"multilingual",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
text-classification | transformers | {} | EMBEDDIA/rubert-tweetsentiment | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | EMBEDDIA/sloberta-tweetsentiment | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
fill-mask | transformers | # Usage
Load in transformers library with:
```
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("EMBEDDIA/sloberta")
model = AutoModelForMaskedLM.from_pretrained("EMBEDDIA/sloberta")
```
# SloBERTa
SloBERTa model is a monolingual Slovene BERT-like model. It is closely related to French Camembert model https://camembert-model.fr/. The corpora used for training the model have 3.47 billion tokens in total. The subword vocabulary contains 32,000 tokens. The scripts and programs used for data preparation and training the model are available on https://github.com/clarinsi/Slovene-BERT-Tool
SloBERTa was trained for 200,000 iterations or about 98 epochs.
## Corpora
The following corpora were used for training the model:
* Gigafida 2.0
* Kas 1.0
* Janes 1.0 (only Janes-news, Janes-forum, Janes-blog, Janes-wiki subcorpora)
* Slovenian parliamentary corpus siParl 2.0
* slWaC
| {"language": ["sl"], "license": "cc-by-sa-4.0"} | EMBEDDIA/sloberta | null | [
"transformers",
"pytorch",
"camembert",
"fill-mask",
"sl",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
fill-mask | transformers |
# bio-lm
## Model description
This model is a [RoBERTa base pre-trained model](https://huggingface.co/roberta-base) that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the [BioLang dataset](https://huggingface.co/datasets/EMBO/biolang).
## Intended uses & limitations
#### How to use
The intended use of this model is to be fine-tuned for downstream tasks, token classification in particular.
To have a quick check of the model as-is in a fill-mask task:
```python
from transformers import pipeline, RobertaTokenizerFast
tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_len=512)
text = "Let us try this model to see if it <mask>."
fill_mask = pipeline(
"fill-mask",
model='EMBO/bio-lm',
tokenizer=tokenizer
)
fill_mask(text)
```
#### Limitations and bias
This model should be fine-tuned on a specifi task like token classification.
The model must be used with the `roberta-base` tokenizer.
## Training data
The model was trained with a masked language modeling taskon the [BioLang dataset](https://huggingface.co/datasets/EMBO/biolang) wich includes 12Mio examples from abstracts and figure legends extracted from papers published in life sciences.
## Training procedure
The training was run on a NVIDIA DGX Station with 4XTesla V100 GPUs.
Training code is available at https://github.com/source-data/soda-roberta
- Command: `python -m lm.train /data/json/oapmc_abstracts_figs/ MLM`
- Tokenizer vocab size: 50265
- Training data: EMBO/biolang MLM
- Training with: 12005390 examples
- Evaluating on: 36713 examples
- Epochs: 3.0
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- tensorboard run: lm-MLM-2021-01-27T15-17-43.113766
End of training:
```
trainset: 'loss': 0.8653350830078125
validation set: 'eval_loss': 0.8192330598831177, 'eval_recall': 0.8154601116513597
```
## Eval results
Eval on test set:
```
recall: 0.814471959728645
```
| {"language": ["english"], "tags": ["language model"], "datasets": ["EMBO/biolang"], "metrics": []} | EMBO/bio-lm | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"language model",
"dataset:EMBO/biolang",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
token-classification | transformers |
# sd-ner
## Model description
This model is a [RoBERTa base model](https://huggingface.co/roberta-base) that was further trained using a masked language modeling task on a compendium of English scientific textual examples from the life sciences using the [BioLang dataset](https://huggingface.co/datasets/EMBO/biolang). It was then fine-tuned for token classification on the SourceData [sd-nlp](https://huggingface.co/datasets/EMBO/sd-nlp) dataset with the `NER` configuration to perform Named Entity Recognition of bioentities.
## Intended uses & limitations
#### How to use
The intended use of this model is for Named Entity Recognition of biological entities used in SourceData annotations (https://sourcedata.embo.org), including small molecules, gene products (genes and proteins), subcellular components, cell line and cell types, organ and tissues, species as well as experimental methods.
To have a quick check of the model:
```python
from transformers import pipeline, RobertaTokenizerFast, RobertaForTokenClassification
example = """<s> F. Western blot of input and eluates of Upf1 domains purification in a Nmd4-HA strain. The band with the # might corresponds to a dimer of Upf1-CH, bands marked with a star correspond to residual signal with the anti-HA antibodies (Nmd4). Fragments in the eluate have a smaller size because the protein A part of the tag was removed by digestion with the TEV protease. G6PDH served as a loading control in the input samples </s>"""
tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_len=512)
model = RobertaForTokenClassification.from_pretrained('EMBO/sd-ner')
ner = pipeline('ner', model, tokenizer=tokenizer)
res = ner(example)
for r in res:
print(r['word'], r['entity'])
```
#### Limitations and bias
The model must be used with the `roberta-base` tokenizer.
## Training data
The model was trained for token classification using the [EMBO/sd-nlp dataset](https://huggingface.co/datasets/EMBO/sd-nlp) dataset which includes manually annotated examples.
## Training procedure
The training was run on an NVIDIA DGX Station with 4XTesla V100 GPUs.
Training code is available at https://github.com/source-data/soda-roberta
- Model fine-tuned: EMBO/bio-lm
- Tokenizer vocab size: 50265
- Training data: EMBO/sd-nlp
- Dataset configuration: NER
- Training with 48771 examples.
- Evaluating on 13801 examples.
- Training on 15 features: O, I-SMALL_MOLECULE, B-SMALL_MOLECULE, I-GENEPROD, B-GENEPROD, I-SUBCELLULAR, B-SUBCELLULAR, I-CELL, B-CELL, I-TISSUE, B-TISSUE, I-ORGANISM, B-ORGANISM, I-EXP_ASSAY, B-EXP_ASSAY
- Epochs: 0.6
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 0.0001
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
## Eval results
Testing on 7178 examples of test set with `sklearn.metrics`:
```
precision recall f1-score support
CELL 0.69 0.81 0.74 5245
EXP_ASSAY 0.56 0.57 0.56 10067
GENEPROD 0.77 0.89 0.82 23587
ORGANISM 0.72 0.82 0.77 3623
SMALL_MOLECULE 0.70 0.80 0.75 6187
SUBCELLULAR 0.65 0.72 0.69 3700
TISSUE 0.62 0.73 0.67 3207
micro avg 0.70 0.79 0.74 55616
macro avg 0.67 0.77 0.72 55616
weighted avg 0.70 0.79 0.74 55616
{'test_loss': 0.1830928772687912, 'test_accuracy_score': 0.9334821000160841, 'test_precision': 0.6987463009514112, 'test_recall': 0.789682825086306, 'test_f1': 0.7414366506288511, 'test_runtime': 61.0547, 'test_samples_per_second': 117.567, 'test_steps_per_second': 1.851}
```
| {"language": ["english"], "license": "agpl-3.0", "tags": ["token classification"], "datasets": ["EMBO/sd-nlp"], "metrics": []} | EMBO/sd-ner | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"token-classification",
"token classification",
"dataset:EMBO/sd-nlp",
"license:agpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
token-classification | transformers |
# sd-panelization
## Model description
This model is a [RoBERTa base model](https://huggingface.co/roberta-base) that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the [BioLang dataset](https://huggingface.co/datasets/EMBO/biolang). It was then fine-tuned for token classification on the SourceData [sd-nlp](https://huggingface.co/datasets/EMBO/sd-nlp) dataset with the `PANELIZATION` task to perform 'parsing' or 'segmentation' of figure legends into fragments corresponding to sub-panels.
Figures are usually composite representations of results obtained with heterogeneous experimental approaches and systems. Breaking figures into panels allows identifying more coherent descriptions of individual scientific experiments.
## Intended uses & limitations
#### How to use
The intended use of this model is for 'parsing' figure legends into sub-fragments corresponding to individual panels as used in SourceData annotations (https://sourcedata.embo.org).
To have a quick check of the model:
```python
from transformers import pipeline, RobertaTokenizerFast, RobertaForTokenClassification
example = """Fig 4. a, Volume density of early (Avi) and late (Avd) autophagic vacuoles.a, Volume density of early (Avi) and late (Avd) autophagic vacuoles from four independent cultures. Examples of Avi and Avd are shown in b and c, respectively. Bars represent 0.4����m. d, Labelling density of cathepsin-D as estimated in two independent experiments. e, Labelling density of LAMP-1."""
tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_len=512)
model = RobertaForTokenClassification.from_pretrained('EMBO/sd-panelization')
ner = pipeline('ner', model, tokenizer=tokenizer)
res = ner(example)
for r in res: print(r['word'], r['entity'])
```
#### Limitations and bias
The model must be used with the `roberta-base` tokenizer.
## Training data
The model was trained for token classification using the [`EMBO/sd-nlp PANELIZATION`](https://huggingface.co/datasets/EMBO/sd-nlp) dataset which includes manually annotated examples.
## Training procedure
The training was run on an NVIDIA DGX Station with 4XTesla V100 GPUs.
Training code is available at https://github.com/source-data/soda-roberta
- Model fine-tuned: EMBO/bio-lm
- Tokenizer vocab size: 50265
- Training data: EMBO/sd-nlp
- Dataset configuration: PANELIZATION
- TTraining with 2175 examples.
- Evaluating on 622 examples.
- Training on 2 features: `O`, `B-PANEL_START`
- Epochs: 1.3
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 0.0001
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
## Eval results
Testing on 1802 examples from test set with `sklearn.metrics`:
```
precision recall f1-score support
PANEL_START 0.89 0.95 0.92 5427
micro avg 0.89 0.95 0.92 5427
macro avg 0.89 0.95 0.92 5427
weighted avg 0.89 0.95 0.92 5427
```
| {"language": ["english"], "license": "agpl-3.0", "tags": ["token classification"], "datasets": ["EMBO/sd-nlp"], "metrics": []} | EMBO/sd-panelization | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"token-classification",
"dataset:EMBO/sd-nlp",
"license:agpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | ERVINLLANERA/Raymond | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | ESPersonnel/DiabloGPT-small-rickandmorty | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers |
# Game of Thrones DialoGPT Model | {"tags": ["conversational"]} | ESPersonnel/DialoGPT-small-got | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | ESPersonnel/policy-distilbert-7d | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers |
# Peppa Pig DialoGPT Model | {"tags": ["conversational"]} | Eagle3ye/DialoGPT-small-PeppaPig | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {"license": "afl-3.0"} | Eashwar/test_chatbot | null | [
"license:afl-3.0",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | ## Bert-base-uncased for Android-Ios Question Classification
**Code**: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/EastHShin/Android-Ios-Classification-Workspace)
<br>
**Android-Ios-Classification DEMO**: [Ainize Endpoint](https://main-android-ios-classification-east-h-shin.endpoint.ainize.ai/)
<br>
**Demo web Code**: [Github](https://github.com/EastHShin/Android-Ios-Classification)
<br>
**Android-Ios-Classification API**: [Ainize API](https://ainize.ai/EastHShin/Android-Ios-Classification)
<br>
<br>
## Overview
**Language model**: bert-base-cased
<br>
**Language**: English
<br>
**Training data**: Question classification Android-Ios dataset from [Kaggle](https://www.kaggle.com/xhlulu/question-classification-android-or-ios)
## Usage
```
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_path = "EasthShin/Android_Ios_Classification"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)
classifier = pipeline('text-classification', model=model_path, tokenizer=tokenizer)
question = "I bought goodnote in Appstore"
result = dict()
result[0] = classifier(question)[0]
``` | {} | EasthShin/Android_Ios_Classification | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers | {} | EasthShin/BTS_Lyrics_GPT-Neo-base | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers | {} | EasthShin/Chatbot-LisaSimpson-DialoGPT | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | EasthShin/Emotion-Classification-bert-base | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
question-answering | transformers |
#### Klue-bert base for Common Sense QA
#### Klue-CommonSense-model DEMO: [Ainize DEMO](https://main-klue-common-sense-qa-east-h-shin.endpoint.ainize.ai/)
#### Klue-CommonSense-model API: [Ainize API](https://ainize.ai/EastHShin/Klue-CommonSense_QA?branch=main)
### Overview
**Language model**: klue/bert-base
<br>
**Language**: Korean
<br>
**Downstream-task**: Extractive QA
<br>
**Training data**: Common sense Data from [Mindslab](https://mindslab.ai:8080/kr/company)
<br>
**Eval data**: Common sense Data from [Mindslab](https://mindslab.ai:8080/kr/company)
<br>
**Code**: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/EastHShin/Klue-CommonSense-workspace)
<br>
### Usage
### In Transformers
```
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("EasthShin/Klue-CommonSense-model")
model = AutoModelForQuestionAnswering.from_pretrained("EasthShin/Klue-CommonSense-model")
context = "your context"
question = "your question"
encodings = tokenizer(context, question, max_length=512, truncation=True,
padding="max_length", return_token_type_ids=False)
encodings = {key: torch.tensor([val]) for key, val in encodings.items()}
input_ids = encodings["input_ids"]
attention_mask = encodings["attention_mask"]
pred = model(input_ids, attention_mask=attention_mask)
start_logits, end_logits = pred.start_logits, pred.end_logits
token_start_index, token_end_index = start_logits.argmax(dim=-1), end_logits.argmax(dim=-1)
pred_ids = input_ids[0][token_start_index: token_end_index + 1]
prediction = tokenizer.decode(pred_ids)
``` | {} | EasthShin/Klue-CommonSense-model | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers | ## Youth_Chatbot_KoGPT2-base
**Demo Web**: [Ainize Endpoint](https://main-youth-chatbot-ko-gpt2-base-east-h-shin.endpoint.ainize.ai/)
<br>
**Demo Web Code**: [Github](https://github.com/EastHShin/Youth_Chatbot_KoGPT2-base)
<br>
**Youth-Chatbot API**: [Ainize API](https://ainize.ai/EastHShin/Youth_Chatbot_KoGPT2-base_API?branch=main)
<br>
<br>
## Overview
**Language model**: KoGPT2
<br>
**Language**: Korean
<br>
**Training data**: [Aihub](https://aihub.or.kr/aidata/7978)
## Usage
```
from transformers import PreTrainedTokenizerFast, GPT2LMHeadModel
U_TKN = '<usr>'
S_TKN = '<sys>'
MASK = '<unused0>'
SENT = '<unused1>'
tokenizer = PreTrainedTokenizerFast.from_pretrained("EasthShin/Youth_Chatbot_Kogpt2-base",
bos_token='</s>', eos_token='</s>', unk_token='<unk>',
pad_token='<pad>', mask_token=MASK)
model = GPT2LMHeadModel.from_pretrained('EasthShin/Youth_Chatbot_Kogpt2-base')
input_ids = tokenizer.encode(U_TKN + {your text} + sent + S_TKN)
gen_ids = model.generate(torch.tensor([input_ids]),
max_length=128,
repetition_penalty= 2.0,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
bos_token_id=tokenizer.bos_token_id,
use_cache=True)
generated = tokenizer.decode(gen_ids[0, :].tolist())
print(generated)
``` | {} | EasthShin/Youth_Chatbot_Kogpt2-base | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | Easton/w2v-ctc_callhome | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
fill-mask | transformers | #Arabic_BERT_Model
#ArBERTMo
| {} | Ebtihal/ArBERTMo | null | [
"transformers",
"tf",
"camembert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
fill-mask | transformers |
# Arabic BERT Model
**AraBERTMo** is an Arabic pre-trained language model based on [Google's BERT architechture](https://github.com/google-research/bert).
AraBERTMo_base uses the same BERT-Base config.
AraBERTMo_base now comes in 10 new variants
All models are available on the `HuggingFace` model page under the [Ebtihal](https://huggingface.co/Ebtihal/) name.
Checkpoints are available in PyTorch formats.
## Pretraining Corpus
`AraBertMo_base_V1' model was pre-trained on ~3 million words:
- [OSCAR](https://traces1.inria.fr/oscar/) - Arabic version "unshuffled_deduplicated_ar".
## Training results
this model achieves the following results:
| Task | Num examples | Num Epochs | Batch Size | steps | Wall time | training loss|
|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|
| Fill-Mask| 10010| 1 | 64 | 157 | 2m 2s | 9.0183 |
## Load Pretrained Model
You can use this model by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Ebtihal/AraBertMo_base_V1")
model = AutoModelForMaskedLM.from_pretrained("Ebtihal/AraBertMo_base_V1")
```
## This model was built for master's degree research in an organization:
- [University of kufa](https://uokufa.edu.iq/).
- [Faculty of Computer Science and Mathematics](https://mathcomp.uokufa.edu.iq/).
- **Department of Computer Science**
| {"language": "ar", "tags": "Fill-Mask", "datasets": "OSCAR", "widget": [{"text": " \u0627\u0644\u0633\u0644\u0627\u0645 \u0639\u0644\u064a\u0643\u0645 \u0648\u0631\u062d\u0645\u0629[MASK] \u0648\u0628\u0631\u0643\u0627\u062a\u0629"}, {"text": " \u0627\u0647\u0644\u0627 \u0648\u0633\u0647\u0644\u0627 \u0628\u0643\u0645 \u0641\u064a [MASK] \u0645\u0646 \u0633\u064a\u0631\u0628\u062d \u0627\u0644\u0645\u0644\u064a\u0648\u0646"}, {"text": " \u0645\u0631\u062d\u0628\u0627 \u0628\u0643 \u0639\u0632\u064a\u0632\u064a \u0627\u0644\u0632\u0627\u0626\u0631 [MASK] \u0645\u0648\u0642\u0639\u0646\u0627 "}]} | Ebtihal/AraBertMo_base_V1 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"Fill-Mask",
"ar",
"dataset:OSCAR",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
fill-mask | transformers |
# Arabic BERT Model
**AraBERTMo** is an Arabic pre-trained language model based on [Google's BERT architechture](https://github.com/google-research/bert).
AraBERTMo_base uses the same BERT-Base config.
AraBERTMo_base now comes in 10 new variants
All models are available on the `HuggingFace` model page under the [Ebtihal](https://huggingface.co/Ebtihal/) name.
Checkpoints are available in PyTorch formats.
## Pretraining Corpus
`AraBertMo_base_V2' model was pre-trained on ~3 million words:
- [OSCAR](https://traces1.inria.fr/oscar/) - Arabic version "unshuffled_deduplicated_ar".
## Training results
this model achieves the following results:
| Task | Num examples | Num Epochs | Batch Size | steps | Wall time | training loss|
|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|
| Fill-Mask| 20020| 2 | 64 | 626 | 19m 2s | 8.437 |
## Load Pretrained Model
You can use this model by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Ebtihal/AraBertMo_base_V2")
model = AutoModelForMaskedLM.from_pretrained("Ebtihal/AraBertMo_base_V2")
```
## This model was built for master's degree research in an organization:
- [University of kufa](https://uokufa.edu.iq/).
- [Faculty of Computer Science and Mathematics](https://mathcomp.uokufa.edu.iq/).
- **Department of Computer Science**
| {"language": "ar", "tags": "Fill-Mask", "datasets": "OSCAR", "widget": [{"text": " \u0627\u0644\u0633\u0644\u0627\u0645 \u0639\u0644\u064a\u0643\u0645 \u0648\u0631\u062d\u0645\u0629[MASK] \u0648\u0628\u0631\u0643\u0627\u062a\u0629"}, {"text": " \u0627\u0647\u0644\u0627 \u0648\u0633\u0647\u0644\u0627 \u0628\u0643\u0645 \u0641\u064a [MASK] \u0645\u0646 \u0633\u064a\u0631\u0628\u062d \u0627\u0644\u0645\u0644\u064a\u0648\u0646"}, {"text": " \u0645\u0631\u062d\u0628\u0627 \u0628\u0643 \u0639\u0632\u064a\u0632\u064a \u0627\u0644\u0632\u0627\u0626\u0631 [MASK] \u0645\u0648\u0642\u0639\u0646\u0627 "}]} | Ebtihal/AraBertMo_base_V2 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"Fill-Mask",
"ar",
"dataset:OSCAR",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
fill-mask | transformers |
# Arabic BERT Model
**AraBERTMo** is an Arabic pre-trained language model based on [Google's BERT architechture](https://github.com/google-research/bert).
AraBERTMo_base uses the same BERT-Base config.
AraBERTMo_base now comes in 10 new variants
All models are available on the `HuggingFace` model page under the [Ebtihal](https://huggingface.co/Ebtihal/) name.
Checkpoints are available in PyTorch formats.
## Pretraining Corpus
`AraBertMo_base_V3' model was pre-trained on ~3 million words:
- [OSCAR](https://traces1.inria.fr/oscar/) - Arabic version "unshuffled_deduplicated_ar".
## Training results
this model achieves the following results:
| Task | Num examples | Num Epochs | Batch Size | steps | Wall time | training loss|
|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|
| Fill-Mask| 30024| 3 | 64 | 1410 | 3h 10m 31s | 8.0201 |
## Load Pretrained Model
You can use this model by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Ebtihal/AraBertMo_base_V3")
model = AutoModelForMaskedLM.from_pretrained("Ebtihal/AraBertMo_base_V3")
```
## This model was built for master's degree research in an organization:
- [University of kufa](https://uokufa.edu.iq/).
- [Faculty of Computer Science and Mathematics](https://mathcomp.uokufa.edu.iq/).
- **Department of Computer Science**
| {"language": "ar", "tags": "Fill-Mask", "datasets": "OSCAR", "widget": [{"text": " \u0627\u0644\u0633\u0644\u0627\u0645 \u0639\u0644\u064a\u0643\u0645 \u0648\u0631\u062d\u0645\u0629[MASK] \u0648\u0628\u0631\u0643\u0627\u062a\u0629"}, {"text": " \u0627\u0647\u0644\u0627 \u0648\u0633\u0647\u0644\u0627 \u0628\u0643\u0645 \u0641\u064a [MASK] \u0645\u0646 \u0633\u064a\u0631\u0628\u062d \u0627\u0644\u0645\u0644\u064a\u0648\u0646"}, {"text": " \u0645\u0631\u062d\u0628\u0627 \u0628\u0643 \u0639\u0632\u064a\u0632\u064a \u0627\u0644\u0632\u0627\u0626\u0631 [MASK] \u0645\u0648\u0642\u0639\u0646\u0627 "}]} | Ebtihal/AraBertMo_base_V3 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"Fill-Mask",
"ar",
"dataset:OSCAR",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
fill-mask | transformers |
# Arabic BERT Model
**AraBERTMo** is an Arabic pre-trained language model based on [Google's BERT architechture](https://github.com/google-research/bert).
AraBERTMo_base uses the same BERT-Base config.
AraBERTMo_base now comes in 10 new variants
All models are available on the `HuggingFace` model page under the [Ebtihal](https://huggingface.co/Ebtihal/) name.
Checkpoints are available in PyTorch formats.
## Pretraining Corpus
`AraBertMo_base_V4' model was pre-trained on ~3 million words:
- [OSCAR](https://traces1.inria.fr/oscar/) - Arabic version "unshuffled_deduplicated_ar".
## Training results
this model achieves the following results:
| Task | Num examples | Num Epochs | Batch Size | steps | Wall time | training loss|
|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|
| Fill-Mask| 40032| 4 | 64 | 2500 | 5h 10m 20s | 7.6544 |
## Load Pretrained Model
You can use this model by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Ebtihal/AraBertMo_base_V4")
model = AutoModelForMaskedLM.from_pretrained("Ebtihal/AraBertMo_base_V4")
```
## This model was built for master's degree research in an organization:
- [University of kufa](https://uokufa.edu.iq/).
- [Faculty of Computer Science and Mathematics](https://mathcomp.uokufa.edu.iq/).
- **Department of Computer Science**
| {"language": "ar", "tags": "Fill-Mask", "datasets": "OSCAR", "widget": [{"text": " \u0627\u0644\u0633\u0644\u0627\u0645 \u0639\u0644\u064a\u0643\u0645 \u0648\u0631\u062d\u0645\u0629[MASK] \u0648\u0628\u0631\u0643\u0627\u062a\u0629"}, {"text": " \u0627\u0647\u0644\u0627 \u0648\u0633\u0647\u0644\u0627 \u0628\u0643\u0645 \u0641\u064a [MASK] \u0645\u0646 \u0633\u064a\u0631\u0628\u062d \u0627\u0644\u0645\u0644\u064a\u0648\u0646"}, {"text": " \u0645\u0631\u062d\u0628\u0627 \u0628\u0643 \u0639\u0632\u064a\u0632\u064a \u0627\u0644\u0632\u0627\u0626\u0631 [MASK] \u0645\u0648\u0642\u0639\u0646\u0627 "}]} | Ebtihal/AraBertMo_base_V4 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"Fill-Mask",
"ar",
"dataset:OSCAR",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
fill-mask | transformers |
# Arabic BERT Model
**AraBERTMo** is an Arabic pre-trained language model based on [Google's BERT architechture](https://github.com/google-research/bert).
AraBERTMo_base uses the same BERT-Base config.
AraBERTMo_base now comes in 10 new variants
All models are available on the `HuggingFace` model page under the [Ebtihal](https://huggingface.co/Ebtihal/) name.
Checkpoints are available in PyTorch formats.
## Pretraining Corpus
`AraBertMo_base_V5' model was pre-trained on ~3 million words:
- [OSCAR](https://traces1.inria.fr/oscar/) - Arabic version "unshuffled_deduplicated_ar".
## Training results
this model achieves the following results:
| Task | Num examples | Num Epochs | Batch Size | steps | Wall time | training loss|
|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|
| Fill-Mask| 50046| 5 | 64 | 3910 | 6h 49m 59s | 7.4599 |
## Load Pretrained Model
You can use this model by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Ebtihal/AraBertMo_base_V5")
model = AutoModelForMaskedLM.from_pretrained("Ebtihal/AraBertMo_base_V5")
```
## This model was built for master's degree research in an organization:
- [University of kufa](https://uokufa.edu.iq/).
- [Faculty of Computer Science and Mathematics](https://mathcomp.uokufa.edu.iq/).
- **Department of Computer Science**
| {"language": "ar", "tags": "Fill-Mask", "datasets": "OSCAR", "widget": [{"text": " \u0627\u0644\u0633\u0644\u0627\u0645 \u0639\u0644\u064a\u0643\u0645 \u0648\u0631\u062d\u0645\u0629[MASK] \u0648\u0628\u0631\u0643\u0627\u062a\u0629"}, {"text": " \u0627\u0647\u0644\u0627 \u0648\u0633\u0647\u0644\u0627 \u0628\u0643\u0645 \u0641\u064a [MASK] \u0645\u0646 \u0633\u064a\u0631\u0628\u062d \u0627\u0644\u0645\u0644\u064a\u0648\u0646"}, {"text": " \u0645\u0631\u062d\u0628\u0627 \u0628\u0643 \u0639\u0632\u064a\u0632\u064a \u0627\u0644\u0632\u0627\u0626\u0631 [MASK] \u0645\u0648\u0642\u0639\u0646\u0627 "}]} | Ebtihal/AraBertMo_base_V5 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"Fill-Mask",
"ar",
"dataset:OSCAR",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
fill-mask | transformers | # Arabic BERT Model
**AraBERTMo** is an Arabic pre-trained language model based on [Google's BERT architechture](https://github.com/google-research/bert).
AraBERTMo_base uses the same BERT-Base config.
AraBERTMo_base now comes in 10 new variants
All models are available on the `HuggingFace` model page under the [Ebtihal](https://huggingface.co/Ebtihal/) name.
Checkpoints are available in PyTorch formats.
## Pretraining Corpus
`AraBertMo_base_V6' model was pre-trained on ~3 million words:
- [OSCAR](https://traces1.inria.fr/oscar/) - Arabic version "unshuffled_deduplicated_ar".
## Training results
this model achieves the following results:
| Task | Num examples | Num Epochs | Batch Size | steps | Wall time | training loss|
|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|
| Fill-Mask| 50046| 6 | 64 | 4692 | 5h 41m 9s | 7.3099 |
## Load Pretrained Model
You can use this model by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Ebtihal/AraBertMo_base_V6")
model = AutoModelForMaskedLM.from_pretrained("Ebtihal/AraBertMo_base_V6")
```
## This model was built for master's degree research in an organization:
- [University of kufa](https://uokufa.edu.iq/).
- [Faculty of Computer Science and Mathematics](https://mathcomp.uokufa.edu.iq/).
- **Department of Computer Science**
| {"language": "ar", "tags": "Fill-Mask", "datasets": "OSCAR", "widget": [{"text": " \u0627\u0644\u0633\u0644\u0627\u0645 \u0639\u0644\u064a\u0643\u0645 \u0648\u0631\u062d\u0645\u0629[MASK] \u0648\u0628\u0631\u0643\u0627\u062a\u0629"}, {"text": " \u0627\u0647\u0644\u0627 \u0648\u0633\u0647\u0644\u0627 \u0628\u0643\u0645 \u0641\u064a [MASK] \u0645\u0646 \u0633\u064a\u0631\u0628\u062d \u0627\u0644\u0645\u0644\u064a\u0648\u0646"}, {"text": " \u0645\u0631\u062d\u0628\u0627 \u0628\u0643 \u0639\u0632\u064a\u0632\u064a \u0627\u0644\u0632\u0627\u0626\u0631 [MASK] \u0645\u0648\u0642\u0639\u0646\u0627 "}]} | Ebtihal/AraBertMo_base_V6 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"Fill-Mask",
"ar",
"dataset:OSCAR",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
fill-mask | transformers | Arabic Model AraBertMo_base_V7
---
language: ar
tags: Fill-Mask
datasets: OSCAR
widget:
- text: " السلام عليكم ورحمة[MASK] وبركاتة"
- text: " اهلا وسهلا بكم في [MASK] من سيربح المليون"
- text: " مرحبا بك عزيزي الزائر [MASK] موقعنا "
---
# Arabic BERT Model
**AraBERTMo** is an Arabic pre-trained language model based on [Google's BERT architechture](https://github.com/google-research/bert).
AraBERTMo_base uses the same BERT-Base config.
AraBERTMo_base now comes in 10 new variants
All models are available on the `HuggingFace` model page under the [Ebtihal](https://huggingface.co/Ebtihal/) name.
Checkpoints are available in PyTorch formats.
## Pretraining Corpus
`AraBertMo_base_V7' model was pre-trained on ~3 million words:
- [OSCAR](https://traces1.inria.fr/oscar/) - Arabic version "unshuffled_deduplicated_ar".
## Training results
this model achieves the following results:
| Task | Num examples | Num Epochs | Batch Size | steps | Wall time | training loss|
|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|
| Fill-Mask| 50046| 7 | 64 | 5915 | 5h 23m 5s | 7.1381 |
## Load Pretrained Model
You can use this model by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Ebtihal/AraBertMo_base_V7")
model = AutoModelForMaskedLM.from_pretrained("Ebtihal/AraBertMo_base_V7")
```
## This model was built for master's degree research in an organization:
- [University of kufa](https://uokufa.edu.iq/).
- [Faculty of Computer Science and Mathematics](https://mathcomp.uokufa.edu.iq/).
- **Department of Computer Science**
| {} | Ebtihal/AraBertMo_base_V7 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
fill-mask | transformers | Arabic Model AraBertMo_base_V8
---
language: ar
tags: Fill-Mask
datasets: OSCAR
widget:
- text: " السلام عليكم ورحمة[MASK] وبركاتة"
- text: " اهلا وسهلا بكم في [MASK] من سيربح المليون"
- text: " مرحبا بك عزيزي الزائر [MASK] موقعنا "
---
# Arabic BERT Model
**AraBERTMo** is an Arabic pre-trained language model based on [Google's BERT architechture](https://github.com/google-research/bert). AraBERTMo_base uses the same BERT-Base config. AraBERTMo_base now comes in 10 new variants All models are available on the `HuggingFace` model page under the [Ebtihal](https://huggingface.co/Ebtihal/) name. Checkpoints are available in PyTorch formats.
## Pretraining Corpus
`AraBertMo_base_V8' model was pre-trained on ~3 million words: [OSCAR](https://traces1.inria.fr/oscar/) - Arabic version "unshuffled_deduplicated_ar".
## Training results
this model achieves the following results:
| Task | Num examples | Num Epochs | Batch Size | steps | Wall time | training loss|
|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|
| Fill-Mask| 40032| 8 | 64 | 5008 | 10h 5m 57s | 7.2164 |
## Load Pretrained Model
You can use this model by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Ebtihal/AraBertMo_base_V8")
model = AutoModelForMaskedLM.from_pretrained("Ebtihal/AraBertMo_base_V8")
```
## This model was built for master's degree research in an organization:
- [University of kufa](https://uokufa.edu.iq/).
- [Faculty of Computer Science and Mathematics](https://mathcomp.uokufa.edu.iq/).
- **Department of Computer Science**
| {} | Ebtihal/AraBertMo_base_V8 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
fill-mask | transformers | Arabic Model AraBertMo_base_V9
---
language: ar
tags: Fill-Mask
datasets: OSCAR
widget:
- text: " السلام عليكم ورحمة[MASK] وبركاتة"
- text: " اهلا وسهلا بكم في [MASK] من سيربح المليون"
- text: " مرحبا بك عزيزي الزائر [MASK] موقعنا "
---
# Arabic BERT Model
**AraBERTMo** is an Arabic pre-trained language model based on [Google's BERT architechture](https://github.com/google-research/bert).
AraBERTMo_base uses the same BERT-Base config.
AraBERTMo_base now comes in 10 new variants
All models are available on the `HuggingFace` model page under the [Ebtihal](https://huggingface.co/Ebtihal/) name.
Checkpoints are available in PyTorch formats.
## Pretraining Corpus
`AraBertMo_base_V9' model was pre-trained on ~3 million words:
- [OSCAR](https://traces1.inria.fr/oscar/) - Arabic version "unshuffled_deduplicated_ar".
## Training results
this model achieves the following results:
| Task | Num examples | Num Epochs | Batch Size | steps | Wall time | training loss|
|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|
| Fill-Mask| 30024| 9 | 64 | 4230 | 7h 57m 42s | 7.3264 |
## Load Pretrained Model
You can use this model by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Ebtihal/AraBertMo_base_V9")
model = AutoModelForMaskedLM.from_pretrained("Ebtihal/AraBertMo_base_V9")
```
## This model was built for master's degree research in an organization:
- [University of kufa](https://uokufa.edu.iq/).
- [Faculty of Computer Science and Mathematics](https://mathcomp.uokufa.edu.iq/).
- **Department of Computer Science**
| {} | Ebtihal/AraBertMo_base_V9 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers | {} | Ebtihal/AraDiaBERT | null | [
"transformers",
"pytorch",
"bert",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers | {} | Ebtihal/AraDiaBERT_V3 | null | [
"transformers",
"pytorch",
"bert",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
fill-mask | transformers | {} | Ebtihal/AraDiaBERTo | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
fill-mask | transformers | {} | Ebtihal/AraDiaBERTo_V1 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
fill-mask | transformers | {} | Ebtihal/AraDiaBERTo_V2 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
fill-mask | transformers | {} | Ebtihal/Aurora | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
fill-mask | transformers | {} | Ebtihal/EsperBERTo | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
fill-mask | transformers | {} | Ebtihal/bert-ar | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
fill-mask | transformers | {} | Ebtihal/bert-en | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
fill-mask | transformers | {} | Ebtihal/dummy-model | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Ed/Test | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | EdCoin/EdCoin1 | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | transformers | {} | Edaiplay/edaiplay-t5model | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-en-ro-finetuned-en-to-ro
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on the wmt16 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2886
- Bleu: 28.1641
- Gen Len: 34.1071
## 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 | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 0.7436 | 1.0 | 38145 | 1.2886 | 28.1641 | 34.1071 |
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
| {"tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model_index": [{"name": "opus-mt-en-ro-finetuned-en-to-ro", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}, "dataset": {"name": "wmt16", "type": "wmt16", "args": "ro-en"}, "metric": {"name": "Bleu", "type": "bleu", "value": 28.1641}}]}]} | Edomonndo/opus-mt-en-ro-finetuned-en-to-ro | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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
| {"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}}]}]} | Edomonndo/opus-mt-ja-en-finetuned-ja-to-en_test | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-ja-en-finetuned-ja-to-en_xml
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: 0.7520
- Bleu: 73.8646
- Gen Len: 27.0884
## 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: 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 1.0512 | 1.0 | 748 | 0.8333 | 59.8234 | 27.905 |
| 0.6076 | 2.0 | 1496 | 0.7817 | 62.5606 | 26.1834 |
| 0.4174 | 3.0 | 2244 | 0.7817 | 64.8346 | 28.2918 |
| 0.2971 | 4.0 | 2992 | 0.7653 | 67.6013 | 27.2222 |
| 0.2172 | 5.0 | 3740 | 0.7295 | 69.4017 | 27.0174 |
| 0.1447 | 6.0 | 4488 | 0.7522 | 68.8355 | 28.2865 |
| 0.0953 | 7.0 | 5236 | 0.7596 | 71.4743 | 27.1861 |
| 0.0577 | 8.0 | 5984 | 0.7469 | 72.0684 | 26.921 |
| 0.04 | 9.0 | 6732 | 0.7526 | 73.2821 | 27.1365 |
| 0.0213 | 10.0 | 7480 | 0.7520 | 73.8646 | 27.0884 |
### Framework versions
- Transformers 4.9.1
- Pytorch 1.10.0+cu111
- Datasets 1.10.2
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model_index": [{"name": "opus-mt-ja-en-finetuned-ja-to-en_xml", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}, "metric": {"name": "Bleu", "type": "bleu", "value": 73.8646}}]}]} | Edomonndo/opus-mt-ja-en-finetuned-ja-to-en_xml | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
automatic-speech-recognition | transformers |
# Wav2vec2 Large 100k Voxpopuli fine-tuned with Common Voice and TTS-Portuguese Corpus in Portuguese
[Wav2vec2 Large 100k Voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) fine-tuned in Portuguese using the Common Voice 7.0 and TTS-Portuguese Corpus.
# Use this model
```python
from transformers import AutoTokenizer, Wav2Vec2ForCTC
tokenizer = AutoTokenizer.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-Common-Voice_plus_TTS-Dataset-portuguese")
model = Wav2Vec2ForCTC.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-Common-Voice_plus_TTS-Dataset-portuguese")
```
# Results
For the results check the [paper](https://arxiv.org/abs/2204.00618)
# Example test with Common Voice Dataset
```python
dataset = load_dataset("common_voice", "pt", split="test", data_dir="./cv-corpus-6.1-2020-12-11")
resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000)
def map_to_array(batch):
speech, _ = torchaudio.load(batch["path"])
batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
batch["sampling_rate"] = resampler.new_freq
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
return batch
```
```python
ds = dataset.map(map_to_array)
result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys()))
print(wer.compute(predictions=result["predicted"], references=result["target"]))
```
| {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "portuguese-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch"], "datasets": ["Common Voice"], "metrics": ["wer"]} | Edresson/wav2vec2-large-100k-voxpopuli-ft-Common-Voice_plus_TTS-Dataset-portuguese | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"portuguese-speech-corpus",
"PyTorch",
"arxiv:2204.00618",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
automatic-speech-recognition | transformers |
# Wav2vec2 Large 100k Voxpopuli fine-tuned with Common Voice and M-AILABS in Russian
[Wav2vec2 Large 100k Voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) fine-tuned in Russian using the Common Voice 7.0 and M-AILABS.
# Use this model
```python
from transformers import AutoTokenizer, Wav2Vec2ForCTC
tokenizer = AutoTokenizer.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-Common-Voice_plus_TTS-Dataset-russian")
model = Wav2Vec2ForCTC.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-Common-Voice_plus_TTS-Dataset-russian")
```
# Results
For the results check the [paper](https://arxiv.org/abs/2204.00618)
# Example test with Common Voice Dataset
```python
dataset = load_dataset("common_voice", "pt", split="test", data_dir="./cv-corpus-6.1-2020-12-11")
resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000)
def map_to_array(batch):
speech, _ = torchaudio.load(batch["path"])
batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
batch["sampling_rate"] = resampler.new_freq
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
return batch
```
```python
ds = dataset.map(map_to_array)
result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys()))
print(wer.compute(predictions=result["predicted"], references=result["target"]))
```
| {"language": "ru", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "ru", "russian-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch"], "datasets": ["Common Voice"], "metrics": ["wer"]} | Edresson/wav2vec2-large-100k-voxpopuli-ft-Common-Voice_plus_TTS-Dataset-russian | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"ru",
"russian-speech-corpus",
"PyTorch",
"arxiv:2204.00618",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
automatic-speech-recognition | transformers |
# Wav2vec2 Large 100k Voxpopuli fine-tuned in Portuguese using the Common Voice 7.0, TTS-Portuguese Corpus plus data augmentation
[Wav2vec2 Large 100k Voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) Wav2vec2 Large 100k Voxpopuli fine-tuned in Portuguese using the Common Voice 7.0, TTS-Portuguese plus data augmentation method based on TTS and voice conversion.
# Use this model
```python
from transformers import AutoTokenizer, Wav2Vec2ForCTC
tokenizer = AutoTokenizer.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-Common_Voice_plus_TTS-Dataset_plus_Data_Augmentation-portuguese")
model = Wav2Vec2ForCTC.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-Common_Voice_plus_TTS-Dataset_plus_Data_Augmentation-portuguese")
```
# Results
For the results check the [paper](https://arxiv.org/abs/2204.00618)
# Example test with Common Voice Dataset
```python
dataset = load_dataset("common_voice", "ru", split="test", data_dir="./cv-corpus-7.0-2021-07-21")
resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000)
def map_to_array(batch):
speech, _ = torchaudio.load(batch["path"])
batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
batch["sampling_rate"] = resampler.new_freq
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
return batch
```
```python
ds = dataset.map(map_to_array)
result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys()))
print(wer.compute(predictions=result["predicted"], references=result["target"]))
```
| {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "Portuguese-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch"], "datasets": ["Common Voice"], "metrics": ["wer"]} | Edresson/wav2vec2-large-100k-voxpopuli-ft-Common_Voice_plus_TTS-Dataset_plus_Data_Augmentation-portuguese | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"Portuguese-speech-corpus",
"PyTorch",
"arxiv:2204.00618",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
automatic-speech-recognition | transformers |
# Wav2vec2 Large 100k Voxpopuli fine-tuned in Russian using the Common Voice 7.0, MAILABS plus data augmentation
[Wav2vec2 Large 100k Voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) Wav2vec2 Large 100k Voxpopuli fine-tuned in Russian using the Common Voice 7.0, M-AILABS plus data augmentation method based on TTS and voice conversion.
# Use this model
```python
from transformers import AutoTokenizer, Wav2Vec2ForCTC
tokenizer = AutoTokenizer.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-Common_Voice_plus_TTS-Dataset_plus_Data_Augmentation-russian")
model = Wav2Vec2ForCTC.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-Common_Voice_plus_TTS-Dataset_plus_Data_Augmentation-russian")
```
# Results
For the results check the [paper](https://arxiv.org/abs/2204.00618)
# Example test with Common Voice Dataset
```python
dataset = load_dataset("common_voice", "ru", split="test", data_dir="./cv-corpus-7.0-2021-07-21")
resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000)
def map_to_array(batch):
speech, _ = torchaudio.load(batch["path"])
batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
batch["sampling_rate"] = resampler.new_freq
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
return batch
```
```python
ds = dataset.map(map_to_array)
result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys()))
print(wer.compute(predictions=result["predicted"], references=result["target"]))
```
| {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "Russian-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch"], "datasets": ["Common Voice"], "metrics": ["wer"]} | Edresson/wav2vec2-large-100k-voxpopuli-ft-Common_Voice_plus_TTS-Dataset_plus_Data_Augmentation-russian | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"Russian-speech-corpus",
"PyTorch",
"arxiv:2204.00618",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
automatic-speech-recognition | transformers |
# Wav2vec2 Large 100k Voxpopuli fine-tuned with a single-speaker dataset plus Data Augmentation in Portuguese
[Wav2vec2 Large 100k Voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) fine-tuned in Portuguese using a single-speaker dataset plus a data augmentation method based on TTS and voice conversion.
# Use this model
```python
from transformers import AutoTokenizer, Wav2Vec2ForCTC
tokenizer = AutoTokenizer.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-plus-data-augmentation-portuguese")
model = Wav2Vec2ForCTC.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-plus-data-augmentation-portuguese")
```
# Results
For the results check the [paper](https://arxiv.org/abs/2204.00618)
# Example test with Common Voice Dataset
```python
dataset = load_dataset("common_voice", "pt", split="test", data_dir="./cv-corpus-7.0-2021-07-21")
resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000)
def map_to_array(batch):
speech, _ = torchaudio.load(batch["path"])
batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
batch["sampling_rate"] = resampler.new_freq
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
return batch
```
```python
ds = dataset.map(map_to_array)
result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys()))
print(wer.compute(predictions=result["predicted"], references=result["target"]))
```
| {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "portuguese-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch"], "datasets": ["Common Voice"], "metrics": ["wer"]} | Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-plus-data-augmentation-portuguese | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"portuguese-speech-corpus",
"PyTorch",
"arxiv:2204.00618",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
automatic-speech-recognition | transformers |
# Wav2vec2 Large 100k Voxpopuli fine-tuned with a single-speaker dataset plus Data Augmentation in Russian
[Wav2vec2 Large 100k Voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) fine-tuned in Russian using a single-speaker dataset plus a data augmentation method based on TTS and voice conversion.
# Use this model
```python
from transformers import AutoTokenizer, Wav2Vec2ForCTC
tokenizer = AutoTokenizer.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-plus-data-augmentation-russian")
model = Wav2Vec2ForCTC.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-plus-data-augmentation-russian")
```
# Results
For the results check the [paper](https://arxiv.org/abs/2204.00618)
# Example test with Common Voice Dataset
```python
dataset = load_dataset("common_voice", "ru", split="test", data_dir="./cv-corpus-7.0-2021-07-21")
resampler = torchaudio.transforms.Resampl(orig_freq=48_000, new_freq=16_000)
def map_to_array(batch):
speech, _ = torchaudio.load(batch["path"])
batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
batch["sampling_rate"] = resampler.new_freq
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
return batch
```
```python
ds = dataset.map(map_to_array)
result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys()))
print(wer.compute(predictions=result["predicted"], references=result["target"]))
```
| {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "Russian-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch"], "datasets": ["Common Voice"], "metrics": ["wer"]} | Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-plus-data-augmentation-russian | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"Russian-speech-corpus",
"PyTorch",
"arxiv:2204.00618",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
automatic-speech-recognition | transformers |
# Wav2vec 2.0 trained with CORAA Portuguese Dataset
This a the demonstration of a fine-tuned Wav2vec model for Portuguese using the following [CORAA dataset](https://github.com/nilc-nlp/CORAA)
# Use this model
```python
from transformers import AutoTokenizer, Wav2Vec2ForCTC
tokenizer = AutoTokenizer.from_pretrained("Edresson/wav2vec2-large-xlsr-coraa-portuguese")
model = Wav2Vec2ForCTC.from_pretrained("Edresson/wav2vec2-large-xlsr-coraa-portuguese")
```
# Results
For the results check the [CORAA article](https://arxiv.org/abs/2110.15731)
# Example test with Common Voice Dataset
```python
dataset = load_dataset("common_voice", "pt", split="test", data_dir="./cv-corpus-6.1-2020-12-11")
resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000)
def map_to_array(batch):
speech, _ = torchaudio.load(batch["path"])
batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
batch["sampling_rate"] = resampler.new_freq
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
return batch
```
```python
ds = dataset.map(map_to_array)
result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys()))
print(wer.compute(predictions=result["predicted"], references=result["target"]))
```
| {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "portuguese-speech-corpus", "automatic-speech-recognition", "hf-asr-leaderboard", "speech", "PyTorch"], "datasets": ["CORAA"], "metrics": ["wer"], "model-index": [{"name": "Edresson Casanova XLSR Wav2Vec2 Large 53 Portuguese", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "CORAA", "type": "CORAA", "args": "pt"}, "metrics": [{"type": "wer", "value": 25.26, "name": "Test CORAA WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice 7", "type": "mozilla-foundation/common_voice_7_0", "args": "pt"}, "metrics": [{"type": "wer", "value": 20.08, "name": "Test WER on Common Voice 7"}]}]}]} | Edresson/wav2vec2-large-xlsr-coraa-portuguese | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"portuguese-speech-corpus",
"hf-asr-leaderboard",
"PyTorch",
"dataset:CORAA",
"arxiv:2110.15731",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | Eduardsvintusevskij/Anna | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Edwardlzy/albert-base-v2-finetuned-cola | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Edwardlzy/albert-base-v2-finetuned-mnli | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Edwardlzy/albert-base-v2-finetuned-race | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Edwardlzy/albert-base-v2-finetuned-squad | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Edwardlzy/albert-base-v2-finetuned-sst2 | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Edwardlzy/albert-base-v2-finetuned-swag | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Edwardlzy/bart-base-finetuned-samsum | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Edwardlzy/bart-base-finetuned-xsum | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Edwardlzy/blenderbot-90M-finetuned-samsum | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Edwardlzy/blenderbot_small-90M-finetuned-xsum | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Edwardlzy/checkpoint-8144-finetuned-squad | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Edwardlzy/led-base-16384-finetuned-samsum | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Edwardlzy/led-base-16384-finetuned-xsum | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Edwardlzy/pegasus-large-finetuned-xsum | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Edwardlzy/pegasus-xsum-finetuned-xsum | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Edwardlzy/t5-small-finetuned-samsum | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Edwardlzy/t5-small-finetuned-xsum | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Eeeeee/DialoGPT-small-harry | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Eeeeee/DialoGPT-small-harrypotter | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Eeeeee/DialoGPT-small-potter | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Egrt/ArcaneGAN | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Egrt/LicenseGAN | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
feature-extraction | transformers | {} | EhsanAghazadeh/bert-base-uncased-random-weights | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | EhsanAghazadeh/bert-based-uncased-sst2-e1 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | EhsanAghazadeh/bert-based-uncased-sst2-e2 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | EhsanAghazadeh/bert-based-uncased-sst2-e3 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | EhsanAghazadeh/bert-based-uncased-sst2-e4 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | EhsanAghazadeh/bert-based-uncased-sst2-e5 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | EhsanAghazadeh/bert-based-uncased-sst2-e6 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | EhsanAghazadeh/bert-large-uncased-CoLA_A | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | EhsanAghazadeh/bert-large-uncased-CoLA_B | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | EhsanAghazadeh/electra-base-avg-2e-5-lcc | null | [
"transformers",
"pytorch",
"electra",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
feature-extraction | transformers | {} | EhsanAghazadeh/electra-base-random-weights | null | [
"transformers",
"pytorch",
"electra",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | EhsanAghazadeh/electra-large-lcc-2e-5-42 | null | [
"transformers",
"pytorch",
"electra",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
fill-mask | transformers | {} | EhsanAghazadeh/melbert-roberta | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
feature-extraction | transformers | {} | EhsanAghazadeh/roberta-base-random-weights | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | EhsanAghazadeh/xlm-roberta-base-lcc-en-2e-5-42 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | EhsanAghazadeh/xlm-roberta-base-lcc-en-fa-2e-5-42 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | EhsanAghazadeh/xlm-roberta-base-lcc-fa-2e-5-42 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
feature-extraction | transformers | {} | EhsanAghazadeh/xlm-roberta-base-random-weights | null | [
"transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | EhsanAghazadeh/xlnet-large-cased-CoLA_A | null | [
"transformers",
"pytorch",
"xlnet",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | EhsanAghazadeh/xlnet-large-cased-CoLA_B | null | [
"transformers",
"pytorch",
"xlnet",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | EhsanAghazadeh/xlnet-large-cased-CoLA_C | null | [
"transformers",
"pytorch",
"xlnet",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
token-classification | transformers | {} | EhsanYB/bert-ehsan-ner-accelerate | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
token-classification | transformers | {} | EhsanYB/distilbert-finetuned-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# PegasusXSUM_GNAD
This model is a fine-tuned version of [Einmalumdiewelt/PegasusXSUM_GNAD](https://huggingface.co/Einmalumdiewelt/PegasusXSUM_GNAD) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4386
- Rouge1: 26.7818
- Rouge2: 7.6864
- Rougel: 18.6264
- Rougelsum: 22.822
- Gen Len: 67.076
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
| {"language": ["de"], "tags": ["generated_from_trainer", "summarization"], "metrics": ["rouge"], "model-index": [{"name": "PegasusXSUM_GNAD", "results": []}]} | Einmalumdiewelt/PegasusXSUM_GNAD | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"summarization",
"de",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
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