repo_id
stringlengths 4
110
| author
stringlengths 2
27
โ | model_type
stringlengths 2
29
โ | files_per_repo
int64 2
15.4k
| downloads_30d
int64 0
19.9M
| library
stringlengths 2
37
โ | likes
int64 0
4.34k
| pipeline
stringlengths 5
30
โ | pytorch
bool 2
classes | tensorflow
bool 2
classes | jax
bool 2
classes | license
stringlengths 2
30
| languages
stringlengths 4
1.63k
โ | datasets
stringlengths 2
2.58k
โ | co2
stringclasses 29
values | prs_count
int64 0
125
| prs_open
int64 0
120
| prs_merged
int64 0
15
| prs_closed
int64 0
28
| discussions_count
int64 0
218
| discussions_open
int64 0
148
| discussions_closed
int64 0
70
| tags
stringlengths 2
513
| has_model_index
bool 2
classes | has_metadata
bool 1
class | has_text
bool 1
class | text_length
int64 401
598k
| is_nc
bool 1
class | readme
stringlengths 0
598k
| hash
stringlengths 32
32
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
clementchadebec/reproduced_aae
|
clementchadebec
| null | 8 | 0 |
pythae
| 0 | null | false | false | false |
apache-2.0
|
['en']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['pythae', 'reproducibility']
| false | true | true | 660 | false |
### Downloading this model from the Hub
This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from pythae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_aae")
```
## Reproducibility
This trained model reproduces the results of Table 1 in [1].
| Model | Dataset | Metric | Obtained value | Reference value |
|:---:|:---:|:---:|:---:|:---:|
| AAE | CELEBA 64 | FID | 43.3 | 42 |
[1] Tolstikhin, O Bousquet, S Gelly, and B Schรถlkopf. Wasserstein auto-encoders. In 6th International Conference on Learning Representations (ICLR 2018), 2018.
|
618982bda9f3d7471c3beaf0221b52ca
|
roscazo/DISO_bsc_test16
|
roscazo
|
roberta
| 14 | 3 |
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 | 2,955 | false |
<!-- 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. -->
# DISO_bsc_test16
This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1732
- Diso Precision: 0.7577
- Diso Recall: 0.7757
- Diso F1: 0.7666
- Diso Number: 4552
- Overall Precision: 0.7577
- Overall Recall: 0.7757
- Overall F1: 0.7666
- Overall Accuracy: 0.9732
## 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: 8e-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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Diso Precision | Diso Recall | Diso F1 | Diso Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------------:|:-----------:|:-------:|:-----------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0948 | 1.0 | 1400 | 0.0766 | 0.7157 | 0.7594 | 0.7369 | 4552 | 0.7157 | 0.7594 | 0.7369 | 0.9710 |
| 0.0631 | 2.0 | 2800 | 0.0818 | 0.7442 | 0.7599 | 0.7520 | 4552 | 0.7442 | 0.7599 | 0.7520 | 0.9726 |
| 0.0454 | 3.0 | 4200 | 0.0842 | 0.7544 | 0.7654 | 0.7599 | 4552 | 0.7544 | 0.7654 | 0.7599 | 0.9728 |
| 0.0311 | 4.0 | 5600 | 0.1113 | 0.7678 | 0.7700 | 0.7689 | 4552 | 0.7678 | 0.7700 | 0.7689 | 0.9732 |
| 0.0217 | 5.0 | 7000 | 0.1231 | 0.7745 | 0.7687 | 0.7716 | 4552 | 0.7745 | 0.7687 | 0.7716 | 0.9743 |
| 0.015 | 6.0 | 8400 | 0.1482 | 0.7651 | 0.7733 | 0.7691 | 4552 | 0.7651 | 0.7733 | 0.7691 | 0.9735 |
| 0.0101 | 7.0 | 9800 | 0.1498 | 0.7576 | 0.7709 | 0.7642 | 4552 | 0.7576 | 0.7709 | 0.7642 | 0.9730 |
| 0.0073 | 8.0 | 11200 | 0.1732 | 0.7577 | 0.7757 | 0.7666 | 4552 | 0.7577 | 0.7757 | 0.7666 | 0.9732 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
9ad7f3bd67d8c0a90b82847fd66aa875
|
vesteinn/IceBERT-finetuned-iec-sentence-bs16
|
vesteinn
|
roberta
| 11 | 3 |
transformers
| 0 |
text-classification
| true | false | false |
gpl-3.0
| null | null | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,557 | false |
<!-- 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. -->
# IceBERT-finetuned-iec-sentence-bs16
This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/IceBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2508
- Matthews Correlation: 0.8169
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------------------:|
| 0.5278 | 1.0 | 3640 | 0.4777 | 0.5396 |
| 0.4648 | 2.0 | 7280 | 0.3886 | 0.6437 |
| 0.3807 | 3.0 | 10920 | 0.3478 | 0.7060 |
| 0.3061 | 4.0 | 14560 | 0.2523 | 0.8083 |
| 0.2477 | 5.0 | 18200 | 0.2508 | 0.8169 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.8.0
- Datasets 1.15.1
- Tokenizers 0.10.3
|
d9f01386cf5d2e6cf46102fa13a0e6f2
|
jaeyeon/korean-aihub-learning-math-16batch
|
jaeyeon
|
wav2vec2
| 13 | 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 | 3,113 | false |
<!-- 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. -->
# korean-aihub-learning-math-16batch
This model is a fine-tuned version of [kresnik/wav2vec2-large-xlsr-korean](https://huggingface.co/kresnik/wav2vec2-large-xlsr-korean) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1497
- Wer: 0.5260
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 20 | 32.0718 | 1.0 |
| No log | 2.0 | 40 | 24.7403 | 1.0808 |
| No log | 3.0 | 60 | 5.8389 | 1.0 |
| No log | 4.0 | 80 | 4.8543 | 1.0 |
| 19.6583 | 5.0 | 100 | 4.4453 | 1.0 |
| 19.6583 | 6.0 | 120 | 4.3923 | 1.0 |
| 19.6583 | 7.0 | 140 | 4.2902 | 1.0 |
| 19.6583 | 8.0 | 160 | 3.9026 | 0.9959 |
| 19.6583 | 9.0 | 180 | 3.0616 | 0.9740 |
| 3.7358 | 10.0 | 200 | 2.2049 | 0.8534 |
| 3.7358 | 11.0 | 220 | 1.6666 | 0.7288 |
| 3.7358 | 12.0 | 240 | 1.4123 | 0.6603 |
| 3.7358 | 13.0 | 260 | 1.3113 | 0.6164 |
| 3.7358 | 14.0 | 280 | 1.2269 | 0.6356 |
| 0.8398 | 15.0 | 300 | 1.2349 | 0.5945 |
| 0.8398 | 16.0 | 320 | 1.1970 | 0.5658 |
| 0.8398 | 17.0 | 340 | 1.2144 | 0.5562 |
| 0.8398 | 18.0 | 360 | 1.2551 | 0.5658 |
| 0.8398 | 19.0 | 380 | 1.1971 | 0.5493 |
| 0.2649 | 20.0 | 400 | 1.1967 | 0.5247 |
| 0.2649 | 21.0 | 420 | 1.2796 | 0.5849 |
| 0.2649 | 22.0 | 440 | 1.2156 | 0.5521 |
| 0.2649 | 23.0 | 460 | 1.2118 | 0.5425 |
| 0.2649 | 24.0 | 480 | 1.1637 | 0.5384 |
| 0.1801 | 25.0 | 500 | 1.1846 | 0.5562 |
| 0.1801 | 26.0 | 520 | 1.1927 | 0.5534 |
| 0.1801 | 27.0 | 540 | 1.2015 | 0.5384 |
| 0.1801 | 28.0 | 560 | 1.2077 | 0.5397 |
| 0.1801 | 29.0 | 580 | 1.1554 | 0.5260 |
| 0.1364 | 30.0 | 600 | 1.1497 | 0.5260 |
### Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
33791677d4908c0af629ea1512354bd7
|
nlp-waseda/roberta-large-japanese-seq512-with-auto-jumanpp
|
nlp-waseda
|
roberta
| 7 | 323 |
transformers
| 1 |
fill-mask
| true | false | false |
cc-by-sa-4.0
|
['ja']
|
['wikipedia', 'cc100']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 2,299 | false |
# nlp-waseda/roberta-large-japanese-seq512-with-auto-jumanpp
## Model description
This is a Japanese RoBERTa large model pretrained on Japanese Wikipedia and the Japanese portion of CC-100 with the maximum sequence length of 512.
## How to use
You can use this model for masked language modeling as follows:
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("nlp-waseda/roberta-large-japanese-seq512-with-auto-jumanpp")
model = AutoModelForMaskedLM.from_pretrained("nlp-waseda/roberta-large-japanese-seq512-with-auto-jumanpp")
sentence = 'ๆฉ็จฒ็ฐๅคงๅญฆใง่ช็ถ่จ่ชๅฆ็ใ[MASK]ใใใ'
encoding = tokenizer(sentence, return_tensors='pt')
...
```
You can fine-tune this model on downstream tasks.
## Tokenization
`BertJapaneseTokenizer` now supports automatic tokenization for [Juman++](https://github.com/ku-nlp/jumanpp). However, if your dataset is large, you may take a long time since `BertJapaneseTokenizer` still does not supoort fast tokenization. You can still do the Juman++ tokenization by your self and use the old model [nlp-waseda/roberta-large-japanese-seq512](https://huggingface.co/nlp-waseda/roberta-large-japanese-seq512).
Juman++ 2.0.0-rc3 was used for pretraining. Each word is tokenized into tokens by [sentencepiece](https://github.com/google/sentencepiece).
## Vocabulary
The vocabulary consists of 32000 tokens including words ([JumanDIC](https://github.com/ku-nlp/JumanDIC)) and subwords induced by the unigram language model of [sentencepiece](https://github.com/google/sentencepiece).
## Training procedure
This model was trained on Japanese Wikipedia (as of 20210920) and the Japanese portion of CC-100 from the checkpoint of [nlp-waseda/roberta-large-japanese](https://huggingface.co/nlp-waseda/roberta-large-japanese). It took a week using eight NVIDIA A100 GPUs.
The following hyperparameters were used during pretraining:
- learning_rate: 6e-5
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 4120 (max_seq_length=128), 4032 (max_seq_length=512)
- max_seq_length: 512
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-6
- lr_scheduler_type: linear
- training_steps: 670000 (max_seq_length=128) + 70000 (max_seq_length=512)
- warmup_steps: 10000
- mixed_precision_training: Native AMP
|
ad8e1b35b922e58246be07ff9496d771
|
malay-patel/bert-finetuned-squad-nq
|
malay-patel
|
roberta
| 9 | 5 |
transformers
| 0 |
question-answering
| false | true | false |
apache-2.0
| null | null | null | 2 | 0 | 2 | 0 | 0 | 0 | 0 |
['generated_from_keras_callback']
| true | true | true | 1,716 | false |
<!-- 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. -->
# malay-patel/bert-finetuned-squad-nq
This model is a fine-tuned version of [nlpconnect/roberta-base-squad2-nq](https://huggingface.co/nlpconnect/roberta-base-squad2-nq) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.5461
- Train End Logits Accuracy: 0.6253
- Train Start Logits Accuracy: 0.6120
- 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 861, '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, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:-----:|
| 1.5548 | 0.6236 | 0.6172 | 0 |
| 1.5423 | 0.6286 | 0.6192 | 1 |
| 1.5461 | 0.6253 | 0.6120 | 2 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.9.2
- Datasets 2.7.1
- Tokenizers 0.13.2
|
15d230072a6ee14ba8fc6e8010c34f86
|
varun1/bert-finetuned-squad
|
varun1
|
bert
| 8 | 5 |
transformers
| 0 |
question-answering
| false | true | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_keras_callback']
| true | true | true | 1,264 | false |
<!-- 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. -->
# varun1/bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.2322
- Epoch: 0
## 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5546, '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, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 1.2322 | 0 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.9.2
- Datasets 2.7.1
- Tokenizers 0.13.2
|
608cbce46cd757fd1e833c0bc7477797
|
emmyapi/distilbart-podimo-data-eval-1
|
emmyapi
|
bart
| 13 | 1 |
transformers
| 0 |
text2text-generation
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,206 | false |
<!-- 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. -->
# distilbart-podimo-data-eval-1
This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3983
- Rouge1: 34.6132
- Rouge2: 7.9113
- Rougel: 17.9418
- Rougelsum: 31.5251
- Gen Len: 141.5587
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:--------:|
| 4.1934 | 0.98 | 44 | 3.7592 | 32.8148 | 6.457 | 16.8696 | 29.6986 | 141.4441 |
| 3.6362 | 1.98 | 88 | 3.5809 | 33.0442 | 6.851 | 17.1323 | 30.1382 | 141.324 |
| 3.3554 | 2.98 | 132 | 3.4835 | 33.66 | 7.1375 | 17.5152 | 30.5783 | 141.2793 |
| 3.1566 | 3.98 | 176 | 3.4301 | 34.524 | 7.757 | 17.995 | 31.5808 | 141.7151 |
| 3.0107 | 4.98 | 220 | 3.4099 | 34.3459 | 7.7512 | 18.0605 | 31.4531 | 141.4106 |
| 2.901 | 5.98 | 264 | 3.4073 | 35.028 | 7.9099 | 17.9907 | 31.8304 | 141.5419 |
| 2.8246 | 6.98 | 308 | 3.3983 | 34.1937 | 7.8606 | 17.7858 | 31.1331 | 141.5279 |
| 2.7306 | 7.98 | 352 | 3.3983 | 34.6132 | 7.9113 | 17.9418 | 31.5251 | 141.5587 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.11.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
6d574fb8269e13f378a6ceb3fe55391c
|
jonatasgrosman/exp_w2v2t_et_vp-it_s222
|
jonatasgrosman
|
wav2vec2
| 10 | 5 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
|
['et']
|
['mozilla-foundation/common_voice_7_0']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['automatic-speech-recognition', 'et']
| false | true | true | 469 | false |
# exp_w2v2t_et_vp-it_s222
Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
6cf6a09cc2c7b788f7e403da08016244
|
nlp04/kobart_64_5e-5_datav2_min30_lp5.0_temperature1.0
|
nlp04
|
bart
| 15 | 2 |
transformers
| 0 |
text2text-generation
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 994 | false |
<!-- 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. -->
# kobart_64_5e-5_datav2_min30_lp5.0_temperature1.0
This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on the None 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: 64
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
36c43d92bf85519f51bd92926446f376
|
Apocalypse-19/Vishu-the-Cat
|
Apocalypse-19
| null | 15 | 476 |
diffusers
| 65 |
text-to-image
| true | false | false |
creativeml-openrail-m
| null | null | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
['pytorch', 'diffusers', 'text-to-image', 'dreambooth-hackathon', 'animal']
| false | true | true | 1,686 | false |
# Dreambooth Model for Animals trained on a custom dataset.
This is a Stable Diffusion model fine-tuned on the animal concept with DreamBooth. It can be used by modifying the `instance_prompt`: **A photo of vishu cat**
This model was created as part of the DreamBooth Hackathon ๐ฅ.
## Description
Model finetuned on the pictures of our cat named Vishu, made for the Dreambooth Hackathon,
finetuned on Stable diffusion 2.1 Base
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('Apocalypse-19/Vishu-the-Cat')
image = pipeline().images[0]
image
```
## Examples
Some examples of images generated with their prompts are (Guidance scale = 7.5 and Number of Inference steps = 50 for all):
Prompt = A photo of vishu cat as a genshin impact character
Prompt = A photo of vishu cat shaking hands with Donald Trump
Prompt = A photo of vishu cat as a Disney Princess
Prompt = A photo of vishu cat cocking a gun
|
9de048516e63822cc85c6d3f45954c33
|
aliosm/ComVE-distilgpt2
|
aliosm
|
gpt2
| 9 | 9 |
transformers
| 0 |
text-generation
| true | false | true |
mit
|
['en']
|
['ComVE']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['exbert', 'commonsense', 'semeval2020', 'comve']
| false | true | true | 2,464 | false |
# ComVE-distilgpt2
## Model description
Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in [SemEval2020 Task4](https://competitions.codalab.org/competitions/21080) using a causal language modeling (CLM) objective.
The model is able to generate a reason why a given natural language statement is against commonsense.
## Intended uses & limitations
You can use the raw model for text generation to generate reasons why natural language statements are against commonsense.
#### How to use
You can use this model directly to generate reasons why the given statement is against commonsense using [`generate.sh`](https://github.com/AliOsm/SemEval2020-Task4-ComVE/tree/master/TaskC-Generation) script.
*Note:* make sure that you are using version `2.4.1` of `transformers` package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.
#### Limitations and bias
The model biased to negate the entered sentence usually instead of producing a factual reason.
## Training data
The model is initialized from the [distilgpt2](https://github.com/huggingface/transformers/blob/master/model_cards/distilgpt2-README.md) model and finetuned using [ComVE](https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation) dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.
## Training procedure
Each natural language statement that against commonsense is concatenated with its reference reason with `<|continue|>` as a separator, then the model finetuned using CLM objective.
The model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 15 epochs, 128 maximum sequence length and 64 batch size.
<center>
<img src="https://i.imgur.com/xKbrwBC.png">
</center>
## Eval results
The model achieved 13.7582/13.8026 BLEU scores on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.
### BibTeX entry and citation info
```bibtex
@article{fadel2020justers,
title={JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models Against Commonsense Validation and Explanation},
author={Fadel, Ali and Al-Ayyoub, Mahmoud and Cambria, Erik},
year={2020}
}
```
<a href="https://huggingface.co/exbert/?model=aliosm/ComVE-distilgpt2">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
a5932900b085fd715c6b61c5fd884ce0
|
PrimeQA/open-nq-colbert-xlmr-large
|
PrimeQA
|
bert
| 9 | 4 |
transformers
| 0 | null | true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 1,217 | false |
# Model Description
This is a retriever model based on ColBERT v2 with [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) language model.<br>
This model was trained with the OpenNQ data.<br>
The architecture of the model and hyper parameters are described in the paper โRelevance-guided Supervision for OpenQA with ColBERTโ.
## Intended uses & limitations
This model uses the xlm-roberta-large LM. Biases associated with the pre-trained language model we used may be present in this ColBERT v2 model.
## Usage
This model can be used with [PrimeQA](https://github.com/primeqa/primeqa)โs [ColBERT](https://github.com/primeqa/primeqa/blob/main/primeqa/ir/README.md) engine.
## BibTeX entry and citation info
```bibtex
@article{Khattab2021RelevanceguidedSF,
title={Relevance-guided Supervision for OpenQA with ColBERT},
author={O. Khattab and Christopher Potts and Matei A. Zaharia},
journal={Transactions of the Association for Computational Linguistics},
year={2021},
}
```
```bibtex
@article{Lee2019LatentRF,
title={Latent Retrieval for Weakly Supervised Open Domain Question Answering},
author={Kenton Lee and Ming-Wei Chang and Kristina Toutanova},
journal={ACL},
year={2019}
}
```
|
abf3b23363a8ed6cd3f233d1c008ac2c
|
ali2066/distilBERT_token_itr0_0.0001_all_01_03_2022-15_22_12
|
ali2066
|
bert
| 13 | 10 |
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 | 1,739 | false |
<!-- 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_token_itr0_0.0001_all_01_03_2022-15_22_12
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: 0.2811
- Precision: 0.3231
- Recall: 0.5151
- F1: 0.3971
- Accuracy: 0.8913
## 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: 32
- 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 30 | 0.2881 | 0.2089 | 0.3621 | 0.2650 | 0.8715 |
| No log | 2.0 | 60 | 0.2500 | 0.2619 | 0.3842 | 0.3115 | 0.8845 |
| No log | 3.0 | 90 | 0.2571 | 0.2327 | 0.4338 | 0.3030 | 0.8809 |
| No log | 4.0 | 120 | 0.2479 | 0.3051 | 0.4761 | 0.3719 | 0.8949 |
| No log | 5.0 | 150 | 0.2783 | 0.3287 | 0.4761 | 0.3889 | 0.8936 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
18f951dcffd8d88164676d434f01cb77
|
Helsinki-NLP/opus-mt-bi-sv
|
Helsinki-NLP
|
marian
| 10 | 9 |
transformers
| 0 |
translation
| true | true | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['translation']
| false | true | true | 768 | false |
### opus-mt-bi-sv
* source languages: bi
* target languages: sv
* OPUS readme: [bi-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bi-sv/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/bi-sv/opus-2020-01-20.zip)
* test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bi-sv/opus-2020-01-20.test.txt)
* test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bi-sv/opus-2020-01-20.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.bi.sv | 22.7 | 0.403 |
|
401e0928a24a4aac0bcc8a1fa07e30ff
|
flax-community/gpt-neo-125M-apps
|
flax-community
|
gpt_neo
| 12 | 51 |
transformers
| 0 |
text-generation
| true | false | true |
mit
|
['en', 'python']
|
['apps']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['gpt_neo', 'code_synthesis']
| false | true | true | 4,833 | false |
# GPT-Neo-125M-APPS
> **Please refer to our new [GitHub Wiki](https://github.com/ncoop57/gpt-code-clippy/wiki) which documents our efforts in detail in creating the open source version of GitHub Copilot**
## Model Description
GPT-Neo-125M-APPS is a GPT-Neo-125M finetuned on APPS dataset. This model is specialized to solve programming tasks.
## Training data
The model is trained on the [Automated Programming Progress Standard (APPS) dataset](https://github.com/hendrycks/apps). The dataset consists of 10,000 coding problems in total, with 131,836 test cases for checking solutions and 232,444 ground-truth solutions written by humans. Problems can be complicated, as the average length of a problem is 293.2 words. The data are split evenly into training and test sets, with 5,000 problems each.
## Training procedure
The training script used to train this model can be found [here](https://github.com/ncoop57/gpt-code-clippy/blob/camera-ready/training/run_clm_apps.py).
Training is done for 5 epochs using AdamW optimizer and leaner decay learning rate schedule with 800 warmup steps. To reproduce the training one can use this command with the above script:
```bash
python run_clm_apps.py \
--output_dir $HOME/gpt-neo-125M-apps \
--model_name_or_path EleutherAI/gpt-neo-125M \
--dataset_name $HOME/gpt-code-clippy/data_processing/apps.py \
--dataset_config_name formatted \
--do_train --do_eval \
--block_size="1024" \
--per_device_train_batch_size="16" \
--per_device_eval_batch_size="16" \
--preprocessing_num_workers="16" \
--learning_rate="8e-5" \
--warmup_steps="800" \
--adam_beta1="0.9" \
--adam_beta2="0.98" \
--weight_decay="0.1" \
--overwrite_output_dir \
--num_train_epochs="5" \
--logging_steps="50" \
--eval_steps="2000" \
--report_to="wandb" \
--dtype="bfloat16" \
--save_strategy epoch \
--gradient_accumulation_steps 2 \
```
## Intended Use and Limitations
The model is finetuned to solve programming problems given a text description and optional starter code.
### How to use
You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
```py
from transformers import AutoModelForCausalLM, AutoTokenizer, FlaxAutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("flax-community/gpt-neo-125M-apps")
tokenizer = AutoTokenizer.from_pretrained("flax-community/gpt-neo-125M-apps")
prompt = """
A function to greet user. Given a user name it should say hello
def greet(name):
ANSWER:
"""
input_ids = tokenizer(prompt, return_tensors='pt').input_ids.to(device)
start = input_ids.size(1)
out = model.generate(input_ids, do_sample=True, max_length=50, num_beams=2,
early_stopping=True, eos_token_id=tokenizer.eos_token_id, )
print(tokenizer.decode(out[0][start:]))
```
### Limitations and Biases
The model is intended to be used for research purposes and comes with no guarantees of quality of generated code.
The paper ["Evaluating Large Language Models Trained on Code"](https://arxiv.org/abs/2107.03374) from OpenAI has a good discussion on what the impact of a large language model trained on code could be. Therefore, some parts of their discuss are highlighted here as it pertains to this dataset and models that may be trained from it. **As well as some differences in views from the paper, particularly around legal implications**.
1. **Over-reliance:** This model may generate plausible solutions that may appear correct, but are not necessarily the correct solution. Not properly evaluating the generated code may cause have negative consequences such as the introduction of bugs, or the introduction of security vulnerabilities. Therefore, it is important that users are aware of the limitations and potential negative consequences of using this language model.
2. **Economic and labor market impacts:** Large language models trained on large code datasets such as this one that are capable of generating high-quality code have the potential to automate part of the software development process. This may negatively impact software developers. However, as discussed in the paper, as shown in the Summary Report of software developers from [O*NET OnLine](https://www.onetonline.org/link/summary/15-1252.00), developers don't just write software.
5. **Biases:** The model is trained on data containing prompt questions formatted in specific way. The performance of the model can be worse if the prompt
formatting is different from that used in APPS dataset.
GPT-CC is finetuned GPT-Neo and might have inhereted biases and limitations from it. See [GPT-Neo model card](https://huggingface.co/EleutherAI/gpt-neo-125M#limitations-and-biases) for details.
## Eval results
Coming soon...
|
e9c5391fdaa32f762ffb4aa25b87eb67
|
hyorea1/KoT5-test-add-data-from5ep
|
hyorea1
|
t5
| 11 | 3 |
transformers
| 0 |
text2text-generation
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,307 | false |
<!-- 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. -->
# KoT5-test-add-data-from5ep
This model is a fine-tuned version of [hyorea1/KoT5-test](https://huggingface.co/hyorea1/KoT5-test) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1737
- Rouge1: 11.8294
- Rouge2: 3.2314
- Rougel: 11.7891
- Rougelsum: 11.8237
- Gen Len: 35.2824
## 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: 4
- eval_batch_size: 4
- seed: 100
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 1.9029 | 0.16 | 400 | 1.1695 | 12.8243 | 3.2659 | 12.7542 | 12.8276 | 35.5743 |
| 1.7971 | 0.32 | 800 | 1.1646 | 12.259 | 3.0668 | 12.1254 | 12.1927 | 35.2353 |
| 1.4396 | 0.48 | 1200 | 1.1681 | 12.1151 | 3.1908 | 11.9507 | 12.0305 | 35.3125 |
| 1.0945 | 0.64 | 1600 | 1.1703 | 12.0576 | 2.9688 | 11.9292 | 11.9792 | 35.0926 |
| 1.1924 | 0.8 | 2000 | 1.1667 | 11.7835 | 2.9605 | 11.6755 | 11.7318 | 35.3596 |
| 1.3711 | 0.97 | 2400 | 1.1668 | 11.9873 | 3.1107 | 11.9369 | 12.0207 | 34.5309 |
| 1.6031 | 1.13 | 2800 | 1.1673 | 11.6049 | 3.1121 | 11.5527 | 11.5976 | 34.6551 |
| 1.5254 | 1.29 | 3200 | 1.1693 | 11.6803 | 2.8527 | 11.6116 | 11.6829 | 34.8066 |
| 1.641 | 1.45 | 3600 | 1.1737 | 11.8294 | 3.2314 | 11.7891 | 11.8237 | 35.2824 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
a0a7eb325240da065672cefa1b3e7f84
|
aemami1/distilbert-base-uncased-finetuned-wnli
|
aemami1
|
distilbert
| 13 | 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 | 1,475 | false |
<!-- 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-wnli
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: 0.6950
- Accuracy: 0.5493
## 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 0.6929 | 0.5211 |
| No log | 2.0 | 80 | 0.6951 | 0.4789 |
| No log | 3.0 | 120 | 0.6950 | 0.5493 |
| No log | 4.0 | 160 | 0.6966 | 0.5352 |
| No log | 5.0 | 200 | 0.6966 | 0.5352 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
2ce6fe602248fceb3fc3bd1b7abca19e
|
jonatasgrosman/exp_w2v2t_ar_unispeech-sat_s504
|
jonatasgrosman
|
unispeech-sat
| 10 | 3 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
|
['ar']
|
['mozilla-foundation/common_voice_7_0']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['automatic-speech-recognition', 'ar']
| false | true | true | 463 | false |
# exp_w2v2t_ar_unispeech-sat_s504
Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (ar)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
a5a794d3ea4908b0511aabc494f0482c
|
Elron/deberta-v3-large-sentiment
|
Elron
|
deberta-v2
| 16 | 4 |
transformers
| 0 |
text-classification
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 10,747 | false |
# deberta-v3-large-sentiment
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an [tweet_eval](https://huggingface.co/datasets/tweet_eval) dataset.
## Model description
Test set results:
| Model | Emotion | Hate | Irony | Offensive | Sentiment |
| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |
| deberta-v3-large | **86.3** | **61.3** | **87.1** | **86.4** | **73.9** |
| BERTweet | 79.3 | - | 82.1 | 79.5 | 73.4 |
| RoB-RT | 79.5 | 52.3 | 61.7 | 80.5 | 69.3 |
[source:papers_with_code](https://paperswithcode.com/sota/sentiment-analysis-on-tweeteval)
## Intended uses & limitations
Classifying attributes of interest on tweeter like data.
## Training and evaluation data
[tweet_eval](https://huggingface.co/datasets/tweet_eval) dataset.
## Training procedure
Fine tuned and evaluated with [run_glue.py]()
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 16
- eval_batch_size: 16
- 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: 50
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0614 | 0.07 | 100 | 1.0196 | 0.4345 |
| 0.8601 | 0.14 | 200 | 0.7561 | 0.6460 |
| 0.734 | 0.21 | 300 | 0.6796 | 0.6955 |
| 0.6753 | 0.28 | 400 | 0.6521 | 0.7000 |
| 0.6408 | 0.35 | 500 | 0.6119 | 0.7440 |
| 0.5991 | 0.42 | 600 | 0.6034 | 0.7370 |
| 0.6069 | 0.49 | 700 | 0.5976 | 0.7375 |
| 0.6122 | 0.56 | 800 | 0.5871 | 0.7425 |
| 0.5908 | 0.63 | 900 | 0.5935 | 0.7445 |
| 0.5884 | 0.7 | 1000 | 0.5792 | 0.7520 |
| 0.5839 | 0.77 | 1100 | 0.5780 | 0.7555 |
| 0.5772 | 0.84 | 1200 | 0.5727 | 0.7570 |
| 0.5895 | 0.91 | 1300 | 0.5601 | 0.7550 |
| 0.5757 | 0.98 | 1400 | 0.5613 | 0.7525 |
| 0.5121 | 1.05 | 1500 | 0.5867 | 0.7600 |
| 0.5254 | 1.12 | 1600 | 0.5595 | 0.7630 |
| 0.5074 | 1.19 | 1700 | 0.5594 | 0.7585 |
| 0.4947 | 1.26 | 1800 | 0.5697 | 0.7575 |
| 0.5019 | 1.33 | 1900 | 0.5665 | 0.7580 |
| 0.5005 | 1.4 | 2000 | 0.5484 | 0.7655 |
| 0.5125 | 1.47 | 2100 | 0.5626 | 0.7605 |
| 0.5241 | 1.54 | 2200 | 0.5561 | 0.7560 |
| 0.5198 | 1.61 | 2300 | 0.5602 | 0.7600 |
| 0.5124 | 1.68 | 2400 | 0.5654 | 0.7490 |
| 0.5096 | 1.75 | 2500 | 0.5803 | 0.7515 |
| 0.4885 | 1.82 | 2600 | 0.5889 | 0.75 |
| 0.5111 | 1.89 | 2700 | 0.5508 | 0.7665 |
| 0.4868 | 1.96 | 2800 | 0.5621 | 0.7635 |
| 0.4599 | 2.04 | 2900 | 0.5995 | 0.7615 |
| 0.4147 | 2.11 | 3000 | 0.6202 | 0.7530 |
| 0.4233 | 2.18 | 3100 | 0.5875 | 0.7625 |
| 0.4324 | 2.25 | 3200 | 0.5794 | 0.7610 |
| 0.4141 | 2.32 | 3300 | 0.5902 | 0.7460 |
| 0.4306 | 2.39 | 3400 | 0.6053 | 0.7545 |
| 0.4266 | 2.46 | 3500 | 0.5979 | 0.7570 |
| 0.4227 | 2.53 | 3600 | 0.5920 | 0.7650 |
| 0.4226 | 2.6 | 3700 | 0.6166 | 0.7455 |
| 0.3978 | 2.67 | 3800 | 0.6126 | 0.7560 |
| 0.3954 | 2.74 | 3900 | 0.6152 | 0.7550 |
| 0.4209 | 2.81 | 4000 | 0.5980 | 0.75 |
| 0.3982 | 2.88 | 4100 | 0.6096 | 0.7490 |
| 0.4016 | 2.95 | 4200 | 0.6541 | 0.7425 |
| 0.3966 | 3.02 | 4300 | 0.6377 | 0.7545 |
| 0.3074 | 3.09 | 4400 | 0.6860 | 0.75 |
| 0.3551 | 3.16 | 4500 | 0.6160 | 0.7550 |
| 0.3323 | 3.23 | 4600 | 0.6714 | 0.7520 |
| 0.3171 | 3.3 | 4700 | 0.6538 | 0.7535 |
| 0.3403 | 3.37 | 4800 | 0.6774 | 0.7465 |
| 0.3396 | 3.44 | 4900 | 0.6726 | 0.7465 |
| 0.3259 | 3.51 | 5000 | 0.6465 | 0.7480 |
| 0.3392 | 3.58 | 5100 | 0.6860 | 0.7460 |
| 0.3251 | 3.65 | 5200 | 0.6697 | 0.7495 |
| 0.3253 | 3.72 | 5300 | 0.6770 | 0.7430 |
| 0.3455 | 3.79 | 5400 | 0.7177 | 0.7360 |
| 0.3323 | 3.86 | 5500 | 0.6943 | 0.7400 |
| 0.3335 | 3.93 | 5600 | 0.6507 | 0.7555 |
| 0.3368 | 4.0 | 5700 | 0.6580 | 0.7485 |
| 0.2479 | 4.07 | 5800 | 0.7667 | 0.7430 |
| 0.2613 | 4.14 | 5900 | 0.7513 | 0.7505 |
| 0.2557 | 4.21 | 6000 | 0.7927 | 0.7485 |
| 0.243 | 4.28 | 6100 | 0.7792 | 0.7450 |
| 0.2473 | 4.35 | 6200 | 0.8107 | 0.7355 |
| 0.2447 | 4.42 | 6300 | 0.7851 | 0.7370 |
| 0.2515 | 4.49 | 6400 | 0.7529 | 0.7465 |
| 0.274 | 4.56 | 6500 | 0.7390 | 0.7465 |
| 0.2674 | 4.63 | 6600 | 0.7658 | 0.7460 |
| 0.2416 | 4.7 | 6700 | 0.7915 | 0.7485 |
| 0.2432 | 4.77 | 6800 | 0.7989 | 0.7435 |
| 0.2595 | 4.84 | 6900 | 0.7850 | 0.7380 |
| 0.2736 | 4.91 | 7000 | 0.7577 | 0.7395 |
| 0.2783 | 4.98 | 7100 | 0.7650 | 0.7405 |
| 0.2304 | 5.05 | 7200 | 0.8542 | 0.7385 |
| 0.1937 | 5.12 | 7300 | 0.8390 | 0.7345 |
| 0.1878 | 5.19 | 7400 | 0.9150 | 0.7330 |
| 0.1921 | 5.26 | 7500 | 0.8792 | 0.7405 |
| 0.1916 | 5.33 | 7600 | 0.8892 | 0.7410 |
| 0.2011 | 5.4 | 7700 | 0.9012 | 0.7325 |
| 0.211 | 5.47 | 7800 | 0.8608 | 0.7420 |
| 0.2194 | 5.54 | 7900 | 0.8852 | 0.7320 |
| 0.205 | 5.61 | 8000 | 0.8803 | 0.7385 |
| 0.1981 | 5.68 | 8100 | 0.8681 | 0.7330 |
| 0.1908 | 5.75 | 8200 | 0.9020 | 0.7435 |
| 0.1942 | 5.82 | 8300 | 0.8780 | 0.7410 |
| 0.1958 | 5.89 | 8400 | 0.8937 | 0.7345 |
| 0.1883 | 5.96 | 8500 | 0.9121 | 0.7360 |
| 0.1819 | 6.04 | 8600 | 0.9409 | 0.7430 |
| 0.145 | 6.11 | 8700 | 1.1390 | 0.7265 |
| 0.1696 | 6.18 | 8800 | 0.9189 | 0.7430 |
| 0.1488 | 6.25 | 8900 | 0.9718 | 0.7400 |
| 0.1637 | 6.32 | 9000 | 0.9702 | 0.7450 |
| 0.1547 | 6.39 | 9100 | 1.0033 | 0.7410 |
| 0.1605 | 6.46 | 9200 | 0.9973 | 0.7355 |
| 0.1552 | 6.53 | 9300 | 1.0491 | 0.7290 |
| 0.1731 | 6.6 | 9400 | 1.0271 | 0.7335 |
| 0.1738 | 6.67 | 9500 | 0.9575 | 0.7430 |
| 0.1669 | 6.74 | 9600 | 0.9614 | 0.7350 |
| 0.1347 | 6.81 | 9700 | 1.0263 | 0.7365 |
| 0.1593 | 6.88 | 9800 | 1.0173 | 0.7360 |
| 0.1549 | 6.95 | 9900 | 1.0398 | 0.7350 |
| 0.1675 | 7.02 | 10000 | 0.9975 | 0.7380 |
| 0.1182 | 7.09 | 10100 | 1.1059 | 0.7350 |
| 0.1351 | 7.16 | 10200 | 1.0933 | 0.7400 |
| 0.1496 | 7.23 | 10300 | 1.0731 | 0.7355 |
| 0.1197 | 7.3 | 10400 | 1.1089 | 0.7360 |
| 0.1111 | 7.37 | 10500 | 1.1381 | 0.7405 |
| 0.1494 | 7.44 | 10600 | 1.0252 | 0.7425 |
| 0.1235 | 7.51 | 10700 | 1.0906 | 0.7360 |
| 0.133 | 7.58 | 10800 | 1.1796 | 0.7375 |
| 0.1248 | 7.65 | 10900 | 1.1332 | 0.7420 |
| 0.1268 | 7.72 | 11000 | 1.1304 | 0.7415 |
| 0.1368 | 7.79 | 11100 | 1.1345 | 0.7380 |
| 0.1228 | 7.86 | 11200 | 1.2018 | 0.7320 |
| 0.1281 | 7.93 | 11300 | 1.1884 | 0.7350 |
| 0.1449 | 8.0 | 11400 | 1.1571 | 0.7345 |
| 0.1025 | 8.07 | 11500 | 1.1538 | 0.7345 |
| 0.1199 | 8.14 | 11600 | 1.2113 | 0.7390 |
| 0.1016 | 8.21 | 11700 | 1.2882 | 0.7370 |
| 0.114 | 8.28 | 11800 | 1.2872 | 0.7390 |
| 0.1019 | 8.35 | 11900 | 1.2876 | 0.7380 |
| 0.1142 | 8.42 | 12000 | 1.2791 | 0.7385 |
| 0.1135 | 8.49 | 12100 | 1.2883 | 0.7380 |
| 0.1139 | 8.56 | 12200 | 1.2829 | 0.7360 |
| 0.1107 | 8.63 | 12300 | 1.2698 | 0.7365 |
| 0.1183 | 8.7 | 12400 | 1.2660 | 0.7345 |
| 0.1064 | 8.77 | 12500 | 1.2889 | 0.7365 |
| 0.0895 | 8.84 | 12600 | 1.3480 | 0.7330 |
| 0.1244 | 8.91 | 12700 | 1.2872 | 0.7325 |
| 0.1209 | 8.98 | 12800 | 1.2681 | 0.7375 |
| 0.1144 | 9.05 | 12900 | 1.2711 | 0.7370 |
| 0.1034 | 9.12 | 13000 | 1.2801 | 0.7360 |
| 0.113 | 9.19 | 13100 | 1.2801 | 0.7350 |
| 0.0994 | 9.26 | 13200 | 1.2920 | 0.7360 |
| 0.0966 | 9.33 | 13300 | 1.2761 | 0.7335 |
| 0.0939 | 9.4 | 13400 | 1.2909 | 0.7365 |
| 0.0975 | 9.47 | 13500 | 1.2953 | 0.7360 |
| 0.0842 | 9.54 | 13600 | 1.3179 | 0.7335 |
| 0.0871 | 9.61 | 13700 | 1.3149 | 0.7385 |
| 0.1162 | 9.68 | 13800 | 1.3124 | 0.7350 |
| 0.085 | 9.75 | 13900 | 1.3207 | 0.7355 |
| 0.0966 | 9.82 | 14000 | 1.3248 | 0.7335 |
| 0.1064 | 9.89 | 14100 | 1.3261 | 0.7335 |
| 0.1046 | 9.96 | 14200 | 1.3255 | 0.7360 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.9.0
- Datasets 2.2.2
- Tokenizers 0.11.6
|
70213760c0d9050af1b75b5637db4d73
|
ozioh/trainer_log
|
ozioh
|
bert
| 8 | 3 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 7,509 | false |
<!-- 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. -->
# trainer_log
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4907
- Accuracy: 0.8742
## 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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.047 | 0.04 | 5 | 0.9927 | 0.5753 |
| 0.938 | 0.08 | 10 | 0.9320 | 0.5753 |
| 0.8959 | 0.12 | 15 | 0.8764 | 0.5773 |
| 0.8764 | 0.16 | 20 | 0.8308 | 0.6639 |
| 0.7968 | 0.2 | 25 | 0.8045 | 0.6577 |
| 0.8644 | 0.25 | 30 | 0.7779 | 0.6639 |
| 0.7454 | 0.29 | 35 | 0.7561 | 0.6412 |
| 0.7008 | 0.33 | 40 | 0.7157 | 0.6845 |
| 0.7627 | 0.37 | 45 | 0.7027 | 0.6907 |
| 0.7568 | 0.41 | 50 | 0.7270 | 0.6763 |
| 0.7042 | 0.45 | 55 | 0.6770 | 0.7031 |
| 0.6683 | 0.49 | 60 | 0.6364 | 0.7134 |
| 0.6312 | 0.53 | 65 | 0.6151 | 0.7278 |
| 0.5789 | 0.57 | 70 | 0.6003 | 0.7443 |
| 0.5964 | 0.61 | 75 | 0.5665 | 0.7835 |
| 0.5178 | 0.66 | 80 | 0.5506 | 0.8 |
| 0.5698 | 0.7 | 85 | 0.5240 | 0.8 |
| 0.5407 | 0.74 | 90 | 0.5223 | 0.7814 |
| 0.6141 | 0.78 | 95 | 0.4689 | 0.8268 |
| 0.4998 | 0.82 | 100 | 0.4491 | 0.8227 |
| 0.4853 | 0.86 | 105 | 0.4448 | 0.8268 |
| 0.4561 | 0.9 | 110 | 0.4646 | 0.8309 |
| 0.5058 | 0.94 | 115 | 0.4317 | 0.8495 |
| 0.4229 | 0.98 | 120 | 0.4014 | 0.8515 |
| 0.2808 | 1.02 | 125 | 0.3834 | 0.8619 |
| 0.3721 | 1.07 | 130 | 0.3829 | 0.8619 |
| 0.3432 | 1.11 | 135 | 0.4212 | 0.8598 |
| 0.3616 | 1.15 | 140 | 0.3930 | 0.8680 |
| 0.3912 | 1.19 | 145 | 0.3793 | 0.8639 |
| 0.4141 | 1.23 | 150 | 0.3646 | 0.8619 |
| 0.2726 | 1.27 | 155 | 0.3609 | 0.8701 |
| 0.2021 | 1.31 | 160 | 0.3640 | 0.8680 |
| 0.3468 | 1.35 | 165 | 0.3655 | 0.8701 |
| 0.2729 | 1.39 | 170 | 0.4054 | 0.8495 |
| 0.3885 | 1.43 | 175 | 0.3559 | 0.8639 |
| 0.446 | 1.48 | 180 | 0.3390 | 0.8680 |
| 0.3337 | 1.52 | 185 | 0.3505 | 0.8660 |
| 0.3507 | 1.56 | 190 | 0.3337 | 0.8804 |
| 0.3864 | 1.6 | 195 | 0.3476 | 0.8660 |
| 0.3495 | 1.64 | 200 | 0.3574 | 0.8577 |
| 0.3388 | 1.68 | 205 | 0.3426 | 0.8701 |
| 0.358 | 1.72 | 210 | 0.3439 | 0.8804 |
| 0.1761 | 1.76 | 215 | 0.3461 | 0.8722 |
| 0.3089 | 1.8 | 220 | 0.3638 | 0.8639 |
| 0.279 | 1.84 | 225 | 0.3527 | 0.8742 |
| 0.3468 | 1.89 | 230 | 0.3497 | 0.8619 |
| 0.2969 | 1.93 | 235 | 0.3572 | 0.8598 |
| 0.2719 | 1.97 | 240 | 0.3391 | 0.8804 |
| 0.1936 | 2.01 | 245 | 0.3415 | 0.8619 |
| 0.2475 | 2.05 | 250 | 0.3477 | 0.8784 |
| 0.1759 | 2.09 | 255 | 0.3718 | 0.8660 |
| 0.2443 | 2.13 | 260 | 0.3758 | 0.8619 |
| 0.2189 | 2.17 | 265 | 0.3670 | 0.8639 |
| 0.1505 | 2.21 | 270 | 0.3758 | 0.8722 |
| 0.2283 | 2.25 | 275 | 0.3723 | 0.8722 |
| 0.155 | 2.3 | 280 | 0.4442 | 0.8330 |
| 0.317 | 2.34 | 285 | 0.3700 | 0.8701 |
| 0.1566 | 2.38 | 290 | 0.4218 | 0.8619 |
| 0.2294 | 2.42 | 295 | 0.3820 | 0.8660 |
| 0.1567 | 2.46 | 300 | 0.3891 | 0.8660 |
| 0.1875 | 2.5 | 305 | 0.3973 | 0.8722 |
| 0.2741 | 2.54 | 310 | 0.4042 | 0.8742 |
| 0.2363 | 2.58 | 315 | 0.3777 | 0.8660 |
| 0.1964 | 2.62 | 320 | 0.3891 | 0.8639 |
| 0.156 | 2.66 | 325 | 0.3998 | 0.8639 |
| 0.1422 | 2.7 | 330 | 0.4022 | 0.8722 |
| 0.2141 | 2.75 | 335 | 0.4239 | 0.8701 |
| 0.1616 | 2.79 | 340 | 0.4094 | 0.8722 |
| 0.1032 | 2.83 | 345 | 0.4263 | 0.8784 |
| 0.2313 | 2.87 | 350 | 0.4579 | 0.8598 |
| 0.0843 | 2.91 | 355 | 0.3989 | 0.8742 |
| 0.2567 | 2.95 | 360 | 0.4051 | 0.8660 |
| 0.1749 | 2.99 | 365 | 0.4136 | 0.8660 |
| 0.1116 | 3.03 | 370 | 0.4312 | 0.8619 |
| 0.1058 | 3.07 | 375 | 0.4007 | 0.8701 |
| 0.1085 | 3.11 | 380 | 0.4174 | 0.8660 |
| 0.0578 | 3.16 | 385 | 0.4163 | 0.8763 |
| 0.1381 | 3.2 | 390 | 0.4578 | 0.8660 |
| 0.1137 | 3.24 | 395 | 0.4259 | 0.8660 |
| 0.2068 | 3.28 | 400 | 0.3976 | 0.8701 |
| 0.0792 | 3.32 | 405 | 0.3824 | 0.8763 |
| 0.1711 | 3.36 | 410 | 0.3793 | 0.8742 |
| 0.0686 | 3.4 | 415 | 0.4013 | 0.8742 |
| 0.1102 | 3.44 | 420 | 0.4113 | 0.8639 |
| 0.1102 | 3.48 | 425 | 0.4276 | 0.8619 |
| 0.0674 | 3.52 | 430 | 0.4222 | 0.8804 |
| 0.0453 | 3.57 | 435 | 0.4326 | 0.8722 |
| 0.0704 | 3.61 | 440 | 0.4684 | 0.8722 |
| 0.1151 | 3.65 | 445 | 0.4640 | 0.8701 |
| 0.1225 | 3.69 | 450 | 0.4408 | 0.8763 |
| 0.0391 | 3.73 | 455 | 0.4520 | 0.8639 |
| 0.0566 | 3.77 | 460 | 0.4558 | 0.8680 |
| 0.1222 | 3.81 | 465 | 0.4599 | 0.8660 |
| 0.1035 | 3.85 | 470 | 0.4630 | 0.8763 |
| 0.1845 | 3.89 | 475 | 0.4796 | 0.8680 |
| 0.087 | 3.93 | 480 | 0.4697 | 0.8742 |
| 0.1599 | 3.98 | 485 | 0.4663 | 0.8784 |
| 0.0632 | 4.02 | 490 | 0.5139 | 0.8536 |
| 0.1218 | 4.06 | 495 | 0.4920 | 0.8722 |
| 0.0916 | 4.1 | 500 | 0.4846 | 0.8763 |
| 0.0208 | 4.14 | 505 | 0.5269 | 0.8722 |
| 0.0803 | 4.18 | 510 | 0.5154 | 0.8784 |
| 0.1318 | 4.22 | 515 | 0.4907 | 0.8742 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
4e10514d670b474372a39a90b5c0ef86
|
LawalAfeez/englishreview-ds
|
LawalAfeez
|
distilbert
| 8 | 2 |
transformers
| 0 |
fill-mask
| false | true | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_keras_callback']
| true | true | true | 962 | false |
<!-- 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. -->
# englishreview-ds
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:
## 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': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.4.0
- Tokenizers 0.12.1
|
c4aabf75d4b7a39dc22bbdb92e83ab90
|
aidiary/xlm-roberta-base-finetuned-panx-de
|
aidiary
|
xlm-roberta
| 10 | 7 |
transformers
| 0 |
token-classification
| true | false | false |
mit
| null |
['xtreme']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,319 | false |
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1375
- F1: 0.8587
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2584 | 1.0 | 525 | 0.1682 | 0.8242 |
| 0.1299 | 2.0 | 1050 | 0.1354 | 0.8447 |
| 0.0822 | 3.0 | 1575 | 0.1375 | 0.8587 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
|
5772fe4bb6ad21e600878ae5ed6b54fc
|
Pro0100Hy6/test_trainer
|
Pro0100Hy6
|
bert
| 6 | 4 |
transformers
| 0 |
text-classification
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,203 | false |
<!-- 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_trainer
This model is a fine-tuned version of [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7773
- Accuracy: 0.6375
## 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
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7753 | 1.0 | 400 | 0.7773 | 0.6375 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
84618b32b573b4b8e7e3246b2c4336ed
|
hackathon-pln-es/Detect-Acoso-Twitter-Es
|
hackathon-pln-es
|
roberta
| 22 | 14 |
transformers
| 4 |
text-classification
| true | false | false |
apache-2.0
|
['es']
|
['hackathon-pln-es/Dataset-Acoso-Twitter-Es']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer', 'es', 'text-classification', 'acoso', 'twitter', 'cyberbullying']
| true | true | true | 1,725 | false |
<!-- 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. -->
# Detecciรณn de acoso en Twitter Espaรฑol
This model is a fine-tuned version of [mrm8488/distilroberta-finetuned-tweets-hate-speech](https://huggingface.co/mrm8488/distilroberta-finetuned-tweets-hate-speech) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1628
- Accuracy: 0.9167
# UNL: Universidad Nacional de Loja
## Miembros del equipo:
- Anderson Quizhpe <br>
- Luis Negrรณn <br>
- David Pacheco <br>
- Bryan Requenes <br>
- Paul Pasaca
## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6732 | 1.0 | 27 | 0.3797 | 0.875 |
| 0.5537 | 2.0 | 54 | 0.3242 | 0.9167 |
| 0.5218 | 3.0 | 81 | 0.2879 | 0.9167 |
| 0.509 | 4.0 | 108 | 0.2606 | 0.9167 |
| 0.4196 | 5.0 | 135 | 0.1628 | 0.9167 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
9437aeb48b9c9577f1124cf6d438053a
|
icon-it-tdtu/mt-vi-en-optimum
|
icon-it-tdtu
|
marian
| 9 | 26 |
transformers
| 1 |
translation
| false | false | false |
apache-2.0
|
['vi', 'en']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['translation']
| false | true | true | 578 | false |
# MarianMT exported to the ONNX format
## Install Optimum
```bash
pip install optimum
```
## Usage example
```python
from transformers import AutoTokenizer
from optimum.onnxruntime import ORTModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("icon-it-tdtu/mt-vi-en-optimum")
model = ORTModelForSeq2SeqLM.from_pretrained("icon-it-tdtu/mt-vi-en-optimum")
text = "Tรดi lร mแปt sinh viรชn."
inputs = tokenizer(text, return_tensors='pt')
outputs = model.generate(**inputs)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
# I am a student.
```
|
761f01bba573f8b6be7d0bd7f842583a
|
vikram15/bert-finetuned-ner
|
vikram15
|
bert
| 12 | 3 |
transformers
| 0 |
token-classification
| true | false | false |
apache-2.0
| null |
['conll2003']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,518 | false |
<!-- 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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0630
- Precision: 0.9310
- Recall: 0.9488
- F1: 0.9398
- Accuracy: 0.9862
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0911 | 1.0 | 1756 | 0.0702 | 0.9197 | 0.9345 | 0.9270 | 0.9826 |
| 0.0336 | 2.0 | 3512 | 0.0623 | 0.9294 | 0.9480 | 0.9386 | 0.9864 |
| 0.0174 | 3.0 | 5268 | 0.0630 | 0.9310 | 0.9488 | 0.9398 | 0.9862 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
|
217d03d0685a2d5d9f7f8df89b6cb83a
|
BiggieW/classification_chnsenticorp_aug
|
BiggieW
|
bert
| 14 | 1 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,352 | false |
<!-- 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. -->
# classification_chnsenticorp_aug
This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3776
- Accuracy: 0.85
## 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: 10
- eval_batch_size: 10
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4438 | 1.0 | 20 | 0.5145 | 0.75 |
| 0.0666 | 2.0 | 40 | 0.4066 | 0.9 |
| 0.0208 | 3.0 | 60 | 0.3776 | 0.85 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
0ee43656a30ddde0333ea1d3441bf132
|
SauravMaheshkar/clr-finetuned-bert-large-uncased
|
SauravMaheshkar
|
bert
| 7 | 8 |
transformers
| 0 |
fill-mask
| true | false | false |
cc0-1.0
| null |
['Commonlit-Readibility']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['kaggle']
| false | true | true | 1,457 | false |
# FineTuning
| **Architecture** | **Weights** | **Training Loss** | **Validation Loss** |
|:-----------------------:|:---------------:|:----------------:|:----------------------:|
| roberta-base | [huggingface/hub](https://huggingface.co/SauravMaheshkar/clr-finetuned-roberta-base) | **0.641** | **0.4728** |
| bert-base-uncased | [huggingface/hub](https://huggingface.co/SauravMaheshkar/clr-finetuned-bert-base-uncased) | 0.6781 | 0.4977 |
| albert-base | [huggingface/hub](https://huggingface.co/SauravMaheshkar/clr-finetuned-albert-base) | 0.7119 | 0.5155 |
| xlm-roberta-base | [huggingface/hub](https://huggingface.co/SauravMaheshkar/clr-finetuned-xlm-roberta-base) | 0.7225 | 0.525 |
| bert-large-uncased | [huggingface/hub](https://huggingface.co/SauravMaheshkar/clr-finetuned-bert-large-uncased) | 0.7482 | 0.5161 |
| albert-large | [huggingface/hub](https://huggingface.co/SauravMaheshkar/clr-finetuned-albert-large) | 1.075 | 0.9921 |
| roberta-large | [huggingface/hub](https://huggingface.co/SauravMaheshkar/clr-finetuned-roberta-large) | 2.749 | 1.075 |
|
5a5156ceaae31d747cd244c8ad44a10d
|
henryscheible/eval_masked_102_mrpc
|
henryscheible
| null | 13 | 0 | null | 0 | null | true | false | false |
apache-2.0
|
['en']
|
['glue']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,049 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# eval_masked_102_mrpc
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5646
- Accuracy: 0.8113
- F1: 0.8702
- Combined Score: 0.8407
## 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1
|
b26d25897135fa275130a44529560131
|
sutd-ai/distilbert-base-uncased-finetuned-squad
|
sutd-ai
|
distilbert
| 12 | 2 |
transformers
| 0 |
question-answering
| true | false | false |
apache-2.0
| null |
['squad_v2']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,287 | false |
<!-- 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-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5027
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2343 | 1.0 | 8235 | 1.3121 |
| 0.9657 | 2.0 | 16470 | 1.2259 |
| 0.7693 | 3.0 | 24705 | 1.5027 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
312602b105ed1f1165f6f1c0a538e6bc
|
kaipo-chang/distilbert-base-uncased-finetuned-squad
|
kaipo-chang
|
distilbert
| 12 | 6 |
transformers
| 0 |
question-answering
| true | false | false |
apache-2.0
| null |
['squad']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 928 | false |
<!-- 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-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
970f29cd9d8e89c58c4055f74b31acec
|
i-am-holmes/vit-base-patch16-224-finetuned-flower
|
i-am-holmes
|
vit
| 7 | 10 |
transformers
| 0 |
image-classification
| true | false | false |
apache-2.0
| null |
['imagefolder']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 964 | false |
<!-- 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. -->
# vit-base-patch16-224-finetuned-flower
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
8f7103656e8c157905f7feebd5956e16
|
Prajeevan/malavika2
|
Prajeevan
| null | 36 | 5 |
diffusers
| 0 |
text-to-image
| false | false | false |
creativeml-openrail-m
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['text-to-image']
| false | true | true | 1,924 | false |
### malavika2 Dreambooth model trained by Prajeevan with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
Sample pictures of:
malavika2 (use that on your prompt)
|
898eabf5f7823aac3ecf2b87804c3191
|
linydub/bart-large-samsum
|
linydub
|
bart
| 18 | 1,924 |
transformers
| 9 |
summarization
| true | false | false |
apache-2.0
|
['en']
|
['samsum']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['summarization', 'azureml', 'azure', 'codecarbon', 'bart']
| true | true | true | 4,296 | false |
## `bart-large-samsum`
This model was trained using Microsoft's [`Azure Machine Learning Service`](https://azure.microsoft.com/en-us/services/machine-learning). It was fine-tuned on the [`samsum`](https://huggingface.co/datasets/samsum) corpus from [`facebook/bart-large`](https://huggingface.co/facebook/bart-large) checkpoint.
## Usage (Inference)
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="linydub/bart-large-samsum")
input_text = '''
Henry: Hey, is Nate coming over to watch the movie tonight?
Kevin: Yea, he said he'll be arriving a bit later at around 7 since he gets off of work at 6. Have you taken out the garbage yet?
Henry: Oh I forgot. I'll do that once I'm finished with my assignment for my math class.
Kevin: Yea, you should take it out as soon as possible. And also, Nate is bringing his girlfriend.
Henry: Nice, I'm really looking forward to seeing them again.
'''
summarizer(input_text)
```
## Fine-tune on AzureML
[](https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2Flinydub%2Fazureml-greenai-txtsum%2Fmain%2F.cloud%2Ftemplate-hub%2Flinydub%2Farm-bart-large-samsum.json) [](http://armviz.io/#/?load=https://raw.githubusercontent.com/linydub/azureml-greenai-txtsum/main/.cloud/template-hub/linydub/arm-bart-large-samsum.json)
More information about the fine-tuning process (including samples and benchmarks):
**[Preview]** https://github.com/linydub/azureml-greenai-txtsum
## Resource Usage
These results were retrieved from [`Azure Monitor Metrics`](https://docs.microsoft.com/en-us/azure/azure-monitor/essentials/data-platform-metrics). All experiments were ran on AzureML low priority compute clusters.
| Key | Value |
| --- | ----- |
| Region | US West 2 |
| AzureML Compute SKU | STANDARD_ND40RS_V2 |
| Compute SKU GPU Device | 8 x NVIDIA V100 32GB (NVLink) |
| Compute Node Count | 1 |
| Run Duration | 6m 48s |
| Compute Cost (Dedicated/LowPriority) | $2.50 / $0.50 USD |
| Average CPU Utilization | 47.9% |
| Average GPU Utilization | 69.8% |
| Average GPU Memory Usage | 25.71 GB |
| Total GPU Energy Usage | 370.84 kJ |
*Compute cost ($) is estimated from the run duration, number of compute nodes utilized, and SKU's price per hour. Updated SKU pricing could be found [here](https://azure.microsoft.com/en-us/pricing/details/machine-learning).
### Carbon Emissions
These results were obtained using [`CodeCarbon`](https://github.com/mlco2/codecarbon). The carbon emissions are estimated from training runtime only (excl. setup and evaluation runtimes).
| Key | Value |
| --- | ----- |
| timestamp | 2021-09-16T23:54:25 |
| duration | 263.2430217266083 |
| emissions | 0.029715544634717518 |
| energy_consumed | 0.09985062041235725 |
| country_name | USA |
| region | Washington |
| cloud_provider | azure |
| cloud_region | westus2 |
## Hyperparameters
- max_source_length: 512
- max_target_length: 90
- fp16: True
- seed: 1
- per_device_train_batch_size: 16
- per_device_eval_batch_size: 16
- gradient_accumulation_steps: 1
- learning_rate: 5e-5
- num_train_epochs: 3.0
- weight_decay: 0.1
## Results
| ROUGE | Score |
| ----- | ----- |
| eval_rouge1 | 55.0234 |
| eval_rouge2 | 29.6005 |
| eval_rougeL | 44.914 |
| eval_rougeLsum | 50.464 |
| predict_rouge1 | 53.4345 |
| predict_rouge2 | 28.7445 |
| predict_rougeL | 44.1848 |
| predict_rougeLsum | 49.1874 |
| Metric | Value |
| ------ | ----- |
| epoch | 3.0 |
| eval_gen_len | 30.6027 |
| eval_loss | 1.4327096939086914 |
| eval_runtime | 22.9127 |
| eval_samples | 818 |
| eval_samples_per_second | 35.701 |
| eval_steps_per_second | 0.306 |
| predict_gen_len | 30.4835 |
| predict_loss | 1.4501988887786865 |
| predict_runtime | 26.0269 |
| predict_samples | 819 |
| predict_samples_per_second | 31.467 |
| predict_steps_per_second | 0.269 |
| train_loss | 1.2014821151207233 |
| train_runtime | 263.3678 |
| train_samples | 14732 |
| train_samples_per_second | 167.811 |
| train_steps_per_second | 1.321 |
| total_steps | 348 |
| total_flops | 4.26008990669865e+16 |
|
bfd68ba83456c9a3f08edaaca797c9a7
|
regisss/distilbert_xnli
|
regisss
|
distilbert
| 10 | 3 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null |
['xnli']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 950 | false |
<!-- 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_xnli
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the xnli 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.1+cu116
- Datasets 2.6.1
- Tokenizers 0.12.1
|
fa265a9cbc165cd3eec6a7ffec4ef501
|
birgermoell/wav2vec2-liepa-1-percent
|
birgermoell
|
wav2vec2
| 11 | 7 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
|
['lt']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['automatic-speech-recognition', 'common_voice', 'generated_from_trainer']
| true | true | true | 5,153 | false |
<!-- 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-liepa-1-percent
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 - LT dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5774
- Wer: 0.5079
## 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: 15.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 0.23 | 100 | 3.3596 | 1.0 |
| No log | 0.46 | 200 | 2.9280 | 1.0 |
| No log | 0.69 | 300 | 1.5091 | 0.9650 |
| No log | 0.93 | 400 | 0.9943 | 0.9177 |
| 3.1184 | 1.16 | 500 | 0.7590 | 0.7793 |
| 3.1184 | 1.39 | 600 | 0.7336 | 0.7408 |
| 3.1184 | 1.62 | 700 | 0.7040 | 0.7618 |
| 3.1184 | 1.85 | 800 | 0.6815 | 0.7233 |
| 3.1184 | 2.08 | 900 | 0.6457 | 0.6865 |
| 0.7917 | 2.31 | 1000 | 0.5705 | 0.6813 |
| 0.7917 | 2.55 | 1100 | 0.5708 | 0.6620 |
| 0.7917 | 2.78 | 1200 | 0.5888 | 0.6462 |
| 0.7917 | 3.01 | 1300 | 0.6509 | 0.6970 |
| 0.7917 | 3.24 | 1400 | 0.5871 | 0.6462 |
| 0.5909 | 3.47 | 1500 | 0.6199 | 0.6813 |
| 0.5909 | 3.7 | 1600 | 0.6230 | 0.5919 |
| 0.5909 | 3.94 | 1700 | 0.5721 | 0.6427 |
| 0.5909 | 4.17 | 1800 | 0.5331 | 0.5867 |
| 0.5909 | 4.4 | 1900 | 0.5561 | 0.6007 |
| 0.4607 | 4.63 | 2000 | 0.5414 | 0.5849 |
| 0.4607 | 4.86 | 2100 | 0.5390 | 0.5587 |
| 0.4607 | 5.09 | 2200 | 0.5313 | 0.5569 |
| 0.4607 | 5.32 | 2300 | 0.5893 | 0.5797 |
| 0.4607 | 5.56 | 2400 | 0.5507 | 0.5954 |
| 0.3933 | 5.79 | 2500 | 0.5521 | 0.6025 |
| 0.3933 | 6.02 | 2600 | 0.5663 | 0.5989 |
| 0.3933 | 6.25 | 2700 | 0.5636 | 0.5832 |
| 0.3933 | 6.48 | 2800 | 0.5464 | 0.5919 |
| 0.3933 | 6.71 | 2900 | 0.5623 | 0.5832 |
| 0.3367 | 6.94 | 3000 | 0.5324 | 0.5692 |
| 0.3367 | 7.18 | 3100 | 0.5907 | 0.5394 |
| 0.3367 | 7.41 | 3200 | 0.5653 | 0.5814 |
| 0.3367 | 7.64 | 3300 | 0.5707 | 0.5814 |
| 0.3367 | 7.87 | 3400 | 0.5754 | 0.5429 |
| 0.2856 | 8.1 | 3500 | 0.5953 | 0.5569 |
| 0.2856 | 8.33 | 3600 | 0.6275 | 0.5394 |
| 0.2856 | 8.56 | 3700 | 0.6253 | 0.5569 |
| 0.2856 | 8.8 | 3800 | 0.5930 | 0.5429 |
| 0.2856 | 9.03 | 3900 | 0.6082 | 0.5219 |
| 0.2522 | 9.26 | 4000 | 0.6026 | 0.5447 |
| 0.2522 | 9.49 | 4100 | 0.6052 | 0.5271 |
| 0.2522 | 9.72 | 4200 | 0.5871 | 0.5219 |
| 0.2522 | 9.95 | 4300 | 0.5870 | 0.5236 |
| 0.2522 | 10.19 | 4400 | 0.5881 | 0.5131 |
| 0.2167 | 10.42 | 4500 | 0.6122 | 0.5289 |
| 0.2167 | 10.65 | 4600 | 0.6128 | 0.5166 |
| 0.2167 | 10.88 | 4700 | 0.6135 | 0.5377 |
| 0.2167 | 11.11 | 4800 | 0.6055 | 0.5184 |
| 0.2167 | 11.34 | 4900 | 0.6725 | 0.5569 |
| 0.1965 | 11.57 | 5000 | 0.6482 | 0.5429 |
| 0.1965 | 11.81 | 5100 | 0.6037 | 0.5096 |
| 0.1965 | 12.04 | 5200 | 0.5931 | 0.5131 |
| 0.1965 | 12.27 | 5300 | 0.5853 | 0.5114 |
| 0.1965 | 12.5 | 5400 | 0.5798 | 0.5219 |
| 0.172 | 12.73 | 5500 | 0.5775 | 0.5009 |
| 0.172 | 12.96 | 5600 | 0.5782 | 0.5044 |
| 0.172 | 13.19 | 5700 | 0.5804 | 0.5184 |
| 0.172 | 13.43 | 5800 | 0.5977 | 0.5219 |
| 0.172 | 13.66 | 5900 | 0.6069 | 0.5236 |
| 0.1622 | 13.89 | 6000 | 0.5850 | 0.5131 |
| 0.1622 | 14.12 | 6100 | 0.5758 | 0.5096 |
| 0.1622 | 14.35 | 6200 | 0.5752 | 0.5009 |
| 0.1622 | 14.58 | 6300 | 0.5727 | 0.5184 |
| 0.1622 | 14.81 | 6400 | 0.5795 | 0.5044 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.10.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
d350a3136c6db49cfc6108fc00e0d2a1
|
hfl/chinese-roberta-wwm-ext-large
|
hfl
|
bert
| 11 | 181,315 |
transformers
| 26 |
fill-mask
| true | true | true |
apache-2.0
|
['zh']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['bert']
| false | true | true | 2,007 | false |
# Please use 'Bert' related functions to load this model!
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on๏ผhttps://github.com/google-research/bert
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese MacBERT: https://github.com/ymcui/MacBERT
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
- Secondary: https://arxiv.org/abs/1906.08101
```
@article{chinese-bert-wwm,
title={Pre-Training with Whole Word Masking for Chinese BERT},
author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
journal={arXiv preprint arXiv:1906.08101},
year={2019}
}
```
|
8952aed5c4f82b0a89c3dfccb964397e
|
nouman-10/roberta_base_model_fine_tuned
|
nouman-10
|
roberta
| 13 | 8 |
transformers
| 0 |
text-classification
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,196 | false |
<!-- 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_model_fine_tuned
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: 0.2488
- Accuracy: 0.9018
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4049 | 1.0 | 875 | 0.2488 | 0.9018 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
36f65e1fa79504d467ab42ae70f2697c
|
muhtasham/bert-small-finetuned-finer
|
muhtasham
|
bert
| 9 | 5 |
transformers
| 0 |
fill-mask
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,285 | false |
<!-- 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-small-finetuned-finer
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6137
## 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: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8994 | 1.0 | 2433 | 1.7597 |
| 1.7226 | 2.0 | 4866 | 1.6462 |
| 1.6752 | 3.0 | 7299 | 1.6137 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
8cb9c1a3e2530adf4d72d93c12e5509e
|
ckiplab/bert-base-chinese-qa
|
ckiplab
|
bert
| 7 | 45 |
transformers
| 0 |
question-answering
| true | false | false |
gpl-3.0
|
['zh']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['pytorch', 'question-answering', 'bert', 'zh']
| false | true | true | 959 | false |
# CKIP BERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
้ๅๅฐๆกๆไพไบ็น้ซไธญๆ็ transformers ๆจกๅ๏ผๅ
ๅซ ALBERTใBERTใGPT2๏ผๅ่ช็ถ่ช่จ่็ๅทฅๅ
ท๏ผๅ
ๅซๆท่ฉใ่ฉๆงๆจ่จใๅฏฆ้ซ่พจ่ญ๏ผใ
## Homepage
- https://github.com/ckiplab/ckip-transformers
## Contributers
- [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer)
## Usage
Please use BertTokenizerFast as tokenizer instead of AutoTokenizer.
่ซไฝฟ็จ BertTokenizerFast ่้ AutoTokenizerใ
```
from transformers import (
BertTokenizerFast,
AutoModel,
)
tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese')
model = AutoModel.from_pretrained('ckiplab/bert-base-chinese-qa')
```
For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers.
ๆ้ๅฎๆดไฝฟ็จๆนๆณๅๅ
ถไป่ณ่จ๏ผ่ซๅ่ฆ https://github.com/ckiplab/ckip-transformers ใ
|
69db767d1a30a7b764e663896afee4df
|
muhtasham/small-mlm-glue-mrpc-target-glue-mnli
|
muhtasham
|
bert
| 10 | 3 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,814 | false |
<!-- 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. -->
# small-mlm-glue-mrpc-target-glue-mnli
This model is a fine-tuned version of [muhtasham/small-mlm-glue-mrpc](https://huggingface.co/muhtasham/small-mlm-glue-mrpc) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6541
- Accuracy: 0.7253
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9151 | 0.04 | 500 | 0.8235 | 0.6375 |
| 0.8111 | 0.08 | 1000 | 0.7776 | 0.6659 |
| 0.7745 | 0.12 | 1500 | 0.7510 | 0.6748 |
| 0.7502 | 0.16 | 2000 | 0.7329 | 0.6886 |
| 0.7431 | 0.2 | 2500 | 0.7189 | 0.6921 |
| 0.7325 | 0.24 | 3000 | 0.7032 | 0.6991 |
| 0.7139 | 0.29 | 3500 | 0.6793 | 0.7129 |
| 0.7031 | 0.33 | 4000 | 0.6678 | 0.7215 |
| 0.6778 | 0.37 | 4500 | 0.6761 | 0.7236 |
| 0.6811 | 0.41 | 5000 | 0.6541 | 0.7253 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
5d46ba970c3e05252984b916af9359e2
|
garyw/clinical-embeddings-600d-ft-cr
|
garyw
| null | 9 | 0 | null | 0 | null | false | false | false |
gpl-3.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 1,548 | false |
Pre-trained word embeddings using the text of published clinical case reports. These embeddings use 600 dimensions and were trained using the fasttext algorithm on published clinical case reports found in the [PMC Open Access Subset](https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/). See the paper here: https://pubmed.ncbi.nlm.nih.gov/34920127/
Citation:
```
@article{flamholz2022word,
title={Word embeddings trained on published case reports are lightweight, effective for clinical tasks, and free of protected health information},
author={Flamholz, Zachary N and Crane-Droesch, Andrew and Ungar, Lyle H and Weissman, Gary E},
journal={Journal of Biomedical Informatics},
volume={125},
pages={103971},
year={2022},
publisher={Elsevier}
}
```
## Quick start
Word embeddings are compatible with the [`gensim` Python package](https://radimrehurek.com/gensim/) format.
First download the files from this archive. Then load the embeddings into Python.
```python
from gensim.models import FastText, Word2Vec, KeyedVectors # KeyedVectors are used to load the GloVe models
# Load the model
model = FastText.load('ft_oa_corp_600d.bin')
# Return 100-dimensional vector representations of each word
model.wv.word_vec('diabetes')
model.wv.word_vec('cardiac_arrest')
model.wv.word_vec('lymphangioleiomyomatosis')
# Try out cosine similarity
model.wv.similarity('copd', 'chronic_obstructive_pulmonary_disease')
model.wv.similarity('myocardial_infarction', 'heart_attack')
model.wv.similarity('lymphangioleiomyomatosis', 'lam')
```
|
c06075b6654357c9e6aca6fb7d993652
|
DOOGLAK/Article_50v7_NER_Model_3Epochs_UNAUGMENTED
|
DOOGLAK
|
bert
| 13 | 6 |
transformers
| 0 |
token-classification
| true | false | false |
apache-2.0
| null |
['article50v7_wikigold_split']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,559 | false |
<!-- 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. -->
# Article_50v7_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article50v7_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7894
- Precision: 0.3333
- Recall: 0.0002
- F1: 0.0005
- Accuracy: 0.7783
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 6 | 1.0271 | 0.1183 | 0.0102 | 0.0188 | 0.7768 |
| No log | 2.0 | 12 | 0.8250 | 0.4 | 0.0005 | 0.0010 | 0.7783 |
| No log | 3.0 | 18 | 0.7894 | 0.3333 | 0.0002 | 0.0005 | 0.7783 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
0b3430bb07e23fcee4a41e9a427bbb12
|
mp6kv/IQA_classification
|
mp6kv
|
roberta
| 15 | 3 |
transformers
| 0 |
text-classification
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,419 | false |
<!-- 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. -->
# IQA_classification
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0718
- Accuracy: 0.4862
- Precision: 0.3398
- Recall: 0.4862
- F1: 0.3270
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.3973 | 1.0 | 28 | 1.1588 | 0.4771 | 0.2276 | 0.4771 | 0.3082 |
| 1.1575 | 2.0 | 56 | 1.0718 | 0.4862 | 0.3398 | 0.4862 | 0.3270 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
478170b013c2873995b89a6dd0432eec
|
Satyamatury/wav2vec2-large-xls-r-300m-hindi-colab
|
Satyamatury
|
wav2vec2
| 18 | 6 |
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 | 1,330 | false |
<!-- 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-hindi-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.
It achieves the following results on the evaluation set:
- Loss: 1.7529
- Wer: 0.9130
## 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: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.2923 | 44.42 | 400 | 1.7529 | 0.9130 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
933695b0fe83d179c8089ca63ca8d1c0
|
kadirnar/strongsort
|
kadirnar
| null | 2 | 0 | null | 0 |
object-detection
| false | false | false |
gpl-3.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['object-detection', 'computer-vision', 'sort', 'tracker', 'strongsort']
| false | true | true | 729 | false |
### Model Description
[StrongSort](https://arxiv.org/abs/2202.13514): Make DeepSORT Great Again
<img src="https://raw.githubusercontent.com/dyhBUPT/StrongSORT/master/assets/MOTA-IDF1-HOTA.png" width="1000"/>
### Installation
```
pip install strongsort
```
### Tracker
```python
from strong_sort import StrongSORT
tracker = StrongSORT(model_weights='model.pt', device='cuda')
pred = model(img)
for i, det in enumerate(pred):
det[i] = tracker[i].update(detection, im0s)
```
### BibTeX Entry and Citation Info
```
@article{du2022strongsort,
title={Strongsort: Make deepsort great again},
author={Du, Yunhao and Song, Yang and Yang, Bo and Zhao, Yanyun},
journal={arXiv preprint arXiv:2202.13514},
year={2022}
}
```
|
d9c19179833fbb544c174d60a643ceb7
|
CLTL/icf-levels-fac
|
CLTL
|
roberta
| 11 | 10 |
transformers
| 1 |
text-classification
| true | false | false |
mit
|
['nl']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 3,447 | false |
# Regression Model for Walking Functioning Levels (ICF d550)
## Description
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing walking functions. The model is based on a pre-trained Dutch medical language model ([link to be added]()): a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. To detect sentences about walking functions in clinical text in Dutch, use the [icf-domains](https://huggingface.co/CLTL/icf-domains) classification model.
## Functioning levels
Level | Meaning
---|---
5 | Patient can walk independently anywhere: level surface, uneven surface, slopes, stairs.
4 | Patient can walk independently on level surface but requires help on stairs, inclines, uneven surface; or, patient can walk independently, but the walking is not fully normal.
3 | Patient requires verbal supervision for walking, without physical contact.
2 | Patient needs continuous or intermittent support of one person to help with balance and coordination.
1 | Patient needs firm continuous support from one person who helps carrying weight and with balance.
0 | Patient cannot walk or needs help from two or more people; or, patient walks on a treadmill.
The predictions generated by the model might sometimes be outside of the scale (e.g. 5.2); this is normal in a regression model.
## Intended uses and limitations
- The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records).
- The model was fine-tuned with the [Simple Transformers](https://simpletransformers.ai/) library. This library is based on Transformers but the model cannot be used directly with Transformers `pipeline` and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled.
## How to use
To generate predictions with the model, use the [Simple Transformers](https://simpletransformers.ai/) library:
```
from simpletransformers.classification import ClassificationModel
model = ClassificationModel(
'roberta',
'CLTL/icf-levels-fac',
use_cuda=False,
)
example = 'kan nog goed traplopen, maar flink ingeleverd aan conditie na Corona'
_, raw_outputs = model.predict([example])
predictions = np.squeeze(raw_outputs)
```
The prediction on the example is:
```
4.2
```
The raw outputs look like this:
```
[[4.20903111]]
```
## Training data
- The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released.
- The annotation guidelines used for the project can be found [here](https://github.com/cltl/a-proof-zonmw/tree/main/resources/annotation_guidelines).
## Training procedure
The default training parameters of Simple Transformers were used, including:
- Optimizer: AdamW
- Learning rate: 4e-5
- Num train epochs: 1
- Train batch size: 8
## Evaluation results
The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals).
| | Sentence-level | Note-level
|---|---|---
mean absolute error | 0.70 | 0.66
mean squared error | 0.91 | 0.93
root mean squared error | 0.95 | 0.96
## Authors and references
### Authors
Jenia Kim, Piek Vossen
### References
TBD
|
ad550cdd009e5862afef2d85d33a6bba
|
asi/albert-act-tiny
|
asi
|
albert_act
| 9 | 4 |
transformers
| 1 | null | true | true | false |
apache-2.0
|
['en']
|
['wikipedia', 'bookcorpus']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| true | true | true | 2,627 | false |
# Adaptive Depth Transformers
Implementation of the paper "How Many Layers and Why? An Analysis of the Model Depth in Transformers". In this study, we investigate the role of the multiple layers in deep transformer models. We design a variant of ALBERT that dynamically adapts the number of layers for each token of the input.
## Model architecture
We augment a multi-layer transformer encoder with a halting mechanism, which dynamically adjusts the number of layers for each token.
We directly adapted this mechanism from Graves ([2016](#graves-2016)). At each iteration, we compute a probability for each token to stop updating its state.
## Model use
The architecture is not yet directly included in the Transformers library. The code used for pre-training is available in the following [github repository](https://github.com/AntoineSimoulin/adaptive-depth-transformers). So you should install the code implementation first:
```bash
!pip install git+https://github.com/AntoineSimoulin/adaptive-depth-transformers$
```
Then you can use the model directly.
```python
from act import AlbertActConfig, AlbertActModel, TFAlbertActModel
from transformers import AlbertTokenizer
tokenizer = AlbertTokenizer.from_pretrained('asi/albert-act-base')
model = AlbertActModel.from_pretrained('asi/albert-act-base')
_ = model.eval()
inputs = tokenizer("a lump in the middle of the monkeys stirred and then fell quiet .", return_tensors="pt")
outputs = model(**inputs)
outputs.updates
# tensor([[[[15., 9., 10., 7., 3., 8., 5., 7., 12., 10., 6., 8., 8., 9., 5., 8.]]]])
```
## Citations
### BibTeX entry and citation info
If you use our iterative transformer model for your scientific publication or your industrial applications, please cite the following [paper](https://aclanthology.org/2021.acl-srw.23/):
```bibtex
@inproceedings{simoulin-crabbe-2021-many,
title = "How Many Layers and Why? {A}n Analysis of the Model Depth in Transformers",
author = "Simoulin, Antoine and
Crabb{\'e}, Benoit",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-srw.23",
doi = "10.18653/v1/2021.acl-srw.23",
pages = "221--228",
}
```
### References
><div id="graves-2016">Alex Graves. 2016. Adaptive computation time for recurrent neural networks. CoRR, abs/1603.08983.</div>
|
fdb0f9dbe1fe677dcde6cc50003501fd
|
IShallRiseAgain/DCAU
|
IShallRiseAgain
| null | 7 | 0 | null | 20 |
text-to-image
| false | false | false |
creativeml-openrail-m
| null | null | null | 1 | 0 | 1 | 0 | 1 | 1 | 0 |
['stable-diffusion', 'text-to-image']
| false | true | true | 513 | false |
**DCAU Diffusion**
Prompt is currently Batman_the_animated_series. In the future it will include all DCAU shows.
**Existing Characters:**
**Characters not in original dataset:**
**Realistic Style:**
|
fc52382070e8e15ed517fac5675a0bcd
|
ligerre/xlm-roberta-base-finetuned-panx-de
|
ligerre
|
xlm-roberta
| 12 | 6 |
transformers
| 0 |
token-classification
| true | false | false |
mit
| null |
['xtreme']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,320 | false |
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1343
- F1: 0.8637
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2578 | 1.0 | 525 | 0.1562 | 0.8273 |
| 0.1297 | 2.0 | 1050 | 0.1330 | 0.8474 |
| 0.0809 | 3.0 | 1575 | 0.1343 | 0.8637 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
4818f1091a3254a857a113953a557b19
|
stanfordnlp/CoreNLP
|
stanfordnlp
| null | 3 | 0 | null | 7 | null | false | false | false |
gpl-2.0
|
['en']
| null | null | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
['corenlp']
| false | true | true | 660 | false |
# Core NLP model for CoreNLP
CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment, quote attributions, and relations.
Find more about it in [our website](https://stanfordnlp.github.io/CoreNLP) and our [GitHub repository](https://github.com/stanfordnlp/CoreNLP).
This card and repo were automatically prepared with `hugging_corenlp.py` in the `stanfordnlp/huggingface-models` repo
Last updated 2023-01-21 01:34:10.792
|
8b6420189afade7ffcf8260e888926e9
|
shripadbhat/whisper-large-v2-sr
|
shripadbhat
|
whisper
| 17 | 0 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
|
['sr']
|
['mozilla-foundation/common_voice_11_0']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['whisper-event', 'generated_from_trainer']
| true | true | true | 1,365 | false |
<!-- 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. -->
# Whisper Large v2 Serbian
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2036
- Wer: 11.8980
## 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: 1e-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: 50
- training_steps: 200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2639 | 0.48 | 100 | 0.2438 | 14.0834 |
| 0.1965 | 0.96 | 200 | 0.2036 | 11.8980 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
6e77512cc5c967656dbd7671654409bf
|
google/tapas-large-finetuned-tabfact
|
google
|
tapas
| 8 | 11 |
transformers
| 0 |
text-classification
| true | true | false |
apache-2.0
|
['en']
|
['tab_fact']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['tapas', 'sequence-classification']
| false | true | true | 4,767 | false |
# TAPAS large model fine-tuned on Tabular Fact Checking (TabFact)
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_tabfact_inter_masklm_large_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training, and then fine-tuned on [TabFact](https://github.com/wenhuchen/Table-Fact-Checking). It uses relative position embeddings by default (i.e. resetting the position index at every cell of the table).
The other (non-default) version which can be used is the one with absolute position embeddings:
- `no_reset`, which corresponds to `tapas_tabfact_inter_masklm_large`
Disclaimer: The team releasing TAPAS did not write a model card for this model so this model card has been written by
the Hugging Face team and contributors.
## Model description
TAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion.
This means it was pretrained on the raw tables and associated texts only, with no humans labelling them in any way (which is why it
can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
was pretrained with two objectives:
- Masked language modeling (MLM): taking a (flattened) table and associated context, the model randomly masks 15% of the words in
the input, then runs the entire (partially masked) sequence through the model. The model then has to predict the masked words.
This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other,
or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional
representation of a table and associated text.
- Intermediate pre-training: to encourage numerical reasoning on tables, the authors additionally pre-trained the model by creating
a balanced dataset of millions of syntactically created training examples. Here, the model must predict (classify) whether a sentence
is supported or refuted by the contents of a table. The training examples are created based on synthetic as well as counterfactual statements.
This way, the model learns an inner representation of the English language used in tables and associated texts, which can then be used
to extract features useful for downstream tasks such as answering questions about a table, or determining whether a sentence is entailed
or refuted by the contents of a table. Fine-tuning is done by adding a classification head on top of the pre-trained model, and then
jointly train this randomly initialized classification head with the base model on TabFact.
## Intended uses & limitations
You can use this model for classifying whether a sentence is supported or refuted by the contents of a table.
For code examples, we refer to the documentation of TAPAS on the HuggingFace website.
## Training procedure
### Preprocessing
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
then of the form:
```
[CLS] Sentence [SEP] Flattened table [SEP]
```
### Fine-tuning
The model was fine-tuned on 32 Cloud TPU v3 cores for 80,000 steps with maximum sequence length 512 and batch size of 512.
In this setup, fine-tuning takes around 14 hours. The optimizer used is Adam with a learning rate of 2e-5, and a warmup
ratio of 0.05. See the [paper](https://arxiv.org/abs/2010.00571) for more details (appendix A2).
### BibTeX entry and citation info
```bibtex
@misc{herzig2020tapas,
title={TAPAS: Weakly Supervised Table Parsing via Pre-training},
author={Jonathan Herzig and Paweล Krzysztof Nowak and Thomas Mรผller and Francesco Piccinno and Julian Martin Eisenschlos},
year={2020},
eprint={2004.02349},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
```
```bibtex
@misc{eisenschlos2020understanding,
title={Understanding tables with intermediate pre-training},
author={Julian Martin Eisenschlos and Syrine Krichene and Thomas Mรผller},
year={2020},
eprint={2010.00571},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@inproceedings{2019TabFactA,
title={TabFact : A Large-scale Dataset for Table-based Fact Verification},
author={Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou and William Yang Wang},
booktitle = {International Conference on Learning Representations (ICLR)},
address = {Addis Ababa, Ethiopia},
month = {April},
year = {2020}
}
```
|
f5cab53ff0dadf9ee0aa5d89cc4120c6
|
csikasote/xls-r-300m-bemba-15hrs
|
csikasote
|
wav2vec2
| 17 | 0 |
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 | 2,071 | false |
<!-- 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. -->
# xls-r-300m-bemba-15hrs
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2754
- Wer: 0.3481
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.5142 | 0.71 | 400 | 0.5585 | 0.7501 |
| 0.6351 | 1.43 | 800 | 0.3185 | 0.5058 |
| 0.4892 | 2.15 | 1200 | 0.2813 | 0.4655 |
| 0.4021 | 2.86 | 1600 | 0.2539 | 0.4159 |
| 0.3505 | 3.58 | 2000 | 0.2411 | 0.4000 |
| 0.3045 | 4.29 | 2400 | 0.2512 | 0.3951 |
| 0.274 | 5.01 | 2800 | 0.2402 | 0.3922 |
| 0.2335 | 5.72 | 3200 | 0.2403 | 0.3764 |
| 0.2032 | 6.44 | 3600 | 0.2383 | 0.3657 |
| 0.1783 | 7.16 | 4000 | 0.2603 | 0.3518 |
| 0.1487 | 7.87 | 4400 | 0.2479 | 0.3577 |
| 0.1281 | 8.59 | 4800 | 0.2638 | 0.3518 |
| 0.113 | 9.3 | 5200 | 0.2754 | 0.3481 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
|
2bdcfa4312132e14348b656eeb53065a
|
lunesco/bert-german-ner
|
lunesco
|
bert
| 13 | 55 |
transformers
| 1 |
token-classification
| true | false | false |
mit
| null |
['conll2003']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,003 | false |
<!-- 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-german-ner
This model is a fine-tuned version of [dbmdz/bert-base-german-cased](https://huggingface.co/dbmdz/bert-base-german-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3196
- Precision: 0.8334
- Recall: 0.8620
- F1: 0.8474
- Accuracy: 0.9292
## 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 300 | 0.3617 | 0.7310 | 0.7733 | 0.7516 | 0.8908 |
| 0.5428 | 2.0 | 600 | 0.2897 | 0.7789 | 0.8395 | 0.8081 | 0.9132 |
| 0.5428 | 3.0 | 900 | 0.2805 | 0.8147 | 0.8465 | 0.8303 | 0.9221 |
| 0.2019 | 4.0 | 1200 | 0.2816 | 0.8259 | 0.8498 | 0.8377 | 0.9260 |
| 0.1215 | 5.0 | 1500 | 0.2942 | 0.8332 | 0.8599 | 0.8463 | 0.9285 |
| 0.1215 | 6.0 | 1800 | 0.3053 | 0.8293 | 0.8619 | 0.8452 | 0.9287 |
| 0.0814 | 7.0 | 2100 | 0.3190 | 0.8249 | 0.8634 | 0.8437 | 0.9267 |
| 0.0814 | 8.0 | 2400 | 0.3196 | 0.8334 | 0.8620 | 0.8474 | 0.9292 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
eb8110e96a1f6d5d176f9af67f20e725
|
kadirnar/OcSort
|
kadirnar
| null | 2 | 0 | null | 0 |
object-detection
| false | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['object-detection', 'computer-vision', 'sort', 'tracker', 'ocsort']
| false | true | true | 982 | false |
### Model Description
Observation-Centric SORT ([OC-SORT(https://arxiv.org/abs/2203.14360)]) is a pure motion-model-based multi-object tracker. It aims to improve tracking robustness in crowded scenes and when objects are in non-linear motion. It is designed by recognizing and fixing limitations in Kalman filter and SORT. It is flexible to integrate with different detectors and matching modules, such as appearance similarity. It remains, Simple, Online and Real-time.
<img src="https://raw.githubusercontent.com/noahcao/OC_SORT/master/assets/teaser.png" width="600"/>
### Installation
```
pip install ocsort
```
### Tracker
```python
from ocsort.ocsort import OCSort
tracker = OCSort(args)
for image in images:
dets = detector(image)
online_targets = tracker.update(dets)
```
### BibTeX Entry and Citation Info
```
, Jinkun and Weng, Xinshuo and Khirodkar, Rawal and Pang, Jiangmiao and Kitani, Kris},
journal={arXiv preprint arXiv:2203.14360},
year={2022}
}
```
|
33b4de78ea91b14730becae7b28c9f20
|
merve/bart-example
|
merve
|
bart
| 10 | 8 |
transformers
| 0 |
text2text-generation
| false | true | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_keras_callback']
| true | true | true | 1,332 | false |
<!-- 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. -->
# bart-example
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.7877
- Validation Loss: 2.4972
- Epoch: 4
## 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': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 6.3670 | 3.2462 | 0 |
| 3.5143 | 2.7551 | 1 |
| 3.0299 | 2.5620 | 2 |
| 2.9364 | 2.7830 | 3 |
| 2.7877 | 2.4972 | 4 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
d67c634082ee4fa52b79102fd02a4b47
|
skpawar1305/wav2vec2-base-finetuned-ks
|
skpawar1305
|
wav2vec2
| 10 | 3 |
transformers
| 0 |
audio-classification
| true | false | false |
apache-2.0
| null |
['superb']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,559 | false |
<!-- 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-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0903
- Accuracy: 0.9834
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7264 | 1.0 | 399 | 0.6319 | 0.9351 |
| 0.2877 | 2.0 | 798 | 0.1846 | 0.9748 |
| 0.175 | 3.0 | 1197 | 0.1195 | 0.9796 |
| 0.1672 | 4.0 | 1596 | 0.0903 | 0.9834 |
| 0.1235 | 5.0 | 1995 | 0.0854 | 0.9825 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
278bdfcbde5c07e5d595102e6a3788f1
|
jonatasgrosman/exp_w2v2t_id_vp-it_s692
|
jonatasgrosman
|
wav2vec2
| 10 | 7 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
|
['id']
|
['mozilla-foundation/common_voice_7_0']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['automatic-speech-recognition', 'id']
| false | true | true | 469 | false |
# exp_w2v2t_id_vp-it_s692
Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (id)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
88ae63ac6bc0176a15056879fcf1dca5
|
naverpapago/garnet
|
naverpapago
| null | 3 | 0 |
pytorch
| 0 | null | true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['Scene Text Removal', 'Image to Image']
| false | true | true | 2,047 | false |
### GaRNet
This is text-removal model that introduced in the paper below and first released at [this page](https://github.com/naver/garnet). \
[The Surprisingly Straightforward Scene Text Removal Method With Gated Attention and Region of Interest Generation: A Comprehensive Prominent Model Analysis](https://arxiv.org/abs/2210.07489). \
Hyeonsu Lee, Chankyu Choi \
Naver Corp. \
In ECCV 2022.
### Model description
GaRNet is a generator that create non-text image with given image and coresponding text box mask. It consists of convolution encoder and decoder. The encoder consists of residual block with attention module called Gated Attention.
Gated Attention module has two Spatial attention branch. Each attention branch finds text stroke or its surrounding regions. The module adjusts the weight of these two domains by trainable parameters.
The model was trained in PatchGAN manner with Region-of-Interest Generation. \
The discriminator is consists of convolution encoder. Given an image, it determines whether each patch, which indicates text-box regions, is real or fake.
All loss functions treat non-textbox regions as 'don't care'.
### Intended uses & limitations
This model can be used for areas that require the process of erasing text from an image, such as concealment private information, text editing.\
You can use the raw model or pre-trained model.\
Note that pre-trained model was trained in both Synthetic and SCUT_EnsText dataset. And the SCUT-EnsText dataset can only be used for non-commercial research purposes.
### How to use
You can use inference code in [this page](https://github.com/naver/garnet).
### BibTeX entry and citation info
```
@inproceedings{lee2022surprisingly,
title={The Surprisingly Straightforward Scene Text Removal Method with Gated Attention and Region of Interest Generation: A Comprehensive Prominent Model Analysis},
author={Lee, Hyeonsu and Choi, Chankyu},
booktitle={European Conference on Computer Vision},
pages={457--472},
year={2022},
organization={Springer}
}
```
|
9172defa0a5587975ac93bb43669a4e9
|
remzicam/xs_blenderbot_onnx
|
remzicam
| null | 6 | 0 | null | 0 | null | false | false | false |
other
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 1,659 | false |
# xs_blenderbot_onnx (only 168 mb)
onnx quantized version of facebook/blenderbot_small-90M model (350 mb)
Faster cpu inference
## INTRO
Before usage:
โข download blender_model.py script from files in this repo
โข pip install onnxruntime
you can use the model with huggingface generate function with its all parameters
# Usage
With text generation pipeline
```python
>>>from blender_model import TextGenerationPipeline
>>>max_answer_length = 100
>>>response_generator_pipe = TextGenerationPipeline(max_length=max_answer_length)
>>>utterance = "Hello, how are you?"
>>>response_generator_pipe(utterance)
i am well. how are you? what do you like to do in your free time?
```
Or you can call the model
```python
>>>from blender_model import OnnxBlender
>>>from transformers import BlenderbotSmallTokenizer
>>>original_repo_id = "facebook/blenderbot_small-90M"
>>>repo_id = "remzicam/xs_blenderbot_onnx"
>>>model_file_names = [
"blenderbot_small-90M-encoder-quantized.onnx",
"blenderbot_small-90M-decoder-quantized.onnx",
"blenderbot_small-90M-init-decoder-quantized.onnx",
]
>>>model=OnnxBlender(original_repo_id, repo_id, model_file_names)
>>>utterance = "Hello, how are you?"
>>>inputs = tokenizer(utterance,
return_tensors="pt")
>>>outputs= model.generate(**inputs,
max_length=max_answer_length)
>>>response = tokenizer.decode(outputs[0],
skip_special_tokens = True)
>>>print(response)
i am well. how are you? what do you like to do in your free time?
```
# Credits
To create the model, I adopted codes from https://github.com/siddharth-sharma7/fast-Bart repository.
|
32f57e1008fcfeb2d94051494f3770a2
|
stanfordnlp/stanza-hi
|
stanfordnlp
| null | 10 | 142 |
stanza
| 0 |
token-classification
| false | false | false |
apache-2.0
|
['hi']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['stanza', 'token-classification']
| false | true | true | 578 | false |
# Stanza model for Hindi (hi)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in [our website](https://stanfordnlp.github.io/stanza) and our [GitHub repository](https://github.com/stanfordnlp/stanza).
This card and repo were automatically prepared with `hugging_stanza.py` in the `stanfordnlp/huggingface-models` repo
Last updated 2022-10-26 21:23:50.098
|
dfb78bce10fbe3ce4e78bdd89ab3168d
|
wietsedv/xlm-roberta-base-ft-udpos28-fo
|
wietsedv
|
xlm-roberta
| 8 | 13 |
transformers
| 0 |
token-classification
| true | false | false |
apache-2.0
|
['fo']
|
['universal_dependencies']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['part-of-speech', 'token-classification']
| true | true | true | 567 | false |
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Faroese
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-fo")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-fo")
```
|
c12ecee01145b258a9ebb99f451ca0db
|
eunbeee/ainize-kobart-news-eb-finetuned-papers
|
eunbeee
|
bart
| 11 | 1 |
transformers
| 0 |
text2text-generation
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,863 | false |
<!-- 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. -->
# ainize-kobart-news-eb-finetuned-papers
This model is a fine-tuned version of [ainize/kobart-news](https://huggingface.co/ainize/kobart-news) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3066
- Rouge1: 14.5433
- Rouge2: 5.2238
- Rougel: 14.4731
- Rougelsum: 14.5183
- Gen Len: 19.9934
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 0.1918 | 1.0 | 7200 | 0.2403 | 14.6883 | 5.2427 | 14.6306 | 14.6489 | 19.9938 |
| 0.1332 | 2.0 | 14400 | 0.2391 | 14.5165 | 5.2443 | 14.493 | 14.4908 | 19.9972 |
| 0.0966 | 3.0 | 21600 | 0.2539 | 14.758 | 5.4976 | 14.6906 | 14.7188 | 19.9941 |
| 0.0736 | 4.0 | 28800 | 0.2782 | 14.6267 | 5.3371 | 14.5578 | 14.6014 | 19.9934 |
| 0.0547 | 5.0 | 36000 | 0.3066 | 14.5433 | 5.2238 | 14.4731 | 14.5183 | 19.9934 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
9c5c7e35f66cae3677037e0accfc27b0
|
Brainergy/zzuurryy
|
Brainergy
| null | 16 | 2 |
diffusers
| 0 |
text-to-image
| false | false | false |
creativeml-openrail-m
| null | null | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
['text-to-image', 'stable-diffusion']
| false | true | true | 419 | false |
### zzuurryy Dreambooth model trained by Brainergy with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
9eb86a4587b74b85f6ee056a6354a809
|
pollcat/pollcat-mnli
|
pollcat
|
distilbert
| 12 | 1 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null |
['glue']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,201 | false |
<!-- 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. -->
# pollcat-mnli
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: 1.8610
- Accuracy: 0.7271
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0633 | 1.0 | 1563 | 1.8610 | 0.7271 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
6c05e4b58907598130d517d812e1e279
|
muhtasham/small-mlm-glue-rte-target-glue-stsb
|
muhtasham
|
bert
| 10 | 3 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
['generated_from_trainer']
| true | true | true | 1,962 | false |
<!-- 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. -->
# small-mlm-glue-rte-target-glue-stsb
This model is a fine-tuned version of [muhtasham/small-mlm-glue-rte](https://huggingface.co/muhtasham/small-mlm-glue-rte) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5419
- Pearson: 0.8754
- Spearmanr: 0.8723
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|
| 0.8054 | 2.78 | 500 | 0.6118 | 0.8682 | 0.8680 |
| 0.2875 | 5.56 | 1000 | 0.5788 | 0.8693 | 0.8682 |
| 0.1718 | 8.33 | 1500 | 0.6133 | 0.8673 | 0.8639 |
| 0.1251 | 11.11 | 2000 | 0.6103 | 0.8716 | 0.8681 |
| 0.0999 | 13.89 | 2500 | 0.5665 | 0.8734 | 0.8707 |
| 0.0825 | 16.67 | 3000 | 0.6035 | 0.8736 | 0.8700 |
| 0.07 | 19.44 | 3500 | 0.5605 | 0.8752 | 0.8716 |
| 0.0611 | 22.22 | 4000 | 0.5661 | 0.8768 | 0.8730 |
| 0.0565 | 25.0 | 4500 | 0.5557 | 0.8739 | 0.8705 |
| 0.0523 | 27.78 | 5000 | 0.5419 | 0.8754 | 0.8723 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
2019ece308a283bf629b6e508f1864a3
|
gokuls/distilbert_add_GLUE_Experiment_logit_kd_cola_96
|
gokuls
|
distilbert
| 17 | 3 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
|
['en']
|
['glue']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,398 | false |
<!-- 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_add_GLUE_Experiment_logit_kd_cola_96
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6839
- Matthews Correlation: 0.0
## 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: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.9137 | 1.0 | 34 | 0.7665 | 0.0 |
| 0.8592 | 2.0 | 68 | 0.7303 | 0.0 |
| 0.8268 | 3.0 | 102 | 0.7043 | 0.0 |
| 0.8074 | 4.0 | 136 | 0.6901 | 0.0 |
| 0.8005 | 5.0 | 170 | 0.6853 | 0.0 |
| 0.7969 | 6.0 | 204 | 0.6842 | 0.0 |
| 0.797 | 7.0 | 238 | 0.6840 | 0.0 |
| 0.7981 | 8.0 | 272 | 0.6840 | 0.0 |
| 0.7971 | 9.0 | 306 | 0.6840 | 0.0 |
| 0.7967 | 10.0 | 340 | 0.6839 | 0.0 |
| 0.7978 | 11.0 | 374 | 0.6839 | 0.0 |
| 0.7979 | 12.0 | 408 | 0.6839 | 0.0 |
| 0.7973 | 13.0 | 442 | 0.6839 | 0.0 |
| 0.7979 | 14.0 | 476 | 0.6840 | 0.0 |
| 0.7972 | 15.0 | 510 | 0.6839 | 0.0 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
39d646c9be5bfb8c052a57d45cb573b3
|
neelrr/xlm-roberta-base-finetuned-panx-hi
|
neelrr
|
xlm-roberta
| 14 | 5 |
transformers
| 0 |
token-classification
| true | false | false |
mit
| null |
['xtreme']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,314 | false |
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-hi
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2211
- F1: 0.8614
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.62 | 1.0 | 209 | 0.3914 | 0.7622 |
| 0.2603 | 2.0 | 418 | 0.2665 | 0.8211 |
| 0.1653 | 3.0 | 627 | 0.2211 | 0.8614 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
9078ae3320c69d13658f8cb8d0b5df20
|
tkazusa/lilt-en-funsd
|
tkazusa
|
lilt
| 23 | 5 |
transformers
| 0 |
token-classification
| true | false | false |
mit
| null |
['funsd-layoutlmv3']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 6,837 | false |
<!-- 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. -->
# lilt-en-funsd
This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6459
- Answer: {'precision': 0.8831942789034565, 'recall': 0.9069767441860465, 'f1': 0.894927536231884, 'number': 817}
- Header: {'precision': 0.6213592233009708, 'recall': 0.5378151260504201, 'f1': 0.5765765765765765, 'number': 119}
- Question: {'precision': 0.8998178506375227, 'recall': 0.9173630454967502, 'f1': 0.9085057471264367, 'number': 1077}
- Overall Precision: 0.8789
- Overall Recall: 0.8907
- Overall F1: 0.8848
- Overall Accuracy: 0.8068
## 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
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.4201 | 10.53 | 200 | 0.8003 | {'precision': 0.8321995464852607, 'recall': 0.8984088127294981, 'f1': 0.8640376692171865, 'number': 817} | {'precision': 0.5714285714285714, 'recall': 0.5714285714285714, 'f1': 0.5714285714285714, 'number': 119} | {'precision': 0.8651079136690647, 'recall': 0.89322191272052, 'f1': 0.8789401553220649, 'number': 1077} | 0.8348 | 0.8763 | 0.8551 | 0.8104 |
| 0.0376 | 21.05 | 400 | 1.3158 | {'precision': 0.8395904436860068, 'recall': 0.9033047735618115, 'f1': 0.8702830188679245, 'number': 817} | {'precision': 0.4785714285714286, 'recall': 0.5630252100840336, 'f1': 0.5173745173745175, 'number': 119} | {'precision': 0.8887814313346228, 'recall': 0.8532961931290622, 'f1': 0.8706774040738986, 'number': 1077} | 0.8397 | 0.8564 | 0.8480 | 0.7934 |
| 0.0119 | 31.58 | 600 | 1.4791 | {'precision': 0.8752941176470588, 'recall': 0.9106487148102815, 'f1': 0.8926214757048591, 'number': 817} | {'precision': 0.5401459854014599, 'recall': 0.6218487394957983, 'f1': 0.578125, 'number': 119} | {'precision': 0.8818681318681318, 'recall': 0.8941504178272981, 'f1': 0.8879668049792531, 'number': 1077} | 0.8567 | 0.8847 | 0.8705 | 0.7961 |
| 0.0061 | 42.11 | 800 | 1.5605 | {'precision': 0.8617886178861789, 'recall': 0.9082007343941249, 'f1': 0.8843861740166865, 'number': 817} | {'precision': 0.5963302752293578, 'recall': 0.5462184873949579, 'f1': 0.5701754385964912, 'number': 119} | {'precision': 0.8747763864042933, 'recall': 0.9080779944289693, 'f1': 0.8911161731207289, 'number': 1077} | 0.8549 | 0.8867 | 0.8705 | 0.7965 |
| 0.0026 | 52.63 | 1000 | 1.5172 | {'precision': 0.8596491228070176, 'recall': 0.8996328029375765, 'f1': 0.8791866028708135, 'number': 817} | {'precision': 0.7176470588235294, 'recall': 0.5126050420168067, 'f1': 0.5980392156862744, 'number': 119} | {'precision': 0.8737864077669902, 'recall': 0.9192200557103064, 'f1': 0.8959276018099548, 'number': 1077} | 0.8616 | 0.8872 | 0.8742 | 0.8014 |
| 0.0019 | 63.16 | 1200 | 1.6132 | {'precision': 0.8735224586288416, 'recall': 0.9045287637698899, 'f1': 0.888755261575466, 'number': 817} | {'precision': 0.6460176991150443, 'recall': 0.6134453781512605, 'f1': 0.6293103448275863, 'number': 119} | {'precision': 0.881508078994614, 'recall': 0.9117920148560817, 'f1': 0.8963943404837974, 'number': 1077} | 0.8654 | 0.8912 | 0.8781 | 0.8040 |
| 0.0012 | 73.68 | 1400 | 1.6459 | {'precision': 0.8831942789034565, 'recall': 0.9069767441860465, 'f1': 0.894927536231884, 'number': 817} | {'precision': 0.6213592233009708, 'recall': 0.5378151260504201, 'f1': 0.5765765765765765, 'number': 119} | {'precision': 0.8998178506375227, 'recall': 0.9173630454967502, 'f1': 0.9085057471264367, 'number': 1077} | 0.8789 | 0.8907 | 0.8848 | 0.8068 |
| 0.0005 | 84.21 | 1600 | 1.5619 | {'precision': 0.8602771362586605, 'recall': 0.9118727050183598, 'f1': 0.8853238265002972, 'number': 817} | {'precision': 0.6631578947368421, 'recall': 0.5294117647058824, 'f1': 0.5887850467289719, 'number': 119} | {'precision': 0.8944494995450409, 'recall': 0.9127205199628597, 'f1': 0.9034926470588234, 'number': 1077} | 0.8694 | 0.8897 | 0.8795 | 0.8155 |
| 0.0003 | 94.74 | 1800 | 1.6571 | {'precision': 0.8649592549476135, 'recall': 0.9094247246022031, 'f1': 0.886634844868735, 'number': 817} | {'precision': 0.6391752577319587, 'recall': 0.5210084033613446, 'f1': 0.5740740740740741, 'number': 119} | {'precision': 0.8971792538671519, 'recall': 0.9155060352831941, 'f1': 0.90625, 'number': 1077} | 0.8715 | 0.8897 | 0.8805 | 0.8098 |
| 0.0003 | 105.26 | 2000 | 1.6731 | {'precision': 0.8672875436554133, 'recall': 0.9118727050183598, 'f1': 0.8890214797136038, 'number': 817} | {'precision': 0.62, 'recall': 0.5210084033613446, 'f1': 0.5662100456621004, 'number': 119} | {'precision': 0.9008264462809917, 'recall': 0.9108635097493036, 'f1': 0.9058171745152355, 'number': 1077} | 0.8730 | 0.8882 | 0.8806 | 0.8071 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
e9d2743b5c345eef3ff203e1b70c5633
|
mahmoudNG/emotion_model
|
mahmoudNG
|
distilbert
| 12 | 5 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null |
['tweet_eval']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,456 | false |
<!-- 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. -->
# emotion_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3046
- Accuracy: 0.7938
## 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 204 | 1.1915 | 0.7854 |
| No log | 2.0 | 408 | 1.1624 | 0.7889 |
| 0.0451 | 3.0 | 612 | 1.1865 | 0.7952 |
| 0.0451 | 4.0 | 816 | 1.2653 | 0.7945 |
| 0.0154 | 5.0 | 1020 | 1.3046 | 0.7938 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
9ab4028a3f4659cff2324c9281b7ceb5
|
renesteeman/whisper-tiny-dutch-25
|
renesteeman
|
whisper
| 15 | 5 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
|
['nl']
|
['mozilla-foundation/common_voice_11_0']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['hf-asr-leaderboard', 'generated_from_trainer']
| true | true | true | 1,535 | false |
<!-- 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. -->
# Whisper Tiny Dutch 25
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7024
- Wer: 42.0655
## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.5563 | 0.78 | 500 | 0.7838 | 47.5002 |
| 0.3949 | 1.56 | 1000 | 0.7301 | 43.9570 |
| 0.2666 | 2.34 | 1500 | 0.7103 | 42.8426 |
| 0.2307 | 3.12 | 2000 | 0.7024 | 42.0655 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
0f9c3d51254413bd399ac4ea494d620e
|
Iwillbeback/ddpm-butterflies-128
|
Iwillbeback
| null | 13 | 3 |
diffusers
| 0 | null | false | false | false |
apache-2.0
|
['en']
|
['huggan/smithsonian_butterflies_subset']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 1,233 | false |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [๐ค Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
๐ [TensorBoard logs](https://huggingface.co/Iwillbeback/ddpm-butterflies-128/tensorboard?#scalars)
|
6028af1ee38d3ee2aa427044ab3ff2aa
|
sd-concepts-library/scarlet-witch
|
sd-concepts-library
| null | 9 | 0 | null | 0 | null | false | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 1,025 | false |
### Scarlet witch on Stable Diffusion
This is the `<sw-mom>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
c8e82f5cd06ced7dccc287ad48672d03
|
ishaankul67/Adult_contemporary_music-clustered
|
ishaankul67
|
distilbert
| 8 | 0 |
transformers
| 0 |
question-answering
| false | true | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_keras_callback']
| true | true | true | 1,878 | false |
<!-- 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. -->
# ishaankul67/Adult_contemporary_music-clustered
This model is a fine-tuned version of [nandysoham16/15-clustered_aug](https://huggingface.co/nandysoham16/15-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3734
- Train End Logits Accuracy: 0.9167
- Train Start Logits Accuracy: 0.8889
- Validation Loss: 0.1582
- Validation End Logits Accuracy: 0.8571
- Validation Start Logits Accuracy: 1.0
- Epoch: 0
## 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': 18, '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 | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.3734 | 0.9167 | 0.8889 | 0.1582 | 0.8571 | 1.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
22642839852fa35cc24ab6b13a4b0880
|
andrewljohnson/segformer-b5-finetuned-magic-cards-230117-3
|
andrewljohnson
|
segformer
| 7 | 4 |
transformers
| 0 |
image-segmentation
| true | false | false |
other
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['vision', 'image-segmentation', 'generated_from_trainer']
| true | true | true | 4,081 | false |
<!-- 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. -->
# segformer-b5-finetuned-magic-cards-230117-3
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the andrewljohnson/magic_cards dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0691
- Mean Iou: 0.6585
- Mean Accuracy: 0.9878
- Overall Accuracy: 0.9912
- Accuracy Unlabeled: nan
- Accuracy Front: 0.9978
- Accuracy Back: 0.9777
- Iou Unlabeled: 0.0
- Iou Front: 0.9978
- Iou Back: 0.9777
## 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: 6e-05
- train_batch_size: 1
- eval_batch_size: 1
- 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 | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Front | Accuracy Back | Iou Unlabeled | Iou Front | Iou Back |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:-------------:|:-------------:|:---------:|:--------:|
| 1.2232 | 0.37 | 20 | 0.4691 | 0.6041 | 0.9201 | 0.9218 | nan | 0.9252 | 0.9150 | 0.0 | 0.9252 | 0.8870 |
| 0.2718 | 0.74 | 40 | 0.1983 | 0.6509 | 0.9764 | 0.9785 | nan | 0.9826 | 0.9702 | 0.0 | 0.9826 | 0.9702 |
| 0.255 | 1.11 | 60 | 0.0939 | 0.6524 | 0.9785 | 0.9794 | nan | 0.9812 | 0.9758 | 0.0 | 0.9812 | 0.9758 |
| 0.1103 | 1.48 | 80 | 0.0682 | 0.6536 | 0.9804 | 0.9813 | nan | 0.9830 | 0.9779 | 0.0 | 0.9830 | 0.9779 |
| 0.1373 | 1.85 | 100 | 0.1260 | 0.6631 | 0.9946 | 0.9961 | nan | 0.9989 | 0.9903 | 0.0 | 0.9989 | 0.9903 |
| 0.0566 | 2.22 | 120 | 0.1558 | 0.6578 | 0.9868 | 0.9912 | nan | 0.9999 | 0.9736 | 0.0 | 0.9999 | 0.9736 |
| 0.1535 | 2.59 | 140 | 0.1330 | 0.6558 | 0.9838 | 0.9883 | nan | 0.9973 | 0.9703 | 0.0 | 0.9973 | 0.9703 |
| 0.0586 | 2.96 | 160 | 0.2317 | 0.6599 | 0.9899 | 0.9933 | nan | 1.0000 | 0.9798 | 0.0 | 1.0000 | 0.9798 |
| 0.0727 | 3.33 | 180 | 0.1018 | 0.6586 | 0.9880 | 0.9919 | nan | 0.9995 | 0.9764 | 0.0 | 0.9995 | 0.9764 |
| 0.3588 | 3.7 | 200 | 0.1151 | 0.6608 | 0.9912 | 0.9939 | nan | 0.9993 | 0.9831 | 0.0 | 0.9993 | 0.9831 |
| 0.0463 | 4.07 | 220 | 0.0538 | 0.6610 | 0.9915 | 0.9934 | nan | 0.9969 | 0.9862 | 0.0 | 0.9969 | 0.9862 |
| 0.046 | 4.44 | 240 | 0.1201 | 0.6581 | 0.9871 | 0.9912 | nan | 0.9991 | 0.9751 | 0.0 | 0.9991 | 0.9751 |
| 0.0468 | 4.81 | 260 | 0.0691 | 0.6585 | 0.9878 | 0.9912 | nan | 0.9978 | 0.9777 | 0.0 | 0.9978 | 0.9777 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.0.dev0
|
645cc79cf2c5b4d172b34944c9cf7809
|
emilios/whisper-sm-farsipal-e5
|
emilios
|
whisper
| 29 | 0 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
|
['el']
|
['mozilla-foundation/common_voice_11_0,google/fleurs']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['whisper-event', 'generated_from_trainer']
| true | true | true | 2,572 | false |
<!-- 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. -->
# Whisper small Greek Farsioal and El Greco
This model is a fine-tuned version of [emilios/whisper-sm-el-farsipal-e4](https://huggingface.co/emilios/whisper-sm-el-farsipal-e4) on the mozilla-foundation/common_voice_11_0,google/fleurs el,el_gr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4871
- Wer: 17.1991
## 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: 1e-06
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 20000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.1259 | 2.49 | 1000 | 0.4834 | 18.3692 |
| 0.1002 | 4.49 | 2000 | 0.4604 | 17.8027 |
| 0.1096 | 6.98 | 3000 | 0.4553 | 17.8770 |
| 0.0885 | 9.46 | 4000 | 0.4551 | 17.9606 |
| 0.0675 | 11.95 | 5000 | 0.4631 | 17.9049 |
| 0.0675 | 14.44 | 6000 | 0.4619 | 17.9049 |
| 0.0645 | 16.93 | 7000 | 0.4678 | 17.6727 |
| 0.0535 | 19.41 | 8000 | 0.4685 | 17.6634 |
| 0.039 | 21.49 | 9000 | 0.4746 | 17.6727 |
| 0.0447 | 23.98 | 10000 | 0.4761 | 17.6634 |
| 0.0393 | 26.46 | 11000 | 0.4792 | 17.7656 |
| 0.0308 | 28.95 | 12000 | 0.4851 | 17.8678 |
| 0.0301 | 31.44 | 13000 | 0.4846 | 17.4499 |
| 0.031 | 33.93 | 14000 | 0.4849 | 17.8306 |
| 0.0263 | 36.41 | 15000 | 0.4880 | 17.6170 |
| 0.0256 | 38.9 | 16000 | 0.4871 | 17.1991 |
| 0.0236 | 41.39 | 17000 | 0.4883 | 17.2641 |
| 0.0195 | 43.88 | 18000 | 0.4880 | 17.5706 |
| 0.0193 | 46.36 | 19000 | 0.4993 | 17.7285 |
| 0.0161 | 48.85 | 20000 | 0.4968 | 17.8306 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 2.0.0.dev20221216+cu116
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
9a8e15177cba9242c5f6eee76019bcf2
|
gabrielgmendonca/bert-base-portuguese-cased-finetuned-chico-xavier-finetuned-chico-xavier
|
gabrielgmendonca
|
bert
| 8 | 2 |
transformers
| 0 |
fill-mask
| false | true | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_keras_callback']
| true | true | true | 1,678 | false |
<!-- 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. -->
# gabrielgmendonca/bert-base-portuguese-cased-finetuned-chico-xavier-finetuned-chico-xavier
This model is a fine-tuned version of [gabrielgmendonca/bert-base-portuguese-cased-finetuned-chico-xavier](https://huggingface.co/gabrielgmendonca/bert-base-portuguese-cased-finetuned-chico-xavier) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.8630
- Validation Loss: 1.7215
- Epoch: 0
## 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3430, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.8630 | 1.7215 | 0 |
### Framework versions
- Transformers 4.22.2
- TensorFlow 2.8.2
- Datasets 2.5.1
- Tokenizers 0.12.1
|
d48219f6baa210c22f9a4673bd68638b
|
premsuresh/bart-finetuned-mathqa-prem
|
premsuresh
|
bart
| 18 | 3 |
transformers
| 0 |
text2text-generation
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 961 | false |
<!-- 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. -->
# bart-finetuned-mathqa-prem
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
09f38f17966ec9c9630eeea10f1ed99a
|
DrishtiSharma/wav2vec2-base-finetuned-sentiment-mesd-v9
|
DrishtiSharma
|
wav2vec2
| 10 | 13 |
transformers
| 0 |
audio-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 7,466 | false |
<!-- 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-finetuned-sentiment-mesd-v9
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: 0.3500
- Accuracy: 0.9154
## 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: 64
- eval_batch_size: 40
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.86 | 3 | 1.7825 | 0.1846 |
| 1.9553 | 1.86 | 6 | 1.7212 | 0.4308 |
| 1.9553 | 2.86 | 9 | 1.6164 | 0.3769 |
| 2.002 | 3.86 | 12 | 1.4904 | 0.3769 |
| 1.6191 | 4.86 | 15 | 1.4426 | 0.4385 |
| 1.6191 | 5.86 | 18 | 1.3516 | 0.5231 |
| 1.6209 | 6.86 | 21 | 1.2176 | 0.5538 |
| 1.6209 | 7.86 | 24 | 1.1683 | 0.5692 |
| 1.371 | 8.86 | 27 | 1.0885 | 0.5923 |
| 1.1568 | 9.86 | 30 | 1.0152 | 0.6385 |
| 1.1568 | 10.86 | 33 | 0.9289 | 0.6385 |
| 1.1023 | 11.86 | 36 | 0.9141 | 0.6308 |
| 1.1023 | 12.86 | 39 | 0.8526 | 0.6462 |
| 0.9448 | 13.86 | 42 | 0.8420 | 0.6769 |
| 0.7972 | 14.86 | 45 | 0.7976 | 0.6692 |
| 0.7972 | 15.86 | 48 | 0.8192 | 0.7308 |
| 0.7793 | 16.86 | 51 | 0.7108 | 0.7615 |
| 0.7793 | 17.86 | 54 | 0.6712 | 0.7769 |
| 0.6468 | 18.86 | 57 | 0.6684 | 0.7923 |
| 0.5083 | 19.86 | 60 | 0.6922 | 0.7385 |
| 0.5083 | 20.86 | 63 | 0.6148 | 0.7923 |
| 0.4988 | 21.86 | 66 | 0.5846 | 0.7923 |
| 0.4988 | 22.86 | 69 | 0.6050 | 0.8154 |
| 0.4123 | 23.86 | 72 | 0.5506 | 0.7846 |
| 0.3511 | 24.86 | 75 | 0.6095 | 0.7846 |
| 0.3511 | 25.86 | 78 | 0.5916 | 0.8154 |
| 0.3268 | 26.86 | 81 | 0.5912 | 0.8077 |
| 0.3268 | 27.86 | 84 | 0.5142 | 0.8538 |
| 0.3036 | 28.86 | 87 | 0.5492 | 0.8077 |
| 0.3066 | 29.86 | 90 | 0.6007 | 0.8231 |
| 0.3066 | 30.86 | 93 | 0.5748 | 0.8231 |
| 0.2538 | 31.86 | 96 | 0.6027 | 0.7692 |
| 0.2538 | 32.86 | 99 | 0.6979 | 0.7462 |
| 0.2281 | 33.86 | 102 | 0.7002 | 0.7615 |
| 0.2183 | 34.86 | 105 | 0.6650 | 0.7769 |
| 0.2183 | 35.86 | 108 | 0.5192 | 0.8462 |
| 0.2202 | 36.86 | 111 | 0.5389 | 0.8308 |
| 0.2202 | 37.86 | 114 | 0.5050 | 0.8385 |
| 0.1906 | 38.86 | 117 | 0.5722 | 0.7769 |
| 0.154 | 39.86 | 120 | 0.5239 | 0.8308 |
| 0.154 | 40.86 | 123 | 0.4448 | 0.8615 |
| 0.1474 | 41.86 | 126 | 0.4623 | 0.8615 |
| 0.1474 | 42.86 | 129 | 0.4282 | 0.8615 |
| 0.1345 | 43.86 | 132 | 0.5087 | 0.8615 |
| 0.1567 | 44.86 | 135 | 0.4859 | 0.8385 |
| 0.1567 | 45.86 | 138 | 0.6603 | 0.8077 |
| 0.1731 | 46.86 | 141 | 0.5379 | 0.8385 |
| 0.1731 | 47.86 | 144 | 0.8666 | 0.7538 |
| 0.1606 | 48.86 | 147 | 0.7518 | 0.8 |
| 0.1484 | 49.86 | 150 | 0.5986 | 0.8385 |
| 0.1484 | 50.86 | 153 | 0.6368 | 0.8231 |
| 0.2256 | 51.86 | 156 | 0.4639 | 0.8692 |
| 0.2256 | 52.86 | 159 | 0.5533 | 0.8462 |
| 0.1178 | 53.86 | 162 | 0.5038 | 0.8615 |
| 0.0815 | 54.86 | 165 | 0.5052 | 0.8692 |
| 0.0815 | 55.86 | 168 | 0.4337 | 0.8846 |
| 0.0998 | 56.86 | 171 | 0.4422 | 0.8769 |
| 0.0998 | 57.86 | 174 | 0.4317 | 0.8692 |
| 0.0855 | 58.86 | 177 | 0.4025 | 0.8923 |
| 0.0962 | 59.86 | 180 | 0.4605 | 0.8769 |
| 0.0962 | 60.86 | 183 | 0.4356 | 0.8769 |
| 0.0763 | 61.86 | 186 | 0.4614 | 0.8769 |
| 0.0763 | 62.86 | 189 | 0.4382 | 0.8846 |
| 0.0902 | 63.86 | 192 | 0.4701 | 0.8692 |
| 0.0654 | 64.86 | 195 | 0.4922 | 0.8692 |
| 0.0654 | 65.86 | 198 | 0.5413 | 0.8538 |
| 0.0651 | 66.86 | 201 | 0.5759 | 0.8615 |
| 0.0651 | 67.86 | 204 | 0.4238 | 0.9 |
| 0.0822 | 68.86 | 207 | 0.3500 | 0.9154 |
| 0.0625 | 69.86 | 210 | 0.3878 | 0.8923 |
| 0.0625 | 70.86 | 213 | 0.4952 | 0.8615 |
| 0.0548 | 71.86 | 216 | 0.4544 | 0.8615 |
| 0.0548 | 72.86 | 219 | 0.5497 | 0.8769 |
| 0.054 | 73.86 | 222 | 0.4434 | 0.8846 |
| 0.0543 | 74.86 | 225 | 0.4732 | 0.8769 |
| 0.0543 | 75.86 | 228 | 0.4425 | 0.8923 |
| 0.0881 | 76.86 | 231 | 0.4788 | 0.8769 |
| 0.0881 | 77.86 | 234 | 0.5448 | 0.8769 |
| 0.061 | 78.86 | 237 | 0.4221 | 0.9077 |
| 0.0567 | 79.86 | 240 | 0.4404 | 0.8769 |
| 0.0567 | 80.86 | 243 | 0.4099 | 0.9 |
| 0.052 | 81.86 | 246 | 0.5259 | 0.8769 |
| 0.052 | 82.86 | 249 | 0.5874 | 0.8692 |
| 0.0444 | 83.86 | 252 | 0.5555 | 0.8846 |
| 0.0332 | 84.86 | 255 | 0.5156 | 0.8615 |
| 0.0332 | 85.86 | 258 | 0.4564 | 0.8615 |
| 0.0449 | 86.86 | 261 | 0.4826 | 0.8692 |
| 0.0449 | 87.86 | 264 | 0.4726 | 0.8615 |
| 0.0385 | 88.86 | 267 | 0.4206 | 0.8846 |
| 0.0356 | 89.86 | 270 | 0.4050 | 0.8769 |
| 0.0356 | 90.86 | 273 | 0.4161 | 0.8923 |
| 0.0391 | 91.86 | 276 | 0.4100 | 0.9077 |
| 0.0391 | 92.86 | 279 | 0.4047 | 0.9 |
| 0.0249 | 93.86 | 282 | 0.4044 | 0.9 |
| 0.0399 | 94.86 | 285 | 0.3968 | 0.8846 |
| 0.0399 | 95.86 | 288 | 0.3802 | 0.9 |
| 0.031 | 96.86 | 291 | 0.3689 | 0.9 |
| 0.031 | 97.86 | 294 | 0.3616 | 0.9077 |
| 0.036 | 98.86 | 297 | 0.3584 | 0.9077 |
| 0.0386 | 99.86 | 300 | 0.3574 | 0.9077 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
b0939940eb87ab4d5ab89988f1b2934a
|
tomekkorbak/pensive_keller
|
tomekkorbak
| null | 2 | 0 | null | 0 | null | false | false | false |
mit
|
['en']
|
['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 8,995 | false |
<!-- 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. -->
# pensive_keller
This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets.
## 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: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 3125
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.24.0
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000',
'tomekkorbak/detoxify-pile-chunk3-50000-100000',
'tomekkorbak/detoxify-pile-chunk3-100000-150000',
'tomekkorbak/detoxify-pile-chunk3-150000-200000',
'tomekkorbak/detoxify-pile-chunk3-200000-250000',
'tomekkorbak/detoxify-pile-chunk3-250000-300000',
'tomekkorbak/detoxify-pile-chunk3-300000-350000',
'tomekkorbak/detoxify-pile-chunk3-350000-400000',
'tomekkorbak/detoxify-pile-chunk3-400000-450000',
'tomekkorbak/detoxify-pile-chunk3-450000-500000',
'tomekkorbak/detoxify-pile-chunk3-500000-550000',
'tomekkorbak/detoxify-pile-chunk3-550000-600000',
'tomekkorbak/detoxify-pile-chunk3-600000-650000',
'tomekkorbak/detoxify-pile-chunk3-650000-700000',
'tomekkorbak/detoxify-pile-chunk3-700000-750000',
'tomekkorbak/detoxify-pile-chunk3-750000-800000',
'tomekkorbak/detoxify-pile-chunk3-800000-850000',
'tomekkorbak/detoxify-pile-chunk3-850000-900000',
'tomekkorbak/detoxify-pile-chunk3-900000-950000',
'tomekkorbak/detoxify-pile-chunk3-950000-1000000',
'tomekkorbak/detoxify-pile-chunk3-1000000-1050000',
'tomekkorbak/detoxify-pile-chunk3-1050000-1100000',
'tomekkorbak/detoxify-pile-chunk3-1100000-1150000',
'tomekkorbak/detoxify-pile-chunk3-1150000-1200000',
'tomekkorbak/detoxify-pile-chunk3-1200000-1250000',
'tomekkorbak/detoxify-pile-chunk3-1250000-1300000',
'tomekkorbak/detoxify-pile-chunk3-1300000-1350000',
'tomekkorbak/detoxify-pile-chunk3-1350000-1400000',
'tomekkorbak/detoxify-pile-chunk3-1400000-1450000',
'tomekkorbak/detoxify-pile-chunk3-1450000-1500000',
'tomekkorbak/detoxify-pile-chunk3-1500000-1550000',
'tomekkorbak/detoxify-pile-chunk3-1550000-1600000',
'tomekkorbak/detoxify-pile-chunk3-1600000-1650000',
'tomekkorbak/detoxify-pile-chunk3-1650000-1700000',
'tomekkorbak/detoxify-pile-chunk3-1700000-1750000',
'tomekkorbak/detoxify-pile-chunk3-1750000-1800000',
'tomekkorbak/detoxify-pile-chunk3-1800000-1850000',
'tomekkorbak/detoxify-pile-chunk3-1850000-1900000',
'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'],
'is_split_by_sentences': True,
'skip_tokens': 1661599744},
'generation': {'every_n_steps': 32,
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048},
{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'challenging_rtp',
'num_samples': 2048,
'prompts_path': 'resources/challenging_rtp.jsonl'}],
'scorer_config': {'device': 'cuda:0'}},
'kl_gpt3_callback': {'every_n_steps': 32,
'max_tokens': 64,
'num_samples': 4096},
'model': {'from_scratch': False,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'model_kwargs': {'revision': '81a1701e025d2c65ae6e8c2103df559071523ee0',
'value_head_config': {'is_detached': False}},
'path_or_name': 'tomekkorbak/goofy_pasteur'},
'objective': {'alpha': 0.5, 'beta': 10, 'name': 'AWR'},
'tokenizer': {'path_or_name': 'gpt2'},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 512,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'pensive_keller',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.001,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output104340',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 3346,
'save_strategy': 'steps',
'seed': 42,
'tokens_already_seen': 1661599744,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/1pk4cf6z
|
940aaa83cd4927049574f7f08a71d83a
|
paola-md/recipe-lr0.0001-wd0.02-bs64
|
paola-md
|
roberta
| 6 | 1 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,470 | false |
<!-- 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. -->
# recipe-lr0.0001-wd0.02-bs64
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2792
- Rmse: 0.5284
- Mse: 0.2792
- Mae: 0.4268
## 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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2799 | 1.0 | 623 | 0.2789 | 0.5281 | 0.2789 | 0.4218 |
| 0.2786 | 2.0 | 1246 | 0.2792 | 0.5284 | 0.2792 | 0.4268 |
| 0.2785 | 3.0 | 1869 | 0.2792 | 0.5284 | 0.2792 | 0.4268 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
d87066ec43997abba4556f2e11dbd6b3
|
jonfd/convbert-small-igc-is
|
jonfd
|
convbert
| 8 | 3 |
transformers
| 0 |
feature-extraction
| true | true | false |
cc-by-4.0
|
['is']
|
['igc']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 607 | false |
# Icelandic ConvBERT-Small
This model was pretrained on the [Icelandic Gigaword Corpus](http://igc.arnastofnun.is/), which contains approximately 1.69B tokens, using default settings. The model uses a Unigram tokenizer with a vocabulary size of 96,000.
# Acknowledgments
This research was supported with Cloud TPUs from Google's TPU Research Cloud (TRC).
This project was funded by the Language Technology Programme for Icelandic 2019-2023. The programme, which is managed and coordinated by [Almannarรณmur](https://almannaromur.is/), is funded by the Icelandic Ministry of Education, Science and Culture.
|
7abac0804c0c97f9352a866739a6d2ff
|
MultiBertGunjanPatrick/multiberts-seed-4-900k
|
MultiBertGunjanPatrick
|
bert
| 7 | 4 |
transformers
| 0 | null | true | false | false |
apache-2.0
|
['en']
|
['bookcorpus', 'wikipedia']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['exbert', 'multiberts', 'multiberts-seed-4']
| false | true | true | 6,483 | false |
# MultiBERTs Seed 4 Checkpoint 900k (uncased)
Seed 4 intermediate checkpoint 900k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint.
The final checkpoint can be found at [multiberts-seed-4](https://hf.co/multberts-seed-4). This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing MultiBERTs did not write a model card for this model so this model card has been written by [gchhablani](https://hf.co/gchhablani).
## Model description
MultiBERTs models are transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
was pretrained with two objectives:
- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
the entire masked sentence through the model and has to predict the masked words. This is different from traditional
recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the
sentence.
- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
predict if the two sentences were following each other or not.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the MultiBERTs model as inputs.
## Intended uses & limitations
You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=multiberts) to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('multiberts-seed-4-900k')
model = BertModel.from_pretrained("multiberts-seed-4-900k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
### Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
predictions. This bias will also affect all fine-tuned versions of this model. For an understanding of bias of this particular
checkpoint, please try out this checkpoint with the snippet present in the [Limitation and bias section](https://huggingface.co/bert-base-uncased#limitations-and-bias) of the [bert-base-uncased](https://huggingface.co/bert-base-uncased) checkpoint.
## Training data
The MultiBERTs models were pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038
unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and
headers).
## Training procedure
### Preprocessing
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
then of the form:
```
[CLS] Sentence A [SEP] Sentence B [SEP]
```
With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
"sentences" has a combined length of less than 512 tokens.
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
### Pretraining
The full model was trained on 16 Cloud TPU v2 chips for two million steps with a batch size
of 256. The sequence length was set to 512 throughout. The optimizer
used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01,
learning rate warmup for 10,000 steps and linear decay of the learning rate after.
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2106-16163,
author = {Thibault Sellam and
Steve Yadlowsky and
Jason Wei and
Naomi Saphra and
Alexander D'Amour and
Tal Linzen and
Jasmijn Bastings and
Iulia Turc and
Jacob Eisenstein and
Dipanjan Das and
Ian Tenney and
Ellie Pavlick},
title = {The MultiBERTs: {BERT} Reproductions for Robustness Analysis},
journal = {CoRR},
volume = {abs/2106.16163},
year = {2021},
url = {https://arxiv.org/abs/2106.16163},
eprinttype = {arXiv},
eprint = {2106.16163},
timestamp = {Mon, 05 Jul 2021 15:15:50 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2106-16163.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
<a href="https://huggingface.co/exbert/?model=multiberts">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
3eae80ffc266b168c728658bca2de666
|
medhabi/distilbert-base-uncased-mlm-ta-local
|
medhabi
|
distilbert
| 10 | 5 |
transformers
| 0 |
fill-mask
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,312 | false |
<!-- 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-mlm-ta-local
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:
- Loss: 2.0658
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4431 | 1.0 | 3125 | 2.1817 |
| 2.2197 | 2.0 | 6250 | 2.0929 |
| 2.1519 | 3.0 | 9375 | 2.0696 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.1
- Datasets 2.0.0
- Tokenizers 0.11.6
|
d4ed788004f1184cb4f9db6888bde7d7
|
microsoft/GODEL-v1_1-base-seq2seq
|
microsoft
|
t5
| 9 | 62,587 |
transformers
| 25 |
conversational
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 2 | 2 | 0 |
['conversational']
| false | true | true | 3,483 | false |
### Large-Scale Pre-Training for Goal-Directed Dialog (GODEL)
GODEL is a large-scale pre-trained model for goal-directed dialogs. It is parameterized with a Transformer-based encoder-decoder model and trained for response generation grounded in external text, which allows more effective fine-tuning on dialog tasks that require conditioning the response on information that is external to the current conversation (e.g., a retrieved document). The pre-trained model can be efficiently fine-tuned and adapted to accomplish a new dialog task with a handful of task-specific dialogs. The v1.1 model is trained on 551M multi-turn dialogs from Reddit discussion thread, and 5M instruction and knowledge grounded dialogs.
##### Multi-turn generation examples from an interactive environment:
Chitchat example:
> Instruction: given a dialog context, you need to response empathically. <br>
> User: Does money buy happiness? <br>
> Agent: It is a question. Money buys you a lot of things, but not enough to buy happiness. <br>
> User: What is the best way to buy happiness ? <br>
> Agent: Happiness is bought through your experience and not money. <br>
Grounded response generation example:
> Instruction: given a dialog context and related knowledge, you need to response safely based on the knowledge. <br>
> Knowledge: The best Stardew Valley mods PCGamesN_0 / About SMAPI <br>
> User: My favorite game is stardew valley. stardew valley is very fun. <br>
> Agent: I love Stardew Valley mods, like PCGamesN_0 / About SMAPI. <br>
Please find the information about preprocessing, training and full details of the GODEL in the [project webpage](https://aka.ms/GODEL).
ArXiv paper: [https://arxiv.org/abs/2206.11309](https://arxiv.org/abs/2206.11309)
### How to use
Now we are ready to try out how the model works as a chatting partner!
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("microsoft/GODEL-v1_1-base-seq2seq")
model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/GODEL-v1_1-base-seq2seq")
def generate(instruction, knowledge, dialog):
if knowledge != '':
knowledge = '[KNOWLEDGE] ' + knowledge
dialog = ' EOS '.join(dialog)
query = f"{instruction} [CONTEXT] {dialog} {knowledge}"
input_ids = tokenizer(f"{query}", return_tensors="pt").input_ids
outputs = model.generate(input_ids, max_length=128, min_length=8, top_p=0.9, do_sample=True)
output = tokenizer.decode(outputs[0], skip_special_tokens=True)
return output
# Instruction for a chitchat task
instruction = f'Instruction: given a dialog context, you need to response empathically.'
# Leave the knowldge empty
knowledge = ''
dialog = [
'Does money buy happiness?',
'It is a question. Money buys you a lot of things, but not enough to buy happiness.',
'What is the best way to buy happiness ?'
]
response = generate(instruction, knowledge, dialog)
print(response)
```
### Citation
if you use this code and data in your research, please cite our arxiv paper:
```
@misc{peng2022godel,
author = {Peng, Baolin and Galley, Michel and He, Pengcheng and Brockett, Chris and Liden, Lars and Nouri, Elnaz and Yu, Zhou and Dolan, Bill and Gao, Jianfeng},
title = {GODEL: Large-Scale Pre-training for Goal-Directed Dialog},
howpublished = {arXiv},
year = {2022},
month = {June},
url = {https://www.microsoft.com/en-us/research/publication/godel-large-scale-pre-training-for-goal-directed-dialog/},
}
```
|
8a69dabbcde05feae031b0c45f18973a
|
jonatasgrosman/exp_w2v2r_de_vp-100k_accent_germany-0_austria-10_s756
|
jonatasgrosman
|
wav2vec2
| 10 | 3 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
|
['de']
|
['mozilla-foundation/common_voice_7_0']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['automatic-speech-recognition', 'de']
| false | true | true | 503 | false |
# exp_w2v2r_de_vp-100k_accent_germany-0_austria-10_s756
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
b0ced49778303150369c458bd0fe5279
|
anki08/t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol-finetuned-nl-to-fol
|
anki08
|
t5
| 14 | 3 |
transformers
| 0 |
text2text-generation
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,100 | false |
<!-- 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. -->
# t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol-finetuned-nl-to-fol
This model is a fine-tuned version of [anki08/t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol](https://huggingface.co/anki08/t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1468
- Bleu: 30.3266
- Gen Len: 18.8824
## 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
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 17 | 0.1486 | 30.3537 | 18.8824 |
| No log | 2.0 | 34 | 0.1474 | 30.2522 | 18.8824 |
| No log | 3.0 | 51 | 0.1465 | 30.2522 | 18.8824 |
| No log | 4.0 | 68 | 0.1461 | 30.2522 | 18.8824 |
| No log | 5.0 | 85 | 0.1469 | 30.2522 | 18.8824 |
| No log | 6.0 | 102 | 0.1457 | 29.8889 | 18.8824 |
| No log | 7.0 | 119 | 0.1470 | 30.3537 | 18.8824 |
| No log | 8.0 | 136 | 0.1469 | 30.3537 | 18.8824 |
| No log | 9.0 | 153 | 0.1469 | 30.3266 | 18.8824 |
| No log | 10.0 | 170 | 0.1468 | 30.3266 | 18.8824 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
263171df3605cdbf87710be2a86d4d15
|
jonatasgrosman/exp_w2v2t_ar_hubert_s947
|
jonatasgrosman
|
hubert
| 10 | 4 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
|
['ar']
|
['mozilla-foundation/common_voice_7_0']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['automatic-speech-recognition', 'ar']
| false | true | true | 452 | false |
# exp_w2v2t_ar_hubert_s947
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (ar)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
755583d5929156a9dc42f047ca66df08
|
GamingDaveUK/HorrorByDave
|
GamingDaveUK
| null | 23 | 0 | null | 2 | null | false | false | false |
wtfpl
| null | null | null | 4 | 0 | 1 | 3 | 0 | 0 | 0 |
[]
| false | true | true | 1,263 | false |
One of the first embeddings I have created, adds a horror atmosphere and monsters to an image.
Download it into the embeddings folder and use it with "by HorrorByDave" or what ever you have renamed the embed.
Samples (if hugging face keeps the png data then you can get the prompt by putting the sample into pnginfo):
.png)
.png)
.png)
.png)
.png)
.png)
.png)
.png)
.png)
.png)
|
6bd0e94ba5544624ee9f85c1c38e06f6
|
Helsinki-NLP/opus-mt-fr-lu
|
Helsinki-NLP
|
marian
| 10 | 9 |
transformers
| 0 |
translation
| true | true | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['translation']
| false | true | true | 768 | false |
### opus-mt-fr-lu
* source languages: fr
* target languages: lu
* OPUS readme: [fr-lu](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-lu/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-lu/opus-2020-01-20.zip)
* test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-lu/opus-2020-01-20.test.txt)
* test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-lu/opus-2020-01-20.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.fr.lu | 25.5 | 0.471 |
|
f1734e6ff501dfa08d5303fd1e48f9d2
|
juro95/fourth_iteration_model
|
juro95
|
xlm-roberta
| 5 | 1 |
transformers
| 0 |
token-classification
| false | true | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_keras_callback']
| true | true | true | 1,162 | false |
<!-- 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. -->
# fourth_iteration_model
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
## 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 65805, '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, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
### Framework versions
- Transformers 4.25.1
- TensorFlow 2.6.5
- Datasets 2.3.2
- Tokenizers 0.13.2
|
96dd4846c0e5853d66c7a5820155f5b5
|
jonatasgrosman/exp_w2v2r_es_vp-100k_gender_male-0_female-10_s33
|
jonatasgrosman
|
wav2vec2
| 10 | 3 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
|
['es']
|
['mozilla-foundation/common_voice_7_0']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['automatic-speech-recognition', 'es']
| false | true | true | 498 | false |
# exp_w2v2r_es_vp-100k_gender_male-0_female-10_s33
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
2d088d06cabd960f66c230c51b240556
|
lighthouse/mdeberta-v3-base-kor-further
|
lighthouse
|
deberta-v2
| 7 | 355 |
transformers
| 3 | null | true | false | false |
mit
|
['multilingual', 'en', 'ko', 'ar', 'bg', 'de', 'el', 'es', 'fr', 'hi', 'ru', 'sw', 'th', 'tr', 'ur', 'vi', 'zh']
| null | null | 0 | 0 | 0 | 0 | 2 | 1 | 1 |
['deberta', 'deberta-v3', 'mdeberta', 'korean', 'pretraining']
| false | true | true | 4,059 | false |
# mDeBERTa-v3-base-kor-further
> ๐ก ์๋ ํ๋ก์ ํธ๋ย KPMG Lighthouse Korea์์ ์งํํ์์ต๋๋ค.
> KPMG Lighthouse Korea์์๋, Financial area์ ๋ค์ํ ๋ฌธ์ ๋ค์ ํด๊ฒฐํ๊ธฐ ์ํด Edge Technology์ NLP/Vision AI๋ฅผ ๋ชจ๋ธ๋งํ๊ณ ์์ต๋๋ค.
> https://kpmgkr.notion.site/
## What is DeBERTa?
- [DeBERTa](https://arxiv.org/abs/2006.03654)๋ `Disentangled Attention` + `Enhanced Mask Decoder` ๋ฅผ ์ ์ฉํ์ฌ ๋จ์ด์ positional information์ ํจ๊ณผ์ ์ผ๋ก ํ์ตํฉ๋๋ค. ์ด์ ๊ฐ์ ์์ด๋์ด๋ฅผ ํตํด, ๊ธฐ์กด์ BERT, RoBERTa์์ ์ฌ์ฉํ๋ absolute position embedding๊ณผ๋ ๋ฌ๋ฆฌ DeBERTa๋ ๋จ์ด์ ์๋์ ์ธ ์์น ์ ๋ณด๋ฅผ ํ์ต ๊ฐ๋ฅํ ๋ฒกํฐ๋ก ํํํ์ฌ ๋ชจ๋ธ์ ํ์ตํ๊ฒ ๋ฉ๋๋ค. ๊ฒฐ๊ณผ์ ์ผ๋ก, BERT, RoBERTA ์ ๋น๊ตํ์ ๋ ๋ ์ค์ํ ์ฑ๋ฅ์ ๋ณด์ฌ์ฃผ์์ต๋๋ค.
- [DeBERTa-v3](https://arxiv.org/abs/2111.09543)์์๋, ์ด์ ๋ฒ์ ์์ ์ฌ์ฉํ๋ MLM (Masked Language Model) ์ RTD (Replaced Token Detection) Task ๋ก ๋์ฒดํ ELECTRA ์คํ์ผ์ ์ฌ์ ํ์ต ๋ฐฉ๋ฒ๊ณผ, Gradient-Disentangled Embedding Sharing ์ ์ ์ฉํ์ฌ ๋ชจ๋ธ ํ์ต์ ํจ์จ์ฑ์ ๊ฐ์ ํ์์ต๋๋ค.
- DeBERTa์ ์ํคํ
์ฒ๋ก ํ๋ถํ ํ๊ตญ์ด ๋ฐ์ดํฐ๋ฅผ ํ์ตํ๊ธฐ ์ํด์, `mDeBERTa-v3-base-kor-further` ๋ microsoft ๊ฐ ๋ฐํํ `mDeBERTa-v3-base` ๋ฅผ ์ฝ 40GB์ ํ๊ตญ์ด ๋ฐ์ดํฐ์ ๋ํด์ **์ถ๊ฐ์ ์ธ ์ฌ์ ํ์ต**์ ์งํํ ์ธ์ด ๋ชจ๋ธ์
๋๋ค.
## How to Use
- Requirements
```
pip install transformers
pip install sentencepiece
```
- Huggingface Hub
```python
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("mdeberta-v3-base-kor-further") # DebertaV2ForModel
tokenizer = AutoTokenizer.from_pretrained("mdeberta-v3-base-kor-further") # DebertaV2Tokenizer (SentencePiece)
```
## Pre-trained Models
- ๋ชจ๋ธ์ ์ํคํ
์ฒ๋ ๊ธฐ์กด microsoft์์ ๋ฐํํ `mdeberta-v3-base`์ ๋์ผํ ๊ตฌ์กฐ์
๋๋ค.
| | Vocabulary(K) | Backbone Parameters(M) | Hidden Size | Layers | Note |
| --- | --- | --- | --- | --- | --- |
| mdeberta-v3-base-kor-further (mdeberta-v3-base์ ๋์ผ) | 250 | 86 | 768 | 12 | 250K new SPM vocab |
## Further Pretraing Details (MLM Task)
- `mDeBERTa-v3-base-kor-further` ๋ `microsoft/mDeBERTa-v3-base` ๋ฅผ ์ฝ 40GB์ ํ๊ตญ์ด ๋ฐ์ดํฐ์ ๋ํด์ MLM Task๋ฅผ ์ ์ฉํ์ฌ ์ถ๊ฐ์ ์ธ ์ฌ์ ํ์ต์ ์งํํ์์ต๋๋ค.
| | Max length | Learning Rate | Batch Size | Train Steps | Warm-up Steps |
| --- | --- | --- | --- | --- | --- |
| mdeberta-v3-base-kor-further | 512 | 2e-5 | 8 | 5M | 50k |
## Datasets
- ๋ชจ๋์ ๋ง๋ญ์น(์ ๋ฌธ, ๊ตฌ์ด, ๋ฌธ์ด), ํ๊ตญ์ด Wiki, ๊ตญ๋ฏผ์ฒญ์ ๋ฑ ์ฝ 40 GB ์ ํ๊ตญ์ด ๋ฐ์ดํฐ์
์ด ์ถ๊ฐ์ ์ธ ์ฌ์ ํ์ต์ ์ฌ์ฉ๋์์ต๋๋ค.
- Train: 10M lines, 5B tokens
- Valid: 2M lines, 1B tokens
- cf) ๊ธฐ์กด mDeBERTa-v3์ XLM-R ๊ณผ ๊ฐ์ด [cc-100 ๋ฐ์ดํฐ์
](https://data.statmt.org/cc-100/)์ผ๋ก ํ์ต๋์์ผ๋ฉฐ, ๊ทธ ์ค ํ๊ตญ์ด ๋ฐ์ดํฐ์
์ ํฌ๊ธฐ๋ 54GB์
๋๋ค.
## Fine-tuning on NLU Tasks - Base Model
| Model | Size | NSMC(acc) | Naver NER(F1) | PAWS (acc) | KorNLI (acc) | KorSTS (spearman) | Question Pair (acc) | KorQuaD (Dev) (EM/F1) | Korean-Hate-Speech (Dev) (F1) |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| XLM-Roberta-Base | 1.03G | 89.03 | 86.65 | 82.80 | 80.23 | 78.45 | 93.80 | 64.70 / 88.94 | 64.06 |
| mdeberta-base | 534M | 90.01 | 87.43 | 85.55 | 80.41 | **82.65** | 94.06 | 65.48 / 89.74 | 62.91 |
| mdeberta-base-kor-further (Ours) | 534M | **90.52** | **87.87** | **85.85** | **80.65** | 81.90 | **94.98** | **66.07 / 90.35** | **68.16** |
## KPMG Lighthouse KR
https://kpmgkr.notion.site/
## Citation
```
@misc{he2021debertav3,
title={DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing},
author={Pengcheng He and Jianfeng Gao and Weizhu Chen},
year={2021},
eprint={2111.09543},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@inproceedings{
he2021deberta,
title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION},
author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=XPZIaotutsD}
}
```
## Reference
- [mDeBERTa-v3-base-kor-further](https://github.com/kpmg-kr/mDeBERTa-v3-base-kor-further)
- [DeBERTa](https://github.com/microsoft/DeBERTa)
- [Huggingface Transformers](https://github.com/huggingface/transformers)
- [๋ชจ๋์ ๋ง๋ญ์น](https://corpus.korean.go.kr/)
- [Korpora: Korean Corpora Archives](https://github.com/ko-nlp/Korpora)
- [sooftware/Korean PLM](https://github.com/sooftware/Korean-PLM)
|
9c087b788db5dcec6fb5ced8d0aabc6d
|
yanaiela/roberta-base-epoch_24
|
yanaiela
|
roberta
| 9 | 3 |
transformers
| 0 |
fill-mask
| true | false | false |
mit
|
['en']
|
['wikipedia', 'bookcorpus']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['roberta-base', 'roberta-base-epoch_24']
| false | true | true | 2,102 | false |
# RoBERTa, Intermediate Checkpoint - Epoch 24
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the training)
to provide the ability to study the training dynamics of such models, and other possible use-cases.
These models were trained in part of a work that studies how simple statistics from data,
such as co-occurrences affects model predictions, which are described in the paper
[Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions](https://arxiv.org/abs/2207.14251).
This is RoBERTa-base epoch_24.
## Model Description
This model was captured during a reproduction of
[RoBERTa-base](https://huggingface.co/roberta-base), for English: it
is a Transformers model pretrained on a large corpus of English data, using the
Masked Language Modelling (MLM).
The intended uses, limitations, training data and training procedure for the fully trained model are similar
to [RoBERTa-base](https://huggingface.co/roberta-base). Two major
differences with the original model:
* We trained our model for 100K steps, instead of 500K
* We only use Wikipedia and the Book Corpus, as corpora which are publicly available.
### How to use
Using code from
[RoBERTa-base](https://huggingface.co/roberta-base), here is an example based on
PyTorch:
```
from transformers import pipeline
model = pipeline("fill-mask", model='yanaiela/roberta-base-epoch_83', device=-1, top_k=10)
model("Hello, I'm the <mask> RoBERTa-base language model")
```
## Citation info
```bibtex
@article{2207.14251,
Author = {Yanai Elazar and Nora Kassner and Shauli Ravfogel and Amir Feder and Abhilasha Ravichander and Marius Mosbach and Yonatan Belinkov and Hinrich Schรผtze and Yoav Goldberg},
Title = {Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions},
Year = {2022},
Eprint = {arXiv:2207.14251},
}
```
|
9764a61c906f419e923e7b72e8ef18e6
|
rafiulrumy/wav2vec2-large-xlsr-hindi-demo-colab
|
rafiulrumy
|
wav2vec2
| 12 | 7 |
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 | 1,108 | false |
<!-- 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-hindi-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.
## 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
|
6bf8593be786fed980bd1484e2c0efcc
|
SetFit/deberta-v3-large__sst2__train-8-8
|
SetFit
|
deberta-v2
| 10 | 5 |
transformers
| 0 |
text-classification
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,135 | false |
<!-- 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. -->
# deberta-v3-large__sst2__train-8-8
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7414
- Accuracy: 0.5623
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6597 | 1.0 | 3 | 0.7716 | 0.25 |
| 0.6376 | 2.0 | 6 | 0.7802 | 0.25 |
| 0.5857 | 3.0 | 9 | 0.6625 | 0.75 |
| 0.4024 | 4.0 | 12 | 0.5195 | 0.75 |
| 0.2635 | 5.0 | 15 | 0.4222 | 1.0 |
| 0.1714 | 6.0 | 18 | 0.4410 | 0.5 |
| 0.1267 | 7.0 | 21 | 0.7773 | 0.75 |
| 0.0582 | 8.0 | 24 | 0.9070 | 0.75 |
| 0.0374 | 9.0 | 27 | 0.9539 | 0.75 |
| 0.0204 | 10.0 | 30 | 1.0507 | 0.75 |
| 0.012 | 11.0 | 33 | 1.2802 | 0.5 |
| 0.0086 | 12.0 | 36 | 1.4272 | 0.5 |
| 0.0049 | 13.0 | 39 | 1.4803 | 0.5 |
| 0.0039 | 14.0 | 42 | 1.4912 | 0.5 |
| 0.0031 | 15.0 | 45 | 1.5231 | 0.5 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
bd544d6560719761ca1159d4d3235134
|
susumu2357/bert-base-swedish-squad2
|
susumu2357
|
bert
| 9 | 11 |
transformers
| 1 |
question-answering
| true | true | true |
apache-2.0
|
['sv']
|
['susumu2357/squad_v2_sv']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['squad']
| false | true | true | 1,271 | false |
# Swedish BERT Fine-tuned on SQuAD v2
This model is a fine-tuning checkpoint of Swedish BERT on SQuAD v2.
## Training data
Fine-tuning was done based on the pre-trained model [KB/bert-base-swedish-cased](https://huggingface.co/KB/bert-base-swedish-cased).
Training and dev datasets are our
[Swedish translation of SQuAD v2](https://github.com/susumu2357/SQuAD_v2_sv).
[Here](https://huggingface.co/datasets/susumu2357/squad_v2_sv) is the HuggingFace Datasets.
## Hyperparameters
```
batch_size = 16
n_epochs = 2
max_seq_len = 386
learning_rate = 3e-5
warmup_steps = 2900 # warmup_proportion = 0.2
doc_stride=128
max_query_length=64
```
## Eval results
```
'exact': 66.72642524202223
'f1': 70.11149581003404
'total': 11156
'HasAns_exact': 55.574745730186144
'HasAns_f1': 62.821693965983044
'HasAns_total': 5211
'NoAns_exact': 76.50126156433979
'NoAns_f1': 76.50126156433979
'NoAns_total': 5945
```
## Limitations and bias
This model may contain biases due to mistranslations of the SQuAD dataset.
## BibTeX entry and citation info
```bibtex
@misc{svSQuADbert,
author = {Susumu Okazawa},
title = {Swedish BERT Fine-tuned on Swedish SQuAD 2.0},
year = {2021},
howpublished = {\url{https://huggingface.co/susumu2357/bert-base-swedish-squad2}},
}
```
|
96d1fcb83567a550e19a8e1949d73931
|
BaxterAI/SentimentClassifier
|
BaxterAI
|
distilbert
| 60 | 1 |
transformers
| 1 |
text-classification
| true | false | false |
apache-2.0
| null |
['amazon_polarity']
| null | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,042 | false |
<!-- 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. -->
# SentimentClassifier
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the amazon_polarity dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4425
- Accuracy: 0.91
- F1: 0.91
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 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
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
ce3f644613e24611548585674bcf8726
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.