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AndrewMcDowell/wav2vec2-xls-r-300m-japanese
|
AndrewMcDowell
|
wav2vec2
| 36 | 5 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
|
['ja']
|
['mozilla-foundation/common_voice_8_0']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'ja', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event']
| true | true | true | 2,877 |
<!-- 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. -->
#
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_8_0 - JA dataset.
Kanji are converted into Hiragana using the [pykakasi](https://pykakasi.readthedocs.io/en/latest/index.html) library during training and evaluation. The model can output both Hiragana and Katakana characters. Since there is no spacing, WER is not a suitable metric for evaluating performance and CER is more suitable.
On mozilla-foundation/common_voice_8_0 it achieved:
- cer: 23.64%
On speech-recognition-community-v2/dev_data it achieved:
- cer: 30.99%
It achieves the following results on the evaluation set:
- Loss: 0.5212
- Wer: 1.3068
## 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: 48
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.0974 | 4.72 | 1000 | 4.0178 | 1.9535 |
| 2.1276 | 9.43 | 2000 | 0.9301 | 1.2128 |
| 1.7622 | 14.15 | 3000 | 0.7103 | 1.5527 |
| 1.6397 | 18.87 | 4000 | 0.6729 | 1.4269 |
| 1.5468 | 23.58 | 5000 | 0.6087 | 1.2497 |
| 1.4885 | 28.3 | 6000 | 0.5786 | 1.3222 |
| 1.451 | 33.02 | 7000 | 0.5726 | 1.3768 |
| 1.3912 | 37.74 | 8000 | 0.5518 | 1.2497 |
| 1.3617 | 42.45 | 9000 | 0.5352 | 1.2694 |
| 1.3113 | 47.17 | 10000 | 0.5228 | 1.2781 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-300m-japanese --dataset mozilla-foundation/common_voice_8_0 --config ja --split test --log_outputs
```
2. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-300m-japanese --dataset speech-recognition-community-v2/dev_data --config de --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
|
Andrey1989/mbert-finetuned-ner
|
Andrey1989
|
bert
| 13 | 27 |
transformers
| 0 |
token-classification
| true | false | false |
apache-2.0
| null |
['wikiann']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,545 |
<!-- 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. -->
# mbert-finetuned-ner
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1264
- Precision: 0.9305
- Recall: 0.9375
- F1: 0.9340
- Accuracy: 0.9700
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.301 | 1.0 | 625 | 0.1756 | 0.8843 | 0.9067 | 0.8953 | 0.9500 |
| 0.1259 | 2.0 | 1250 | 0.1248 | 0.9285 | 0.9335 | 0.9310 | 0.9688 |
| 0.0895 | 3.0 | 1875 | 0.1264 | 0.9305 | 0.9375 | 0.9340 | 0.9700 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Andrija/M-bert-NER
|
Andrija
|
bert
| 9 | 11 |
transformers
| 0 |
token-classification
| true | false | false |
apache-2.0
|
['hr', 'sr', 'multilingual']
|
['hr500k']
| null | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 583 |
Named Entity Recognition (Token Classification Head) for Serbian / Croatian languges.
Abbreviation|Description
-|-
O|Outside of a named entity
B-MIS |Beginning of a miscellaneous entity right after another miscellaneous entity
I-MIS | Miscellaneous entity
B-PER |Beginning of a person's name right after another person's name
B-DERIV-PER| Begginning derivative that describes relation to a person
I-PER |Person's name
B-ORG |Beginning of an organization right after another organization
I-ORG |organization
B-LOC |Beginning of a location right after another location
I-LOC |Location
|
Andrija/RobertaFastBPE
|
Andrija
| null | 8 | 0 | null | 0 | null | false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | false | true | 504 |
from transformers import RobertaTokenizerFast
tokenizer = RobertaTokenizerFast.from_pretrained('Andrija/RobertaFastBPE', bos_token="<s>", eos_token="</s>")
encoded = tokenizer('Stručnjaci te bolnice, predvođeni dr Alisom Lim')
# {'input_ids': [0, 47541, 34632, 603, 24817, 16, 27540, 6768, 2350, 2803, 3991, 2733, 81, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
tokenizer.decode(encoded['input_ids'])
# <s>Stručnjaci te bolnice, predvođeni dr Alisom Lim</s>
|
Andrija/SRoBERTa-F
|
Andrija
|
roberta
| 13 | 6 |
transformers
| 0 |
fill-mask
| true | false | false |
apache-2.0
|
['hr', 'sr', 'multilingual']
|
['oscar', 'srwac', 'leipzig', 'cc100', 'hrwac']
| null | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
['masked-lm']
| false | true | true | 775 |
# Transformer language model for Croatian and Serbian
Trained on 43GB datasets that contain Croatian and Serbian language for one epochs (9.6 mil. steps, 3 epochs).
Leipzig Corpus, OSCAR, srWac, hrWac, cc100-hr and cc100-sr datasets
Validation number of exampels run for perplexity:1620487 sentences
Perplexity:6.02
Start loss: 8.6
Final loss: 2.0
Thoughts: Model could be trained more, the training did not stagnate.
| Model | #params | Arch. | Training data |
|--------------------------------|--------------------------------|-------|-----------------------------------|
| `Andrija/SRoBERTa-F` | 80M | Fifth | Leipzig Corpus, OSCAR, srWac, hrWac, cc100-hr and cc100-sr (43 GB of text) |
|
Andrija/SRoBERTa-L-NER
|
Andrija
|
roberta
| 10 | 8 |
transformers
| 0 |
token-classification
| true | false | false |
apache-2.0
|
['hr', 'sr', 'multilingual']
|
['hr500k']
| null | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 583 |
Named Entity Recognition (Token Classification Head) for Serbian / Croatian languges.
Abbreviation|Description
-|-
O|Outside of a named entity
B-MIS |Beginning of a miscellaneous entity right after another miscellaneous entity
I-MIS | Miscellaneous entity
B-PER |Beginning of a person’s name right after another person’s name
B-DERIV-PER| Begginning derivative that describes relation to a person
I-PER |Person’s name
B-ORG |Beginning of an organization right after another organization
I-ORG |organization
B-LOC |Beginning of a location right after another location
I-LOC |Location
|
Andrija/SRoBERTa-L
|
Andrija
|
roberta
| 10 | 7 |
transformers
| 0 |
fill-mask
| true | false | false |
apache-2.0
|
['hr', 'sr', 'multilingual']
|
['oscar', 'srwac', 'leipzig']
| null | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
['masked-lm']
| false | true | true | 509 |
# Transformer language model for Croatian and Serbian
Trained on 6GB datasets that contain Croatian and Serbian language for two epochs (500k steps).
Leipzig, OSCAR and srWac datasets
| Model | #params | Arch. | Training data |
|--------------------------------|--------------------------------|-------|-----------------------------------|
| `Andrija/SRoBERTa-L` | 80M | Third | Leipzig Corpus, OSCAR and srWac (6 GB of text) |
|
Andrija/SRoBERTa-NER
|
Andrija
|
roberta
| 10 | 13 |
transformers
| 0 |
token-classification
| true | false | false |
apache-2.0
|
['hr', 'sr', 'multilingual']
|
['hr500k']
| null | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 583 |
Named Entity Recognition (Token Classification Head) for Serbian / Croatian languges.
Abbreviation|Description
-|-
O|Outside of a named entity
B-MIS |Beginning of a miscellaneous entity right after another miscellaneous entity
I-MIS | Miscellaneous entity
B-PER |Beginning of a person’s name right after another person’s name
B-DERIV-PER| Begginning derivative that describes relation to a person
I-PER |Person’s name
B-ORG |Beginning of an organization right after another organization
I-ORG |organization
B-LOC |Beginning of a location right after another location
I-LOC |Location
|
Andrija/SRoBERTa-XL-NER
|
Andrija
|
roberta
| 10 | 19 |
transformers
| 0 |
token-classification
| true | false | false |
apache-2.0
|
['hr', 'sr', 'multilingual']
|
['hr500k']
| null | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 584 |
Named Entity Recognition (Token Classification Head) for Serbian / Croatian languges.
Abbreviation|Description
-|-
O|Outside of a named entity
B-MIS |Beginning of a miscellaneous entity right after another miscellaneous entity
I-MIS | Miscellaneous entity
B-PER |Beginning of a person's name right after another person's name
B-DERIV-PER| Begginning derivative that describes relation to a person
I-PER |Person's name
B-ORG |Beginning of an organization right after another organization
I-ORG |organization
B-LOC |Beginning of a location right after another location
I-LOC |Location
|
Andrija/SRoBERTa-XL
|
Andrija
|
roberta
| 10 | 14 |
transformers
| 0 |
fill-mask
| true | false | false |
apache-2.0
|
['hr', 'sr', 'multilingual']
|
['oscar', 'srwac', 'leipzig', 'cc100', 'hrwac']
| null | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
['masked-lm']
| false | true | true | 579 |
# Transformer language model for Croatian and Serbian
Trained on 28GB datasets that contain Croatian and Serbian language for one epochs (3 mil. steps).
Leipzig Corpus, OSCAR, srWac, hrWac, cc100-hr and cc100-sr datasets
| Model | #params | Arch. | Training data |
|--------------------------------|--------------------------------|-------|-----------------------------------|
| `Andrija/SRoBERTa-XL` | 80M | Forth | Leipzig Corpus, OSCAR, srWac, hrWac, cc100-hr and cc100-sr (28 GB of text) |
|
Andrija/SRoBERTa-base-NER
|
Andrija
|
roberta
| 10 | 13 |
transformers
| 0 |
token-classification
| true | false | false |
apache-2.0
|
['hr', 'sr', 'multilingual']
|
['hr500k']
| null | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 583 |
Named Entity Recognition (Token Classification Head) for Serbian / Croatian languges.
Abbreviation|Description
-|-
O|Outside of a named entity
B-MIS |Beginning of a miscellaneous entity right after another miscellaneous entity
I-MIS | Miscellaneous entity
B-PER |Beginning of a person’s name right after another person’s name
B-DERIV-PER| Begginning derivative that describes relation to a person
I-PER |Person’s name
B-ORG |Beginning of an organization right after another organization
I-ORG |organization
B-LOC |Beginning of a location right after another location
I-LOC |Location
|
Andrija/SRoBERTa-base
|
Andrija
|
roberta
| 7 | 7 |
transformers
| 0 |
fill-mask
| true | false | false |
apache-2.0
|
['hr', 'sr', 'multilingual']
|
['oscar', 'leipzig']
| null | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
['masked-lm']
| false | true | true | 512 |
# Transformer language model for Croatian and Serbian
Trained on 3GB datasets that contain Croatian and Serbian language for two epochs.
Leipzig and OSCAR datasets
# Information of dataset
| Model | #params | Arch. | Training data |
|--------------------------------|--------------------------------|-------|-----------------------------------|
| `Andrija/SRoBERTa-base` | 80M | Second | Leipzig Corpus and OSCAR (3 GB of text) |
|
Andrija/SRoBERTa
|
Andrija
|
roberta
| 8 | 4 |
transformers
| 1 |
fill-mask
| true | false | false |
apache-2.0
|
['hr', 'sr', 'multilingual']
|
['leipzig']
| null | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
['masked-lm']
| false | true | true | 718 |
# Transformer language model for Croatian and Serbian
Trained on 0.7GB dataset Croatian and Serbian language for one epoch.
Dataset from Leipzig Corpora.
# Information of dataset
| Model | #params | Arch. | Training data |
|--------------------------------|--------------------------------|-------|-----------------------------------|
| `Andrija/SRoBERTa` | 120M | First | Leipzig Corpus (0.7 GB of text) |
# How to use in code
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Andrija/SRoBERTa")
model = AutoModelForMaskedLM.from_pretrained("Andrija/SRoBERTa")
```
|
Ann2020/distilbert-base-uncased-finetuned-ner
|
Ann2020
|
distilbert
| 13 | 11 |
transformers
| 0 |
token-classification
| true | false | false |
apache-2.0
| null |
['conll2003']
| null | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| false | true | true | 1,554 |
<!-- 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: 0.0609
- Precision: 0.9275
- Recall: 0.9365
- F1: 0.9320
- Accuracy: 0.9840
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2527 | 1.0 | 878 | 0.0706 | 0.9120 | 0.9181 | 0.9150 | 0.9803 |
| 0.0517 | 2.0 | 1756 | 0.0603 | 0.9174 | 0.9349 | 0.9261 | 0.9830 |
| 0.031 | 3.0 | 2634 | 0.0609 | 0.9275 | 0.9365 | 0.9320 | 0.9840 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
Anonymous/ReasonBERT-BERT
|
Anonymous
|
bert
| 4 | 0 |
transformers
| 0 |
feature-extraction
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | false | true | 237 |
Pre-trained to have better reasoning ability, try this if you are working with task like QA. For more details please see https://openreview.net/forum?id=cGB7CMFtrSx
This is based on bert-base-uncased model and pre-trained for text input
|
Anonymous/ReasonBERT-RoBERTa
|
Anonymous
|
roberta
| 4 | 0 |
transformers
| 0 |
feature-extraction
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | false | true | 232 |
Pre-trained to have better reasoning ability, try this if you are working with task like QA. For more details please see https://openreview.net/forum?id=cGB7CMFtrSx
This is based on roberta-base model and pre-trained for text input
|
Anonymous/ReasonBERT-TAPAS
|
Anonymous
|
tapas
| 4 | 0 |
transformers
| 0 |
feature-extraction
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | false | true | 241 |
Pre-trained to have better reasoning ability, try this if you are working with task like QA. For more details please see https://openreview.net/forum?id=cGB7CMFtrSx
This is based on tapas-base(no_reset) model and pre-trained for table input
|
Anthos23/distilbert-base-uncased-finetuned-sst2
|
Anthos23
|
distilbert
| 18 | 1 |
transformers
| 0 |
text-classification
| false | true | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_keras_callback']
| true | true | true | 1,535 |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Anthos23/distilbert-base-uncased-finetuned-sst2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0662
- Validation Loss: 0.2623
- Train Accuracy: 0.9083
- Epoch: 2
## 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:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 21045, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.2101 | 0.2373 | 0.9083 | 0 |
| 0.1065 | 0.2645 | 0.9060 | 1 |
| 0.0662 | 0.2623 | 0.9083 | 2 |
### Framework versions
- Transformers 4.17.0.dev0
- TensorFlow 2.5.0
- Datasets 1.18.3
- Tokenizers 0.11.0
|
Apoorva/k2t-test
|
Apoorva
|
t5
| 8 | 2 |
transformers
| 0 |
text2text-generation
| true | false | false | null |
['en']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['keytotext', 'k2t', 'Keywords to Sentences']
| false | true | true | 234 |
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
Potential use case can include:
- Marketing
- Search Engine Optimization
- Topic generation etc.
- Fine tuning of topic modeling models
|
ArBert/albert-base-v2-finetuned-ner
|
ArBert
|
albert
| 17 | 11 |
transformers
| 2 |
token-classification
| true | false | false |
apache-2.0
| null |
['conll2003']
| null | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,521 |
<!-- 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. -->
# albert-base-v2-finetuned-ner
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0700
- Precision: 0.9301
- Recall: 0.9376
- F1: 0.9338
- Accuracy: 0.9852
## 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: 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.096 | 1.0 | 1756 | 0.0752 | 0.9163 | 0.9201 | 0.9182 | 0.9811 |
| 0.0481 | 2.0 | 3512 | 0.0761 | 0.9169 | 0.9293 | 0.9231 | 0.9830 |
| 0.0251 | 3.0 | 5268 | 0.0700 | 0.9301 | 0.9376 | 0.9338 | 0.9852 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
|
ArBert/bert-base-uncased-finetuned-ner-kmeans
|
ArBert
|
bert
| 12 | 19 |
transformers
| 0 |
token-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,582 |
<!-- 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. -->
# bert-base-uncased-finetuned-ner-kmeans
This model is a fine-tuned version of [ArBert/bert-base-uncased-finetuned-ner](https://huggingface.co/ArBert/bert-base-uncased-finetuned-ner) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1169
- Precision: 0.9084
- Recall: 0.9245
- F1: 0.9164
- Accuracy: 0.9792
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.036 | 1.0 | 1123 | 0.1010 | 0.9086 | 0.9117 | 0.9101 | 0.9779 |
| 0.0214 | 2.0 | 2246 | 0.1094 | 0.9033 | 0.9199 | 0.9115 | 0.9784 |
| 0.014 | 3.0 | 3369 | 0.1169 | 0.9084 | 0.9245 | 0.9164 | 0.9792 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
ArBert/bert-base-uncased-finetuned-ner
|
ArBert
|
bert
| 12 | 4 |
transformers
| 0 |
token-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,533 |
<!-- 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. -->
# bert-base-uncased-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0905
- Precision: 0.9068
- Recall: 0.9200
- F1: 0.9133
- Accuracy: 0.9787
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1266 | 1.0 | 1123 | 0.0952 | 0.8939 | 0.8869 | 0.8904 | 0.9742 |
| 0.0741 | 2.0 | 2246 | 0.0866 | 0.8936 | 0.9247 | 0.9089 | 0.9774 |
| 0.0496 | 3.0 | 3369 | 0.0905 | 0.9068 | 0.9200 | 0.9133 | 0.9787 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
ArBert/roberta-base-finetuned-ner-agglo-twitter
|
ArBert
|
roberta
| 13 | 5 |
transformers
| 0 |
token-classification
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,878 |
<!-- 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. -->
# roberta-base-finetuned-ner-agglo-twitter
This model is a fine-tuned version of [ArBert/roberta-base-finetuned-ner](https://huggingface.co/ArBert/roberta-base-finetuned-ner) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6645
- Precision: 0.6885
- Recall: 0.7665
- F1: 0.7254
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| No log | 1.0 | 245 | 0.2820 | 0.6027 | 0.7543 | 0.6700 |
| No log | 2.0 | 490 | 0.2744 | 0.6308 | 0.7864 | 0.7000 |
| 0.2301 | 3.0 | 735 | 0.2788 | 0.6433 | 0.7637 | 0.6984 |
| 0.2301 | 4.0 | 980 | 0.3255 | 0.6834 | 0.7221 | 0.7022 |
| 0.1153 | 5.0 | 1225 | 0.3453 | 0.6686 | 0.7439 | 0.7043 |
| 0.1153 | 6.0 | 1470 | 0.3988 | 0.6797 | 0.7420 | 0.7094 |
| 0.0617 | 7.0 | 1715 | 0.4711 | 0.6702 | 0.7259 | 0.6969 |
| 0.0617 | 8.0 | 1960 | 0.4904 | 0.6904 | 0.7505 | 0.7192 |
| 0.0328 | 9.0 | 2205 | 0.5088 | 0.6591 | 0.7713 | 0.7108 |
| 0.0328 | 10.0 | 2450 | 0.5709 | 0.6468 | 0.7788 | 0.7067 |
| 0.019 | 11.0 | 2695 | 0.5570 | 0.6642 | 0.7533 | 0.7059 |
| 0.019 | 12.0 | 2940 | 0.5574 | 0.6899 | 0.7656 | 0.7258 |
| 0.0131 | 13.0 | 3185 | 0.5858 | 0.6952 | 0.7609 | 0.7265 |
| 0.0131 | 14.0 | 3430 | 0.6239 | 0.6556 | 0.7826 | 0.7135 |
| 0.0074 | 15.0 | 3675 | 0.5931 | 0.6825 | 0.7599 | 0.7191 |
| 0.0074 | 16.0 | 3920 | 0.6364 | 0.6785 | 0.7580 | 0.7161 |
| 0.005 | 17.0 | 4165 | 0.6437 | 0.6855 | 0.7580 | 0.7199 |
| 0.005 | 18.0 | 4410 | 0.6610 | 0.6779 | 0.7599 | 0.7166 |
| 0.0029 | 19.0 | 4655 | 0.6625 | 0.6853 | 0.7656 | 0.7232 |
| 0.0029 | 20.0 | 4900 | 0.6645 | 0.6885 | 0.7665 | 0.7254 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
ArBert/roberta-base-finetuned-ner-kmeans-twitter
|
ArBert
|
roberta
| 15 | 8 |
transformers
| 0 |
token-classification
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,879 |
<!-- 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. -->
# roberta-base-finetuned-ner-kmeans-twitter
This model is a fine-tuned version of [ArBert/roberta-base-finetuned-ner](https://huggingface.co/ArBert/roberta-base-finetuned-ner) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6645
- Precision: 0.6885
- Recall: 0.7665
- F1: 0.7254
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| No log | 1.0 | 245 | 0.2820 | 0.6027 | 0.7543 | 0.6700 |
| No log | 2.0 | 490 | 0.2744 | 0.6308 | 0.7864 | 0.7000 |
| 0.2301 | 3.0 | 735 | 0.2788 | 0.6433 | 0.7637 | 0.6984 |
| 0.2301 | 4.0 | 980 | 0.3255 | 0.6834 | 0.7221 | 0.7022 |
| 0.1153 | 5.0 | 1225 | 0.3453 | 0.6686 | 0.7439 | 0.7043 |
| 0.1153 | 6.0 | 1470 | 0.3988 | 0.6797 | 0.7420 | 0.7094 |
| 0.0617 | 7.0 | 1715 | 0.4711 | 0.6702 | 0.7259 | 0.6969 |
| 0.0617 | 8.0 | 1960 | 0.4904 | 0.6904 | 0.7505 | 0.7192 |
| 0.0328 | 9.0 | 2205 | 0.5088 | 0.6591 | 0.7713 | 0.7108 |
| 0.0328 | 10.0 | 2450 | 0.5709 | 0.6468 | 0.7788 | 0.7067 |
| 0.019 | 11.0 | 2695 | 0.5570 | 0.6642 | 0.7533 | 0.7059 |
| 0.019 | 12.0 | 2940 | 0.5574 | 0.6899 | 0.7656 | 0.7258 |
| 0.0131 | 13.0 | 3185 | 0.5858 | 0.6952 | 0.7609 | 0.7265 |
| 0.0131 | 14.0 | 3430 | 0.6239 | 0.6556 | 0.7826 | 0.7135 |
| 0.0074 | 15.0 | 3675 | 0.5931 | 0.6825 | 0.7599 | 0.7191 |
| 0.0074 | 16.0 | 3920 | 0.6364 | 0.6785 | 0.7580 | 0.7161 |
| 0.005 | 17.0 | 4165 | 0.6437 | 0.6855 | 0.7580 | 0.7199 |
| 0.005 | 18.0 | 4410 | 0.6610 | 0.6779 | 0.7599 | 0.7166 |
| 0.0029 | 19.0 | 4655 | 0.6625 | 0.6853 | 0.7656 | 0.7232 |
| 0.0029 | 20.0 | 4900 | 0.6645 | 0.6885 | 0.7665 | 0.7254 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
ArBert/roberta-base-finetuned-ner-kmeans
|
ArBert
|
roberta
| 13 | 6 |
transformers
| 0 |
token-classification
| true | false | false |
mit
| null |
['conll2003']
| null | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,498 |
<!-- 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. -->
# roberta-base-finetuned-ner-kmeans
This model is a fine-tuned version of [ArBert/roberta-base-finetuned-ner](https://huggingface.co/ArBert/roberta-base-finetuned-ner) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0592
- Precision: 0.9559
- Recall: 0.9615
- F1: 0.9587
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.0248 | 1.0 | 878 | 0.0609 | 0.9507 | 0.9561 | 0.9534 |
| 0.0163 | 2.0 | 1756 | 0.0640 | 0.9515 | 0.9578 | 0.9546 |
| 0.0089 | 3.0 | 2634 | 0.0592 | 0.9559 | 0.9615 | 0.9587 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
ArBert/roberta-base-finetuned-ner
|
ArBert
|
roberta
| 19 | 4 |
transformers
| 0 |
token-classification
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,518 |
<!-- 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. -->
# roberta-base-finetuned-ner
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0738
- Precision: 0.9232
- Recall: 0.9437
- F1: 0.9333
- Accuracy: 0.9825
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1397 | 1.0 | 1368 | 0.0957 | 0.9141 | 0.9048 | 0.9094 | 0.9753 |
| 0.0793 | 2.0 | 2736 | 0.0728 | 0.9274 | 0.9324 | 0.9299 | 0.9811 |
| 0.0499 | 3.0 | 4104 | 0.0738 | 0.9232 | 0.9437 | 0.9333 | 0.9825 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
Arnold/wav2vec2-hausa2-demo-colab
|
Arnold
|
wav2vec2
| 12 | 9 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
| null |
['common_voice']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,424 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-hausa2-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2032
- Wer: 0.7237
## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1683 | 12.49 | 400 | 1.0279 | 0.7211 |
| 0.0995 | 24.98 | 800 | 1.2032 | 0.7237 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
Arnold/wav2vec2-large-xlsr-hausa2-demo-colab
|
Arnold
|
wav2vec2
| 18 | 11 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
| null |
['common_voice']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,556 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-hausa2-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2993
- Wer: 0.4826
## 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: 9.6e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 13
- gradient_accumulation_steps: 3
- total_train_batch_size: 36
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.1549 | 12.5 | 400 | 2.7289 | 1.0 |
| 2.0566 | 25.0 | 800 | 0.4582 | 0.6768 |
| 0.4423 | 37.5 | 1200 | 0.3037 | 0.5138 |
| 0.2991 | 50.0 | 1600 | 0.2993 | 0.4826 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
Aron/distilbert-base-uncased-finetuned-emotion
|
Aron
|
distilbert
| 12 | 41 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null |
['emotion']
| null | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,343 |
<!-- 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
|
ArvinZhuang/BiTAG-t5-large
|
ArvinZhuang
|
t5
| 8 | 1 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 724 |
```
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model = AutoModelForSeq2SeqLM.from_pretrained("ArvinZhuang/BiTAG-t5-large")
tokenizer = AutoTokenizer.from_pretrained("ArvinZhuang/BiTAG-t5-large")
text = "abstract: [your abstract]" # use 'title:' as the prefix for title_to_abs task.
input_ids = tokenizer.encode(text, return_tensors='pt')
outputs = model.generate(
input_ids,
do_sample=True,
max_length=500,
top_p=0.9,
top_k=20,
temperature=1,
num_return_sequences=10,
)
print("Output:\n" + 100 * '-')
for i, output in enumerate(outputs):
print("{}: {}".format(i+1, tokenizer.decode(output, skip_special_tokens=True)))
```
GitHub: https://github.com/ArvinZhuang/BiTAG
|
AryanLala/autonlp-Scientific_Title_Generator-34558227
|
AryanLala
|
pegasus
| 9 | 12 |
transformers
| 19 |
text2text-generation
| true | false | false | null |
['en']
|
['AryanLala/autonlp-data-Scientific_Title_Generator']
|
137.60574081887984
| 0 | 0 | 0 | 0 | 0 | 0 | 0 |
autonlp
| false | true | true | 988 |
# Model Trained Using AutoNLP
- Model: Google's Pegasus (https://huggingface.co/google/pegasus-xsum)
- Problem type: Summarization
- Model ID: 34558227
- CO2 Emissions (in grams): 137.60574081887984
- Spaces: https://huggingface.co/spaces/TitleGenerators/ArxivTitleGenerator
- Dataset: arXiv Dataset (https://www.kaggle.com/Cornell-University/arxiv)
- Data subset used: https://huggingface.co/datasets/AryanLala/autonlp-data-Scientific_Title_Generator
## Validation Metrics
- Loss: 2.578599214553833
- Rouge1: 44.8482
- Rouge2: 24.4052
- RougeL: 40.1716
- RougeLsum: 40.1396
- Gen Len: 11.4675
## Social
- LinkedIn: https://www.linkedin.com/in/aryanlala/
- Twitter: https://twitter.com/AryanLala20
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/AryanLala/autonlp-Scientific_Title_Generator-34558227
```
|
Ashkanmh/bert-base-parsbert-uncased-finetuned
|
Ashkanmh
|
bert
| 13 | 4 |
transformers
| 0 |
fill-mask
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| false | true | true | 1,206 |
<!-- 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. -->
# bert-base-parsbert-uncased-finetuned
This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-uncased](https://huggingface.co/HooshvareLab/bert-base-parsbert-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2045
## 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: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 3.5596 | 1.0 | 515 | 3.2097 |
### Framework versions
- Transformers 4.10.0
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
Aurora/asdawd
|
Aurora
| null | 2 | 0 | null | 0 | null | false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | false | true | 431 |
https://www.geogebra.org/m/bbuczchu
https://www.geogebra.org/m/xwyasqje
https://www.geogebra.org/m/mx2cqkwr
https://www.geogebra.org/m/tkqqqthm
https://www.geogebra.org/m/asdaf9mj
https://www.geogebra.org/m/ywuaj7p5
https://www.geogebra.org/m/jkfkayj3
https://www.geogebra.org/m/hptnn7ar
https://www.geogebra.org/m/de9cwmrf
https://www.geogebra.org/m/yjc5hdep
https://www.geogebra.org/m/nm8r56w5
https://www.geogebra.org/m/j7wfcpxj
|
Aurora/community.afpglobal
|
Aurora
| null | 2 | 0 | null | 0 | null | false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | false | true | 1,399 |
https://community.afpglobal.org/network/members/profile?UserKey=b0b38adc-86c7-4d30-85c6-ac7d15c5eeb0
https://community.afpglobal.org/network/members/profile?UserKey=f4ddef89-b508-4695-9d1e-3d4d1a583279
https://community.afpglobal.org/network/members/profile?UserKey=36081479-5e7b-41ba-8370-ecf72989107a
https://community.afpglobal.org/network/members/profile?UserKey=e1a88332-be7f-4997-af4e-9fcb7bb366da
https://community.afpglobal.org/network/members/profile?UserKey=4738b405-2017-4025-9e5f-eadbf7674840
https://community.afpglobal.org/network/members/profile?UserKey=eb96d91c-31ae-46e1-8297-a3c8551f2e6a
https://u.mpi.org/network/members/profile?UserKey=9867e2d9-d22a-4dab-8bcf-3da5c2f30745
https://u.mpi.org/network/members/profile?UserKey=5af232f2-a66e-438f-a5ab-9768321f791d
https://community.afpglobal.org/network/members/profile?UserKey=481305df-48ea-4c50-bca4-a82008efb427
https://u.mpi.org/network/members/profile?UserKey=039fbb91-52c6-40aa-b58d-432fb4081e32
https://www.geogebra.org/m/jkfkayj3
https://www.geogebra.org/m/hptnn7ar
https://www.geogebra.org/m/de9cwmrf
https://www.geogebra.org/m/yjc5hdep
https://www.geogebra.org/m/nm8r56w5
https://www.geogebra.org/m/j7wfcpxj
https://www.geogebra.org/m/bbuczchu
https://www.geogebra.org/m/xwyasqje
https://www.geogebra.org/m/mx2cqkwr
https://www.geogebra.org/m/tkqqqthm
https://www.geogebra.org/m/asdaf9mj
https://www.geogebra.org/m/ywuaj7p5
|
Axon/resnet18-v1
|
Axon
| null | 3 | 0 | null | 1 | null | false | false | false |
apache-2.0
| null |
['ImageNet']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['Axon', 'Elixir']
| false | true | true | 3,463 |
# ResNet
This ResNet18 model was translated from the ONNX ResNetv1 model found
at https://github.com/onnx/models/tree/main/vision/classification/resnet into Axon using [AxonOnnx](https://github.com/elixir-nx/axon_onnx)
The following description is copied from the relevant description at the ONNX repository.
## Use cases
These ResNet models perform image classification - they take images as input and classify the major object in the image into a set of pre-defined classes. They are trained on ImageNet dataset which contains images from 1000 classes. ResNet models provide very high accuracies with affordable model sizes. They are ideal for cases when high accuracy of classification is required.
ImageNet trained models are often used as the base layers for a transfer learning approach to training a model in your domain. Transfer learning can significantly reduce the processing necessary to train an accurate model in your domain. This model was published here with the expectation that it would be useful to the Elixir community for transfer learning and other similar approaches.
## Description
Deeper neural networks are more difficult to train. Residual learning framework ease the training of networks that are substantially deeper. The research explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. It also provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset the residual nets were evaluated with a depth of up to 152 layers — 8× deeper than VGG nets but still having lower complexity.
## Model
ResNet models consists of residual blocks and came up to counter the effect of deteriorating accuracies with more layers due to network not learning the initial layers.
ResNet v1 uses post-activation for the residual blocks.
### Input
All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (N x 3 x H x W), where N is the batch size, and H and W are expected to be at least 224.
The inference was done using jpeg image.
### Preprocessing
The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225]. The transformation should preferably happen at preprocessing.
### Output
The model outputs image scores for each of the 1000 classes of ImageNet.
### Postprocessing
The post-processing involves calculating the softmax probability scores for each class. You can also sort them to report the most probable classes. Check [imagenet_postprocess.py](../imagenet_postprocess.py) for code.
## Dataset
Dataset used for train and validation: [ImageNet (ILSVRC2012)](http://www.image-net.org/challenges/LSVRC/2012/). Check [imagenet_prep](../imagenet_prep.md) for guidelines on preparing the dataset.
## References
* **ResNetv1**
[Deep residual learning for image recognition](https://arxiv.org/abs/1512.03385)
He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778. 2016.
* **ONNX source model**
[onnx/models vision/classification/resnet resnet18-v1-7.onnx](https://github.com/onnx/models/tree/main/vision/classification/resnet/README)
|
Axon/resnet34-v1
|
Axon
| null | 3 | 0 | null | 0 | null | false | false | false |
apache-2.0
| null |
['ImageNet']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['Axon', 'Elixir']
| false | true | true | 3,463 |
# ResNet
This ResNet34 model was translated from the ONNX ResNetv1 model found
at https://github.com/onnx/models/tree/main/vision/classification/resnet into Axon using [AxonOnnx](https://github.com/elixir-nx/axon_onnx)
The following description is copied from the relevant description at the ONNX repository.
## Use cases
These ResNet models perform image classification - they take images as input and classify the major object in the image into a set of pre-defined classes. They are trained on ImageNet dataset which contains images from 1000 classes. ResNet models provide very high accuracies with affordable model sizes. They are ideal for cases when high accuracy of classification is required.
ImageNet trained models are often used as the base layers for a transfer learning approach to training a model in your domain. Transfer learning can significantly reduce the processing necessary to train an accurate model in your domain. This model was published here with the expectation that it would be useful to the Elixir community for transfer learning and other similar approaches.
## Description
Deeper neural networks are more difficult to train. Residual learning framework ease the training of networks that are substantially deeper. The research explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. It also provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset the residual nets were evaluated with a depth of up to 152 layers — 8× deeper than VGG nets but still having lower complexity.
## Model
ResNet models consists of residual blocks and came up to counter the effect of deteriorating accuracies with more layers due to network not learning the initial layers.
ResNet v1 uses post-activation for the residual blocks.
### Input
All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (N x 3 x H x W), where N is the batch size, and H and W are expected to be at least 224.
The inference was done using jpeg image.
### Preprocessing
The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225]. The transformation should preferably happen at preprocessing.
### Output
The model outputs image scores for each of the 1000 classes of ImageNet.
### Postprocessing
The post-processing involves calculating the softmax probability scores for each class. You can also sort them to report the most probable classes. Check [imagenet_postprocess.py](../imagenet_postprocess.py) for code.
## Dataset
Dataset used for train and validation: [ImageNet (ILSVRC2012)](http://www.image-net.org/challenges/LSVRC/2012/). Check [imagenet_prep](../imagenet_prep.md) for guidelines on preparing the dataset.
## References
* **ResNetv1**
[Deep residual learning for image recognition](https://arxiv.org/abs/1512.03385)
He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778. 2016.
* **ONNX source model**
[onnx/models vision/classification/resnet resnet34-v1-7.onnx](https://github.com/onnx/models/tree/main/vision/classification/resnet/README)
|
Axon/resnet50-v1
|
Axon
| null | 3 | 0 | null | 0 | null | false | false | false |
apache-2.0
| null |
['ImageNet']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['Axon', 'Elixir']
| false | true | true | 3,463 |
# ResNet
This ResNet50 model was translated from the ONNX ResNetv1 model found
at https://github.com/onnx/models/tree/main/vision/classification/resnet into Axon using [AxonOnnx](https://github.com/elixir-nx/axon_onnx)
The following description is copied from the relevant description at the ONNX repository.
## Use cases
These ResNet models perform image classification - they take images as input and classify the major object in the image into a set of pre-defined classes. They are trained on ImageNet dataset which contains images from 1000 classes. ResNet models provide very high accuracies with affordable model sizes. They are ideal for cases when high accuracy of classification is required.
ImageNet trained models are often used as the base layers for a transfer learning approach to training a model in your domain. Transfer learning can significantly reduce the processing necessary to train an accurate model in your domain. This model was published here with the expectation that it would be useful to the Elixir community for transfer learning and other similar approaches.
## Description
Deeper neural networks are more difficult to train. Residual learning framework ease the training of networks that are substantially deeper. The research explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. It also provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset the residual nets were evaluated with a depth of up to 152 layers — 8× deeper than VGG nets but still having lower complexity.
## Model
ResNet models consists of residual blocks and came up to counter the effect of deteriorating accuracies with more layers due to network not learning the initial layers.
ResNet v1 uses post-activation for the residual blocks.
### Input
All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (N x 3 x H x W), where N is the batch size, and H and W are expected to be at least 224.
The inference was done using jpeg image.
### Preprocessing
The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225]. The transformation should preferably happen at preprocessing.
### Output
The model outputs image scores for each of the 1000 classes of ImageNet.
### Postprocessing
The post-processing involves calculating the softmax probability scores for each class. You can also sort them to report the most probable classes. Check [imagenet_postprocess.py](../imagenet_postprocess.py) for code.
## Dataset
Dataset used for train and validation: [ImageNet (ILSVRC2012)](http://www.image-net.org/challenges/LSVRC/2012/). Check [imagenet_prep](../imagenet_prep.md) for guidelines on preparing the dataset.
## References
* **ResNetv1**
[Deep residual learning for image recognition](https://arxiv.org/abs/1512.03385)
He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778. 2016.
* **ONNX source model**
[onnx/models vision/classification/resnet resnet50-v1-7.onnx](https://github.com/onnx/models/tree/main/vision/classification/resnet/README)
|
Ayham/albert_bert_summarization_cnn_dailymail
|
Ayham
|
encoder-decoder
| 8 | 1 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['cnn_dailymail']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 993 |
<!-- 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. -->
# albert_bert_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Ayham/albert_distilgpt2_summarization_cnn_dailymail
|
Ayham
|
encoder-decoder
| 8 | 2 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['cnn_dailymail']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 994 |
<!-- 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. -->
# albert_distilgpt2_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
Ayham/albert_gpt2_Full_summarization_cnndm
|
Ayham
|
encoder-decoder
| 8 | 6 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['cnn_dailymail']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 990 |
<!-- 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. -->
# albert_gpt2_Full_summarization_cnndm
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
Ayham/albert_gpt2_summarization_cnndm
|
Ayham
|
encoder-decoder
| 8 | 2 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['cnn_dailymail']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 991 |
<!-- 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. -->
# albert_large_gpt2_summarization_cnndm
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
Ayham/albert_gpt2_summarization_xsum
|
Ayham
|
encoder-decoder
| 8 | 1 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['xsum']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 975 |
<!-- 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. -->
# albert_gpt2_summarization_xsum
This model is a fine-tuned version of [](https://huggingface.co/) on the xsum 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Ayham/albert_roberta_summarization_cnn_dailymail
|
Ayham
|
encoder-decoder
| 8 | 1 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['cnn_dailymail']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,000 |
<!-- 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. -->
# albert_roberta_new_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Ayham/bert_bert_summarization_cnn_dailymail
|
Ayham
|
encoder-decoder
| 12 | 4 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['cnn_dailymail']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 991 |
<!-- 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. -->
# bert_bert_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Ayham/bert_distilgpt2_summarization_cnn_dailymail
|
Ayham
|
encoder-decoder
| 8 | 1 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['cnn_dailymail']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 992 |
<!-- 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. -->
# bert_distilgpt2_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
Ayham/bert_gpt2_summarization_cnndm
|
Ayham
|
encoder-decoder
| 8 | 2 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['cnn_dailymail']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 983 |
<!-- 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. -->
# bert_gpt2_summarization_cnndm
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Ayham/bert_gpt2_summarization_cnndm_new
|
Ayham
|
encoder-decoder
| 8 | 3 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['cnn_dailymail']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 987 |
<!-- 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. -->
# bert_gpt2_summarization_cnndm_new
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Ayham/bert_gpt2_summarization_xsum
|
Ayham
|
encoder-decoder
| 8 | 15 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['xsum']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 973 |
<!-- 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. -->
# bert_gpt2_summarization_xsum
This model is a fine-tuned version of [](https://huggingface.co/) on the xsum 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Ayham/bert_roberta_summarization_cnn_dailymail
|
Ayham
|
encoder-decoder
| 8 | 1 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['cnn_dailymail']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 994 |
<!-- 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. -->
# bert_roberta_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Ayham/bertgpt2_cnn
|
Ayham
|
encoder-decoder
| 8 | 1 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 954 |
<!-- 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. -->
# bertgpt2_cnn
This model is a fine-tuned version of [](https://huggingface.co/) 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Ayham/distilbert_bert_summarization_cnn_dailymail
|
Ayham
|
encoder-decoder
| 8 | 1 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['cnn_dailymail']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 997 |
<!-- 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_bert_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Ayham/distilbert_distilgpt2_summarization_cnn_dailymail
|
Ayham
|
encoder-decoder
| 8 | 1 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['cnn_dailymail']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 998 |
<!-- 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_distilgpt2_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
Ayham/distilbert_gpt2_summarization_cnndm
|
Ayham
|
encoder-decoder
| 8 | 5 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['cnn_dailymail']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 989 |
<!-- 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_gpt2_summarization_cnndm
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Ayham/distilbert_gpt2_summarization_xsum
|
Ayham
|
encoder-decoder
| 8 | 2 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['xsum']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 979 |
<!-- 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_gpt2_summarization_xsum
This model is a fine-tuned version of [](https://huggingface.co/) on the xsum 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Ayham/distilbert_roberta_summarization_cnn_dailymail
|
Ayham
|
encoder-decoder
| 8 | 3 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['cnn_dailymail']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,000 |
<!-- 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_roberta_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Ayham/ernie_gpt2_summarization_cnn_dailymail
|
Ayham
|
encoder-decoder
| 8 | 1 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['cnn_dailymail']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 992 |
<!-- 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. -->
# ernie_gpt2_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Ayham/roberta_bert_summarization_cnn_dailymail
|
Ayham
|
encoder-decoder
| 8 | 8 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['cnn_dailymail']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 994 |
<!-- 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. -->
# roberta_bert_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Ayham/roberta_distilgpt2_summarization_cnn_dailymail
|
Ayham
|
encoder-decoder
| 8 | 1 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['cnn_dailymail']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 995 |
<!-- 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. -->
# roberta_distilgpt2_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.2
- Tokenizers 0.11.0
|
Ayham/roberta_gpt2_new_max64_summarization_cnndm
|
Ayham
|
encoder-decoder
| 8 | 1 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['cnn_dailymail']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 996 |
<!-- 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. -->
# roberta_gpt2_new_max64_summarization_cnndm
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
Ayham/roberta_gpt2_summarization_cnn_dailymail
|
Ayham
|
encoder-decoder
| 10 | 5 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['cnn_dailymail']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,645 |
<!-- 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. -->
# roberta_gpt2_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset.
## Model description
This model uses RoBerta encoder and GPT2 decoder and fine-tuned on the summarization task. It got Rouge scores as follows:
Rouge1= 35.886
Rouge2= 16.292
RougeL= 23.499
## Intended uses & limitations
To use its API:
from transformers import RobertaTokenizerFast, GPT2Tokenizer, EncoderDecoderModel
model = EncoderDecoderModel.from_pretrained("Ayham/roberta_gpt2_summarization_cnn_dailymail")
input_tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base')
output_tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
article = """Your Input Text"""
input_ids = input_tokenizer(article, return_tensors="pt").input_ids
output_ids = model.generate(input_ids)
print(output_tokenizer.decode(output_ids[0], skip_special_tokens=True))
More information needed
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Ayham/roberta_gpt2_summarization_xsum
|
Ayham
|
encoder-decoder
| 8 | 3 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['xsum']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 976 |
<!-- 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. -->
# roberta_gpt2_summarization_xsum
This model is a fine-tuned version of [](https://huggingface.co/) on the xsum 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Ayham/roberta_roberta_summarization_cnn_dailymail
|
Ayham
|
encoder-decoder
| 13 | 1 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['cnn_dailymail']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 997 |
<!-- 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. -->
# roberta_roberta_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Ayham/robertagpt2_cnn
|
Ayham
|
encoder-decoder
| 8 | 1 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 957 |
<!-- 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. -->
# robertagpt2_cnn
This model is a fine-tuned version of [](https://huggingface.co/) 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Ayham/robertagpt2_xsum
|
Ayham
|
encoder-decoder
| 8 | 1 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 958 |
<!-- 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. -->
# robertagpt2_xsum
This model is a fine-tuned version of [](https://huggingface.co/) 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Ayham/robertagpt2_xsum2
|
Ayham
|
encoder-decoder
| 8 | 1 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 959 |
<!-- 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. -->
# robertagpt2_xsum2
This model is a fine-tuned version of [](https://huggingface.co/) 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Ayham/robertagpt2_xsum4
|
Ayham
|
encoder-decoder
| 8 | 1 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 959 |
<!-- 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. -->
# robertagpt2_xsum4
This model is a fine-tuned version of [](https://huggingface.co/) 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Ayham/xlmroberta_gpt2_summarization_xsum
|
Ayham
|
encoder-decoder
| 8 | 2 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['xsum']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 979 |
<!-- 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. -->
# xlmroberta_gpt2_summarization_xsum
This model is a fine-tuned version of [](https://huggingface.co/) on the xsum 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
Ayham/xlmroberta_large_gpt2_summarization_cnndm
|
Ayham
|
encoder-decoder
| 8 | 2 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['cnn_dailymail']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 995 |
<!-- 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. -->
# xlmroberta_large_gpt2_summarization_cnndm
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
Ayham/xlnet_bert_summarization_cnn_dailymail
|
Ayham
|
encoder-decoder
| 8 | 4 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['cnn_dailymail']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 992 |
<!-- 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. -->
# xlnet_bert_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Ayham/xlnet_distilgpt2_summarization_cnn_dailymail
|
Ayham
|
encoder-decoder
| 8 | 1 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['cnn_dailymail']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 993 |
<!-- 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. -->
# xlnet_distilgpt2_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
Ayham/xlnet_gpt2_summarization_cnn_dailymail
|
Ayham
|
encoder-decoder
| 8 | 4 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['cnn_dailymail']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 992 |
<!-- 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. -->
# xlnet_gpt2_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Ayham/xlnet_gpt2_summarization_xsum
|
Ayham
|
encoder-decoder
| 8 | 1 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['xsum']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 974 |
<!-- 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. -->
# xlnet_gpt2_summarization_xsum
This model is a fine-tuned version of [](https://huggingface.co/) on the xsum 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Ayham/xlnet_gpt_xsum
|
Ayham
|
encoder-decoder
| 8 | 2 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 932 |
<!-- 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. -->
# xlnet_gpt_xsum
This model is a fine-tuned version of [](https://huggingface.co/) 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Ayham/xlnet_roberta_summarization_cnn_dailymail
|
Ayham
|
encoder-decoder
| 8 | 3 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null |
['cnn_dailymail']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 995 |
<!-- 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. -->
# xlnet_roberta_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Ayham/xlnetgpt2_xsum7
|
Ayham
|
encoder-decoder
| 8 | 1 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 957 |
<!-- 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. -->
# xlnetgpt2_xsum7
This model is a fine-tuned version of [](https://huggingface.co/) 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
AyushPJ/ai-club-inductions-21-nlp-ALBERT
|
AyushPJ
|
albert
| 10 | 5 |
transformers
| 0 |
question-answering
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 845 |
<!-- 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. -->
# ai-club-inductions-21-nlp-ALBERT
This model was trained from scratch 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: 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: 10
### Framework versions
- Transformers 4.11.3
- Pytorch 1.7.1+cpu
- Datasets 1.14.0
- Tokenizers 0.10.3
|
AyushPJ/ai-club-inductions-21-nlp-ELECTRA-base-squad
|
AyushPJ
|
electra
| 10 | 5 |
transformers
| 0 |
question-answering
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,064 |
<!-- 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. -->
# ai-club-inductions-21-nlp-ELECTRA-base-squad
This model is the deepset/electra-base-squad2 pre-trained model trained on data from AI Inductions 21 NLP competition (https://www.kaggle.com/c/ai-inductions-21-nlp) for extractive QA.
## Model description
More information needed
## Intended uses & limitations
AI Inductions 21 NLP competition
## Training and evaluation data
AI Inductions 21 NLP competition data
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- max_length = 512
- doc_stride = 384
- learning_rate: 2e-05
- weight_decay=0.01
- 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: 10
### Framework versions
- Transformers 4.11.3
- Pytorch 1.7.1+cpu
- Datasets 1.14.0
- Tokenizers 0.10.3
|
AyushPJ/ai-club-inductions-21-nlp-XLNet
|
AyushPJ
|
xlnet
| 10 | 5 |
transformers
| 0 |
question-answering
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 844 |
<!-- 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. -->
# ai-club-inductions-21-nlp-XLNet
This model was trained from scratch 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: 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: 10
### Framework versions
- Transformers 4.11.3
- Pytorch 1.7.1+cpu
- Datasets 1.14.0
- Tokenizers 0.10.3
|
AyushPJ/ai-club-inductions-21-nlp-distilBERT
|
AyushPJ
|
distilbert
| 10 | 7 |
transformers
| 0 |
question-answering
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 851 |
<!-- 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. -->
# ai-club-inductions-21-nlp-distilBERT
This model was trained from scratch 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: 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: 10
### Framework versions
- Transformers 4.11.3
- Pytorch 1.7.1+cu110
- Datasets 1.14.0
- Tokenizers 0.10.3
|
AyushPJ/ai-club-inductions-21-nlp-roBERTa-base-squad-v2
|
AyushPJ
|
roberta
| 11 | 5 |
transformers
| 0 |
question-answering
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 859 |
<!-- 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. -->
# ai-club-inductions-21-nlp-roBERTa-base-squad-v2
This model was trained from scratch 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: 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
### Framework versions
- Transformers 4.11.3
- Pytorch 1.7.1+cpu
- Datasets 1.14.0
- Tokenizers 0.10.3
|
AyushPJ/ai-club-inductions-21-nlp-roBERTa
|
AyushPJ
|
roberta
| 11 | 5 |
transformers
| 0 |
question-answering
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 846 |
<!-- 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. -->
# ai-club-inductions-21-nlp-roBERTa
This model was trained from scratch 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: 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: 10
### Framework versions
- Transformers 4.11.3
- Pytorch 1.7.1+cpu
- Datasets 1.14.0
- Tokenizers 0.10.3
|
AyushPJ/test-squad-trained-finetuned-squad
|
AyushPJ
|
distilbert
| 12 | 4 |
transformers
| 0 |
question-answering
| true | false | false | null | null |
['squad']
| null | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 847 |
<!-- 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. -->
# test-squad-trained-finetuned-squad
This model was trained from scratch on the squad 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: 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: 3
### Framework versions
- Transformers 4.11.3
- Pytorch 1.7.1+cu110
- Datasets 1.13.3
- Tokenizers 0.10.3
|
BAHIJA/distilbert-base-uncased-finetuned-cola
|
BAHIJA
|
distilbert
| 38 | 1 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null |
['glue']
| null | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,572 |
<!-- 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: 0.7371
- Matthews Correlation: 0.5481
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5298 | 1.0 | 535 | 0.5333 | 0.4142 |
| 0.3619 | 2.0 | 1070 | 0.5174 | 0.5019 |
| 0.2449 | 3.0 | 1605 | 0.6394 | 0.4921 |
| 0.1856 | 4.0 | 2140 | 0.7371 | 0.5481 |
| 0.133 | 5.0 | 2675 | 0.8600 | 0.5327 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
|
BME-TMIT/foszt2oszt
|
BME-TMIT
|
encoder-decoder
| 7 | 4 |
transformers
| 1 |
text2text-generation
| true | false | false | null |
['hu']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 1,354 |
[Paper](https://hlt.bme.hu/en/publ/foszt2oszt)
We publish an abstractive summarizer for Hungarian, an
encoder-decoder model initialized with [huBERT](huggingface.co/SZTAKI-HLT/hubert-base-cc), and fine-tuned on the
[ELTE.DH](https://elte-dh.hu/) corpus of former Hungarian news portals. The model produces fluent output in the correct topic, but it hallucinates frequently.
Our quantitative evaluation on automatic and human transcripts of news
(with automatic and human-made punctuation, [Tündik et al. (2019)](https://www.isca-speech.org/archive/interspeech_2019/tundik19_interspeech.html), [Tündik and Szaszák (2019)](https://www.isca-speech.org/archive/interspeech_2019/szaszak19_interspeech.html)) shows that the model is
robust with respect to errors in either automatic speech recognition or
automatic punctuation restoration. In fine-tuning and inference, we followed [a jupyter notebook by Patrick von
Platen](https://github.com/patrickvonplaten/notebooks/blob/master/BERT2BERT_for_CNN_Dailymail.ipynb). Most hyper-parameters are the same as those by von Platen, but we
found it advantageous to change the minimum length of the summary to 8 word-
pieces (instead of 56), and the number of beams in beam search to 5 (instead
of 4). Our model was fine-tuned on a server of the [SZTAKI-HLT](hlt.bme.hu/) group, which kindly
provided access to it.
|
BSC-LT/RoBERTalex
|
BSC-LT
|
roberta
| 9 | 0 |
transformers
| 3 |
fill-mask
| true | false | false |
apache-2.0
|
['es']
|
['legal_ES', 'temu_legal']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['legal', 'spanish']
| false | true | true | 1,241 |
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/RoBERTalex
# Spanish Legal-domain RoBERTa
There are few models trained for the Spanish language. Some of the models have been trained with a low resource, unclean corpora. The ones derived from the Spanish National Plan for Language Technologies are proficient solving several tasks and have been trained using large scale clean corpora. However, the Spanish Legal domain language could be think of an independent language on its own. We therefore created a Spanish Legal model from scratch trained exclusively on legal corpora.
## Citing
```
@misc{gutierrezfandino2021legal,
title={Spanish Legalese Language Model and Corpora},
author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Aitor Gonzalez-Agirre and Marta Villegas},
year={2021},
eprint={2110.12201},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
For more information visit our [GitHub repository](https://github.com/PlanTL-GOB-ES/lm-legal-es)
## Funding
This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.
|
BSC-LT/roberta-base-biomedical-clinical-es
|
BSC-LT
|
roberta
| 11 | 0 |
transformers
| 5 |
fill-mask
| true | false | false |
apache-2.0
|
['es']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['biomedical', 'clinical', 'spanish']
| false | true | true | 11,595 |
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-clinical-es
# Biomedical-clinical language model for Spanish
Biomedical pretrained language model for Spanish. For more details about the corpus, the pretraining and the evaluation, check the official [repository](https://github.com/PlanTL-SANIDAD/lm-biomedical-clinical-es) and read our [preprint](https://arxiv.org/abs/2109.03570) "_Carrino, C. P., Armengol-Estapé, J., Gutiérrez-Fandiño, A., Llop-Palao, J., Pàmies, M., Gonzalez-Agirre, A., & Villegas, M. (2021). Biomedical and Clinical Language Models for Spanish: On the Benefits of Domain-Specific Pretraining in a Mid-Resource Scenario._".
## Tokenization and model pretraining
This model is a [RoBERTa-based](https://github.com/pytorch/fairseq/tree/master/examples/roberta) model trained on a
**biomedical-clinical** corpus in Spanish collected from several sources (see next section).
The training corpus has been tokenized using a byte version of [Byte-Pair Encoding (BPE)](https://github.com/openai/gpt-2)
used in the original [RoBERTA](https://github.com/pytorch/fairseq/tree/master/examples/roberta) model with a vocabulary size of 52,000 tokens. The pretraining consists of a masked language model training at the subword level following the approach employed for the RoBERTa base model with the same hyperparameters as in the original work. The training lasted a total of 48 hours with 16 NVIDIA V100 GPUs of 16GB DDRAM, using Adam optimizer with a peak learning rate of 0.0005 and an effective batch size of 2,048 sentences.
## Training corpora and preprocessing
The training corpus is composed of several biomedical corpora in Spanish, collected from publicly available corpora and crawlers, and a real-world clinical corpus collected from more than 278K clinical documents and notes. To obtain a high-quality training corpus while retaining the idiosyncrasies of the clinical language, a cleaning pipeline has been applied only to the biomedical corpora, keeping the clinical corpus uncleaned. Essentially, the cleaning operations used are:
- data parsing in different formats
- sentence splitting
- language detection
- filtering of ill-formed sentences
- deduplication of repetitive contents
- keep the original document boundaries
Then, the biomedical corpora are concatenated and further global deduplication among the biomedical corpora have been applied.
Eventually, the clinical corpus is concatenated to the cleaned biomedical corpus resulting in a medium-size biomedical-clinical corpus for Spanish composed of more than 1B tokens. The table below shows some basic statistics of the individual cleaned corpora:
| Name | No. tokens | Description |
|-----------------------------------------------------------------------------------------|-------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [Medical crawler](https://zenodo.org/record/4561970) | 745,705,946 | Crawler of more than 3,000 URLs belonging to Spanish biomedical and health domains. |
| Clinical cases misc. | 102,855,267 | A miscellany of medical content, essentially clinical cases. Note that a clinical case report is a scientific publication where medical practitioners share patient cases and it is different from a clinical note or document. |
| Clinical notes/documents | 91,250,080 | Collection of more than 278K clinical documents, including discharge reports, clinical course notes and X-ray reports, for a total of 91M tokens. |
| [Scielo](https://github.com/PlanTL-SANIDAD/SciELO-Spain-Crawler) | 60,007,289 | Publications written in Spanish crawled from the Spanish SciELO server in 2017. |
| [BARR2_background](https://temu.bsc.es/BARR2/downloads/background_set.raw_text.tar.bz2) | 24,516,442 | Biomedical Abbreviation Recognition and Resolution (BARR2) containing Spanish clinical case study sections from a variety of clinical disciplines. |
| Wikipedia_life_sciences | 13,890,501 | Wikipedia articles crawled 04/01/2021 with the [Wikipedia API python library](https://pypi.org/project/Wikipedia-API/) starting from the "Ciencias\_de\_la\_vida" category up to a maximum of 5 subcategories. Multiple links to the same articles are then discarded to avoid repeating content. |
| Patents | 13,463,387 | Google Patent in Medical Domain for Spain (Spanish). The accepted codes (Medical Domain) for Json files of patents are: "A61B", "A61C","A61F", "A61H", "A61K", "A61L","A61M", "A61B", "A61P". |
| [EMEA](http://opus.nlpl.eu/download.php?f=EMEA/v3/moses/en-es.txt.zip) | 5,377,448 | Spanish-side documents extracted from parallel corpora made out of PDF documents from the European Medicines Agency. |
| [mespen_Medline](https://zenodo.org/record/3562536#.YTt1fH2xXbR) | 4,166,077 | Spanish-side articles extracted from a collection of Spanish-English parallel corpus consisting of biomedical scientific literature. The collection of parallel resources are aggregated from the MedlinePlus source. |
| PubMed | 1,858,966 | Open-access articles from the PubMed repository crawled in 2017. |
## Evaluation and results
The model has been evaluated on the Named Entity Recognition (NER) using the following datasets:
- [PharmaCoNER](https://zenodo.org/record/4270158): is a track on chemical and drug mention recognition from Spanish medical texts (for more info see: https://temu.bsc.es/pharmaconer/).
- [CANTEMIST](https://zenodo.org/record/3978041#.YTt5qH2xXbQ): is a shared task specifically focusing on named entity recognition of tumor morphology, in Spanish (for more info see: https://zenodo.org/record/3978041#.YTt5qH2xXbQ).
- ICTUSnet: consists of 1,006 hospital discharge reports of patients admitted for stroke from 18 different Spanish hospitals. It contains more than 79,000 annotations for 51 different kinds of variables.
The evaluation results are compared against the [mBERT](https://huggingface.co/bert-base-multilingual-cased) and [BETO](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) models:
| F1 - Precision - Recall | roberta-base-biomedical-clinical-es | mBERT | BETO |
|---------------------------|----------------------------|-------------------------------|-------------------------|
| PharmaCoNER | **90.04** - **88.92** - **91.18** | 87.46 - 86.50 - 88.46 | 88.18 - 87.12 - 89.28 |
| CANTEMIST | **83.34** - **81.48** - **85.30** | 82.61 - 81.12 - 84.15 | 82.42 - 80.91 - 84.00 |
| ICTUSnet | **88.08** - **84.92** - **91.50** | 86.75 - 83.53 - 90.23 | 85.95 - 83.10 - 89.02 |
## Intended uses & limitations
The model is ready-to-use only for masked language modelling to perform the Fill Mask task (try the inference API or read the next section)
However, the is intended to be fine-tuned on downstream tasks such as Named Entity Recognition or Text Classification.
## Cite
If you use our models, please cite our latest preprint:
```bibtex
@misc{carrino2021biomedical,
title={Biomedical and Clinical Language Models for Spanish: On the Benefits of Domain-Specific Pretraining in a Mid-Resource Scenario},
author={Casimiro Pio Carrino and Jordi Armengol-Estapé and Asier Gutiérrez-Fandiño and Joan Llop-Palao and Marc Pàmies and Aitor Gonzalez-Agirre and Marta Villegas},
year={2021},
eprint={2109.03570},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
If you use our Medical Crawler corpus, please cite the preprint:
```bibtex
@misc{carrino2021spanish,
title={Spanish Biomedical Crawled Corpus: A Large, Diverse Dataset for Spanish Biomedical Language Models},
author={Casimiro Pio Carrino and Jordi Armengol-Estapé and Ona de Gibert Bonet and Asier Gutiérrez-Fandiño and Aitor Gonzalez-Agirre and Martin Krallinger and Marta Villegas},
year={2021},
eprint={2109.07765},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
---
---
## How to use
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("BSC-TeMU/roberta-base-biomedical-es")
model = AutoModelForMaskedLM.from_pretrained("BSC-TeMU/roberta-base-biomedical-es")
from transformers import pipeline
unmasker = pipeline('fill-mask', model="BSC-TeMU/roberta-base-biomedical-es")
unmasker("El único antecedente personal a reseñar era la <mask> arterial.")
```
```
# Output
[
{
"sequence": " El único antecedente personal a reseñar era la hipertensión arterial.",
"score": 0.9855039715766907,
"token": 3529,
"token_str": " hipertensión"
},
{
"sequence": " El único antecedente personal a reseñar era la diabetes arterial.",
"score": 0.0039140828885138035,
"token": 1945,
"token_str": " diabetes"
},
{
"sequence": " El único antecedente personal a reseñar era la hipotensión arterial.",
"score": 0.002484665485098958,
"token": 11483,
"token_str": " hipotensión"
},
{
"sequence": " El único antecedente personal a reseñar era la Hipertensión arterial.",
"score": 0.0023484621196985245,
"token": 12238,
"token_str": " Hipertensión"
},
{
"sequence": " El único antecedente personal a reseñar era la presión arterial.",
"score": 0.0008009297889657319,
"token": 2267,
"token_str": " presión"
}
]
```
|
BSC-LT/roberta-base-biomedical-es
|
BSC-LT
|
roberta
| 11 | 3 |
transformers
| 3 |
fill-mask
| true | false | false |
apache-2.0
|
['es']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['biomedical', 'spanish']
| false | true | true | 10,778 |
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-es
# Biomedical language model for Spanish
Biomedical pretrained language model for Spanish. For more details about the corpus, the pretraining and the evaluation, check the official [repository](https://github.com/PlanTL-SANIDAD/lm-biomedical-clinical-es) and read our [preprint](https://arxiv.org/abs/2109.03570) "_Carrino, C. P., Armengol-Estapé, J., Gutiérrez-Fandiño, A., Llop-Palao, J., Pàmies, M., Gonzalez-Agirre, A., & Villegas, M. (2021). Biomedical and Clinical Language Models for Spanish: On the Benefits of Domain-Specific Pretraining in a Mid-Resource Scenario._".
## Tokenization and model pretraining
This model is a [RoBERTa-based](https://github.com/pytorch/fairseq/tree/master/examples/roberta) model trained on a
**biomedical** corpus in Spanish collected from several sources (see next section).
The training corpus has been tokenized using a byte version of [Byte-Pair Encoding (BPE)](https://github.com/openai/gpt-2)
used in the original [RoBERTA](https://github.com/pytorch/fairseq/tree/master/examples/roberta) model with a vocabulary size of 52,000 tokens. The pretraining consists of a masked language model training at the subword level following the approach employed for the RoBERTa base model with the same hyperparameters as in the original work. The training lasted a total of 48 hours with 16 NVIDIA V100 GPUs of 16GB DDRAM, using Adam optimizer with a peak learning rate of 0.0005 and an effective batch size of 2,048 sentences.
## Training corpora and preprocessing
The training corpus is composed of several biomedical corpora in Spanish, collected from publicly available corpora and crawlers.
To obtain a high-quality training corpus, a cleaning pipeline with the following operations has been applied:
- data parsing in different formats
- sentence splitting
- language detection
- filtering of ill-formed sentences
- deduplication of repetitive contents
- keep the original document boundaries
Finally, the corpora are concatenated and further global deduplication among the corpora have been applied.
The result is a medium-size biomedical corpus for Spanish composed of about 963M tokens. The table below shows some basic statistics of the individual cleaned corpora:
| Name | No. tokens | Description |
|-----------------------------------------------------------------------------------------|-------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [Medical crawler](https://zenodo.org/record/4561970) | 745,705,946 | Crawler of more than 3,000 URLs belonging to Spanish biomedical and health domains. |
| Clinical cases misc. | 102,855,267 | A miscellany of medical content, essentially clinical cases. Note that a clinical case report is a scientific publication where medical practitioners share patient cases and it is different from a clinical note or document. |
| [Scielo](https://github.com/PlanTL-SANIDAD/SciELO-Spain-Crawler) | 60,007,289 | Publications written in Spanish crawled from the Spanish SciELO server in 2017. |
| [BARR2_background](https://temu.bsc.es/BARR2/downloads/background_set.raw_text.tar.bz2) | 24,516,442 | Biomedical Abbreviation Recognition and Resolution (BARR2) containing Spanish clinical case study sections from a variety of clinical disciplines. |
| Wikipedia_life_sciences | 13,890,501 | Wikipedia articles crawled 04/01/2021 with the [Wikipedia API python library](https://pypi.org/project/Wikipedia-API/) starting from the "Ciencias\_de\_la\_vida" category up to a maximum of 5 subcategories. Multiple links to the same articles are then discarded to avoid repeating content. |
| Patents | 13,463,387 | Google Patent in Medical Domain for Spain (Spanish). The accepted codes (Medical Domain) for Json files of patents are: "A61B", "A61C","A61F", "A61H", "A61K", "A61L","A61M", "A61B", "A61P". |
| [EMEA](http://opus.nlpl.eu/download.php?f=EMEA/v3/moses/en-es.txt.zip) | 5,377,448 | Spanish-side documents extracted from parallel corpora made out of PDF documents from the European Medicines Agency. |
| [mespen_Medline](https://zenodo.org/record/3562536#.YTt1fH2xXbR) | 4,166,077 | Spanish-side articles extracted from a collection of Spanish-English parallel corpus consisting of biomedical scientific literature. The collection of parallel resources are aggregated from the MedlinePlus source. |
| PubMed | 1,858,966 | Open-access articles from the PubMed repository crawled in 2017. |
## Evaluation and results
The model has been evaluated on the Named Entity Recognition (NER) using the following datasets:
- [PharmaCoNER](https://zenodo.org/record/4270158): is a track on chemical and drug mention recognition from Spanish medical texts (for more info see: https://temu.bsc.es/pharmaconer/).
- [CANTEMIST](https://zenodo.org/record/3978041#.YTt5qH2xXbQ): is a shared task specifically focusing on named entity recognition of tumor morphology, in Spanish (for more info see: https://zenodo.org/record/3978041#.YTt5qH2xXbQ).
- ICTUSnet: consists of 1,006 hospital discharge reports of patients admitted for stroke from 18 different Spanish hospitals. It contains more than 79,000 annotations for 51 different kinds of variables.
The evaluation results are compared against the [mBERT](https://huggingface.co/bert-base-multilingual-cased) and [BETO](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) models:
| F1 - Precision - Recall | roberta-base-biomedical-es | mBERT | BETO |
|---------------------------|----------------------------|-------------------------------|-------------------------|
| PharmaCoNER | **89.48** - **87.85** - **91.18** | 87.46 - 86.50 - 88.46 | 88.18 - 87.12 - 89.28 |
| CANTEMIST | **83.87** - **81.70** - **86.17** | 82.61 - 81.12 - 84.15 | 82.42 - 80.91 - 84.00 |
| ICTUSnet | **88.12** - **85.56** - **90.83** | 86.75 - 83.53 - 90.23 | 85.95 - 83.10 - 89.02 |
## Intended uses & limitations
The model is ready-to-use only for masked language modelling to perform the Fill Mask task (try the inference API or read the next section)
However, the is intended to be fine-tuned on downstream tasks such as Named Entity Recognition or Text Classification.
## Cite
If you use our models, please cite our latest preprint:
```bibtex
@misc{carrino2021biomedical,
title={Biomedical and Clinical Language Models for Spanish: On the Benefits of Domain-Specific Pretraining in a Mid-Resource Scenario},
author={Casimiro Pio Carrino and Jordi Armengol-Estapé and Asier Gutiérrez-Fandiño and Joan Llop-Palao and Marc Pàmies and Aitor Gonzalez-Agirre and Marta Villegas},
year={2021},
eprint={2109.03570},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
If you use our Medical Crawler corpus, please cite the preprint:
```bibtex
@misc{carrino2021spanish,
title={Spanish Biomedical Crawled Corpus: A Large, Diverse Dataset for Spanish Biomedical Language Models},
author={Casimiro Pio Carrino and Jordi Armengol-Estapé and Ona de Gibert Bonet and Asier Gutiérrez-Fandiño and Aitor Gonzalez-Agirre and Martin Krallinger and Marta Villegas},
year={2021},
eprint={2109.07765},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
---
## How to use
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("BSC-TeMU/roberta-base-biomedical-es")
model = AutoModelForMaskedLM.from_pretrained("BSC-TeMU/roberta-base-biomedical-es")
from transformers import pipeline
unmasker = pipeline('fill-mask', model="BSC-TeMU/roberta-base-biomedical-es")
unmasker("El único antecedente personal a reseñar era la <mask> arterial.")
```
```
# Output
[
{
"sequence": " El único antecedente personal a reseñar era la hipertensión arterial.",
"score": 0.9855039715766907,
"token": 3529,
"token_str": " hipertensión"
},
{
"sequence": " El único antecedente personal a reseñar era la diabetes arterial.",
"score": 0.0039140828885138035,
"token": 1945,
"token_str": " diabetes"
},
{
"sequence": " El único antecedente personal a reseñar era la hipotensión arterial.",
"score": 0.002484665485098958,
"token": 11483,
"token_str": " hipotensión"
},
{
"sequence": " El único antecedente personal a reseñar era la Hipertensión arterial.",
"score": 0.0023484621196985245,
"token": 12238,
"token_str": " Hipertensión"
},
{
"sequence": " El único antecedente personal a reseñar era la presión arterial.",
"score": 0.0008009297889657319,
"token": 2267,
"token_str": " presión"
}
]
```
|
BSC-LT/roberta-base-bne-capitel-ner-plus
|
BSC-LT
|
roberta
| 9 | 0 |
transformers
| 1 |
token-classification
| true | false | false |
apache-2.0
|
['es']
|
['bne', 'capitel']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['national library of spain', 'spanish', 'bne', 'capitel', 'ner']
| false | true | true | 3,135 |
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-capitel-ner-plus
# Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset.
RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-base-bne
## Dataset
The dataset used is the one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 1).
**IMPORTANT ABOUT THIS MODEL:** We modified the dataset to make this model more robust to general Spanish input. In the Spanish language all the name entities are capitalized, as this dataset has been elaborated by experts, it is totally correct in terms of Spanish language. We randomly took some entities and we lower-cased some of them for the model to learn not only that the named entities are capitalized, but also the structure of a sentence that should contain a named entity. For instance: "My name is [placeholder]", this [placeholder] should be a named entity even though it is not written capitalized. The model trained on the original capitel dataset can be found here: https://huggingface.co/BSC-TeMU/roberta-base-bne-capitel-ner
Examples:
This model:
- "Me llamo asier y vivo en barcelona todo el año." → "Me llamo {as:S-PER}{ier:S-PER} y vivo en {barcelona:S-LOC} todo el año."
- "Hoy voy a visitar el parc güell tras salir del barcelona supercomputing center." → "Hoy voy a visitar el {par:B-LOC}{k:I-LOC} {gü:E-LOC}{ell:E-LOC} tras salir del {barcelona:B-ORG} {super:I-ORG}{com:I-ORG}{pu:I-ORG}{ting:I-ORG} {cen:E-ORG}{ter:E-ORG}."
Model trained on original data:
- "Me llamo asier y vivo en barcelona todo el año." → "Me llamo asier y vivo en barcelona todo el año." (nothing)
- "Hoy voy a visitar el parc güell tras salir del barcelona supercomputing center." → "Hoy voy a visitar el parc güell tras salir del barcelona supercomputing center." (nothing)
## Evaluation and results
F1 Score: 0.8867
For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish).
## Citing
Check out our paper for all the details: https://arxiv.org/abs/2107.07253
```
@misc{gutierrezfandino2021spanish,
title={Spanish Language Models},
author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas},
year={2021},
eprint={2107.07253},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
BSC-LT/roberta-base-bne-capitel-ner
|
BSC-LT
|
roberta
| 9 | 0 |
transformers
| 1 |
token-classification
| true | false | false |
apache-2.0
|
['es']
|
['bne', 'capitel']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['national library of spain', 'spanish', 'bne', 'capitel', 'ner']
| false | true | true | 1,652 |
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-capitel-ner
# Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset.
RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-base-bne
## Dataset
The dataset used is the one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 1).
## Evaluation and results
F1 Score: 0.8960
For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish).
## Citing
Check out our paper for all the details: https://arxiv.org/abs/2107.07253
```
@misc{gutierrezfandino2021spanish,
title={Spanish Language Models},
author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas},
year={2021},
eprint={2107.07253},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
BSC-LT/roberta-base-bne-capitel-pos
|
BSC-LT
|
roberta
| 9 | 1 |
transformers
| 3 |
token-classification
| true | false | false |
apache-2.0
|
['es']
|
['bne', 'capitel']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['national library of spain', 'spanish', 'bne', 'capitel', 'pos']
| false | true | true | 1,662 |
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-capitel-pos
# Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Part of Speech (POS) dataset
RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-base-bne
## Dataset
The dataset used is the one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 2).
## Evaluation and results
F1 Score: 0.9846 (average of 5 runs).
For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish).
## Citing
Check out our paper for all the details: https://arxiv.org/abs/2107.07253
```
@misc{gutierrezfandino2021spanish,
title={Spanish Language Models},
author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas},
year={2021},
eprint={2107.07253},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
BSC-LT/roberta-base-bne-sqac
|
BSC-LT
|
roberta
| 9 | 3 |
transformers
| 3 |
question-answering
| true | false | false |
apache-2.0
|
['es']
|
['BSC-TeMU/SQAC']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['national library of spain', 'spanish', 'bne', 'qa', 'question answering']
| false | true | true | 1,622 |
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-sqac
# Spanish RoBERTa-base trained on BNE finetuned for Spanish Question Answering Corpus (SQAC) dataset.
RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-base-bne
## Dataset
The dataset used is the [SQAC corpus](https://huggingface.co/datasets/BSC-TeMU/SQAC).
## Evaluation and results
F1 Score: 0.7923 (average of 5 runs).
For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish).
## Citing
Check out our paper for all the details: https://arxiv.org/abs/2107.07253
```
@misc{gutierrezfandino2021spanish,
title={Spanish Language Models},
author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas},
year={2021},
eprint={2107.07253},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
BSC-LT/roberta-base-bne
|
BSC-LT
|
roberta
| 10 | 1 |
transformers
| 8 |
fill-mask
| true | false | false |
apache-2.0
|
['es']
|
['bne']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['national library of spain', 'spanish', 'bne']
| false | true | true | 2,808 |
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne
# RoBERTa base trained with data from National Library of Spain (BNE)
## Model Description
RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
## Training corpora and preprocessing
The [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) crawls all .es domains once a year. The training corpus consists of 59TB of WARC files from these crawls, carried out from 2009 to 2019.
To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents. During the process document boundaries are kept. This resulted into 2TB of Spanish clean corpus. Further global deduplication among the corpus is applied, resulting into 570GB of text.
Some of the statistics of the corpus:
| Corpora | Number of documents | Number of tokens | Size (GB) |
|---------|---------------------|------------------|-----------|
| BNE | 201,080,084 | 135,733,450,668 | 570GB |
## Tokenization and pre-training
The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original [RoBERTA](https://arxiv.org/abs/1907.11692) model with a vocabulary size of 50,262 tokens. The RoBERTa-base-bne pre-training consists of a masked language model training that follows the approach employed for the RoBERTa base. The training lasted a total of 48 hours with 16 computing nodes each one with 4 NVIDIA V100 GPUs of 16GB VRAM.
## Evaluation and results
For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish).
## Citing
Check out our paper for all the details: https://arxiv.org/abs/2107.07253
```
@misc{gutierrezfandino2021spanish,
title={Spanish Language Models},
author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas},
year={2021},
eprint={2107.07253},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
BSC-LT/roberta-base-ca
|
BSC-LT
|
roberta
| 9 | 4 |
transformers
| 3 |
fill-mask
| true | false | false |
apache-2.0
|
['ca']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['masked-lm', 'BERTa', 'catalan']
| false | true | true | 11,501 |
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-base-ca
# BERTa: RoBERTa-based Catalan language model
## BibTeX citation
If you use any of these resources (datasets or models) in your work, please cite our latest paper:
```bibtex
@inproceedings{armengol-estape-etal-2021-multilingual,
title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
author = "Armengol-Estap{\'e}, Jordi and
Carrino, Casimiro Pio and
Rodriguez-Penagos, Carlos and
de Gibert Bonet, Ona and
Armentano-Oller, Carme and
Gonzalez-Agirre, Aitor and
Melero, Maite and
Villegas, Marta",
booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-acl.437",
doi = "10.18653/v1/2021.findings-acl.437",
pages = "4933--4946",
}
```
## Model description
BERTa is a transformer-based masked language model for the Catalan language.
It is based on the [RoBERTA](https://github.com/pytorch/fairseq/tree/master/examples/roberta) base model
and has been trained on a medium-size corpus collected from publicly available corpora and crawlers.
## Training corpora and preprocessing
The training corpus consists of several corpora gathered from web crawling and public corpora.
The publicly available corpora are:
1. the Catalan part of the [DOGC](http://opus.nlpl.eu/DOGC-v2.php) corpus, a set of documents from the Official Gazette of the Catalan Government
2. the [Catalan Open Subtitles](http://opus.nlpl.eu/download.php?f=OpenSubtitles/v2018/mono/OpenSubtitles.raw.ca.gz), a collection of translated movie subtitles
3. the non-shuffled version of the Catalan part of the [OSCAR](https://traces1.inria.fr/oscar/) corpus \\\\cite{suarez2019asynchronous},
a collection of monolingual corpora, filtered from [Common Crawl](https://commoncrawl.org/about/)
4. The [CaWac](http://nlp.ffzg.hr/resources/corpora/cawac/) corpus, a web corpus of Catalan built from the .cat top-level-domain in late 2013
the non-deduplicated version
5. the [Catalan Wikipedia articles](https://ftp.acc.umu.se/mirror/wikimedia.org/dumps/cawiki/20200801/) downloaded on 18-08-2020.
The crawled corpora are:
6. The Catalan General Crawling, obtained by crawling the 500 most popular .cat and .ad domains
7. the Catalan Government Crawling, obtained by crawling the .gencat domain and subdomains, belonging to the Catalan Government
8. the ACN corpus with 220k news items from March 2015 until October 2020, crawled from the [Catalan News Agency](https://www.acn.cat/)
To obtain a high-quality training corpus, each corpus have preprocessed with a pipeline of operations, including among the others,
sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents.
During the process, we keep document boundaries are kept.
Finally, the corpora are concatenated and further global deduplication among the corpora is applied.
The final training corpus consists of about 1,8B tokens.
## Tokenization and pretraining
The training corpus has been tokenized using a byte version of [Byte-Pair Encoding (BPE)](https://github.com/openai/gpt-2)
used in the original [RoBERTA](https://github.com/pytorch/fairseq/tree/master/examples/roberta) model with a vocabulary size of 52,000 tokens.
The BERTa pretraining consists of a masked language model training that follows the approach employed for the RoBERTa base model
with the same hyperparameters as in the original work.
The training lasted a total of 48 hours with 16 NVIDIA V100 GPUs of 16GB DDRAM.
## Evaluation
## CLUB benchmark
The BERTa model has been fine-tuned on the downstream tasks of the Catalan Language Understanding Evaluation benchmark (CLUB),
that has been created along with the model.
It contains the following tasks and their related datasets:
1. Part-of-Speech Tagging (POS)
Catalan-Ancora: from the [Universal Dependencies treebank](https://github.com/UniversalDependencies/UD_Catalan-AnCora) of the well-known Ancora corpus
2. Named Entity Recognition (NER)
**[AnCora Catalan 2.0.0](https://zenodo.org/record/4762031#.YKaFjqGxWUk)**: extracted named entities from the original [Ancora](https://doi.org/10.5281/zenodo.4762030) version,
filtering out some unconventional ones, like book titles, and transcribed them into a standard CONLL-IOB format
3. Text Classification (TC)
**[TeCla](https://doi.org/10.5281/zenodo.4627197)**: consisting of 137k news pieces from the Catalan News Agency ([ACN](https://www.acn.cat/)) corpus
4. Semantic Textual Similarity (STS)
**[Catalan semantic textual similarity](https://doi.org/10.5281/zenodo.4529183)**: consisting of more than 3000 sentence pairs, annotated with the semantic similarity between them,
scraped from the [Catalan Textual Corpus](https://doi.org/10.5281/zenodo.4519349)
5. Question Answering (QA):
**[ViquiQuAD](https://doi.org/10.5281/zenodo.4562344)**: consisting of more than 15,000 questions outsourced from Catalan Wikipedia randomly chosen from a set of 596 articles that were originally written in Catalan.
**[XQuAD](https://doi.org/10.5281/zenodo.4526223)**: the Catalan translation of XQuAD, a multilingual collection of manual translations of 1,190 question-answer pairs from English Wikipedia used only as a _test set_
Here are the train/dev/test splits of the datasets:
| Task (Dataset) | Total | Train | Dev | Test |
|:--|:--|:--|:--|:--|
| NER (Ancora) |13,581 | 10,628 | 1,427 | 1,526 |
| POS (Ancora)| 16,678 | 13,123 | 1,709 | 1,846 |
| STS | 3,073 | 2,073 | 500 | 500 |
| TC (TeCla) | 137,775 | 110,203 | 13,786 | 13,786|
| QA (ViquiQuAD) | 14,239 | 11,255 | 1,492 | 1,429 |
_The fine-tuning on downstream tasks have been performed with the HuggingFace [**Transformers**](https://github.com/huggingface/transformers) library_
## Results
Below the evaluation results on the CLUB tasks compared with the multilingual mBERT, XLM-RoBERTa models and
the Catalan WikiBERT-ca model
| Task | NER (F1) | POS (F1) | STS (Pearson) | TC (accuracy) | QA (ViquiQuAD) (F1/EM) | QA (XQuAD) (F1/EM) |
| ------------|:-------------:| -----:|:------|:-------|:------|:----|
| BERTa | **88.13** | **98.97** | **79.73** | **74.16** | **86.97/72.29** | **68.89/48.87** |
| mBERT | 86.38 | 98.82 | 76.34 | 70.56 | 86.97/72.22 | 67.15/46.51 |
| XLM-RoBERTa | 87.66 | 98.89 | 75.40 | 71.68 | 85.50/70.47 | 67.10/46.42 |
| WikiBERT-ca | 77.66 | 97.60 | 77.18 | 73.22 | 85.45/70.75 | 65.21/36.60 |
## Intended uses & limitations
The model is ready-to-use only for masked language modelling to perform the Fill Mask task (try the inference API or read the next section)
However, the is intended to be fine-tuned on non-generative downstream tasks such as Question Answering, Text Classification or Named Entity Recognition.
---
## Using BERTa
## Load model and tokenizer
``` python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("BSC-TeMU/roberta-base-ca-cased")
model = AutoModelForMaskedLM.from_pretrained("BSC-TeMU/roberta-base-ca-cased")
```
## Fill Mask task
Below, an example of how to use the masked language modelling task with a pipeline.
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='BSC-TeMU/roberta-base-ca-cased')
>>> unmasker("Situada a la costa de la mar Mediterrània, <mask> s'assenta en una plana formada "
"entre els deltes de les desembocadures dels rius Llobregat, al sud-oest, "
"i Besòs, al nord-est, i limitada pel sud-est per la línia de costa,"
"i pel nord-oest per la serralada de Collserola "
"(amb el cim del Tibidabo, 516,2 m, com a punt més alt) que segueix paral·lela "
"la línia de costa encaixant la ciutat en un perímetre molt definit.")
[
{
"sequence": " Situada a la costa de la mar Mediterrània, <mask> s'assenta en una plana formada "
"entre els deltes de les desembocadures dels rius Llobregat, al sud-oest, "
"i Besòs, al nord-est, i limitada pel sud-est per la línia de costa,"
"i pel nord-oest per la serralada de Collserola "
"(amb el cim del Tibidabo, 516,2 m, com a punt més alt) que segueix paral·lela "
"la línia de costa encaixant la ciutat en un perímetre molt definit.",
"score": 0.4177263379096985,
"token": 734,
"token_str": " Barcelona"
},
{
"sequence": " Situada a la costa de la mar Mediterrània, <mask> s'assenta en una plana formada "
"entre els deltes de les desembocadures dels rius Llobregat, al sud-oest, "
"i Besòs, al nord-est, i limitada pel sud-est per la línia de costa,"
"i pel nord-oest per la serralada de Collserola "
"(amb el cim del Tibidabo, 516,2 m, com a punt més alt) que segueix paral·lela "
"la línia de costa encaixant la ciutat en un perímetre molt definit.",
"score": 0.10696165263652802,
"token": 3849,
"token_str": " Badalona"
},
{
"sequence": " Situada a la costa de la mar Mediterrània, <mask> s'assenta en una plana formada "
"entre els deltes de les desembocadures dels rius Llobregat, al sud-oest, "
"i Besòs, al nord-est, i limitada pel sud-est per la línia de costa,"
"i pel nord-oest per la serralada de Collserola "
"(amb el cim del Tibidabo, 516,2 m, com a punt més alt) que segueix paral·lela "
"la línia de costa encaixant la ciutat en un perímetre molt definit.",
"score": 0.08135009557008743,
"token": 19349,
"token_str": " Collserola"
},
{
"sequence": " Situada a la costa de la mar Mediterrània, <mask> s'assenta en una plana formada "
"entre els deltes de les desembocadures dels rius Llobregat, al sud-oest, "
"i Besòs, al nord-est, i limitada pel sud-est per la línia de costa,"
"i pel nord-oest per la serralada de Collserola "
"(amb el cim del Tibidabo, 516,2 m, com a punt més alt) que segueix paral·lela "
"la línia de costa encaixant la ciutat en un perímetre molt definit.",
"score": 0.07330769300460815,
"token": 4974,
"token_str": " Terrassa"
},
{
"sequence": " Situada a la costa de la mar Mediterrània, <mask> s'assenta en una plana formada "
"entre els deltes de les desembocadures dels rius Llobregat, al sud-oest, "
"i Besòs, al nord-est, i limitada pel sud-est per la línia de costa,"
"i pel nord-oest per la serralada de Collserola "
"(amb el cim del Tibidabo, 516,2 m, com a punt més alt) que segueix paral·lela "
"la línia de costa encaixant la ciutat en un perímetre molt definit.",
"score": 0.03317456692457199,
"token": 14333,
"token_str": " Gavà"
}
]
```
This model was originally published as [bsc/roberta-base-ca-cased](https://huggingface.co/bsc/roberta-base-ca-cased).
|
BSC-LT/roberta-large-bne-capitel-ner
|
BSC-LT
|
roberta
| 9 | 1 |
transformers
| 0 |
token-classification
| true | false | false |
apache-2.0
|
['es']
|
['bne', 'capitel']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['national library of spain', 'spanish', 'bne', 'capitel', 'ner']
| false | true | true | 1,656 |
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne-capitel-ner
# Spanish RoBERTa-large trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset.
RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) large model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-large-bne
## Dataset
The dataset used is the one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 1).
## Evaluation and results
F1 Score: 0.8998
For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish).
## Citing
Check out our paper for all the details: https://arxiv.org/abs/2107.07253
```
@misc{gutierrezfandino2021spanish,
title={Spanish Language Models},
author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas},
year={2021},
eprint={2107.07253},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
BSC-LT/roberta-large-bne-capitel-pos
|
BSC-LT
|
roberta
| 9 | 0 |
transformers
| 3 |
token-classification
| true | false | false |
apache-2.0
|
['es']
|
['bne', 'capitel']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['national library of spain', 'spanish', 'bne', 'capitel', 'pos']
| false | true | true | 1,667 |
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne-capitel-pos
# Spanish RoBERTa-large trained on BNE finetuned for CAPITEL Part of Speech (POS) dataset
RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) large model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-large-bne
## Dataset
The dataset used is the one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 2).
## Evaluation and results
F1 Score: 0.9851 (average of 5 runs).
For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish).
## Citing
Check out our paper for all the details: https://arxiv.org/abs/2107.07253
```
@misc{gutierrezfandino2021spanish,
title={Spanish Language Models},
author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas},
year={2021},
eprint={2107.07253},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
BSC-LT/roberta-large-bne-sqac
|
BSC-LT
|
roberta
| 9 | 0 |
transformers
| 3 |
question-answering
| true | false | false |
apache-2.0
|
['es']
|
['BSC-TeMU/SQAC']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['national library of spain', 'spanish', 'bne', 'qa', 'question answering']
| false | true | true | 1,627 |
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne-sqac
# Spanish RoBERTa-large trained on BNE finetuned for Spanish Question Answering Corpus (SQAC) dataset.
RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) large model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-large-bne
## Dataset
The dataset used is the [SQAC corpus](https://huggingface.co/datasets/BSC-TeMU/SQAC).
## Evaluation and results
F1 Score: 0.7993 (average of 5 runs).
For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish).
## Citing
Check out our paper for all the details: https://arxiv.org/abs/2107.07253
```
@misc{gutierrezfandino2021spanish,
title={Spanish Language Models},
author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas},
year={2021},
eprint={2107.07253},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
BSC-LT/roberta-large-bne
|
BSC-LT
|
roberta
| 10 | 0 |
transformers
| 7 |
fill-mask
| true | false | false |
apache-2.0
|
['es']
|
['bne']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['national library of spain', 'spanish', 'bne']
| false | true | true | 2,814 |
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne
# RoBERTa large trained with data from National Library of Spain (BNE)
## Model Description
RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) large model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
## Training corpora and preprocessing
The [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) crawls all .es domains once a year. The training corpus consists of 59TB of WARC files from these crawls, carried out from 2009 to 2019.
To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents. During the process document boundaries are kept. This resulted into 2TB of Spanish clean corpus. Further global deduplication among the corpus is applied, resulting into 570GB of text.
Some of the statistics of the corpus:
| Corpora | Number of documents | Number of tokens | Size (GB) |
|---------|---------------------|------------------|-----------|
| BNE | 201,080,084 | 135,733,450,668 | 570GB |
## Tokenization and pre-training
The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original [RoBERTA](https://arxiv.org/abs/1907.11692) model with a vocabulary size of 50,262 tokens. The RoBERTa-large-bne pre-training consists of a masked language model training that follows the approach employed for the RoBERTa large. The training lasted a total of 96 hours with 32 computing nodes each one with 4 NVIDIA V100 GPUs of 16GB VRAM.
## Evaluation and results
For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish).
## Citing
Check out our paper for all the details: https://arxiv.org/abs/2107.07253
```
@misc{gutierrezfandino2021spanish,
title={Spanish Language Models},
author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas},
year={2021},
eprint={2107.07253},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
BSen/wav2vec2-base-timit-demo-colab
|
BSen
|
wav2vec2
| 14 | 5 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,341 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4877
- Wer: 0.4895
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.6615 | 4.0 | 500 | 1.7423 | 1.0723 |
| 0.8519 | 8.0 | 1000 | 0.4877 | 0.4895 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
BSen/wav2vec2-large-xls-r-300m-turkish-colab
|
BSen
|
wav2vec2
| 13 | 5 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
| null |
['common_voice']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,105 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
Babelscape/rebel-large
|
Babelscape
|
bart
| 10 | 216,008 |
transformers
| 48 |
text2text-generation
| true | false | false |
cc-by-nc-sa-4.0
|
['en']
|
['Babelscape/rebel-dataset']
| null | 0 | 0 | 0 | 0 | 3 | 0 | 3 |
['seq2seq', 'relation-extraction']
| true | true | true | 8,298 |
[](https://paperswithcode.com/sota/relation-extraction-on-nyt?p=rebel-relation-extraction-by-end-to-end)
[](https://paperswithcode.com/sota/relation-extraction-on-conll04?p=rebel-relation-extraction-by-end-to-end)
[](https://paperswithcode.com/sota/joint-entity-and-relation-extraction-on-3?p=rebel-relation-extraction-by-end-to-end)
[](https://paperswithcode.com/sota/relation-extraction-on-ade-corpus?p=rebel-relation-extraction-by-end-to-end)
[](https://paperswithcode.com/sota/relation-extraction-on-re-tacred?p=rebel-relation-extraction-by-end-to-end)
# REBEL <img src="https://i.ibb.co/qsLzNqS/hf-rebel.png" width="30" alt="hf-rebel" border="0" style="display:inline; white-space:nowrap;">: Relation Extraction By End-to-end Language generation
This is the model card for the Findings of EMNLP 2021 paper [REBEL: Relation Extraction By End-to-end Language generation](https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf). We present a new linearization approach and a reframing of Relation Extraction as a seq2seq task. The paper can be found [here](https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf). If you use the code, please reference this work in your paper:
@inproceedings{huguet-cabot-navigli-2021-rebel-relation,
title = "{REBEL}: Relation Extraction By End-to-end Language generation",
author = "Huguet Cabot, Pere-Llu{\'\i}s and
Navigli, Roberto",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.204",
pages = "2370--2381",
abstract = "Extracting relation triplets from raw text is a crucial task in Information Extraction, enabling multiple applications such as populating or validating knowledge bases, factchecking, and other downstream tasks. However, it usually involves multiple-step pipelines that propagate errors or are limited to a small number of relation types. To overcome these issues, we propose the use of autoregressive seq2seq models. Such models have previously been shown to perform well not only in language generation, but also in NLU tasks such as Entity Linking, thanks to their framing as seq2seq tasks. In this paper, we show how Relation Extraction can be simplified by expressing triplets as a sequence of text and we present REBEL, a seq2seq model based on BART that performs end-to-end relation extraction for more than 200 different relation types. We show our model{'}s flexibility by fine-tuning it on an array of Relation Extraction and Relation Classification benchmarks, with it attaining state-of-the-art performance in most of them.",
}
The original repository for the paper can be found [here](https://github.com/Babelscape/rebel)
Be aware that the inference widget at the right does not output special tokens, which are necessary to distinguish the subject, object and relation types. For a demo of REBEL and its pre-training dataset check the [Spaces demo](https://huggingface.co/spaces/Babelscape/rebel-demo).
## Pipeline usage
```python
from transformers import pipeline
triplet_extractor = pipeline('text2text-generation', model='Babelscape/rebel-large', tokenizer='Babelscape/rebel-large')
# We need to use the tokenizer manually since we need special tokens.
extracted_text = triplet_extractor.tokenizer.batch_decode([triplet_extractor("Punta Cana is a resort town in the municipality of Higuey, in La Altagracia Province, the eastern most province of the Dominican Republic", return_tensors=True, return_text=False)[0]["generated_token_ids"]])
print(extracted_text[0])
# Function to parse the generated text and extract the triplets
def extract_triplets(text):
triplets = []
relation, subject, relation, object_ = '', '', '', ''
text = text.strip()
current = 'x'
for token in text.replace("<s>", "").replace("<pad>", "").replace("</s>", "").split():
if token == "<triplet>":
current = 't'
if relation != '':
triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})
relation = ''
subject = ''
elif token == "<subj>":
current = 's'
if relation != '':
triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})
object_ = ''
elif token == "<obj>":
current = 'o'
relation = ''
else:
if current == 't':
subject += ' ' + token
elif current == 's':
object_ += ' ' + token
elif current == 'o':
relation += ' ' + token
if subject != '' and relation != '' and object_ != '':
triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})
return triplets
extracted_triplets = extract_triplets(extracted_text[0])
print(extracted_triplets)
```
## Model and Tokenizer using transformers
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
def extract_triplets(text):
triplets = []
relation, subject, relation, object_ = '', '', '', ''
text = text.strip()
current = 'x'
for token in text.replace("<s>", "").replace("<pad>", "").replace("</s>", "").split():
if token == "<triplet>":
current = 't'
if relation != '':
triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})
relation = ''
subject = ''
elif token == "<subj>":
current = 's'
if relation != '':
triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})
object_ = ''
elif token == "<obj>":
current = 'o'
relation = ''
else:
if current == 't':
subject += ' ' + token
elif current == 's':
object_ += ' ' + token
elif current == 'o':
relation += ' ' + token
if subject != '' and relation != '' and object_ != '':
triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})
return triplets
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("Babelscape/rebel-large")
model = AutoModelForSeq2SeqLM.from_pretrained("Babelscape/rebel-large")
gen_kwargs = {
"max_length": 256,
"length_penalty": 0,
"num_beams": 3,
"num_return_sequences": 3,
}
# Text to extract triplets from
text = 'Punta Cana is a resort town in the municipality of Higüey, in La Altagracia Province, the easternmost province of the Dominican Republic.'
# Tokenizer text
model_inputs = tokenizer(text, max_length=256, padding=True, truncation=True, return_tensors = 'pt')
# Generate
generated_tokens = model.generate(
model_inputs["input_ids"].to(model.device),
attention_mask=model_inputs["attention_mask"].to(model.device),
**gen_kwargs,
)
# Extract text
decoded_preds = tokenizer.batch_decode(generated_tokens, skip_special_tokens=False)
# Extract triplets
for idx, sentence in enumerate(decoded_preds):
print(f'Prediction triplets sentence {idx}')
print(extract_triplets(sentence))
```
|
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