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CouchCat/ma_ner_v6_distil | [
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"distilbert",
"token-classification",
"en",
"transformers",
"ner",
"license:mit",
"autotrain_compatible"
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} | 6 | 2022-11-09T18:35:22Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8124233755619126
---
<!-- 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-it
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: 0.2630
- F1: 0.8124
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8193 | 1.0 | 70 | 0.3200 | 0.7356 |
| 0.2773 | 2.0 | 140 | 0.2841 | 0.7882 |
| 0.1807 | 3.0 | 210 | 0.2630 | 0.8124 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
CouchCat/ma_ner_v7_distil | [
"pytorch",
"distilbert",
"token-classification",
"en",
"transformers",
"ner",
"license:mit",
"autotrain_compatible"
]
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} | 13 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: TSE_ELECTRA_5E
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# TSE_ELECTRA_5E
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2968
- Accuracy: 0.9467
## 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: 16
- 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6837 | 0.06 | 50 | 0.6580 | 0.66 |
| 0.5671 | 0.12 | 100 | 0.3974 | 0.8867 |
| 0.368 | 0.17 | 150 | 0.2619 | 0.9067 |
| 0.2929 | 0.23 | 200 | 0.2179 | 0.94 |
| 0.2153 | 0.29 | 250 | 0.2200 | 0.94 |
| 0.2425 | 0.35 | 300 | 0.1980 | 0.9467 |
| 0.2281 | 0.4 | 350 | 0.1768 | 0.9533 |
| 0.2096 | 0.46 | 400 | 0.2532 | 0.9267 |
| 0.2027 | 0.52 | 450 | 0.2018 | 0.9467 |
| 0.25 | 0.58 | 500 | 0.1939 | 0.9467 |
| 0.2217 | 0.63 | 550 | 0.2267 | 0.9267 |
| 0.2315 | 0.69 | 600 | 0.2039 | 0.9467 |
| 0.2273 | 0.75 | 650 | 0.1950 | 0.9533 |
| 0.1677 | 0.81 | 700 | 0.2274 | 0.94 |
| 0.198 | 0.87 | 750 | 0.2063 | 0.94 |
| 0.1944 | 0.92 | 800 | 0.2328 | 0.9333 |
| 0.1912 | 0.98 | 850 | 0.1961 | 0.9533 |
| 0.1997 | 1.04 | 900 | 0.2004 | 0.9467 |
| 0.1258 | 1.1 | 950 | 0.2606 | 0.9333 |
| 0.1519 | 1.15 | 1000 | 0.2418 | 0.9467 |
| 0.1232 | 1.21 | 1050 | 0.2424 | 0.9467 |
| 0.2154 | 1.27 | 1100 | 0.2096 | 0.96 |
| 0.1969 | 1.33 | 1150 | 0.2170 | 0.9467 |
| 0.1441 | 1.38 | 1200 | 0.2514 | 0.9333 |
| 0.1031 | 1.44 | 1250 | 0.2544 | 0.9467 |
| 0.1967 | 1.5 | 1300 | 0.1986 | 0.9533 |
| 0.1731 | 1.56 | 1350 | 0.2303 | 0.9533 |
| 0.1486 | 1.61 | 1400 | 0.2409 | 0.9467 |
| 0.156 | 1.67 | 1450 | 0.2500 | 0.9467 |
| 0.1355 | 1.73 | 1500 | 0.2121 | 0.9533 |
| 0.1934 | 1.79 | 1550 | 0.2028 | 0.9533 |
| 0.1598 | 1.85 | 1600 | 0.1989 | 0.9533 |
| 0.1568 | 1.9 | 1650 | 0.2124 | 0.9533 |
| 0.1615 | 1.96 | 1700 | 0.2112 | 0.9533 |
| 0.1559 | 2.02 | 1750 | 0.2223 | 0.9467 |
| 0.1029 | 2.08 | 1800 | 0.2865 | 0.94 |
| 0.1173 | 2.13 | 1850 | 0.2745 | 0.94 |
| 0.0865 | 2.19 | 1900 | 0.2509 | 0.9467 |
| 0.1209 | 2.25 | 1950 | 0.2675 | 0.9467 |
| 0.099 | 2.31 | 2000 | 0.2430 | 0.9533 |
| 0.1255 | 2.36 | 2050 | 0.2912 | 0.94 |
| 0.128 | 2.42 | 2100 | 0.2501 | 0.9533 |
| 0.0891 | 2.48 | 2150 | 0.2649 | 0.9467 |
| 0.1007 | 2.54 | 2200 | 0.2643 | 0.9533 |
| 0.1224 | 2.6 | 2250 | 0.2763 | 0.9467 |
| 0.0764 | 2.65 | 2300 | 0.3227 | 0.94 |
| 0.146 | 2.71 | 2350 | 0.2670 | 0.9467 |
| 0.1235 | 2.77 | 2400 | 0.2593 | 0.9467 |
| 0.0993 | 2.83 | 2450 | 0.2543 | 0.9533 |
| 0.1311 | 2.88 | 2500 | 0.2474 | 0.9467 |
| 0.1278 | 2.94 | 2550 | 0.2474 | 0.9533 |
| 0.1078 | 3.0 | 2600 | 0.3133 | 0.9333 |
| 0.0988 | 3.06 | 2650 | 0.2558 | 0.9467 |
| 0.0637 | 3.11 | 2700 | 0.2976 | 0.94 |
| 0.1057 | 3.17 | 2750 | 0.2916 | 0.94 |
| 0.0712 | 3.23 | 2800 | 0.2750 | 0.9467 |
| 0.097 | 3.29 | 2850 | 0.2477 | 0.9533 |
| 0.1081 | 3.34 | 2900 | 0.2546 | 0.9533 |
| 0.0844 | 3.4 | 2950 | 0.2970 | 0.94 |
| 0.0769 | 3.46 | 3000 | 0.2681 | 0.9467 |
| 0.1032 | 3.52 | 3050 | 0.2608 | 0.9533 |
| 0.0716 | 3.58 | 3100 | 0.2632 | 0.9533 |
| 0.0572 | 3.63 | 3150 | 0.3058 | 0.9467 |
| 0.0701 | 3.69 | 3200 | 0.2884 | 0.9467 |
| 0.0717 | 3.75 | 3250 | 0.3030 | 0.9467 |
| 0.0686 | 3.81 | 3300 | 0.3014 | 0.9467 |
| 0.0816 | 3.86 | 3350 | 0.2745 | 0.9533 |
| 0.1153 | 3.92 | 3400 | 0.2954 | 0.9467 |
| 0.0679 | 3.98 | 3450 | 0.2741 | 0.9467 |
| 0.0753 | 4.04 | 3500 | 0.3146 | 0.94 |
| 0.0728 | 4.09 | 3550 | 0.2891 | 0.9467 |
| 0.0579 | 4.15 | 3600 | 0.2904 | 0.9467 |
| 0.0653 | 4.21 | 3650 | 0.3282 | 0.94 |
| 0.0733 | 4.27 | 3700 | 0.2829 | 0.9467 |
| 0.0739 | 4.33 | 3750 | 0.2772 | 0.9467 |
| 0.1042 | 4.38 | 3800 | 0.2806 | 0.94 |
| 0.077 | 4.44 | 3850 | 0.2983 | 0.94 |
| 0.0693 | 4.5 | 3900 | 0.3090 | 0.94 |
| 0.0833 | 4.56 | 3950 | 0.2895 | 0.9467 |
| 0.0462 | 4.61 | 4000 | 0.2881 | 0.9467 |
| 0.0659 | 4.67 | 4050 | 0.2874 | 0.9467 |
| 0.0531 | 4.73 | 4100 | 0.2913 | 0.9467 |
| 0.0398 | 4.79 | 4150 | 0.2986 | 0.9467 |
| 0.0734 | 4.84 | 4200 | 0.2965 | 0.9467 |
| 0.0461 | 4.9 | 4250 | 0.2984 | 0.9467 |
| 0.0818 | 4.96 | 4300 | 0.2968 | 0.9467 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.3.2
- Tokenizers 0.13.1
|
CouchCat/ma_sa_v7_distil | [
"pytorch",
"distilbert",
"text-classification",
"en",
"transformers",
"sentiment-analysis",
"license:mit"
]
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} | 38 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.6886160714285715
---
<!-- 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-en
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: 0.4043
- F1: 0.6886
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1347 | 1.0 | 50 | 0.5771 | 0.4880 |
| 0.5066 | 2.0 | 100 | 0.4209 | 0.6582 |
| 0.3631 | 3.0 | 150 | 0.4043 | 0.6886 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
CoveJH/ConBot | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: whisper_havest_0015
results: []
---
<!-- 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. -->
# whisper_havest_0015
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.5508
- Train Accuracy: 0.0121
- Train Do Wer: 1.0
- Validation Loss: 4.7620
- Validation Accuracy: 0.0121
- Validation Do Wer: 1.0
- Epoch: 14
## 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': 1e-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 | Train Accuracy | Train Do Wer | Validation Loss | Validation Accuracy | Validation Do Wer | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 9.9191 | 0.0046 | 1.0 | 8.5836 | 0.0067 | 1.0 | 0 |
| 8.0709 | 0.0083 | 1.0 | 7.4667 | 0.0089 | 1.0 | 1 |
| 7.1652 | 0.0100 | 1.0 | 6.8204 | 0.0112 | 1.0 | 2 |
| 6.7196 | 0.0114 | 1.0 | 6.5192 | 0.0114 | 1.0 | 3 |
| 6.4115 | 0.0115 | 1.0 | 6.2357 | 0.0115 | 1.0 | 4 |
| 6.1085 | 0.0115 | 1.0 | 5.9657 | 0.0115 | 1.0 | 5 |
| 5.8206 | 0.0115 | 1.0 | 5.7162 | 0.0115 | 1.0 | 6 |
| 5.5567 | 0.0115 | 1.0 | 5.4963 | 0.0115 | 1.0 | 7 |
| 5.3223 | 0.0116 | 1.0 | 5.3096 | 0.0116 | 1.0 | 8 |
| 5.1222 | 0.0117 | 1.0 | 5.1600 | 0.0117 | 1.0 | 9 |
| 4.9580 | 0.0117 | 1.0 | 5.0391 | 0.0118 | 1.0 | 10 |
| 4.8251 | 0.0119 | 1.0 | 4.9427 | 0.0118 | 1.0 | 11 |
| 4.7171 | 0.0119 | 1.0 | 4.8691 | 0.0119 | 1.0 | 12 |
| 4.6284 | 0.0121 | 1.0 | 4.8123 | 0.0120 | 1.0 | 13 |
| 4.5508 | 0.0121 | 1.0 | 4.7620 | 0.0121 | 1.0 | 14 |
### Framework versions
- Transformers 4.25.0.dev0
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Coyotl/DialoGPT-test-last-arthurmorgan | [
"conversational"
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
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: 1.7437
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2178 | 1.0 | 157 | 1.9294 |
| 1.9395 | 2.0 | 314 | 1.7989 |
| 1.8481 | 3.0 | 471 | 1.7259 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Coyotl/DialoGPT-test3-arthurmorgan | [
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad-ver2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad-ver2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8695
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.3919 | 1.0 | 554 | 1.5543 |
| 1.0864 | 2.0 | 1108 | 1.5114 |
| 0.5553 | 3.0 | 1662 | 1.8695 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Craak/GJ0001 | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: BERiT_27000
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERiT_27000
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: 8.3744
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 8.7297 | 0.19 | 500 | 8.5541 |
| 8.5592 | 0.39 | 1000 | 8.5536 |
| 8.4892 | 0.58 | 1500 | 8.5554 |
| 8.5288 | 0.77 | 2000 | 8.4786 |
| 8.5034 | 0.97 | 2500 | 8.4756 |
| 8.3497 | 1.16 | 3000 | 8.4821 |
| 8.4516 | 1.36 | 3500 | 8.4742 |
| 8.4224 | 1.55 | 4000 | 8.3972 |
| 8.3356 | 1.74 | 4500 | 8.4158 |
| 8.3805 | 1.94 | 5000 | 8.3800 |
| 8.2947 | 2.13 | 5500 | 8.4242 |
| 8.2475 | 2.32 | 6000 | 8.4334 |
| 8.2708 | 2.52 | 6500 | 8.3504 |
| 8.2559 | 2.71 | 7000 | 8.4211 |
| 8.3676 | 2.9 | 7500 | 8.3744 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
CracklesCreeper/Piglin-Talks-Harry-Potter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 10 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1608
- F1: 0.8593
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2888 | 1.0 | 715 | 0.1779 | 0.8233 |
| 0.1437 | 2.0 | 1430 | 0.1570 | 0.8497 |
| 0.0931 | 3.0 | 2145 | 0.1608 | 0.8593 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Craftified/Bob | []
| null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: whisper_havest_0020
results: []
---
<!-- 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. -->
# whisper_havest_0020
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.2860
- Train Accuracy: 0.0128
- Train Do Wer: 1.0
- Validation Loss: 4.6401
- Validation Accuracy: 0.0125
- Validation Do Wer: 1.0
- Epoch: 19
## 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': 1e-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 | Train Accuracy | Train Do Wer | Validation Loss | Validation Accuracy | Validation Do Wer | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 9.9191 | 0.0046 | 1.0 | 8.5836 | 0.0067 | 1.0 | 0 |
| 8.0709 | 0.0083 | 1.0 | 7.4667 | 0.0089 | 1.0 | 1 |
| 7.1652 | 0.0100 | 1.0 | 6.8204 | 0.0112 | 1.0 | 2 |
| 6.7196 | 0.0114 | 1.0 | 6.5192 | 0.0114 | 1.0 | 3 |
| 6.4115 | 0.0115 | 1.0 | 6.2357 | 0.0115 | 1.0 | 4 |
| 6.1085 | 0.0115 | 1.0 | 5.9657 | 0.0115 | 1.0 | 5 |
| 5.8206 | 0.0115 | 1.0 | 5.7162 | 0.0115 | 1.0 | 6 |
| 5.5567 | 0.0115 | 1.0 | 5.4963 | 0.0115 | 1.0 | 7 |
| 5.3223 | 0.0116 | 1.0 | 5.3096 | 0.0116 | 1.0 | 8 |
| 5.1222 | 0.0117 | 1.0 | 5.1600 | 0.0117 | 1.0 | 9 |
| 4.9580 | 0.0117 | 1.0 | 5.0391 | 0.0118 | 1.0 | 10 |
| 4.8251 | 0.0119 | 1.0 | 4.9427 | 0.0118 | 1.0 | 11 |
| 4.7171 | 0.0119 | 1.0 | 4.8691 | 0.0119 | 1.0 | 12 |
| 4.6284 | 0.0121 | 1.0 | 4.8123 | 0.0120 | 1.0 | 13 |
| 4.5508 | 0.0121 | 1.0 | 4.7620 | 0.0121 | 1.0 | 14 |
| 4.4855 | 0.0123 | 1.0 | 4.7260 | 0.0121 | 1.0 | 15 |
| 4.4305 | 0.0124 | 1.0 | 4.7018 | 0.0123 | 1.0 | 16 |
| 4.3788 | 0.0125 | 1.0 | 4.6738 | 0.0123 | 1.0 | 17 |
| 4.3305 | 0.0127 | 1.0 | 4.6525 | 0.0124 | 1.0 | 18 |
| 4.2860 | 0.0128 | 1.0 | 4.6401 | 0.0125 | 1.0 | 19 |
### Framework versions
- Transformers 4.25.0.dev0
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Craig/mGqFiPhu | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
]
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: TSE_DistilBERT_5E
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# TSE_DistilBERT_5E
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: 0.3301
- Accuracy: 0.9333
## 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: 16
- 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6534 | 0.06 | 50 | 0.5269 | 0.8333 |
| 0.3926 | 0.12 | 100 | 0.2674 | 0.9133 |
| 0.275 | 0.17 | 150 | 0.2063 | 0.94 |
| 0.2341 | 0.23 | 200 | 0.1896 | 0.9333 |
| 0.2436 | 0.29 | 250 | 0.2132 | 0.9133 |
| 0.2561 | 0.35 | 300 | 0.2474 | 0.9 |
| 0.2536 | 0.4 | 350 | 0.2092 | 0.9267 |
| 0.2048 | 0.46 | 400 | 0.2135 | 0.92 |
| 0.2119 | 0.52 | 450 | 0.2382 | 0.9133 |
| 0.2152 | 0.58 | 500 | 0.2322 | 0.9267 |
| 0.2072 | 0.63 | 550 | 0.2182 | 0.9333 |
| 0.2134 | 0.69 | 600 | 0.2457 | 0.9133 |
| 0.2093 | 0.75 | 650 | 0.2476 | 0.92 |
| 0.2145 | 0.81 | 700 | 0.2489 | 0.9267 |
| 0.2191 | 0.87 | 750 | 0.2374 | 0.9267 |
| 0.2198 | 0.92 | 800 | 0.2347 | 0.92 |
| 0.2126 | 0.98 | 850 | 0.2015 | 0.9467 |
| 0.1373 | 1.04 | 900 | 0.2246 | 0.9467 |
| 0.1367 | 1.1 | 950 | 0.2875 | 0.9133 |
| 0.1726 | 1.15 | 1000 | 0.2641 | 0.94 |
| 0.1968 | 1.21 | 1050 | 0.2653 | 0.9333 |
| 0.1607 | 1.27 | 1100 | 0.2323 | 0.94 |
| 0.1437 | 1.33 | 1150 | 0.2900 | 0.9267 |
| 0.1707 | 1.38 | 1200 | 0.2430 | 0.94 |
| 0.1174 | 1.44 | 1250 | 0.2553 | 0.94 |
| 0.1662 | 1.5 | 1300 | 0.2442 | 0.9467 |
| 0.1374 | 1.56 | 1350 | 0.2365 | 0.9467 |
| 0.1632 | 1.61 | 1400 | 0.2794 | 0.9133 |
| 0.1558 | 1.67 | 1450 | 0.2428 | 0.94 |
| 0.1717 | 1.73 | 1500 | 0.2380 | 0.92 |
| 0.1301 | 1.79 | 1550 | 0.2006 | 0.94 |
| 0.1757 | 1.85 | 1600 | 0.2327 | 0.9467 |
| 0.1997 | 1.9 | 1650 | 0.2160 | 0.94 |
| 0.1611 | 1.96 | 1700 | 0.2797 | 0.92 |
| 0.1638 | 2.02 | 1750 | 0.2433 | 0.9333 |
| 0.1041 | 2.08 | 1800 | 0.2389 | 0.94 |
| 0.1172 | 2.13 | 1850 | 0.2381 | 0.9467 |
| 0.1332 | 2.19 | 1900 | 0.2650 | 0.94 |
| 0.1299 | 2.25 | 1950 | 0.2869 | 0.9333 |
| 0.0992 | 2.31 | 2000 | 0.2308 | 0.9533 |
| 0.1012 | 2.36 | 2050 | 0.2552 | 0.9467 |
| 0.0948 | 2.42 | 2100 | 0.2823 | 0.9267 |
| 0.1081 | 2.48 | 2150 | 0.2634 | 0.9467 |
| 0.1157 | 2.54 | 2200 | 0.2864 | 0.9333 |
| 0.1154 | 2.6 | 2250 | 0.2987 | 0.9267 |
| 0.1259 | 2.65 | 2300 | 0.2879 | 0.9333 |
| 0.1084 | 2.71 | 2350 | 0.2661 | 0.94 |
| 0.1342 | 2.77 | 2400 | 0.2711 | 0.94 |
| 0.12 | 2.83 | 2450 | 0.2362 | 0.9467 |
| 0.0839 | 2.88 | 2500 | 0.2712 | 0.9333 |
| 0.1546 | 2.94 | 2550 | 0.2433 | 0.9467 |
| 0.1321 | 3.0 | 2600 | 0.2421 | 0.9467 |
| 0.101 | 3.06 | 2650 | 0.2820 | 0.9333 |
| 0.061 | 3.11 | 2700 | 0.2990 | 0.9267 |
| 0.0608 | 3.17 | 2750 | 0.2512 | 0.9467 |
| 0.0983 | 3.23 | 2800 | 0.3033 | 0.9333 |
| 0.0806 | 3.29 | 2850 | 0.2621 | 0.9467 |
| 0.0788 | 3.34 | 2900 | 0.2672 | 0.9467 |
| 0.0827 | 3.4 | 2950 | 0.2797 | 0.9467 |
| 0.0912 | 3.46 | 3000 | 0.2802 | 0.9467 |
| 0.0771 | 3.52 | 3050 | 0.2693 | 0.9467 |
| 0.0842 | 3.58 | 3100 | 0.2758 | 0.9467 |
| 0.086 | 3.63 | 3150 | 0.2921 | 0.9333 |
| 0.1102 | 3.69 | 3200 | 0.3066 | 0.9333 |
| 0.1124 | 3.75 | 3250 | 0.2808 | 0.9333 |
| 0.0762 | 3.81 | 3300 | 0.2863 | 0.94 |
| 0.074 | 3.86 | 3350 | 0.3159 | 0.9333 |
| 0.062 | 3.92 | 3400 | 0.2977 | 0.9333 |
| 0.1027 | 3.98 | 3450 | 0.3449 | 0.9267 |
| 0.0734 | 4.04 | 3500 | 0.3165 | 0.9333 |
| 0.0375 | 4.09 | 3550 | 0.2960 | 0.9333 |
| 0.0377 | 4.15 | 3600 | 0.3245 | 0.9333 |
| 0.0661 | 4.21 | 3650 | 0.3262 | 0.9333 |
| 0.079 | 4.27 | 3700 | 0.3085 | 0.9333 |
| 0.0801 | 4.33 | 3750 | 0.3219 | 0.9333 |
| 0.0865 | 4.38 | 3800 | 0.3336 | 0.9267 |
| 0.058 | 4.44 | 3850 | 0.3083 | 0.9333 |
| 0.0689 | 4.5 | 3900 | 0.3351 | 0.9267 |
| 0.0345 | 4.56 | 3950 | 0.3412 | 0.9267 |
| 0.0557 | 4.61 | 4000 | 0.3236 | 0.9333 |
| 0.0758 | 4.67 | 4050 | 0.3224 | 0.9333 |
| 0.0682 | 4.73 | 4100 | 0.3241 | 0.9333 |
| 0.0534 | 4.79 | 4150 | 0.3349 | 0.9333 |
| 0.0707 | 4.84 | 4200 | 0.3254 | 0.9333 |
| 0.0672 | 4.9 | 4250 | 0.3277 | 0.9333 |
| 0.1033 | 4.96 | 4300 | 0.3301 | 0.9333 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.3.2
- Tokenizers 0.13.1
|
Crasher222/kaggle-comp-test | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:Crasher222/autonlp-data-kaggle-test",
"transformers",
"autonlp",
"co2_eq_emissions"
]
| text-classification | {
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}
} | 29 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-burak-new-300-v2-6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-burak-new-300-v2-6
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3074
- Wer: 0.2340
## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 151
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 6.3136 | 9.61 | 500 | 3.1262 | 1.0 |
| 1.8247 | 19.23 | 1000 | 0.4049 | 0.5065 |
| 0.5387 | 28.83 | 1500 | 0.2828 | 0.3462 |
| 0.3713 | 38.45 | 2000 | 0.2761 | 0.3125 |
| 0.293 | 48.08 | 2500 | 0.2872 | 0.3001 |
| 0.2436 | 57.68 | 3000 | 0.2912 | 0.2904 |
| 0.2116 | 67.3 | 3500 | 0.2910 | 0.2725 |
| 0.1859 | 76.91 | 4000 | 0.2937 | 0.2533 |
| 0.1731 | 86.53 | 4500 | 0.2985 | 0.2485 |
| 0.1569 | 96.15 | 5000 | 0.3022 | 0.2409 |
| 0.1471 | 105.76 | 5500 | 0.3070 | 0.2374 |
| 0.1385 | 115.38 | 6000 | 0.2954 | 0.2429 |
| 0.1289 | 124.99 | 6500 | 0.3016 | 0.2361 |
| 0.1268 | 134.61 | 7000 | 0.3000 | 0.2368 |
| 0.12 | 144.23 | 7500 | 0.3074 | 0.2340 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
CrayonShinchan/bart_fine_tune_test | []
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} | 0 | null | ---
language:
- es
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- facebook/multilingual_librispeech
metrics:
- wer
model-index:
- name: Whisper Small Es - Sanchit Gandhi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Multilingual LibriSpeech
type: facebook/multilingual_librispeech
args: 'config: es, split: test'
metrics:
- name: Wer
type: wer
value: 60.16226172047142
---
<!-- 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 Es - Sanchit Gandhi
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Multilingual LibriSpeech dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2668
- Wer: 60.1623
## 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-08
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- 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: 2500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 2.2112 | 0.2 | 500 | 1.7394 | 61.1126 |
| 1.4913 | 0.4 | 1000 | 1.3758 | 62.8143 |
| 1.6651 | 0.6 | 1500 | 1.3100 | 61.3261 |
| 1.7031 | 0.8 | 2000 | 1.2752 | 60.5261 |
| 1.4289 | 1.0 | 2500 | 1.2668 | 60.1623 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.0
- Datasets 2.6.2.dev0
- Tokenizers 0.12.1
|
CrayonShinchan/fine_tune_try_1 | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad-ver3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad-ver3
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5641
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5441 | 1.0 | 554 | 1.5467 |
| 1.2502 | 2.0 | 1108 | 1.4265 |
| 0.8088 | 3.0 | 1662 | 1.5641 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
CrisLeaf/generador-de-historias-de-tolkien | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 8 | null | ---
license: mit
---
### Glitch on Stable Diffusion via Dreambooth
#### model by BakkerHenk
This your the Stable Diffusion model fine-tuned the Glitch concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **a photo in sks glitched style**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can 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), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Here are the images used for training this concept:
|
Crisblair/Wkwk | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: whisper_havest_0025
results: []
---
<!-- 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. -->
# whisper_havest_0025
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.0701
- Train Accuracy: 0.0135
- Train Do Wer: 1.0
- Validation Loss: 4.6038
- Validation Accuracy: 0.0128
- Validation Do Wer: 1.0
- Epoch: 24
## 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': 1e-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 | Train Accuracy | Train Do Wer | Validation Loss | Validation Accuracy | Validation Do Wer | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 9.9191 | 0.0046 | 1.0 | 8.5836 | 0.0067 | 1.0 | 0 |
| 8.0709 | 0.0083 | 1.0 | 7.4667 | 0.0089 | 1.0 | 1 |
| 7.1652 | 0.0100 | 1.0 | 6.8204 | 0.0112 | 1.0 | 2 |
| 6.7196 | 0.0114 | 1.0 | 6.5192 | 0.0114 | 1.0 | 3 |
| 6.4115 | 0.0115 | 1.0 | 6.2357 | 0.0115 | 1.0 | 4 |
| 6.1085 | 0.0115 | 1.0 | 5.9657 | 0.0115 | 1.0 | 5 |
| 5.8206 | 0.0115 | 1.0 | 5.7162 | 0.0115 | 1.0 | 6 |
| 5.5567 | 0.0115 | 1.0 | 5.4963 | 0.0115 | 1.0 | 7 |
| 5.3223 | 0.0116 | 1.0 | 5.3096 | 0.0116 | 1.0 | 8 |
| 5.1222 | 0.0117 | 1.0 | 5.1600 | 0.0117 | 1.0 | 9 |
| 4.9580 | 0.0117 | 1.0 | 5.0391 | 0.0118 | 1.0 | 10 |
| 4.8251 | 0.0119 | 1.0 | 4.9427 | 0.0118 | 1.0 | 11 |
| 4.7171 | 0.0119 | 1.0 | 4.8691 | 0.0119 | 1.0 | 12 |
| 4.6284 | 0.0121 | 1.0 | 4.8123 | 0.0120 | 1.0 | 13 |
| 4.5508 | 0.0121 | 1.0 | 4.7620 | 0.0121 | 1.0 | 14 |
| 4.4855 | 0.0123 | 1.0 | 4.7260 | 0.0121 | 1.0 | 15 |
| 4.4305 | 0.0124 | 1.0 | 4.7018 | 0.0123 | 1.0 | 16 |
| 4.3788 | 0.0125 | 1.0 | 4.6738 | 0.0123 | 1.0 | 17 |
| 4.3305 | 0.0127 | 1.0 | 4.6525 | 0.0124 | 1.0 | 18 |
| 4.2860 | 0.0128 | 1.0 | 4.6401 | 0.0125 | 1.0 | 19 |
| 4.2451 | 0.0130 | 1.0 | 4.6234 | 0.0126 | 1.0 | 20 |
| 4.1994 | 0.0132 | 1.0 | 4.6077 | 0.0128 | 1.0 | 21 |
| 4.1521 | 0.0133 | 1.0 | 4.6098 | 0.0129 | 1.0 | 22 |
| 4.1148 | 0.0134 | 1.0 | 4.5919 | 0.0129 | 1.0 | 23 |
| 4.0701 | 0.0135 | 1.0 | 4.6038 | 0.0128 | 1.0 | 24 |
### Framework versions
- Transformers 4.25.0.dev0
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Crispy/dialopt-small-kratos | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: BERiT_14500
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERiT_14500
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: 8.0316
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 8.3825 | 0.19 | 500 | 8.3006 |
| 8.2426 | 0.39 | 1000 | 8.2751 |
| 8.1622 | 0.58 | 1500 | 8.2504 |
| 8.1673 | 0.77 | 2000 | 8.1935 |
| 8.1597 | 0.97 | 2500 | 8.1928 |
| 8.0644 | 1.16 | 3000 | 8.1111 |
| 8.0724 | 1.36 | 3500 | 8.0820 |
| 8.0654 | 1.55 | 4000 | 8.0655 |
| 8.0649 | 1.74 | 4500 | 8.0896 |
| 8.051 | 1.94 | 5000 | 8.0838 |
| 8.0003 | 2.13 | 5500 | 8.0989 |
| 7.9795 | 2.32 | 6000 | 8.0729 |
| 7.9984 | 2.52 | 6500 | 8.0566 |
| 7.9935 | 2.71 | 7000 | 8.0757 |
| 7.9652 | 2.9 | 7500 | 8.0316 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Crives/distilbert-base-uncased-finetuned-emotion | [
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"dataset:emotion",
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} | 31 | null | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
- laion-2b
---
# Model card for vit_base_patch16_clip_384.laion2b_ft_in1k
A Vision Transformer (ViT) image classification model. Pretrained on LAION-2B image-text pairs using OpenCLIP. Fine-tuned on ImageNet-1k in `timm`. See recipes in [Reproducible scaling laws](https://arxiv.org/abs/2212.07143).
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 86.9
- GMACs: 49.4
- Activations (M): 48.3
- Image size: 384 x 384
- **Papers:**
- OpenCLIP: https://github.com/mlfoundations/open_clip
- Reproducible scaling laws for contrastive language-image learning: https://arxiv.org/abs/2212.07143
- LAION-5B: An open large-scale dataset for training next generation image-text models: https://arxiv.org/abs/2210.08402
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
- **Dataset:** ImageNet-1k
- **Pretrain Dataset:**
- LAION-2B
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('vit_base_patch16_clip_384.laion2b_ft_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_base_patch16_clip_384.laion2b_ft_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 577, 768) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@software{ilharco_gabriel_2021_5143773,
author = {Ilharco, Gabriel and
Wortsman, Mitchell and
Wightman, Ross and
Gordon, Cade and
Carlini, Nicholas and
Taori, Rohan and
Dave, Achal and
Shankar, Vaishaal and
Namkoong, Hongseok and
Miller, John and
Hajishirzi, Hannaneh and
Farhadi, Ali and
Schmidt, Ludwig},
title = {OpenCLIP},
month = jul,
year = 2021,
note = {If you use this software, please cite it as below.},
publisher = {Zenodo},
version = {0.1},
doi = {10.5281/zenodo.5143773},
url = {https://doi.org/10.5281/zenodo.5143773}
}
```
```bibtex
@article{cherti2022reproducible,
title={Reproducible scaling laws for contrastive language-image learning},
author={Cherti, Mehdi and Beaumont, Romain and Wightman, Ross and Wortsman, Mitchell and Ilharco, Gabriel and Gordon, Cade and Schuhmann, Christoph and Schmidt, Ludwig and Jitsev, Jenia},
journal={arXiv preprint arXiv:2212.07143},
year={2022}
}
```
```bibtex
@inproceedings{schuhmann2022laionb,
title={{LAION}-5B: An open large-scale dataset for training next generation image-text models},
author={Christoph Schuhmann and
Romain Beaumont and
Richard Vencu and
Cade W Gordon and
Ross Wightman and
Mehdi Cherti and
Theo Coombes and
Aarush Katta and
Clayton Mullis and
Mitchell Wortsman and
Patrick Schramowski and
Srivatsa R Kundurthy and
Katherine Crowson and
Ludwig Schmidt and
Robert Kaczmarczyk and
Jenia Jitsev},
booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2022},
url={https://openreview.net/forum?id=M3Y74vmsMcY}
}
```
```bibtex
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
Crumped/imdb-simpleRNN | [
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tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
- wit-400m
---
# Model card for vit_base_patch16_clip_384.openai_ft_in1k
A Vision Transformer (ViT) image classification model. Pretrained on WIT-400M image-text pairs by OpenAI using CLIP. Fine-tuned on ImageNet-1k in `timm`. See recipes in [Reproducible scaling laws](https://arxiv.org/abs/2212.07143).
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 86.9
- GMACs: 49.4
- Activations (M): 48.3
- Image size: 384 x 384
- **Papers:**
- Learning Transferable Visual Models From Natural Language Supervision: https://arxiv.org/abs/2103.00020
- Reproducible scaling laws for contrastive language-image learning: https://arxiv.org/abs/2212.07143
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
- **Dataset:** ImageNet-1k
- **Pretrain Dataset:**
- WIT-400M
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('vit_base_patch16_clip_384.openai_ft_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_base_patch16_clip_384.openai_ft_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 577, 768) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@inproceedings{Radford2021LearningTV,
title={Learning Transferable Visual Models From Natural Language Supervision},
author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
booktitle={ICML},
year={2021}
}
```
```bibtex
@article{cherti2022reproducible,
title={Reproducible scaling laws for contrastive language-image learning},
author={Cherti, Mehdi and Beaumont, Romain and Wightman, Ross and Wortsman, Mitchell and Ilharco, Gabriel and Gordon, Cade and Schuhmann, Christoph and Schmidt, Ludwig and Jitsev, Jenia},
journal={arXiv preprint arXiv:2212.07143},
year={2022}
}
```
```bibtex
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
CrypticT1tan/DialoGPT-medium-harrypotter | []
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} | 0 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-hausa2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-hausa2
This model is a fine-tuned version of [Davlan/xlm-roberta-base-finetuned-hausa](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-hausa) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6335
- Accuracy: 0.7310
- F1: 0.7296
## 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-06
- 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
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
|
Culmenus/IceBERT-finetuned-ner | [
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"dataset:mim_gold_ner",
"transformers",
"generated_from_trainer",
"license:gpl-3.0",
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} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad-ver4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad-ver4
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4931
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.8147 | 1.0 | 554 | 1.6712 |
| 1.4844 | 2.0 | 1108 | 1.4681 |
| 1.0993 | 3.0 | 1662 | 1.4931 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Culmenus/XLMR-ENIS-finetuned-ner | [
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"dataset:mim_gold_ner",
"transformers",
"generated_from_trainer",
"license:agpl-3.0",
"model-index",
"autotrain_compatible"
]
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"XLMRobertaForTokenClassification"
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} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- librispeech_asr
metrics:
- wer
model-index:
- name: whisper-small-en
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: librispeech_asr
type: librispeech_asr
config: clean
split: test
args: clean
metrics:
- name: Wer
type: wer
value: 124.51154529307283
---
<!-- 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-en
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 6.7832
- Wer: 124.5115
## 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: 0.0005
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| 9.6259 | 1.57 | 5 | 10.7408 | 1127.3535 |
| 11.5288 | 3.29 | 10 | 9.2534 | 100.0 |
| 10.9249 | 4.86 | 15 | 7.8357 | 100.0 |
| 7.0442 | 6.57 | 20 | 6.9971 | 595.3819 |
| 8.6762 | 8.29 | 25 | 5.6135 | 312.2558 |
| 5.4239 | 9.86 | 30 | 5.4885 | 97.1581 |
| 4.986 | 11.57 | 35 | 5.2888 | 628.7744 |
| 6.708 | 13.29 | 40 | 4.9665 | 277.6199 |
| 3.9096 | 14.86 | 45 | 5.0861 | 631.9716 |
| 3.2326 | 16.57 | 50 | 5.0090 | 279.7513 |
| 3.9691 | 18.29 | 55 | 5.0804 | 133.2149 |
| 1.8661 | 19.86 | 60 | 5.4423 | 317.5844 |
| 1.1588 | 21.57 | 65 | 5.7955 | 119.5382 |
| 1.0355 | 23.29 | 70 | 6.0458 | 190.2309 |
| 0.3455 | 24.86 | 75 | 6.3057 | 106.7496 |
| 0.142 | 26.57 | 80 | 6.5767 | 209.9467 |
| 0.1722 | 28.29 | 85 | 6.5937 | 101.4210 |
| 0.0816 | 29.86 | 90 | 6.7679 | 149.7336 |
| 0.079 | 31.57 | 95 | 6.8008 | 133.5702 |
| 0.1007 | 33.29 | 100 | 6.7832 | 124.5115 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Culmenus/checkpoint-168500-finetuned-de-to-is_nr2 | []
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} | 0 | null | ---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# BERT base model (cased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is case-sensitive: it makes a difference between
english and English.
Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by
the Hugging Face team.
## Model description
BERT is a 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 BERT 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=bert) 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
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-cased')
>>> unmasker("Hello I'm a [MASK] model.")
[{'sequence': "[CLS] Hello I'm a fashion model. [SEP]",
'score': 0.09019174426794052,
'token': 4633,
'token_str': 'fashion'},
{'sequence': "[CLS] Hello I'm a new model. [SEP]",
'score': 0.06349995732307434,
'token': 1207,
'token_str': 'new'},
{'sequence': "[CLS] Hello I'm a male model. [SEP]",
'score': 0.06228214129805565,
'token': 2581,
'token_str': 'male'},
{'sequence': "[CLS] Hello I'm a professional model. [SEP]",
'score': 0.0441727414727211,
'token': 1848,
'token_str': 'professional'},
{'sequence': "[CLS] Hello I'm a super model. [SEP]",
'score': 0.03326151892542839,
'token': 7688,
'token_str': 'super'}]
```
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('bert-base-cased')
model = BertModel.from_pretrained("bert-base-cased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
model = TFBertModel.from_pretrained("bert-base-cased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
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:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-cased')
>>> unmasker("The man worked as a [MASK].")
[{'sequence': '[CLS] The man worked as a lawyer. [SEP]',
'score': 0.04804691672325134,
'token': 4545,
'token_str': 'lawyer'},
{'sequence': '[CLS] The man worked as a waiter. [SEP]',
'score': 0.037494491785764694,
'token': 17989,
'token_str': 'waiter'},
{'sequence': '[CLS] The man worked as a cop. [SEP]',
'score': 0.035512614995241165,
'token': 9947,
'token_str': 'cop'},
{'sequence': '[CLS] The man worked as a detective. [SEP]',
'score': 0.031271643936634064,
'token': 9140,
'token_str': 'detective'},
{'sequence': '[CLS] The man worked as a doctor. [SEP]',
'score': 0.027423162013292313,
'token': 3995,
'token_str': 'doctor'}]
>>> unmasker("The woman worked as a [MASK].")
[{'sequence': '[CLS] The woman worked as a nurse. [SEP]',
'score': 0.16927455365657806,
'token': 7439,
'token_str': 'nurse'},
{'sequence': '[CLS] The woman worked as a waitress. [SEP]',
'score': 0.1501094549894333,
'token': 15098,
'token_str': 'waitress'},
{'sequence': '[CLS] The woman worked as a maid. [SEP]',
'score': 0.05600163713097572,
'token': 13487,
'token_str': 'maid'},
{'sequence': '[CLS] The woman worked as a housekeeper. [SEP]',
'score': 0.04838843643665314,
'token': 26458,
'token_str': 'housekeeper'},
{'sequence': '[CLS] The woman worked as a cook. [SEP]',
'score': 0.029980547726154327,
'token': 9834,
'token_str': 'cook'}]
```
This bias will also affect all fine-tuned versions of this model.
## Training data
The BERT model was 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 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 model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size
of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. 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.
## Evaluation results
When fine-tuned on downstream tasks, this model achieves the following results:
Glue test results:
| Task | MNLI-(m/mm) | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | Average |
|:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:|
| | 84.6/83.4 | 71.2 | 90.5 | 93.5 | 52.1 | 85.8 | 88.9 | 66.4 | 79.6 |
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-1810-04805,
author = {Jacob Devlin and
Ming{-}Wei Chang and
Kenton Lee and
Kristina Toutanova},
title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
Understanding},
journal = {CoRR},
volume = {abs/1810.04805},
year = {2018},
url = {http://arxiv.org/abs/1810.04805},
archivePrefix = {arXiv},
eprint = {1810.04805},
timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
<a href="https://huggingface.co/exbert/?model=bert-base-cased">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
Culmenus/opus-mt-de-is-finetuned-de-to-is | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
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"MarianMTModel"
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}
} | 1 | null | ---
language: zh
---
# Bert-base-chinese
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
# Model Details
- **Model Description:**
This model has been pre-trained for Chinese, training and random input masking has been applied independently to word pieces (as in the original BERT paper).
- **Developed by:** HuggingFace team
- **Model Type:** Fill-Mask
- **Language(s):** Chinese
- **License:** [More Information needed]
- **Parent Model:** See the [BERT base uncased model](https://huggingface.co/bert-base-uncased) for more information about the BERT base model.
## Uses
#### Direct Use
This model can be used for masked language modeling
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
## Training
#### Training Procedure
* **type_vocab_size:** 2
* **vocab_size:** 21128
* **num_hidden_layers:** 12
#### Training Data
[More Information Needed]
## Evaluation
#### Results
[More Information Needed]
## How to Get Started With the Model
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese")
model = AutoModelForMaskedLM.from_pretrained("bert-base-chinese")
```
|
Culmenus/opus-mt-de-is-finetuned-de-to-is_35g65cc | []
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} | 0 | null | ---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by
the Hugging Face team.
## Model description
BERT is a 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 labeling 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 masks 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 BERT model as inputs.
## Model variations
BERT has originally been released in base and large variations, for cased and uncased input text. The uncased models also strips out an accent markers.
Chinese and multilingual uncased and cased versions followed shortly after.
Modified preprocessing with whole word masking has replaced subpiece masking in a following work, with the release of two models.
Other 24 smaller models are released afterward.
The detailed release history can be found on the [google-research/bert readme](https://github.com/google-research/bert/blob/master/README.md) on github.
| Model | #params | Language |
|------------------------|--------------------------------|-------|
| [`bert-base-uncased`](https://huggingface.co/bert-base-uncased) | 110M | English |
| [`bert-large-uncased`](https://huggingface.co/bert-large-uncased) | 340M | English | sub
| [`bert-base-cased`](https://huggingface.co/bert-base-cased) | 110M | English |
| [`bert-large-cased`](https://huggingface.co/bert-large-cased) | 340M | English |
| [`bert-base-chinese`](https://huggingface.co/bert-base-chinese) | 110M | Chinese |
| [`bert-base-multilingual-cased`](https://huggingface.co/bert-base-multilingual-cased) | 110M | Multiple |
| [`bert-large-uncased-whole-word-masking`](https://huggingface.co/bert-large-uncased-whole-word-masking) | 340M | English |
| [`bert-large-cased-whole-word-masking`](https://huggingface.co/bert-large-cased-whole-word-masking) | 340M | English |
## 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=bert) to look for
fine-tuned versions of 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
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
>>> unmasker("Hello I'm a [MASK] model.")
[{'sequence': "[CLS] hello i'm a fashion model. [SEP]",
'score': 0.1073106899857521,
'token': 4827,
'token_str': 'fashion'},
{'sequence': "[CLS] hello i'm a role model. [SEP]",
'score': 0.08774490654468536,
'token': 2535,
'token_str': 'role'},
{'sequence': "[CLS] hello i'm a new model. [SEP]",
'score': 0.05338378623127937,
'token': 2047,
'token_str': 'new'},
{'sequence': "[CLS] hello i'm a super model. [SEP]",
'score': 0.04667217284440994,
'token': 3565,
'token_str': 'super'},
{'sequence': "[CLS] hello i'm a fine model. [SEP]",
'score': 0.027095865458250046,
'token': 2986,
'token_str': 'fine'}]
```
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('bert-base-uncased')
model = BertModel.from_pretrained("bert-base-uncased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertModel.from_pretrained("bert-base-uncased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
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:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
>>> unmasker("The man worked as a [MASK].")
[{'sequence': '[CLS] the man worked as a carpenter. [SEP]',
'score': 0.09747550636529922,
'token': 10533,
'token_str': 'carpenter'},
{'sequence': '[CLS] the man worked as a waiter. [SEP]',
'score': 0.0523831807076931,
'token': 15610,
'token_str': 'waiter'},
{'sequence': '[CLS] the man worked as a barber. [SEP]',
'score': 0.04962705448269844,
'token': 13362,
'token_str': 'barber'},
{'sequence': '[CLS] the man worked as a mechanic. [SEP]',
'score': 0.03788609802722931,
'token': 15893,
'token_str': 'mechanic'},
{'sequence': '[CLS] the man worked as a salesman. [SEP]',
'score': 0.037680890411138535,
'token': 18968,
'token_str': 'salesman'}]
>>> unmasker("The woman worked as a [MASK].")
[{'sequence': '[CLS] the woman worked as a nurse. [SEP]',
'score': 0.21981462836265564,
'token': 6821,
'token_str': 'nurse'},
{'sequence': '[CLS] the woman worked as a waitress. [SEP]',
'score': 0.1597415804862976,
'token': 13877,
'token_str': 'waitress'},
{'sequence': '[CLS] the woman worked as a maid. [SEP]',
'score': 0.1154729500412941,
'token': 10850,
'token_str': 'maid'},
{'sequence': '[CLS] the woman worked as a prostitute. [SEP]',
'score': 0.037968918681144714,
'token': 19215,
'token_str': 'prostitute'},
{'sequence': '[CLS] the woman worked as a cook. [SEP]',
'score': 0.03042375110089779,
'token': 5660,
'token_str': 'cook'}]
```
This bias will also affect all fine-tuned versions of this model.
## Training data
The BERT model was 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 model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size
of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. 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.
## Evaluation results
When fine-tuned on downstream tasks, this model achieves the following results:
Glue test results:
| Task | MNLI-(m/mm) | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | Average |
|:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:|
| | 84.6/83.4 | 71.2 | 90.5 | 93.5 | 52.1 | 85.8 | 88.9 | 66.4 | 79.6 |
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-1810-04805,
author = {Jacob Devlin and
Ming{-}Wei Chang and
Kenton Lee and
Kristina Toutanova},
title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
Understanding},
journal = {CoRR},
volume = {abs/1810.04805},
year = {2018},
url = {http://arxiv.org/abs/1810.04805},
archivePrefix = {arXiv},
eprint = {1810.04805},
timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
<a href="https://huggingface.co/exbert/?model=bert-base-uncased">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
Culmenus/opus-mt-de-is-finetuned-de-to-is_35g65cc_1 | []
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} | 0 | null | ---
language: en
tags:
- exbert
license: mit
datasets:
- bookcorpus
- wikipedia
---
# RoBERTa base model
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1907.11692) and first released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). This model is case-sensitive: it
makes a difference between english and English.
Disclaimer: The team releasing RoBERTa did not write a model card for this model so this model card has been written by
the Hugging Face team.
## Model description
RoBERTa is a 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 the Masked language modeling (MLM) objective. 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.
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 BERT model as inputs.
## Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
See the [model hub](https://huggingface.co/models?filter=roberta) 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
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='roberta-base')
>>> unmasker("Hello I'm a <mask> model.")
[{'sequence': "<s>Hello I'm a male model.</s>",
'score': 0.3306540250778198,
'token': 2943,
'token_str': 'Ġmale'},
{'sequence': "<s>Hello I'm a female model.</s>",
'score': 0.04655390977859497,
'token': 2182,
'token_str': 'Ġfemale'},
{'sequence': "<s>Hello I'm a professional model.</s>",
'score': 0.04232972860336304,
'token': 2038,
'token_str': 'Ġprofessional'},
{'sequence': "<s>Hello I'm a fashion model.</s>",
'score': 0.037216778844594955,
'token': 2734,
'token_str': 'Ġfashion'},
{'sequence': "<s>Hello I'm a Russian model.</s>",
'score': 0.03253649175167084,
'token': 1083,
'token_str': 'ĠRussian'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaModel.from_pretrained('roberta-base')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = TFRobertaModel.from_pretrained('roberta-base')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
### Limitations and bias
The training data used for this model contains a lot of unfiltered content from the internet, which is far from
neutral. Therefore, the model can have biased predictions:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='roberta-base')
>>> unmasker("The man worked as a <mask>.")
[{'sequence': '<s>The man worked as a mechanic.</s>',
'score': 0.08702439814805984,
'token': 25682,
'token_str': 'Ġmechanic'},
{'sequence': '<s>The man worked as a waiter.</s>',
'score': 0.0819653645157814,
'token': 38233,
'token_str': 'Ġwaiter'},
{'sequence': '<s>The man worked as a butcher.</s>',
'score': 0.073323555290699,
'token': 32364,
'token_str': 'Ġbutcher'},
{'sequence': '<s>The man worked as a miner.</s>',
'score': 0.046322137117385864,
'token': 18678,
'token_str': 'Ġminer'},
{'sequence': '<s>The man worked as a guard.</s>',
'score': 0.040150221437215805,
'token': 2510,
'token_str': 'Ġguard'}]
>>> unmasker("The Black woman worked as a <mask>.")
[{'sequence': '<s>The Black woman worked as a waitress.</s>',
'score': 0.22177888453006744,
'token': 35698,
'token_str': 'Ġwaitress'},
{'sequence': '<s>The Black woman worked as a prostitute.</s>',
'score': 0.19288744032382965,
'token': 36289,
'token_str': 'Ġprostitute'},
{'sequence': '<s>The Black woman worked as a maid.</s>',
'score': 0.06498628109693527,
'token': 29754,
'token_str': 'Ġmaid'},
{'sequence': '<s>The Black woman worked as a secretary.</s>',
'score': 0.05375480651855469,
'token': 2971,
'token_str': 'Ġsecretary'},
{'sequence': '<s>The Black woman worked as a nurse.</s>',
'score': 0.05245552211999893,
'token': 9008,
'token_str': 'Ġnurse'}]
```
This bias will also affect all fine-tuned versions of this model.
## Training data
The RoBERTa model was pretrained on the reunion of five datasets:
- [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books;
- [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers) ;
- [CC-News](https://commoncrawl.org/2016/10/news-dataset-available/), a dataset containing 63 millions English news
articles crawled between September 2016 and February 2019.
- [OpenWebText](https://github.com/jcpeterson/openwebtext), an opensource recreation of the WebText dataset used to
train GPT-2,
- [Stories](https://arxiv.org/abs/1806.02847) a dataset containing a subset of CommonCrawl data filtered to match the
story-like style of Winograd schemas.
Together theses datasets weight 160GB of text.
## Training procedure
### Preprocessing
The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50,000. The inputs of
the model take pieces of 512 contiguous token that may span over documents. The beginning of a new document is marked
with `<s>` and the end of one by `</s>`
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.
Contrary to BERT, the masking is done dynamically during pretraining (e.g., it changes at each epoch and is not fixed).
### Pretraining
The model was trained on 1024 V100 GPUs for 500K steps with a batch size of 8K and a sequence length of 512. The
optimizer used is Adam with a learning rate of 6e-4, \\(\beta_{1} = 0.9\\), \\(\beta_{2} = 0.98\\) and
\\(\epsilon = 1e-6\\), a weight decay of 0.01, learning rate warmup for 24,000 steps and linear decay of the learning
rate after.
## Evaluation results
When fine-tuned on downstream tasks, this model achieves the following results:
Glue test results:
| Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE |
|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|
| | 87.6 | 91.9 | 92.8 | 94.8 | 63.6 | 91.2 | 90.2 | 78.7 |
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-1907-11692,
author = {Yinhan Liu and
Myle Ott and
Naman Goyal and
Jingfei Du and
Mandar Joshi and
Danqi Chen and
Omer Levy and
Mike Lewis and
Luke Zettlemoyer and
Veselin Stoyanov},
title = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach},
journal = {CoRR},
volume = {abs/1907.11692},
year = {2019},
url = {http://arxiv.org/abs/1907.11692},
archivePrefix = {arXiv},
eprint = {1907.11692},
timestamp = {Thu, 01 Aug 2019 08:59:33 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
<a href="https://huggingface.co/exbert/?model=roberta-base">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
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} | 0 | null | ---
language: zh
datasets: CLUECorpusSmall
widget:
- text: "这是很久之前的事情了"
---
# Chinese GPT2 Model
## Model description
The model is used to generate Chinese texts. You can download the model either from the [GPT2-Chinese Github page](https://github.com/Morizeyao/GPT2-Chinese), or via HuggingFace from the link [gpt2-chinese-cluecorpussmall](https://huggingface.co/uer/gpt2-chinese-cluecorpussmall).
## How to use
You can use the model directly with a pipeline for text generation:
```python
>>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline
>>> tokenizer = BertTokenizer.from_pretrained("uer/gpt2-chinese-cluecorpussmall")
>>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-cluecorpussmall")
>>> text_generator = TextGenerationPipeline(model, tokenizer)
>>> text_generator("这是很久之前的事情了", max_length=100, do_sample=True)
[{'generated_text': '这是很久之前的事情了 ， 我 曾 经 把 这 个 当 做 一 种 思 想 的 传 承 ， 或 者 是 人 生 的 回 顾 ， 当 时 我 们 是 一 个 刚 刚 加 入 的 时 候 就 想 要 加 入 他 们 ， 于 是 我 们 每 天 看 到 他 们 ， 加 上 他 们 的 各 种 不 可 思 议 的 行 为 ， 直 到 现 在 ， 我 们 的 人 生 才 完 整 起 来 。'}]
```
## Training data
[CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020/) is used as training data.
## Training procedure
The model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). We pre-train 1,000,000 steps with a sequence length of 128 and then pre-train 250,000 additional steps with a sequence length of 1024.
Stage1:
```
python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
--vocab_path models/google_zh_vocab.txt \
--dataset_path cluecorpussmall_lm_seq128_dataset.pt \
--seq_length 128 --processes_num 32 --data_processor lm
```
```
python3 pretrain.py --dataset_path cluecorpussmall_lm_seq128_dataset.pt \
--vocab_path models/google_zh_vocab.txt \
--config_path models/gpt2/config.json \
--output_model_path models/cluecorpussmall_gpt2_seq128_model.bin \
--world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
--total_steps 1000000 --save_checkpoint_steps 100000 --report_steps 50000 \
--learning_rate 1e-4 --batch_size 64
```
Stage2:
```
python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
--vocab_path models/google_zh_vocab.txt \
--dataset_path cluecorpussmall_lm_seq1024_dataset.pt \
--seq_length 1024 --processes_num 32 --data_processor lm
```
```
python3 pretrain.py --dataset_path cluecorpussmall_lm_seq1024_dataset.pt \
--vocab_path models/google_zh_vocab.txt \
--pretrained_model_path models/cluecorpussmall_gpt2_seq128_model.bin-1000000 \
--config_path models/gpt2/config.json \
--output_model_path models/cluecorpussmall_gpt2_seq1024_model.bin \
--world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
--total_steps 250000 --save_checkpoint_steps 50000 --report_steps 10000 \
--learning_rate 5e-5 --batch_size 16
```
Finally, we convert the pre-trained model into Huggingface's format:
```
python3 scripts/convert_gpt2_from_uer_to_huggingface.py --input_model_path cluecorpussmall_gpt2_seq1024_model.bin-250000 \
--output_model_path pytorch_model.bin \
--layers_num 12
```
### BibTeX entry and citation info
```
@article{radford2019language,
title={Language Models are Unsupervised Multitask Learners},
author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
year={2019}
}
@article{zhao2019uer,
title={UER: An Open-Source Toolkit for Pre-training Models},
author={Zhao, Zhe and Chen, Hui and Zhang, Jinbin and Zhao, Xin and Liu, Tao and Lu, Wei and Chen, Xi and Deng, Haotang and Ju, Qi and Du, Xiaoyong},
journal={EMNLP-IJCNLP 2019},
pages={241},
year={2019}
}
``` |
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} | 0 | null | ---
language: en
---
This is a Hugging Face transformers-compatible conversion of the original dense 125M-parameter model from the paper "[Efficient Large Scale Language Modeling with Mixtures of Experts](https://arxiv.org/abs/2112.10684)" from Artetxe et al. Please refer to the original model card, which can be found at https://github.com/facebookresearch/fairseq/blob/main/examples/moe_lm/model_card.md.
|
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} | 0 | null | ---
language: de
widget:
- text: "Heute ist sehr schönes Wetter in"
license: mit
---
# German GPT-2 model
In this repository we release (yet another) GPT-2 model, that was trained on various texts for German.
The model is meant to be an entry point for fine-tuning on other texts, and it is definitely not as good or "dangerous" as the English GPT-3 model. We do not plan extensive PR or staged releases for this model 😉
**Note**: The model was initially released under an anonymous alias (`anonymous-german-nlp/german-gpt2`) so we now "de-anonymize" it.
More details about GPT-2 can be found in the great [Hugging Face](https://huggingface.co/transformers/model_doc/gpt2.html) documentation.
# Changelog
16.08.2021: Public release of re-trained version of our German GPT-2 model with better results.
15.11.2020: Initial release. Please use the tag `v1.0` for [this older version](https://huggingface.co/dbmdz/german-gpt2/tree/v1.0).
# Training corpora
We use pretty much the same corpora as used for training the DBMDZ BERT model, that can be found in [this repository](https://github.com/dbmdz/berts).
Thanks to the awesome Hugging Face team, it is possible to create byte-level BPE with their awesome [Tokenizers](https://github.com/huggingface/tokenizers) library.
With the previously mentioned awesome Tokenizers library we created a 50K byte-level BPE vocab based on the training corpora.
After creating the vocab, we could train the GPT-2 for German on a v3-8 TPU over the complete training corpus for 20 epochs. All hyperparameters
can be found in the official JAX/FLAX documentation [here](https://github.com/huggingface/transformers/blob/master/examples/flax/language-modeling/README.md)
from Transformers.
# Using the model
The model itself can be used in this way:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("dbmdz/german-gpt2")
model = AutoModelWithLMHead.from_pretrained("dbmdz/german-gpt2")
```
However, text generation is a bit more interesting, so here's an example that shows how to use the great Transformers *Pipelines* for generating text:
```python
from transformers import pipeline
pipe = pipeline('text-generation', model="dbmdz/german-gpt2",
tokenizer="dbmdz/german-gpt2")
text = pipe("Der Sinn des Lebens ist es", max_length=100)[0]["generated_text"]
print(text)
```
This could output this beautiful text:
```
Der Sinn des Lebens ist es, im Geist zu verweilen, aber nicht in der Welt zu sein, sondern ganz im Geist zu leben.
Die Menschen beginnen, sich nicht nach der Natur und nach der Welt zu richten, sondern nach der Seele,'
```
# License
All models are licensed under [MIT](LICENSE).
# Huggingface model hub
All models are available on the [Huggingface model hub](https://huggingface.co/dbmdz).
# Contact (Bugs, Feedback, Contribution and more)
For questions about our BERT models just open an issue
[here](https://github.com/stefan-it/german-gpt/issues/new) 🤗
# Acknowledgments
Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).
Thanks for providing access to the TFRC ❤️
Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team,
it is possible to download both cased and uncased models from their S3 storage 🤗
|
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} | 0 | null | ---
language: en
license: mit
---
# OpenAI GPT
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications](#technical-specifications)
- [Citation Information](#citation-information)
- [Model Card Authors](#model-card-authors)
## Model Details
**Model Description:** `openai-gpt` is a transformer-based language model created and released by OpenAI. The model is a causal (unidirectional) transformer pre-trained using language modeling on a large corpus with long range dependencies.
- **Developed by:** Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever. See [associated research paper](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) and [GitHub repo](https://github.com/openai/finetune-transformer-lm) for model developers and contributors.
- **Model Type:** Transformer-based language model
- **Language(s):** English
- **License:** [MIT License](https://github.com/openai/finetune-transformer-lm/blob/master/LICENSE)
- **Related Models:** [GPT2](https://huggingface.co/gpt2), [GPT2-Medium](https://huggingface.co/gpt2-medium), [GPT2-Large](https://huggingface.co/gpt2-large) and [GPT2-XL](https://huggingface.co/gpt2-xl)
- **Resources for more information:**
- [Research Paper](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf)
- [OpenAI Blog Post](https://openai.com/blog/language-unsupervised/)
- [GitHub Repo](https://github.com/openai/finetune-transformer-lm)
- Test the full generation capabilities here: https://transformer.huggingface.co/doc/gpt
## How to Get Started with the Model
Use the code below to get started with the model. You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='openai-gpt')
>>> set_seed(42)
>>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)
[{'generated_text': "Hello, I'm a language model,'he said, when i was finished.'ah well,'said the man,'that's"},
{'generated_text': 'Hello, I\'m a language model, " she said. \n she reached the bottom of the shaft and leaned a little further out. it was'},
{'generated_text': 'Hello, I\'m a language model, " she laughed. " we call that a\'white girl.\'or as we are called by the'},
{'generated_text': 'Hello, I\'m a language model, " said mr pin. " an\'the ones with the funny hats don\'t. " the rest of'},
{'generated_text': 'Hello, I\'m a language model, was\'ere \'bout to do some more dancin \', " he said, then his voice lowered to'}]
```
Here is how to use this model in PyTorch:
```python
from transformers import OpenAIGPTTokenizer, OpenAIGPTModel
import torch
tokenizer = OpenAIGPTTokenizer.from_pretrained("openai-gpt")
model = OpenAIGPTModel.from_pretrained("openai-gpt")
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```
and in TensorFlow:
```python
from transformers import OpenAIGPTTokenizer, TFOpenAIGPTModel
tokenizer = OpenAIGPTTokenizer.from_pretrained("openai-gpt")
model = TFOpenAIGPTModel.from_pretrained("openai-gpt")
inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
outputs = model(inputs)
last_hidden_states = outputs.last_hidden_state
```
## Uses
#### Direct Use
This model can be used for language modeling tasks.
#### Downstream Use
Potential downstream uses of this model include tasks that leverage language models. In the [associated paper](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf), the model developers discuss evaluations of the model for tasks including natural language inference (NLI), question answering, semantic similarity, and text classification.
#### Misuse and Out-of-scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
## Risks, Limitations and Biases
#### Biases
**CONTENT WARNING: Readers should be aware that language generated by this model can be disturbing or offensive to some and can propagate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
Predictions generated by this model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. For example:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='openai-gpt')
>>> set_seed(42)
>>> generator("The man worked as a", max_length=10, num_return_sequences=5)
[{'generated_text': 'The man worked as a teacher for the college he'},
{'generated_text': 'The man worked as a janitor at the club.'},
{'generated_text': 'The man worked as a bodyguard in america. the'},
{'generated_text': 'The man worked as a clerk for one of the'},
{'generated_text': 'The man worked as a nurse, but there was'}]
>>> set_seed(42)
>>> generator("The woman worked as a", max_length=10, num_return_sequences=5)
[{'generated_text': 'The woman worked as a medical intern but is a'},
{'generated_text': 'The woman worked as a midwife, i know that'},
{'generated_text': 'The woman worked as a prostitute in a sex club'},
{'generated_text': 'The woman worked as a secretary for one of the'},
{'generated_text': 'The woman worked as a nurse, but she had'}]
```
This bias may also affect fine-tuned versions of this model. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
#### Risks and Limitations
The model developers also wrote in a [blog post](https://openai.com/blog/language-unsupervised/) about risks and limitations of the model, including:
> - **Compute Requirements:** Many previous approaches to NLP tasks train relatively small models on a single GPU from scratch. Our approach requires an expensive pre-training step - 1 month on 8 GPUs. Luckily, this only has to be done once and we’re releasing our model so others can avoid it. It is also a large model (in comparison to prior work) and consequently uses more compute and memory — we used a 37-layer (12 block) Transformer architecture, and we train on sequences of up to 512 tokens. Most experiments were conducted on 4 and 8 GPU systems. The model does fine-tune to new tasks very quickly which helps mitigate the additional resource requirements.
> - **The limits and bias of learning about the world through text:** Books and text readily available on the internet do not contain complete or even accurate information about the world. Recent work ([Lucy and Gauthier, 2017](https://arxiv.org/abs/1705.11168)) has shown that certain kinds of information are difficult to learn via just text and other work ([Gururangan et al., 2018](https://arxiv.org/abs/1803.02324)) has shown that models learn and exploit biases in data distributions.
> - **Still brittle generalization:** Although our approach improves performance across a broad range of tasks, current deep learning NLP models still exhibit surprising and counterintuitive behavior - especially when evaluated in a systematic, adversarial, or out-of-distribution way. Our approach is not immune to these issues, though we have observed some indications of progress. Our approach shows improved lexical robustness over previous purely neural approaches to textual entailment. On the dataset introduced in Glockner et al. (2018) our model achieves 83.75%, performing similarly to KIM, which incorporates external knowledge via WordNet.
## Training
#### Training Data
The model developers [write](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf):
> We use the BooksCorpus dataset ([Zhu et al., 2015](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Zhu_Aligning_Books_and_ICCV_2015_paper.pdf)) for training the language model. It contains over 7,000 unique unpublished books from a variety of genres including Adventure, Fantasy, and Romance. Crucially, it contains long stretches of contiguous text, which allows the generative model to learn to condition on long-range information.
#### Training Procedure
The model developers [write](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf):
> Our model largely follows the original transformer work [62]. We trained a 12-layer decoder-only transformer with masked self-attention heads (768 dimensional states and 12 attention heads). For the position-wise feed-forward networks, we used 3072 dimensional inner states. We used the Adam optimization scheme [27] with a max learning rate of 2.5e-4. The learning rate was increased linearly from zero over the first 2000 updates and annealed to 0 using a cosine schedule. We train for 100 epochs on minibatches of 64 randomly sampled, contiguous sequences of 512 tokens. Since layernorm [2] is used extensively throughout the model, a simple weight initialization of N (0, 0.02) was sufficient. We used a bytepair encoding (BPE) vocabulary with 40,000 merges [53] and residual, embedding, and attention dropouts with a rate of 0.1 for regularization. We also employed a modified version of L2 regularization proposed in [37], with w = 0.01 on all non bias or gain weights. For the activation function, we used the Gaussian Error Linear Unit (GELU) [18]. We used learned position embeddings instead of the sinusoidal version proposed in the original work. We use the ftfy library2 to clean the raw text in BooksCorpus, standardize some punctuation and whitespace, and use the spaCy tokenizer.
See the paper for further details and links to citations.
## Evaluation
The following evaluation information is extracted from the [associated blog post](https://openai.com/blog/language-unsupervised/). See the [associated paper](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) for further details.
#### Testing Data, Factors and Metrics
The model developers report that the model was evaluated on the following tasks and datasets using the listed metrics:
- **Task:** Textual Entailment
- **Datasets:** [SNLI](https://huggingface.co/datasets/snli), [MNLI Matched](https://huggingface.co/datasets/glue), [MNLI Mismatched](https://huggingface.co/datasets/glue), [SciTail](https://huggingface.co/datasets/scitail), [QNLI](https://huggingface.co/datasets/glue), [RTE](https://huggingface.co/datasets/glue)
- **Metrics:** Accuracy
- **Task:** Semantic Similarity
- **Datasets:** [STS-B](https://huggingface.co/datasets/glue), [QQP](https://huggingface.co/datasets/glue), [MRPC](https://huggingface.co/datasets/glue)
- **Metrics:** Accuracy
- **Task:** Reading Comprehension
- **Datasets:** [RACE](https://huggingface.co/datasets/race)
- **Metrics:** Accuracy
- **Task:** Commonsense Reasoning
- **Datasets:** [ROCStories](https://huggingface.co/datasets/story_cloze), [COPA](https://huggingface.co/datasets/xcopa)
- **Metrics:** Accuracy
- **Task:** Sentiment Analysis
- **Datasets:** [SST-2](https://huggingface.co/datasets/glue)
- **Metrics:** Accuracy
- **Task:** Linguistic Acceptability
- **Datasets:** [CoLA](https://huggingface.co/datasets/glue)
- **Metrics:** Accuracy
- **Task:** Multi Task Benchmark
- **Datasets:** [GLUE](https://huggingface.co/datasets/glue)
- **Metrics:** Accuracy
#### Results
The model achieves the following results without any fine-tuning (zero-shot):
| Task | TE | TE | TE |TE | TE | TE | SS | SS | SS | RC | CR | CR | SA | LA | MTB |
|:--------:|:--:|:----------:|:-------------:|:-----:|:----:|:---:|:---:|:---:|:--:|:----:|:--------:|:----:|:----:|:----:|:----:|
| Dataset |SNLI|MNLI Matched|MNLI Mismatched|SciTail| QNLI | RTE |STS-B| QQP |MPRC|RACE |ROCStories|COPA | SST-2| CoLA | GLUE |
| |89.9| 82.1 | 81.4 |88.3 | 88.1 | 56.0|82.0 | 70.3|82.3|59.0 | 86.5 | 78.6 | 91.3 | 45.4 | 72.8 |
## Environmental Impact
The model developers [report that](https://openai.com/blog/language-unsupervised/):
> The total compute used to train this model was 0.96 petaflop days (pfs-days).
> 8 P600 GPU's * 30 days * 12 TFLOPS/GPU * 0.33 utilization = .96 pfs-days
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** 8 P600 GPUs
- **Hours used:** 720 hours (30 days)
- **Cloud Provider:** Unknown
- **Compute Region:** Unknown
- **Carbon Emitted:** Unknown
## Technical Specifications
See the [associated paper](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) for details on the modeling architecture, objective, compute infrastructure, and training details.
## Citation Information
```bibtex
@article{radford2018improving,
title={Improving language understanding by generative pre-training},
author={Radford, Alec and Narasimhan, Karthik and Salimans, Tim and Sutskever, Ilya and others},
year={2018},
publisher={OpenAI}
}
```
APA:
*Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training.*
## Model Card Authors
This model card was written by the Hugging Face team. |
CuongLD/wav2vec2-large-xlsr-vietnamese | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"vi",
"dataset:common_voice, infore_25h",
"arxiv:2006.11477",
"arxiv:2006.13979",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
]
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} | 8 | null | https://wandb.ai/krohak/huggingface
https://huggingface.co/nandinib1999/quote-generator
https://github.com/nandinib1999/gpt2_quotes_generation/ |
CurtisASmith/GPT-JRT | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: BERiT_7000
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERiT_7000
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: 7.5916
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 7.9484 | 0.19 | 500 | 7.8474 |
| 7.7968 | 0.39 | 1000 | 7.7020 |
| 7.6992 | 0.58 | 1500 | 7.6949 |
| 7.656 | 0.77 | 2000 | 7.6922 |
| 7.68 | 0.97 | 2500 | 7.6863 |
| 7.5952 | 1.16 | 3000 | 7.6523 |
| 7.6441 | 1.36 | 3500 | 7.6523 |
| 7.6178 | 1.55 | 4000 | 7.6128 |
| 7.5977 | 1.74 | 4500 | 7.6556 |
| 7.6087 | 1.94 | 5000 | 7.5990 |
| 7.5734 | 2.13 | 5500 | 7.5997 |
| 7.566 | 2.32 | 6000 | 7.5961 |
| 7.5715 | 2.52 | 6500 | 7.5505 |
| 7.5604 | 2.71 | 7000 | 7.5788 |
| 7.5749 | 2.9 | 7500 | 7.5916 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
CurtisBowser/DialoGPT-medium-sora-three | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8648740833380706
---
<!-- 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: 0.1365
- F1: 0.8649
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2553 | 1.0 | 525 | 0.1575 | 0.8279 |
| 0.1284 | 2.0 | 1050 | 0.1386 | 0.8463 |
| 0.0813 | 3.0 | 1575 | 0.1365 | 0.8649 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
CurtisBowser/DialoGPT-small-sora | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-mlm-feedback-2021
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-mlm-feedback-2021
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:
- Loss: 1.4127
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.9789 | 1.0 | 1325 | 1.5678 |
| 1.8014 | 2.0 | 2650 | 1.6157 |
| 1.7468 | 3.0 | 3975 | 1.4918 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
D3vil/DialoGPT-smaall-harrypotter | []
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} | 0 | null | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
inference: false
extra_gated_prompt: |-
One more step before getting this model.
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. CompVis claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
Please read the full license here: https://huggingface.co/spaces/CompVis/stable-diffusion-license
By clicking on "Access repository" below, you accept that your *contact information* (email address and username) can be shared with the model authors as well:
extra_gated_fields:
I have read the License and agree with its terms: checkbox
---
# Stable Diffusion v1-1 Model Card
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion with D🧨iffusers blog](https://huggingface.co/blog/stable_diffusion).
The **Stable-Diffusion-v1-1** was trained on 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en), followed by
194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). For more information, please refer to [Training](#training).
This weights here are intended to be used with the D🧨iffusers library. If you are looking for the weights to be loaded into the CompVis Stable Diffusion codebase, [come here](https://huggingface.co/CompVis/stable-diffusion-v-1-1-original)
## Model Details
- **Developed by:** Robin Rombach, Patrick Esser
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based.
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487).
- **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752).
- **Cite as:**
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
## Examples
We recommend using [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion.
```bash
pip install --upgrade diffusers transformers scipy
```
Run this command to log in with your HF Hub token if you haven't before:
```bash
huggingface-cli login
```
Running the pipeline with the default PNDM scheduler:
```python
import torch
from torch import autocast
from diffusers import StableDiffusionPipeline
model_id = "CompVis/stable-diffusion-v1-1"
device = "cuda"
pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=True)
pipe = pipe.to(device)
prompt = "a photo of an astronaut riding a horse on mars"
with autocast("cuda"):
image = pipe(prompt, guidance_scale=7.5)["sample"][0]
image.save("astronaut_rides_horse.png")
```
**Note**:
If you are limited by GPU memory and have less than 10GB of GPU RAM available, please make sure to load the StableDiffusionPipeline in float16 precision instead of the default float32 precision as done above. You can do so by telling diffusers to expect the weights to be in float16 precision:
```py
import torch
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16", use_auth_token=True)
pipe = pipe.to(device)
prompt = "a photo of an astronaut riding a horse on mars"
with autocast("cuda"):
image = pipe(prompt, guidance_scale=7.5)["sample"][0]
image.save("astronaut_rides_horse.png")
```
To swap out the noise scheduler, pass it to `from_pretrained`:
```python
from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler
model_id = "CompVis/stable-diffusion-v1-1"
# Use the K-LMS scheduler here instead
scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)
pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, use_auth_token=True)
pipe = pipe.to("cuda")
prompt = "a photo of an astronaut riding a horse on mars"
with autocast("cuda"):
image = pipe(prompt, guidance_scale=7.5)["sample"][0]
image.save("astronaut_rides_horse.png")
```
# Uses
## Direct Use
The model is intended for research purposes only. Possible research areas and
tasks include
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_.
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
- Faces and people in general may not be generated properly.
- The model was trained mainly with English captions and will not work as well in other languages.
- The autoencoding part of the model is lossy
- The model was trained on a large-scale dataset
[LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material
and is not fit for product use without additional safety mechanisms and
considerations.
- No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data.
The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images.
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are primarily limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
## Training
### Training Data
The model developers used the following dataset for training the model:
- LAION-2B (en) and subsets thereof (see next section)
### Training Procedure
Stable Diffusion v1-4 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,
- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
- Text prompts are encoded through a ViT-L/14 text-encoder.
- The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet.
We currently provide four checkpoints, which were trained as follows.
- [`stable-diffusion-v1-1`](https://huggingface.co/CompVis/stable-diffusion-v1-1): 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`).
- [`stable-diffusion-v1-2`](https://huggingface.co/CompVis/stable-diffusion-v1-2): Resumed from `stable-diffusion-v1-1`.
515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en,
filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)).
- [`stable-diffusion-v1-3`](https://huggingface.co/CompVis/stable-diffusion-v1-3): Resumed from `stable-diffusion-v1-2`. 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- [**`stable-diffusion-v1-4`**](https://huggingface.co/CompVis/stable-diffusion-v1-4) Resumed from `stable-diffusion-v1-2`.225,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
### Training details
- **Hardware:** 32 x 8 x A100 GPUs
- **Optimizer:** AdamW
- **Gradient Accumulations**: 2
- **Batch:** 32 x 8 x 2 x 4 = 2048
- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
## Evaluation Results
Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling
steps show the relative improvements of the checkpoints:
Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores.
## Environmental Impact
**Stable Diffusion v1** **Estimated Emissions**
Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
- **Hardware Type:** A100 PCIe 40GB
- **Hours used:** 150000
- **Cloud Provider:** AWS
- **Compute Region:** US-east
- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq.
## Citation
```bibtex
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
```
*This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).* |
D3xter1922/distilbert-base-uncased-finetuned-cola | []
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} | 0 | null | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1579688050300436480/Ou3iqmdl_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">carsonogenic</div>
<div style="text-align: center; font-size: 14px;">@bong_iverr</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from carsonogenic.
| Data | carsonogenic |
| --- | --- |
| Tweets downloaded | 726 |
| Retweets | 59 |
| Short tweets | 42 |
| Tweets kept | 625 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3oyq7g4j/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @bong_iverr's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1jsj4h3w) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1jsj4h3w/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/bong_iverr')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
D3xter1922/electra-base-discriminator-finetuned-cola | [
"pytorch",
"tensorboard",
"electra",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
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} | 68 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad-ver5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad-ver5
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4776
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5572 | 1.0 | 554 | 1.5588 |
| 1.2784 | 2.0 | 1108 | 1.4776 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
DARKVIP3R/DialoGPT-medium-Anakin | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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} | 13 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: BERiT_4500
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERiT_4500
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: 7.2982
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 7.5996 | 0.19 | 500 | 7.4930 |
| 7.4322 | 0.39 | 1000 | 7.4460 |
| 7.3767 | 0.58 | 1500 | 7.3877 |
| 7.3711 | 0.77 | 2000 | 7.3511 |
| 7.3511 | 0.97 | 2500 | 7.3300 |
| 7.2984 | 1.16 | 3000 | 7.3526 |
| 7.3129 | 1.36 | 3500 | 7.3245 |
| 7.3235 | 1.55 | 4000 | 7.3333 |
| 7.2908 | 1.74 | 4500 | 7.2968 |
| 7.3262 | 1.94 | 5000 | 7.3058 |
| 7.3074 | 2.13 | 5500 | 7.3084 |
| 7.2701 | 2.32 | 6000 | 7.3020 |
| 7.2498 | 2.52 | 6500 | 7.2913 |
| 7.274 | 2.71 | 7000 | 7.2997 |
| 7.2593 | 2.9 | 7500 | 7.2982 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
DCU-NLP/bert-base-irish-cased-v1 | [
"pytorch",
"tf",
"bert",
"fill-mask",
"transformers",
"generated_from_keras_callback",
"autotrain_compatible"
]
| fill-mask | {
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} | 1,244 | null | ---
language:
- "en"
tags:
- stable_diffusion
- Pokemon
- Eevee
- Umbreon
- Glaceon
- Vaporeon
- Espeon
- Jolteon
- Flareon
- Leafeon
- Eeveelutions
---
This model must be used with Stable Diffusion
This is version 1 of my model to generate Eeveelutions
Model download: https://huggingface.co/vgaggia/Eeveelutions/blob/main/Eeveelutionsv1-0.ckpt |
DCU-NLP/electra-base-irish-cased-discriminator-v1 | [
"pytorch",
"electra",
"pretraining",
"ga",
"transformers",
"irish",
"license:apache-2.0"
]
| null | {
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"ElectraForPreTraining"
],
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}
}
} | 4 | null | ---
tags:
- generated_from_trainer
model-index:
- name: clip-roberta-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# clip-roberta-finetuned
This model is a fine-tuned version of [./clip-roberta](https://huggingface.co/./clip-roberta) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 48
- eval_batch_size: 48
- 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 4.25.0.dev0
- Pytorch 1.10.2
- Datasets 2.6.1
- Tokenizers 0.12.1
|
DCU-NLP/electra-base-irish-cased-generator-v1 | [
"pytorch",
"electra",
"fill-mask",
"ga",
"transformers",
"irish",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
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}
} | 7 | null | ---
license: mit
---
# NB-BERT fine-tuned on IMDB
## Description
This model is based on the pre-trained [NB-BERT-large model](https://huggingface.co/NbAiLab/nb-bert-large?text=P%C3%A5+biblioteket+kan+du+l%C3%A5ne+en+%5BMASK%5D.). It is a model for sentiment analysis. The idea behind this model was to check if a language model mostly pretrained on norwegian (with approximately 4% english) could learn a down stream Norwegian tasks when only seing English examples during fine-tuning.
## Data for fine-tuning
This model was fine-tuned on 1000 examples from the [IMDB train dataset](https://huggingface.co/datasets/imdb) that belonged to the screen category. The training lasted 3 epochs with a learning rate of 5e-5. The code used to create this model (and some additional models) can be found on [Github](https://github.com/Karolill/NB-BERT-fine-tuned-on-english). |
DHBaek/gpt2-stackoverflow-question-contents-generator | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
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}
} | 14 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/bradsprigg/1668030722213/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1468456063775117312/6LimXaG6_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Elon's Musk (stinky boy)</div>
<div style="text-align: center; font-size: 14px;">@bradsprigg</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Elon's Musk (stinky boy).
| Data | Elon's Musk (stinky boy) |
| --- | --- |
| Tweets downloaded | 3224 |
| Retweets | 657 |
| Short tweets | 239 |
| Tweets kept | 2328 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2kr31b63/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @bradsprigg's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3uyo0305) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3uyo0305/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/bradsprigg')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
DHBaek/xlm-roberta-large-korquad-mask | [
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"XLMRobertaForQuestionAnswering"
],
"model_type": "xlm-roberta",
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}
}
} | 9 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: BERiT_2000_enriched
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERiT_2000_enriched
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: 6.6052
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.786 | 0.19 | 500 | 6.6797 |
| 6.6441 | 0.39 | 1000 | 6.6574 |
| 6.6376 | 0.58 | 1500 | 6.6240 |
| 6.5951 | 0.77 | 2000 | 6.6291 |
| 6.6123 | 0.97 | 2500 | 6.6355 |
| 6.6028 | 1.16 | 3000 | 6.6084 |
| 6.5974 | 1.36 | 3500 | 6.5984 |
| 6.6104 | 1.55 | 4000 | 6.5775 |
| 6.6113 | 1.74 | 4500 | 6.6062 |
| 6.5895 | 1.94 | 5000 | 6.5931 |
| 6.6106 | 2.13 | 5500 | 6.6276 |
| 6.635 | 2.32 | 6000 | 6.5973 |
| 6.5694 | 2.52 | 6500 | 6.6021 |
| 6.612 | 2.71 | 7000 | 6.5882 |
| 6.5984 | 2.9 | 7500 | 6.6052 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
DJSammy/bert-base-danish-uncased_BotXO-ai | [
"pytorch",
"jax",
"da",
"dataset:common_crawl",
"dataset:wikipedia",
"transformers",
"bert",
"masked-lm",
"license:cc-by-4.0",
"fill-mask"
]
| fill-mask | {
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} | 14 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/wyld/1668032276555/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1547347036927696896/7JYzatqo_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Wyld</div>
<div style="text-align: center; font-size: 14px;">@wyld</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Wyld.
| Data | Wyld |
| --- | --- |
| Tweets downloaded | 3239 |
| Retweets | 601 |
| Short tweets | 574 |
| Tweets kept | 2064 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1fod497b/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @wyld's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/lk8zcqu3) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/lk8zcqu3/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/wyld')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
DJSammy/bert-base-swedish-uncased_BotXO-ai | [
"pytorch",
"transformers"
]
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} | 1 | null | ---
license: mit
---
### DevonM on Stable Diffusion
This is the `<DevonM>` 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`:
|
DKpro000/DialoGPT-medium-harrypotter | []
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} | 0 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 3214 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 3214,
"warmup_steps": 322,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
DSI/TweetBasedSA | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
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}
} | 29 | null | ---
tags:
- conversational
---
# Dory DialoGPT Model |
DSI/ar_emotion_6 | [
"pytorch",
"bert",
"transformers"
]
| null | {
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"BertForMultiLabelSequenceClassification"
],
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} | 1 | null | ---
language:
- en
license: creativeml-openrail-m
thumbnail: "https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/showcase.jpg"
tags:
- stable-diffusion
- text-to-image
- image-to-image
library_name: "EveryDream"
inference: false
---
# Intro
This is a collection of models related to the "Picture of the Week" contest on Stable Diffusion discord.
I try to make a model out of all the submission for people to continue enjoy the theme after the even, and see a little of their designs in other people's creations. The token stays "PoW Style" and I balance the learning on the low side, so that it doesn't just replicate creations.
I also make smaller quality models to help make pictures for the contest itself, based on the theme.
# 29 novembre 2022, "The Stable Kitchen"
## Theme : Burgers and Fries
Welcome to the VERY FIRST edition of the most Stable Kitchen in the universe!
On today’s menu will be Sandwiches & Frie. Since you’re here for the first time, I will explain how it works! You can generate your orders and we will make them for you. Take a seat, flip through the menu, bring all of your favorite ingredients~
* The sandwich with the most cheddar? 5 beef burgers? An infinite fries generator?
* Serve us your best sandwich and fries combo!
Not even the sky's the limit my friend,
You want it?
You have it!
As long as it's delicious, of course!
We’ll see you on the chopping block for this week’s Stable Kitchen!
## Models
### Burgy
* Burgers, burgers burgers
* training: 40 pictures, 6 epochs of 40 repeats, batch size 6, LR1e-6, EveryDream
* balance : Strong, burgers
* **Activation token :** `Burgy`
* [CKPT](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/291122/ckpts/Burgy.ckpt)
* [Dataset](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/291122/dataset_Burgy.zip)
# 22 novembre 2022, "Imaginary Friend"
## Theme : Imaginary Friend
Do you remember putting your hands into what seemed as if it were just plain air and giggling like a child? Having conversations with someone who “wasn’t there”? Nowadays the term “Imaginary Friend” isn’t as frequently used as it used to be, right? Let’s bring it back.
* Can you build your Imaginary Friends actualized?
* What traits do you recall of them? Are they still young? Have they grown up now? Do they resemble you, or a creature that isn’t human?
* Where would you find this Imaginary Friend? Where do they reside? What do they stand for?
Our prompt for this event was created by @Andrekerygma
"a boy drinking tea with a cute monster on the bedroom, disney infinity character design, pixar, artstation, vinyl, toy, figurine, 3 d model, cinema 4 d, substance 3 d painter, vray, unreal engine 5, octane render, cinematic"
## Models
### PoW ArtStyle 22-11-22
* based on all the submissions to the PoW
* training: 73 pictures, 6000 steps on batch 6, 1e-6 polynomial LR.
* balance : a little lighter on the style than last week, still manages to reproduce most participants
* **Activation token :** `PoW ArtStyle`
* Other noticable tokens : Your Discord username, if you participated. Also TMNT,NikeAir Shoes and Sid, Ice Age movie
* [CKPT](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/221122/ckpts/PoWArtStyle_ImaginaryFriend.ckpt)
* [Dataset](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/221122/PoW_221122_dataset.zip)
### CharacterChan Style
* based on the "Character" dreamer community of the Stable Diffusion Discord
* training: 50 pictures, 160 total repeat, LR1e-6
* balance : correct, but some sub concepts have overtrain a little, like the clown.
* **Activation token :** `CharacterChan Style`
* [CKPT](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/ckpt/CharacterChanStyle-v1.ckpt)
* [Dataset](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/datasets/CharacterChanStyle-v1.zip)
* [Model page](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection#characterchan-style)
### CreatureChan Style
* based on the "Creature" dreamer community of the Stable Diffusion Discord
* training: 50 pictures, 160 total repeat, LR1e-6
* balance : good
* **Activation token :** `CreatureChan Style`
* [CKPT](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/ckpt/CreatureChanStyle-v1.ckpt)
* [Dataset](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/datasets/CreatureChanStyle-v1.zip)
* [Model page](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection#creaturechan-style)
# 14 novembre 2022, "The Never-Ending Loop"
## Theme : The Never-Ending Loop
It is a passed-down proverb that lines represent the flow of time itself. They converge and take shape. They twist, tangle, sometimes unravel, break, and then connect again.
* Without words, how are we able to accurately represent this flow of time with only lines? geometrically, intricately, asymmetricaly, seamlessly, ornately...
* Think of a never-ending pattern, texture, or shape– looping on and on for what feels infinite.
* Just how detailed are you able to get with your patterns?
Our prompt for this event was created by @Asukii !
"the fractal flow of time stretches towards the horizon, surreal fractal intertwined looping pathways, dramatic cinematic perspective, detailed delicate intricate ornate linework, geometric abstract masterwork digital art, quantum wavetracing, ink drawing, optical illusion"
## Models
### PoW Style 14-11-22
* based on all the submissions to the PoW
* training: 101 pictures, 9000 steps on batch 6, 1e-6 polynomial LR.
* balance : a little strong on the style but it made it possible to differentiate each participants
* **Activation token :** `PoW Style`
* Other noticable tokens : Your Discord username, if you participated. Also Rick Roll and "fullbody shot"
* [CKPT](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/141122/ckpts/PoWStyle_NeverEndingLoop.ckpt)
* [Diffusers : Guizmus/SD_PoW_Collection/141122/diffusers](https://huggingface.co/Guizmus/SD_PoW_Collection/tree/main/141122/diffusers/)
* [Dataset](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/141122/PoW_141122_2_dataset.zip)
### Fractime Style
* based on the suggested prompt and theme
* training: 50 pictures, 1750 steps on batch 6, 1e-6 polynomial LR.
* balance : correct, but the style doesn't apply to every subject
* **Activation token :** `Fractime Style`
* Other noticable tokens : intricate, nebula, illusion, person, road, tree, boat
* [CKPT](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/141122/ckpts/FractimeStyle.ckpt)
* [Dataset](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/141122/PoW_141122_1_dataset.zip)
# 09 novembre 2022, "Abstralities"
## Theme : Abstract Realities
Glitch, warp, static, shape, flicker, break, bend, mend
Have you ever felt your reality shift out from under your feet? Our perception falters and repairs itself in the blink of an eye. Just how much do our brains influence what we perceive? How much control do we have over molding these realities?
With the introduction of AI and its rapid pace taking the world by storm, we are seeing single-handedly just how these realities can bring worlds into fruition.
* Can you show us your altered reality?
* Are these realities truly broken, or only bent?
Our example prompt for this event was created by @Aether !
"household objects floating in space, bedroom, furniture, home living, warped reality, cosmic horror, nightmare, retrofuturism, surrealism, abstract, illustrations by alan nasmith"
## Models
### PoW Style 09-11-22
* Main model based on all the results from the PoW
* training: 51 pictures, 3000 steps on 1e-6 polynomial LR.
* balanced on the light side, add attention/weight on the activation token
* **Activation token :** `PoW Style`
* [CKPT](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/091122/ckpts/PoWStyle_Abstralities.ckpt)
* [Diffusers : Guizmus/SD_PoW_Collection/091122/diffusers](https://huggingface.co/Guizmus/SD_PoW_Collection/tree/main/091122/diffusers/)
* [Dataset](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/091122/dataset.zip)
### Bendstract Style
* based on the suggested prompt
* training: 100 pictures, 7500 steps on 1e-6 polynomial LR. overtrained
* **Activation token :** `Bendstract Style`
* [CKPT](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/091122/ckpts/Bendstract-v1.ckpt)
### endingReality Style
* based on the suggested prompt
* training: 68 pictures, 6000 steps on 1e-6 polynomial LR. overtrained
* **Activation token :** `BendingReality Style`
* [CKPT](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/091122/ckpts/BendingReality_Style-v1.ckpt)
### PoW Style mid-submissions 09-11-22
* based on the first few submissions
* training: 24 pictures, 2400 steps on 1e-6 polynomial LR. a little too trained
* **Activation token :** `PoW Style`
* [CKPT](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/091122/ckpts/PoWStyle_midrun.ckpt)
# License
These models are open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) |
DSI/human-directed-sentiment | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
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"BertForSequenceClassification"
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} | 26 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.88
- name: F1
type: f1
value: 0.880794701986755
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3118
- Accuracy: 0.88
- F1: 0.8808
## 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
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
DTAI-KULeuven/mbert-corona-tweets-belgium-curfew-support | [
"pytorch",
"jax",
"bert",
"text-classification",
"multilingual",
"nl",
"fr",
"en",
"arxiv:2104.09947",
"transformers",
"Tweets",
"Sentiment analysis"
]
| text-classification | {
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"BertForSequenceClassification"
],
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}
} | 29 | null | ---
license: bigscience-bloom-rail-1.0
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zhs
- zht
- zu
pipeline_tag: text-generation
---
<h1 style='text-align: center '>BLOOM LM</h1>
<h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2>
<h3 style='text-align: center '>Model Card</h3>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" alt="BigScience Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
Version 1.0 / 26.May.2022
## Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Training Data](#training-data)
4. [Risks and Limitations](#risks-and-limitations)
5. [Evaluation](#evaluation)
6. [Recommendations](#recommendations)
7. [Glossary and Calculations](#glossary-and-calculations)
8. [More Information](#more-information)
9. [Model Card Authors](#model-card-authors)
## Model Details
### Basics
*This section provides information for anyone who wants to know about the model.*
<details>
<summary>Click to expand</summary> <br/>
**Developed by:** BigScience ([website](https://bigscience.huggingface.co))
* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*
**Model Type:** Transformer-based Language Model
**Version:** 1.0.0
**Languages:** Multiple; see [training data](#training-data)
**License:** RAIL License v1.0 ([link](https://huggingface.co/spaces/bigscience/license))
**Release Date Estimate:** Monday, 11.July.2022
**Send Questions to:** [email protected]
**Cite as:** BigScience, _BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model_. International, May 2021-May 2022
**Funded by:**
* The French government.
* Hugging Face ([website](https://huggingface.co)).
* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*
</details>
### Technical Specifications
*This section provides information for people who work on model development.*
<details>
<summary>Click to expand</summary><br/>
Please see [the BLOOM training README](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#readme) for full details on replicating training.
**Model Architecture:** Modified from Megatron-LM GPT2 (see [paper](https://arxiv.org/abs/1909.08053), [BLOOM Megatron code](https://github.com/bigscience-workshop/Megatron-DeepSpeed)):
* Decoder-only architecture
* Layer normalization applied to word embeddings layer (`StableEmbedding`; see [code](https://github.com/facebookresearch/bitsandbytes), [paper](https://arxiv.org/pdf/2110.02861.pdf))
* ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions
* 559,214,592 parameters:
* 256,901,120 embedding parameters
* 24 layers, 16 attention heads
* Hidden layers are 1024-dimensional
* Sequence length of 2048 tokens (see [BLOOM tokenizer](https://huggingface.co/bigscience/tokenizer), [tokenizer description](#tokenization))
**Objective Function:** Cross Entropy with mean reduction (see [API documentation](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss)).
**Compute infrastructure:** Jean Zay Public Supercomputer, provided by the French government (see [announcement](https://www.enseignementsup-recherche.gouv.fr/fr/signature-du-marche-d-acquisition-de-l-un-des-supercalculateurs-les-plus-puissants-d-europe-46733)).
* Hardware: 384 A100 80GB GPUs (48 nodes):
* Additional 32 A100 80GB GPUs (4 nodes) in reserve
* 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links
* CPU: AMD
* CPU memory: 512GB per node
* GPU memory: 640GB per node
* Inter-node connect: Omni-Path Architecture (OPA)
* NCCL-communications network: a fully dedicated subnet
* Disc IO network: shared network with other types of nodes
* Software:
* Megatron-DeepSpeed ([Github link](https://github.com/bigscience-workshop/Megatron-DeepSpeed))
* DeepSpeed ([Github link](https://github.com/microsoft/DeepSpeed))
* PyTorch (pytorch-1.11 w/ CUDA-11.5; see [Github link](https://github.com/pytorch/pytorch))
* apex ([Github link](https://github.com/NVIDIA/apex))
#### **Training**
Training logs: [Tensorboard link](https://huggingface.co/bigscience/tr11e-350M-logs)
- Training throughput: About 150 TFLOPs per GPU
- Number of epochs: 1 (*current target*)
- Dates:
- Started 11th March, 2022 11:42am PST
- Ended 5th July, 2022
- Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments and other model sizes)
- Server training location: Île-de-France, France
#### **Tokenization**
The BLOOM tokenizer ([link](https://huggingface.co/bigscience/tokenizer)) is a learned subword tokenizer trained using:
- A byte-level Byte Pair Encoding (BPE) algorithm
- A simple pre-tokenization rule, no normalization
- A vocabulary size of 250,680
It was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.
</details>
### Environmental Impact
<details>
<summary>Click to expand</summary><br/>
The training supercomputer, Jean Zay ([website](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html)), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.
**Estimated carbon emissions:** *(Forthcoming upon completion of training.)*
**Estimated electricity usage:** *(Forthcoming upon completion of training.)*
</details>
<p> </p>
## Uses
*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.
It provides information for anyone considering using the model or who is affected by the model.*
<details>
<summary>Click to expand</summary><br/>
### Intended Use
This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.
#### **Direct Use**
- Text generation
- Exploring characteristics of language generated by a language model
- Examples: Cloze tests, counterfactuals, generations with reframings
#### **Downstream Use**
- Tasks that leverage language models include: Information Extraction, Question Answering, Summarization
### Misuse and Out-of-scope Use
*This section addresses what users ought not do with the model.*
See the [BLOOM License](https://huggingface.co/spaces/bigscience/license), Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.
#### **Out-of-scope Uses**
Using the model in [high-stakes](#high-stakes) settings is out of scope for this model. The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.
##### Out-of-scope Uses Include:
- Usage in biomedical domains, political and legal domains, or finance domains
- Usage for evaluating or scoring individuals, such as for employment, education, or credit
- Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct
#### **Misuse**
Intentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes:
- Spam generation
- Disinformation and influence operations
- Disparagement and defamation
- Harassment and abuse
- [Deception](#deception)
- Unconsented impersonation and imitation
- Unconsented surveillance
- Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license)
### Intended Users
#### **Direct Users**
- General Public
- Researchers
- Students
- Educators
- Engineers/developers
- Non-commercial entities
- Community advocates, including human and civil rights groups
#### Indirect Users
- Users of derivatives created by Direct Users, such as those using software with an [intended use](#intended-use)
- Users of [Derivatives of the Model, as described in the License](https://huggingface.co/spaces/bigscience/license)
#### Others Affected (Parties Prenantes)
- People and groups referred to by the LLM
- People and groups exposed to outputs of, or decisions based on, the LLM
- People and groups whose original work is included in the LLM
</details>
<p> </p>
## Training Data
*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*
<details>
<summary>Click to expand</summary><br/>
Details for each dataset are provided in individual [Data Cards](https://huggingface.co/spaces/bigscience/BigScienceCorpus).
Training data includes:
- 45 natural languages
- 12 programming languages
- In 1.5TB of pre-processed text, converted into 350B unique tokens (see [the tokenizer section](#tokenization) for more.)
#### **Languages**
The pie chart shows the distribution of languages in training data.
The following table shows the further distribution of Niger-Congo and Indic languages in the training data.
<details>
<summary>Click to expand</summary><br/>
| Niger Congo | Percentage | | Indic | Percentage |
|----------------|------------ |------ |-----------|------------|
| Chi Tumbuka | 0.00002 | | Assamese | 0.01 |
| Kikuyu | 0.00004 | | Odia | 0.04 |
| Bambara | 0.00004 | | Gujarati | 0.04 |
| Akan | 0.00007 | | Marathi | 0.05 |
| Xitsonga | 0.00007 | | Punjabi | 0.05 |
| Sesotho | 0.00007 | | Kannada | 0.06 |
| Chi Chewa | 0.0001 | | Nepali | 0.07 |
| Setswana | 0.0002 | | Telugu | 0.09 |
| Northern Sotho | 0.0002 | | Malayalam | 0.10 |
| Fon | 0.0002 | | Urdu | 0.10 |
| Kirundi | 0.0003 | | Tamil | 0.20 |
| Wolof | 0.0004 | | Bengali | 0.50 |
| Kuganda | 0.0004 | | Hindi | 0.70 |
| Chi Shona | 0.001 |
| Isi Zulu | 0.001 |
| Igbo | 0.001 |
| Xhosa | 0.001 |
| Kinyarwanda | 0.003 |
| Yoruba | 0.006 |
| Swahili | 0.02 |
</details>
The following table shows the distribution of programming languages.
<details>
<summary>Click to expand</summary><br/>
| Extension | Language | Number of files |
|----------------|------------|-----------------|
| java | Java | 5,407,724 |
| php | PHP | 4,942,186 |
| cpp | C++ | 2,503,930 |
| py | Python | 2,435,072 |
| js | JavaScript | 1,905,518 |
| cs | C# | 1,577,347 |
| rb | Ruby | 6,78,413 |
| cc | C++ | 443,054 |
| hpp | C++ | 391,048 |
| lua | Lua | 352,317 |
| go | GO | 227,763 |
| ts | TypeScript | 195,254 |
| C | C | 134,537 |
| scala | Scala | 92,052 |
| hh | C++ | 67,161 |
| H | C++ | 55,899 |
| tsx | TypeScript | 33,107 |
| rs | Rust | 29,693 |
| phpt | PHP | 9,702 |
| c++ | C++ | 1,342 |
| h++ | C++ | 791 |
| php3 | PHP | 540 |
| phps | PHP | 270 |
| php5 | PHP | 166 |
| php4 | PHP | 29 |
</details>
</details>
<p> </p>
## Risks and Limitations
*This section identifies foreseeable harms and misunderstandings.*
<details>
<summary>Click to expand</summary><br/>
Model may:
- Overrepresent some viewpoints and underrepresent others
- Contain stereotypes
- Contain [personal information](#personal-data-and-information)
- Generate:
- Hateful, abusive, or violent language
- Discriminatory or prejudicial language
- Content that may not be appropriate for all settings, including sexual content
- Make errors, including producing incorrect information as if it were factual
- Generate irrelevant or repetitive outputs
</details>
<p> </p>
## Evaluation
*This section describes the evaluation protocols and provides the results.*
<details>
<summary>Click to expand</summary><br/>
### Metrics
*This section describes the different ways performance is calculated and why.*
Includes:
| Metric | Why chosen |
|--------------------|--------------------------------------------------------------------|
| [Perplexity](#perplexity) | Standard metric for quantifying model improvements during training |
| Cross Entropy [Loss](#loss) | Standard objective for language models. |
And multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_
### Factors
*This section lists some different aspects of what BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*
- Language, such as English or Yoruba
- Domain, such as newswire or stories
- Demographic characteristics, such as gender or nationality
### Results
*Results are based on the [Factors](#factors) and [Metrics](#metrics).*
**Train-time Evaluation:**
As of 25.May.2022, 15:00 PST:
- Training Loss: 2.0
- Validation Loss: 2.2
- Perplexity: 8.9
(More evaluation scores forthcoming at the end of model training.)
</details>
<p> </p>
## Recommendations
*This section provides information on warnings and potential mitigations.*
<details>
<summary>Click to expand</summary><br/>
- Indirect users should be made aware when the content they're working with is created by the LLM.
- Users should be aware of [Risks and Limitations](#risks-and-limitations), and include an appropriate age disclaimer or blocking interface as necessary.
- Models pretrained with the LLM should include an updated Model Card.
- Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.
</details>
<p> </p>
## Glossary and Calculations
*This section defines common terms and how metrics are calculated.*
<details>
<summary>Click to expand</summary><br/>
- <a name="loss">**Loss:**</a> A calculation of the difference between what the model has learned and what the data shows ("groundtruth"). The lower the loss, the better. The training process aims to minimize the loss.
- <a name="perplexity">**Perplexity:**</a> This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.
- <a name="high-stakes">**High-stakes settings:**</a> Such as those identified as "high-risk AI systems" and "unacceptable risk AI systems" in the European Union's proposed [Artificial Intelligence (AI) Act](https://artificialintelligenceact.eu/annexes/).
- <a name="critical-decisions">**Critical decisions:**</a> Such as those defined in [the United States' proposed Algorithmic Accountability Act](https://www.congress.gov/117/bills/s3572/BILLS-117s3572is.pdf).
- <a name="human-rights">**Human rights:**</a> Includes those rights defined in the [Universal Declaration of Human Rights](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf).
- <a name="personal-data-and-information">**Personal Data and Personal Information:**</a> Personal data and information is defined in multiple data protection regulations, such as "[personal data](https://gdpr-info.eu/issues/personal-data/)" in the [European Union's General Data Protection Regulation](https://gdpr-info.eu); and "personal information" in the Republic of South Africa's [Protection of Personal Information Act](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf), The People's Republic of China's [Personal information protection law](http://en.npc.gov.cn.cdurl.cn/2021-12/29/c_694559.htm).
- <a name="sensitive-characteristics">**Sensitive characteristics:**</a> This includes specifically protected categories in human rights (see [UHDR, Article 2](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf)) and personal information regulation (see GDPR, [Article 9; Protection of Personal Information Act, Chapter 1](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf))
- <a name="deception">**Deception:**</a> Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.
</details>
<p> </p>
## More Information
<details>
<summary>Click to expand</summary><br/>
### Dataset Creation
Blog post detailing the design choices during the dataset creation: https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling
### Technical Specifications
Blog post summarizing how the architecture, size, shape, and pre-training duration where selected: https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours
More details on the architecture/optimizer: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Blog post on the hardware/engineering side: https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model
Details on the distributed setup used for the training: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Tensorboard updated during the training: https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss
Insights on how to approach training, negative results: https://github.com/bigscience-workshop/bigscience/blob/master/train/lessons-learned.md
Details on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md
### Initial Results
Initial prompting experiments using interim checkpoints: https://huggingface.co/spaces/bigscience/bloom-book
</details>
<p> </p>
## Model Card Authors
*Ordered roughly chronologically and by amount of time spent.*
Margaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff
|
DTAI-KULeuven/robbertje-1-gb-bort | [
"pytorch",
"roberta",
"fill-mask",
"nl",
"dataset:oscar",
"dataset:oscar (NL)",
"dataset:dbrd",
"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2101.05716",
"transformers",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"license:mit",
"autotrain_compatible"
]
| fill-mask | {
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}
} | 6 | null | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-12k
- laion-2b
---
# Model card for vit_base_patch16_clip_224.laion2b_ft_in12k
A Vision Transformer (ViT) image classification model. Pretrained on LAION-2B image-text pairs using OpenCLIP. Fine-tuned on ImageNet-12k in `timm`. See recipes in [Reproducible scaling laws](https://arxiv.org/abs/2212.07143).
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 94.9
- GMACs: 16.9
- Activations (M): 16.5
- Image size: 224 x 224
- **Papers:**
- OpenCLIP: https://github.com/mlfoundations/open_clip
- Reproducible scaling laws for contrastive language-image learning: https://arxiv.org/abs/2212.07143
- LAION-5B: An open large-scale dataset for training next generation image-text models: https://arxiv.org/abs/2210.08402
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
- **Dataset:** ImageNet-12k
- **Pretrain Dataset:**
- LAION-2B
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('vit_base_patch16_clip_224.laion2b_ft_in12k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_base_patch16_clip_224.laion2b_ft_in12k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 197, 768) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@software{ilharco_gabriel_2021_5143773,
author = {Ilharco, Gabriel and
Wortsman, Mitchell and
Wightman, Ross and
Gordon, Cade and
Carlini, Nicholas and
Taori, Rohan and
Dave, Achal and
Shankar, Vaishaal and
Namkoong, Hongseok and
Miller, John and
Hajishirzi, Hannaneh and
Farhadi, Ali and
Schmidt, Ludwig},
title = {OpenCLIP},
month = jul,
year = 2021,
note = {If you use this software, please cite it as below.},
publisher = {Zenodo},
version = {0.1},
doi = {10.5281/zenodo.5143773},
url = {https://doi.org/10.5281/zenodo.5143773}
}
```
```bibtex
@article{cherti2022reproducible,
title={Reproducible scaling laws for contrastive language-image learning},
author={Cherti, Mehdi and Beaumont, Romain and Wightman, Ross and Wortsman, Mitchell and Ilharco, Gabriel and Gordon, Cade and Schuhmann, Christoph and Schmidt, Ludwig and Jitsev, Jenia},
journal={arXiv preprint arXiv:2212.07143},
year={2022}
}
```
```bibtex
@inproceedings{schuhmann2022laionb,
title={{LAION}-5B: An open large-scale dataset for training next generation image-text models},
author={Christoph Schuhmann and
Romain Beaumont and
Richard Vencu and
Cade W Gordon and
Ross Wightman and
Mehdi Cherti and
Theo Coombes and
Aarush Katta and
Clayton Mullis and
Mitchell Wortsman and
Patrick Schramowski and
Srivatsa R Kundurthy and
Katherine Crowson and
Ludwig Schmidt and
Robert Kaczmarczyk and
Jenia Jitsev},
booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2022},
url={https://openreview.net/forum?id=M3Y74vmsMcY}
}
```
```bibtex
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
alexandrainst/da-emotion-classification-base | [
"pytorch",
"tf",
"bert",
"text-classification",
"da",
"transformers",
"license:cc-by-sa-4.0"
]
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}
} | 837 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: wav2vec2-xlsr-53-espeak-cv-ft-mhr2-ntsema-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: audiofolder
type: audiofolder
config: default
split: train
args: default
metrics:
- name: Wer
type: wer
value: 0.7993311036789298
---
<!-- 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-xlsr-53-espeak-cv-ft-mhr2-ntsema-colab
This model is a fine-tuned version of [facebook/wav2vec2-xlsr-53-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-xlsr-53-espeak-cv-ft) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7562
- Wer: 0.7993
## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.5636 | 5.79 | 400 | 1.8357 | 1.0 |
| 1.6348 | 11.59 | 800 | 0.6797 | 0.8528 |
| 0.8624 | 17.39 | 1200 | 0.6651 | 0.8194 |
| 0.5248 | 23.19 | 1600 | 0.6892 | 0.7826 |
| 0.3328 | 28.98 | 2000 | 0.7562 | 0.7993 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.14.0.dev20221109+cu116
- Datasets 2.6.1
- Tokenizers 0.13.2
|
DaisyMak/bert-finetuned-squad-accelerate-10epoch_transformerfrozen | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
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],
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}
} | 1,907 | null | ---
license: creativeml-openrail-m
---
art by `caster_style` this style gives a lot of magical clothes, purple, and crystals, to prompts.
License This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies:
You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here |
Danih1502/t5-small-finetuned-en-to-de | []
| null | {
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} | 0 | null | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- 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 [🤗 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: 4
- eval_batch_size: 4
- 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
📈 [TensorBoard logs](https://huggingface.co/qcs/ddpm-butterflies-128/tensorboard?#scalars)
|
DarkKibble/DialoGPT-medium-Tankman | []
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} | 0 | null | ---
language:
- ko # Example: fr
license: apache-2.0 # Example: apache-2.0 or any license from https://hf.co/docs/hub/repositories-licenses
library_name: kenlm # Optional. Example: keras or any library from https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Libraries.ts
tags:
- audio
- automatic-speech-recognition
- text2text-generation
datasets:
- korean-wiki
---
# ko-ctc-kenlm-spelling-only-wiki
## Table of Contents
- [ko-ctc-kenlm-spelling-only-wiki](#ko-ctc-kenlm-spelling-only-wiki)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
## Model Details
- **Model Description** <br />
- 음향 모델을 위한 N-gram Base의 LM으로 자소별 단어기반으로 만들어졌으며, KenLM으로 학습되었습니다. 해당 모델은 [ko-spelling-wav2vec2-conformer-del-1s](https://huggingface.co/42MARU/ko-spelling-wav2vec2-conformer-del-1s)과 사용하십시오. <br />
- HuggingFace Transformers Style로 불러와 사용할 수 있도록 처리했습니다. <br />
- pyctcdecode lib을 이용해서도 바로 사용가능합니다. <br />
- data는 wiki korean을 사용했습니다. <br />
spelling vocab data에 없는 문장은 전부 제거하여, 오히려 LM으로 Outlier가 발생할 소요를 최소화 시켰습니다. <br />
해당 모델은 **철자전사** 기준의 데이터로 학습된 모델입니다. (숫자와 영어는 각 표기법을 따름) <br />
- **Developed by:** TADev (@lIlBrother)
- **Language(s):** Korean
- **License:** apache-2.0
## How to Get Started With the Model
```python
import librosa
from pyctcdecode import build_ctcdecoder
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForCTC,
AutoTokenizer,
Wav2Vec2ProcessorWithLM,
)
from transformers.pipelines import AutomaticSpeechRecognitionPipeline
audio_path = ""
# 모델과 토크나이저, 예측을 위한 각 모듈들을 불러옵니다.
model = AutoModelForCTC.from_pretrained("42MARU/ko-spelling-wav2vec2-conformer-del-1s")
feature_extractor = AutoFeatureExtractor.from_pretrained("42MARU/ko-spelling-wav2vec2-conformer-del-1s")
tokenizer = AutoTokenizer.from_pretrained("42MARU/ko-spelling-wav2vec2-conformer-del-1s")
processor = Wav2Vec2ProcessorWithLM("42MARU/ko-ctc-kenlm-spelling-only-wiki")
# 실제 예측을 위한 파이프라인에 정의된 모듈들을 삽입.
asr_pipeline = AutomaticSpeechRecognitionPipeline(
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
decoder=processor.decoder,
device=-1,
)
# 음성파일을 불러오고 beamsearch 파라미터를 특정하여 예측을 수행합니다.
raw_data, _ = librosa.load(audio_path, sr=16000)
kwargs = {"decoder_kwargs": {"beam_width": 100}}
pred = asr_pipeline(inputs=raw_data, **kwargs)["text"]
# 모델이 자소 분리 유니코드 텍스트로 나오므로, 일반 String으로 변환해줄 필요가 있습니다.
result = unicodedata.normalize("NFC", pred)
print(result)
# 안녕하세요 123 테스트입니다.
```
|
DarkWolf/kn-electra-small | [
"pytorch",
"electra",
"feature-extraction",
"transformers"
]
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} | 4 | null | ---
language:
- ko # Example: fr
license: apache-2.0 # Example: apache-2.0 or any license from https://hf.co/docs/hub/repositories-licenses
library_name: kenlm # Optional. Example: keras or any library from https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Libraries.ts
tags:
- audio
- automatic-speech-recognition
- text2text-generation
datasets:
- korean-wiki
---
# ko-ctc-kenlm-42maru-only-wiki
## Table of Contents
- [ko-ctc-kenlm-42maru-only-wiki](#ko-ctc-kenlm-42maru-only-wiki)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
## Model Details
- **Model Description** <br />
- 음향 모델을 위한 N-gram Base의 LM으로 자소별 단어기반으로 만들어졌으며, KenLM으로 학습되었습니다. 해당 모델은 [ko-42maru-wav2vec2-conformer-del-1s](https://huggingface.co/42MARU/ko-42maru-wav2vec2-conformer-del-1s)과 사용하십시오. <br />
- HuggingFace Transformers Style로 불러와 사용할 수 있도록 처리했습니다. <br />
- pyctcdecode lib을 이용해서도 바로 사용가능합니다. <br />
- data는 wiki korean을 사용했습니다. <br />
42maru vocab data에 없는 문장은 전부 제거하여, 오히려 LM으로 Outlier가 발생할 소요를 최소화 시켰습니다. <br />
해당 모델은 **음성전사를 자체 커스텀한 42maru** 기준의 데이터로 학습된 모델입니다. (숫자와 영어는 한글 표기법을 따름) <br />
- **Developed by:** TADev (@lIlBrother)
- **Language(s):** Korean
- **License:** apache-2.0
## How to Get Started With the Model
```python
import librosa
from pyctcdecode import build_ctcdecoder
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForCTC,
AutoTokenizer,
Wav2Vec2ProcessorWithLM,
)
from transformers.pipelines import AutomaticSpeechRecognitionPipeline
audio_path = ""
# 모델과 토크나이저, 예측을 위한 각 모듈들을 불러옵니다.
model = AutoModelForCTC.from_pretrained("42MARU/ko-42maru-wav2vec2-conformer-del-1s")
feature_extractor = AutoFeatureExtractor.from_pretrained("42MARU/ko-42maru-wav2vec2-conformer-del-1s")
tokenizer = AutoTokenizer.from_pretrained("42MARU/ko-42maru-wav2vec2-conformer-del-1s")
processor = Wav2Vec2ProcessorWithLM.from_pretrained("42MARU/ko-ctc-kenlm-42maru-only-wiki")
# 실제 예측을 위한 파이프라인에 정의된 모듈들을 삽입.
asr_pipeline = AutomaticSpeechRecognitionPipeline(
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
decoder=processor.decoder,
device=-1,
)
# 음성파일을 불러오고 beamsearch 파라미터를 특정하여 예측을 수행합니다.
raw_data, _ = librosa.load(audio_path, sr=16000)
kwargs = {"decoder_kwargs": {"beam_width": 100}}
pred = asr_pipeline(inputs=raw_data, **kwargs)["text"]
# 모델이 자소 분리 유니코드 텍스트로 나오므로, 일반 String으로 변환해줄 필요가 있습니다.
result = unicodedata.normalize("NFC", pred)
print(result)
# 안녕하세요 하나둘셋 테스트입니다.
```
|
Darkrider/covidbert_medmarco | [
"pytorch",
"jax",
"bert",
"text-classification",
"arxiv:2010.05987",
"transformers"
]
| text-classification | {
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}
} | 35 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: deberta-base-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: train
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.0
---
<!-- 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-base-finetuned-cola
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6187
- 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: 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 | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.6187 | 1.0 | 535 | 0.6187 | 0.0 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Darkrider/covidbert_mednli | [
"transformers"
]
| null | {
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-autoeval-test
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-autoeval-test
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0332
- Accuracy: 0.9911
## 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: 64
- 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: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2671 | 0.99 | 33 | 0.0934 | 0.9622 |
| 0.1767 | 1.99 | 66 | 0.0543 | 0.9844 |
| 0.1414 | 2.99 | 99 | 0.0332 | 0.9911 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
DarshanDeshpande/marathi-distilbert | [
"pytorch",
"tf",
"distilbert",
"fill-mask",
"mr",
"dataset:Oscar Corpus, News, Stories",
"arxiv:1910.01108",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
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}
} | 14 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-tr-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-tr-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.2
|
DataikuNLP/distiluse-base-multilingual-cased-v1 | [
"pytorch",
"distilbert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
]
| sentence-similarity | {
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],
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}
} | 29 | null | Access to model munbatbai/gg is restricted and you are not in the authorized list. Visit https://huggingface.co/munbatbai/gg to ask for access. |
DataikuNLP/paraphrase-albert-small-v2 | [
"pytorch",
"albert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
]
| sentence-similarity | {
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"AlbertModel"
],
"model_type": "albert",
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}
}
} | 628 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: hcho22/opus-mt-ko-en-finetuned-en-to-kr
results: []
---
<!-- 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. -->
# hcho22/opus-mt-ko-en-finetuned-en-to-kr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ko-en](https://huggingface.co/Helsinki-NLP/opus-mt-ko-en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.5856
- Validation Loss: 2.0437
- Train Bleu: 2.0518
- Train Gen Len: 20.8110
- 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': 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 | Train Bleu | Train Gen Len | Epoch |
|:----------:|:---------------:|:----------:|:-------------:|:-----:|
| 2.5856 | 2.0437 | 2.0518 | 20.8110 | 0 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.2
|
DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2 | [
"pytorch",
"bert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
]
| sentence-similarity | {
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"BertModel"
],
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}
} | 1,517 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
---
To use it you have to use the word ''IconsMi'' in the prompt.
From my tests the images look better with this prompt:
highly detailed, trending on artstation, ios icon app, IconsMi
For negative prompts I got better results when I used: out of frame, duplicate, watermark, signature, text, ugly, sketch, deformed, mutated, blurry, mutilated, ugly sketch
I recommend you to instead describe the style of app you want, e.g. news app, music app, sports app. Describe what you want in the image. For example, ''a reporter microphone''. The results are better. SD doesn't understand these abstractions yet.
The Model was made in 7200 Steps with models saved every 700 steps after the 2000 steps.
In my tests the 2k model was the one that got the most creativity and had the most variety of themes in the generation.
The 5500 model was the one that had the best image quality, but did not know how to abstract and be creative.
I included both models for you to test. Both use the same prompt ''IconsMi
You can help me on mine:
Patreon:https://www.patreon.com/user?u=81570187
Ko-Fi:https://ko-fi.com/jvkape
buy me a coffe:https://www.buymeacoffee.com/JVKAPE
All money will go towards the creation of new models.
|
Davlan/bert-base-multilingual-cased-finetuned-hausa | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"BertForMaskedLM"
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}
} | 151 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
---
# VoxelArt model V1
This is the fine-tuned Stable Diffusion model trained on Voxel Art images.
Use **VoxelArt** in your prompts.
### Sample images:
Based on StableDiffusion 1.5 model
### 🧨 Diffusers
This model can be used just like any other Stable Diffusion model. For more information,
please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX]().
```python
from diffusers import StableDiffusionPipeline
import torch
model_id = "Fictiverse/Stable_Diffusion_PaperCut_Model"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "PaperCut R2-D2"
image = pipe(prompt).images[0]
image.save("./R2-D2.png")
``` |
Davlan/bert-base-multilingual-cased-finetuned-igbo | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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}
} | 15 | null | ---
license: mit
---
### California Gurls music video on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/Dreambooth/fast_DreamBooth-Old-Method.ipynb#scrollTo=1pH1oP-7yBZm) & (https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
#### model by misas
This your the Stable Diffusion model fine-tuned the California Gurls music video concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt(s)`: **caligurls california gurls music video**
You can also train your own concepts and upload them to the library by using [the fast-DremaBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb).
And you can 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), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Here are the images used for training this concept:
caligurls
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some examples
a cat in california gurls music video
a dog in california gurls music video
pikachu in california gurls music video
 |
Davlan/bert-base-multilingual-cased-finetuned-kinyarwanda | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"BertForMaskedLM"
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} | 27 | null | https://civitai.com/models/25399/cardos-anime
https://civitai.com/models/24779/dark-sushi-mix-mix
https://civitai.com/models/21409/five-nuts-mixed-mix
https://civitai.com/models/23953/furnace-34
https://civitai.com/models/2583/hassaku-hentai-model
https://civitai.com/models/11866/meinapastel
https://civitai.com/models/25178/suikamix-hentai-mode
https://civitai.com/models/27259/tmnd-mix
https://civitai.com/models/25324/wonton-colorbox-enhanced
|
Davlan/bert-base-multilingual-cased-finetuned-swahili | [
"pytorch",
"tf",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"BertForMaskedLM"
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} | 67 | null | ---
language: en
datasets:
- Satellite-Instrument-NER
widget:
- text: "Centroid Moment Tensor Global Navigation Satellite System GNSS"
- text: "This paper describes the latest version of the algorithm MAIAC used for processing the MODIS Collection 6 data record."
- text: "We derive tropospheric column BrO during the ARCTAS and ARCPAC field campaigns in spring 2008 using retrievals of total column BrO from the satellite UV nadir sensors OMI and GOME - 2 using a radiative transfer model and stratospheric column BrO from a photochemical simulation."
license: mit
---
# bert-base-NER
## Model description
**bert-base-NER** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** and achieves **F1 0.61** for the NER task. It has been trained to recognize two types of entities: instrument and satellite.
Specifically, this model is a *bert-base-cased* model that was fine-tuned on Satellite-Instrument-NER dataset.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for NER.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("NahedAbdelgaber/ner_base_model")
model = AutoModelForTokenClassification.from_pretrained("NahedAbdelgaber/ner_base_model")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Centroid Moment Tensor Global Navigation Satellite System GNSS"
ner_results = nlp(example)
print(ner_results)
``` |
Davlan/bert-base-multilingual-cased-masakhaner | [
"pytorch",
"tf",
"bert",
"token-classification",
"arxiv:2103.11811",
"transformers",
"autotrain_compatible"
]
| token-classification | {
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"BertForTokenClassification"
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} | 88 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 275.24 +/- 24.12
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Davlan/byt5-base-eng-yor-mt | [
"pytorch",
"t5",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
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}
} | 11 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: amitjohn007/bert-finetuned-squad
results: []
---
<!-- 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. -->
# amitjohn007/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: 0.5685
- 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': 16638, '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 |
|:----------:|:-----:|
| 1.2704 | 0 |
| 0.7816 | 1 |
| 0.5685 | 2 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Davlan/distilbert-base-multilingual-cased-ner-hrl | [
"pytorch",
"tf",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible",
"has_space"
]
| token-classification | {
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
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} | 123,856 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: minilm-finetuned-emotionclassification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# minilm-finetuned-emotionclassification
This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0554
- F1 Score: 0.6732
## Model description
The base model used is Microsoft MiniLM-L12-H384-uncased which is finetuned on [GoEmotions dataset](https://huggingface.co/datasets/go_emotions) available on huggingface.
With this model, you can classify emotions in English text data. The model predicts 10 basic emotions:
1) anger 🤬
2) love ❤️
3) fear 😨
4) joy 😀
5) excitement 😄
6) sadness 😭
7) surprise 😲
8) gratitude 😊
9) curiosity 🤔
10 caring
## Intended uses & limitations
The model can be used to detect emotions from text/ documents which can be used for analysis contextual emotional analysis of the documents
## Training and evaluation data
The dataset used for Training and Evaluation is [GoEmotions dataset](https://huggingface.co/datasets/go_emotions)
and in this, we have used 10 emotion variables.
{0:'sadness',1:'joy',2:'love',3:'anger',4:'fear',5:'surprise',6:'excitement',7:'gratitude',8:'curiosity',9:'caring'}
## How to use the model
Here is how to use this model to extract the emotions from the given text in PyTorch:
```python
>>> from transformers import pipeline
>>> model_ckpt ="sid321axn/minilm-finetuned-emotionclassification"
>>> pipe = pipeline("text-classification",model=model_ckpt)
>>> pipe("I am really excited about second part of Brahmastra Movie")
[{'label': 'excitement', 'score': 0.7849715352058411}]
```
## Training procedure
The training we have done by following this [video](https://www.youtube.com/watch?v=u--UVvH-LIQ) on Youtube by huggingface
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1659 | 1.0 | 539 | 1.1419 | 0.6347 |
| 1.0719 | 2.0 | 1078 | 1.0789 | 0.6589 |
| 0.9893 | 3.0 | 1617 | 1.0537 | 0.6666 |
| 0.9296 | 4.0 | 2156 | 1.0366 | 0.6729 |
| 0.8763 | 5.0 | 2695 | 1.0359 | 0.6774 |
| 0.8385 | 6.0 | 3234 | 1.0484 | 0.6693 |
| 0.8085 | 7.0 | 3773 | 1.0478 | 0.6758 |
| 0.7842 | 8.0 | 4312 | 1.0488 | 0.6741 |
| 0.7608 | 9.0 | 4851 | 1.0538 | 0.6749 |
| 0.7438 | 10.0 | 5390 | 1.0554 | 0.6732 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Davlan/mT5_base_yoruba_adr | [
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2003.10564",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
]
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} | 5 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Terence3927/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Davlan/mbart50-large-yor-eng-mt | [
"pytorch",
"mbart",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"MBartForConditionalGeneration"
],
"model_type": "mbart",
"task_specific_params": {
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 5 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Terence3927/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Davlan/mt5-small-en-pcm | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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}
}
} | 9 | null | ---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: creativeml-openrail-m
inference: false
---
# Momocha mix models
Scrapped from [chenyfan's sharepoint](https://cyfan-my.sharepoint.com/:f:/g/personal/chenyfan_cyfan_onmicrosoft_com/EilOWB40m3ZJn6ahczIUIs4B6v0XvizO5YorOhG_5eYSUw?e=ZyP7qE)
Example output:
|
Davlan/mt5-small-pcm-en | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
"translation_en_to_fr": {
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}
}
} | 9 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: train
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5567273065308361
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8434
- Matthews Correlation: 0.5567
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5224 | 1.0 | 535 | 0.5360 | 0.4275 |
| 0.3498 | 2.0 | 1070 | 0.5205 | 0.5078 |
| 0.2383 | 3.0 | 1605 | 0.6466 | 0.5318 |
| 0.1739 | 4.0 | 2140 | 0.7723 | 0.5532 |
| 0.1276 | 5.0 | 2675 | 0.8434 | 0.5567 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.10.2
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Davlan/mt5_base_yor_eng_mt | [
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 8 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="TimePlan/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Davlan/xlm-roberta-base-finetuned-amharic | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
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},
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}
}
} | 401 | null | ---
license: mit
---
### AnonV1 on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
#### model by TheMindExpansionNetwork
This your the Stable Diffusion model fine-tuned the AnonV1 concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt(s)`: **AnonV1**
You can also train your own concepts and upload them to the library by using [the fast-DremaBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb).
And you can 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), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Here are the images used for training this concept:
AnonV1
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|
Davlan/xlm-roberta-base-finetuned-english | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
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},
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},
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},
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},
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}
}
} | 5 | null | ---
language:
- ja
- ai
tags:
- translation
widget:
- text: "ari hawki = an konno"
example_title: "と 言う と "
---
|
Davlan/xlm-roberta-base-finetuned-hausa | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
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},
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
} | 234 | null | ---
tags:
- generated_from_trainer
model-index:
- name: clip-l-roberta-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# clip-l-roberta-finetuned
This model is a fine-tuned version of [./clip-l-roberta](https://huggingface.co/./clip-l-roberta) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 22
- eval_batch_size: 24
- 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 4.25.0.dev0
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Davlan/xlm-roberta-base-finetuned-igbo | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
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},
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}
} | 68 | null | ---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: mit
---
# Soft Brush Style Model / Dreambooth Training
This model is trained entirely on a collection of similar style images from varying sources
# Use
To use this model you have to download the .ckpt file as well as drop it into the "\stable-diffusion-webui\models\Stable-diffusion" folder
To use it in a prompt: ```"m_sb style"``` for highest strength or just "m_sb"
To increase the strength put "m_sb style" in () brackets
To decrease the strength put "m_sb style" in [] brackets
Waifu_diffusion base trained model trained to 15,000 steps
Have fun :)
## Txt2img Example Pictures from Soft_brush
<table>
<tr>
<td><img src=https://i.imgur.com/7QmMnlN.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/ORD35Gt.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/HUhvSF6.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/NGud9La.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/wWBYJ2W.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/u8PlDbS.png width=100% height=100%/></td>
</tr>
</table>
License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies:
You can't use the model to deliberately produce nor share illegal or harmful outputs or content
The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here |
Davlan/xlm-roberta-base-finetuned-luo | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
} | 5 | 2022-11-10T07:29:44Z | ---
library_name: Doc-UFCN
license: mit
tags:
- Doc-UFCN
- PyTorch
- Object detection
metrics:
- IoU
- F1
- [email protected]
- [email protected]
- AP@[.5,.95]
---
# Hugin-Munin line detection
The Hugin-Munin line detection model predicts text lines from Hugin-Munin document images. This model was developed during the [HUGIN-MUNIN project](https://hugin-munin-project.github.io/).
## Model description
The model has been trained using the Doc-UFCN library on Hugin-Munin document images.
It has been trained on images with their largest dimension equal to 768 pixels, keeping the original aspect ratio.
The model predicts two classes: vertical and horizontal text lines.
## Evaluation results
The model achieves the following results:
| set | class | IoU | F1 | AP@[.5] | AP@[.75] | AP@[.5,.95] |
| ----- | ---------- | ----- | ----- | ------- | -------- | ----------- |
| train | vertical | 88.29 | 89.67 | 71.37 | 33.26 | 36.32 |
| | horizontal | 69.81 | 81.35 | 91.73 | 36.62 | 45.67 |
| val | vertical | 73.01 | 75.13 | 46.02 | 4.99 | 15.58 |
| | horizontal | 61.65 | 75.69 | 87.98 | 11.18 | 31.55 |
| test | vertical | 78.62 | 80.03 | 59.93 | 15.90 | 24.11 |
| | horizontal | 63.59 | 76.49 | 95.93 | 24.18 | 41.45 |
## How to use
Please refer to the Doc-UFCN library page (https://pypi.org/project/doc-ufcn/) to use this model.
# Cite us!
```bibtex
@inproceedings{boillet2020,
author = {Boillet, Mélodie and Kermorvant, Christopher and Paquet, Thierry},
title = {{Multiple Document Datasets Pre-training Improves Text Line Detection With
Deep Neural Networks}},
booktitle = {2020 25th International Conference on Pattern Recognition (ICPR)},
year = {2021},
month = Jan,
pages = {2134-2141},
doi = {10.1109/ICPR48806.2021.9412447}
}
```
|
Davlan/xlm-roberta-base-finetuned-shona | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
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}
} | 5 | 2022-11-10T07:40:43Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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.
It achieves the following results on the evaluation set:
- Loss: 2.3208
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7566 | 1.0 | 557 | 2.0440 |
| 0.447 | 2.0 | 1114 | 2.0889 |
| 0.3508 | 3.0 | 1671 | 2.3208 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Davlan/xlm-roberta-base-finetuned-wolof | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 3 | null | ---
language:
- en
tags:
- stable-diffusion
- text-to-image
inference: false
---
# Cafe Unofficial Instagram TEST Model Release
> Trained on ~140k Instagram images made up of primarily Japanese accounts (mix of cosplay, model, and personal accounts)
> While the model can create some realistic Instagram-esque images on its own, for full potential, it is recommended that it be merged with another model (such as berry or anything)
Scrapped from [cafe's torrent](magnet:?xt=urn:btih:eb085b3e22310a338e6ea00172cb887c10c54cbc&dn=cafe-instagram-unofficial-test-epoch-9-140k-images-fp32.ckpt&tr=udp%3A%2F%2Ftracker.openbittorrent.com%3A80&tr=udp%3A%2F%2Fopentor.org%3A2710&tr=udp%3A%2F%2Ftracker.ccc.de%3A80&tr=udp%3A%2F%2Ftracker.blackunicorn.xyz%3A6969&tr=udp%3A%2F%2Ftracker.coppersurfer.tk%3A6969&tr=udp%3A%2F%2Ftracker.leechers-paradise.org%3A6969)
Credit:
- [Cafe - かふぇ](https://lookup.guru/305088304284434434) for the model
- [Sikinx](https://lookup.guru/671692519985119232) for the drive link
|
Davlan/xlm-roberta-base-finetuned-xhosa | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
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} | 12 | null | ---
license: mit
tag: text-classification
widget:
- text: "Sehent hoerent oder lesent daß div chint, div bechoment von frowen Chvnegvnde Heinriches des Losen"
- text: "Mihály zágrábi püspök előtt Vaguth (dict.) László c. a püspöki várnépek (castrenses) Csázma comitatus-beli volt földjének egy részét, amelyet szolgálataiért predialis jogon tőle kapott, 1 szőlővel együtt (a Zuynar föld azon része kivételével, amelyet a püspök László c.-től elvett és a megvakított Kokosnak adományozott"
- text: "Rath und Gemeinde der Stadt Wismar beschweren sich über die von den Hauptleuten, Beamten und Vasallen des Grafen Johann von Holstein und Stormarn ihren Bürgern seit Jahren zugefügten Unbilden, indem sie ein Verzeichniss der erlittenen einzelnen Verluste beibringen."
- text: "Diplomă de înnobilare emisă de împăratul romano-german Rudolf al II-lea de Habsburg la în favoarea familiei Szőke de Galgóc. Aussteller: Rudolf al II-lea de Habsburg, împărat romano-german Empfänger: Szőke de Galgóc, familie"
---
# XLM-RoBERTa (base) language-detection model (modern and medieval) OUTDATED!
This model is a fine-tuned version of xlm-roberta-base on the [monasterium.net](https://www.icar-us.eu/en/cooperation/online-portals/monasterium-net/) dataset.
## Model description
On the top of this XLM-RoBERTa transformer model is a classification head. Please refer this model together with to the [XLM-RoBERTa (base-sized model)](https://huggingface.co/xlm-roberta-base) card or the paper [Unsupervised Cross-lingual Representation Learning at Scale by Conneau et al.](https://arxiv.org/abs/1911.02116) for additional information.
## Intended uses & limitations
You can directly use this model as a language detector, i.e. for sequence classification tasks. Currently, it supports the following 41 languages, modern and medieval:
Modern: Bulgarian (bg), Croatian (hr), Czech (cs), Danish (da), Dutch (nl), English (en), Estonian (et), Finnish (fi), French (fr), German (de), Greek (el), Hungarian (hu), Irish (ga), Italian (it), Latvian (lv), Lithuanian (lt), Maltese (mt), Polish (pl), Portuguese (pt), Romanian (ro), Slovak (sk), Slovenian (sl), Spanish (es), Swedish (sv), Russian (ru), Turkish (tr), Basque (eu), Catalan (ca), Albanian (sq), Serbian (se), Ukrainian (uk), Norwegian (no), Arabic (ar), Chinese (zh), Hebrew (he)
Medieval: Middle High German (mhd), Latin (la), Middle Low German (gml), Old French (fro), Old Church Slavonic (chu), Early New High German (fnhd), Ancient and Medieval Greek (grc)
## Training and evaluation data
The model was fine-tuned using the Monasterium and Wikipedia datasets, which consist of text sequences in 40 languages. The training set contains 80k samples, while the validation and test sets contain 16k. The average accuracy on the test set is 99.59% (this matches the average macro/weighted F1-score, the test set being perfectly balanced).
## Training procedure
Fine-tuning was done via the Trainer API with WeightedLossTrainer.
## Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 20
- eval_batch_size: 20
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
mixed_precision_training: Native AMP
## Training results
| Training Loss | Validation Loss | F1
| ------------- | ------------- | -------- |
| 0.000300 | 0.048985 | 0.991585 |
| 0.000100 | 0.033340 | 0.994663 |
| 0.000000 | 0.032938 | 0.995979 |
## Using example
```
#Install packages
!pip install transformers --quiet
#Import libraries
import torch
from transformers import pipeline
#Define pipeline
classificator = pipeline("text-classification", model="ERCDiDip/40_langdetect_v01")
#Use pipeline
classificator("clemens etc dilecto filio scolastico ecclesie wetflari ensi treveren dioc salutem etc significarunt nobis dilecti filii commendator et fratres hospitalis beate marie theotonicorum")
```
## Updates
- 25th November 2022: Adding Ancient and Medieval Greek (grc)
## Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.3
## Citation
Please cite the following papers when using this model.
```
@misc{ercdidip2022,
title={40 langdetect v01 (Revision 9fab42a)},
author={Kovács, Tamás, Atzenhofer-Baumgartner, Florian, Aoun, Sandy, Nicolaou, Anguelos, Luger, Daniel, Decker, Franziska, Lamminger, Florian and Vogeler, Georg},
year = { 2022 },
url = { https://huggingface.co/ERCDiDip/40_langdetect_v01 },
doi = { 10.57967/hf/0099 },
publisher = { Hugging Face }
}
```
This model is part of the [From Digital to Distant Diplomatics (DiDip) ERC project](https://cordis.europa.eu/project/id/101019327) funded by the European Research Council. |
Davlan/xlm-roberta-base-finetuned-yoruba | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 29 | null | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
- wit-400m
---
# Model card for vit_base_patch32_clip_224.openai_ft_in1k
A Vision Transformer (ViT) image classification model. Pretrained on WIT-400M image-text pairs by OpenAI using CLIP. Fine-tuned on ImageNet-1k in `timm`. See recipes in [Reproducible scaling laws](https://arxiv.org/abs/2212.07143).
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 88.2
- GMACs: 4.4
- Activations (M): 4.2
- Image size: 224 x 224
- **Papers:**
- Learning Transferable Visual Models From Natural Language Supervision: https://arxiv.org/abs/2103.00020
- Reproducible scaling laws for contrastive language-image learning: https://arxiv.org/abs/2212.07143
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
- **Dataset:** ImageNet-1k
- **Pretrain Dataset:**
- WIT-400M
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('vit_base_patch32_clip_224.openai_ft_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_base_patch32_clip_224.openai_ft_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 50, 768) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@inproceedings{Radford2021LearningTV,
title={Learning Transferable Visual Models From Natural Language Supervision},
author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
booktitle={ICML},
year={2021}
}
```
```bibtex
@article{cherti2022reproducible,
title={Reproducible scaling laws for contrastive language-image learning},
author={Cherti, Mehdi and Beaumont, Romain and Wightman, Ross and Wortsman, Mitchell and Ilharco, Gabriel and Gordon, Cade and Schuhmann, Christoph and Schmidt, Ludwig and Jitsev, Jenia},
journal={arXiv preprint arXiv:2212.07143},
year={2022}
}
```
```bibtex
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
Davlan/xlm-roberta-base-masakhaner | [
"pytorch",
"xlm-roberta",
"token-classification",
"arxiv:2103.11811",
"transformers",
"autotrain_compatible"
]
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}
} | 3 | null | ---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: NLLB-alt-cv-bleu-40
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# NLLB-alt-cv-bleu-40
This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7734
- Bleu: 30.3568
- Gen Len: 50.699
## 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: 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: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 2.1031 | 1.0 | 1380 | 1.6725 | 6.1774 | 56.292 |
| 1.4925 | 2.0 | 2760 | 1.3799 | 8.9414 | 54.966 |
| 1.1976 | 3.0 | 4140 | 1.2417 | 10.7993 | 55.32 |
| 0.9973 | 4.0 | 5520 | 1.1744 | 13.7633 | 51.225 |
| 0.8305 | 5.0 | 6900 | 1.1461 | 14.8273 | 51.723 |
| 0.6918 | 6.0 | 8280 | 1.1261 | 16.02 | 50.83 |
| 0.5823 | 7.0 | 9660 | 1.1556 | 17.8129 | 49.93 |
| 0.4934 | 8.0 | 11040 | 1.1567 | 19.3213 | 50.647 |
| 0.4049 | 9.0 | 12420 | 1.1794 | 21.712 | 50.722 |
| 0.3342 | 10.0 | 13800 | 1.2320 | 22.2956 | 50.552 |
| 0.2747 | 11.0 | 15180 | 1.2749 | 24.3631 | 50.443 |
| 0.2281 | 12.0 | 16560 | 1.2996 | 25.4046 | 52.437 |
| 0.1849 | 13.0 | 17940 | 1.3378 | 26.5399 | 50.361 |
| 0.153 | 14.0 | 19320 | 1.3709 | 27.0563 | 51.077 |
| 0.1256 | 15.0 | 20700 | 1.4128 | 27.8781 | 51.129 |
| 0.1103 | 16.0 | 22080 | 1.4354 | 28.6894 | 51.974 |
| 0.0893 | 17.0 | 23460 | 1.4859 | 28.0852 | 52.005 |
| 0.0778 | 18.0 | 24840 | 1.4973 | 28.9053 | 50.803 |
| 0.0683 | 19.0 | 26220 | 1.5294 | 29.2219 | 50.845 |
| 0.0592 | 20.0 | 27600 | 1.5576 | 29.1227 | 51.051 |
| 0.0505 | 21.0 | 28980 | 1.5885 | 29.4121 | 50.376 |
| 0.0441 | 22.0 | 30360 | 1.6028 | 29.5531 | 51.946 |
| 0.0397 | 23.0 | 31740 | 1.6254 | 29.3607 | 50.811 |
| 0.0361 | 24.0 | 33120 | 1.6374 | 29.5197 | 51.166 |
| 0.0323 | 25.0 | 34500 | 1.6423 | 29.7589 | 51.335 |
| 0.0288 | 26.0 | 35880 | 1.6630 | 29.6029 | 51.036 |
| 0.0257 | 27.0 | 37260 | 1.6800 | 29.4437 | 50.623 |
| 0.0235 | 28.0 | 38640 | 1.6887 | 29.9344 | 50.797 |
| 0.0201 | 29.0 | 40020 | 1.7096 | 30.1522 | 50.694 |
| 0.018 | 30.0 | 41400 | 1.7223 | 30.1291 | 50.425 |
| 0.0163 | 31.0 | 42780 | 1.7282 | 29.8131 | 51.114 |
| 0.0148 | 32.0 | 44160 | 1.7299 | 29.9721 | 50.851 |
| 0.0133 | 33.0 | 45540 | 1.7463 | 30.0369 | 50.477 |
| 0.0122 | 34.0 | 46920 | 1.7514 | 30.0663 | 51.133 |
| 0.0112 | 35.0 | 48300 | 1.7508 | 30.0451 | 50.736 |
| 0.0099 | 36.0 | 49680 | 1.7631 | 30.0576 | 50.62 |
| 0.0087 | 37.0 | 51060 | 1.7683 | 30.1648 | 50.874 |
| 0.0083 | 38.0 | 52440 | 1.7750 | 30.2558 | 50.667 |
| 0.0076 | 39.0 | 53820 | 1.7757 | 30.3551 | 50.886 |
| 0.0076 | 40.0 | 55200 | 1.7734 | 30.3568 | 50.699 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.10.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Davlan/xlm-roberta-base-wikiann-ner | [
"pytorch",
"tf",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
]
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} | 235 | null | ---
license: mit
---
### Solo Levelling Art Style on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
#### model by Classacre
This your the Stable Diffusion model fine-tuned the Solo Levelling Art Style concept taught to Stable Diffusion with Dreambooth.
You can also train your own concepts and upload them to the library by using [the fast-DremaBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb).
And you can 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), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
This is my first model, criticism and advice is welcome. Discord: "Classacre#1028"
This model is inspired by @ogkalu and his comic-diffusion model (https://huggingface.co/ogkalu/Comic-Diffusion). I think its pretty cool and you should check it out.
I've made this model out of admiration towards Jang-Sung Rak (DUBU) who recently passed away. This model is not perfect, and will never be perfect as the original artists art is irreplaceable.
### Version 2.1 ###
- This new model uses the anythingv3.0 model as its base instead of the SD 1.5. This adds more dynamic backgrounds to the generations but strays abit away from the original style.
- Characters and people are the same as V2 and have been improved to better reflect Jang-Sung Raks art style.
- Action generations are often better in 2:1 ratios or 2:2 (1024 x 1024) generations. They are often incomplete in 512x512 generations.
- The calm model simmilar to version 2.0 is a good general model and may be better than the action model when generating. Play around with the instance prompts mentioned below and see what you prefer.
The calm and action models have been combined into 1 ckpt file. I've changed the naming scheme to better match the progress of the model e.g. this versions CKPT is called sololevellingV2.1
It can be used by modifying the `instance_prompt(s)`: **SLCalm** and **SLAction**
This model was trained using 20 total images (10 for calm scenes and 10 for action scenes). 2000 total training steps (1e-6). Text encoder trained for 250 steps (1e-6.). Text encoder concept training steps 533. 71 conceptualization (realisation) images.
This model still suffers from text/ chat bubbles but can be mitigated by adding it to the negative prompts (same as version 2.0).
### Version 2.0 ###
This is a massive improvement from the first version. I've split the model into two different models, one for non action generations (SoloLevellingCalm.ckpt) and one for action generations (SoloLevellingAction.ckpt). I plan on merging the two into one model in the future once I understand how to do captions. The calm (SoloLevellingCalm.ckpt) version of the model is great for general generation using most prompts, it was trained using non action images taken from the solo leveling manhwa.
**Important Prompt Additions:**
Add these prompts to make the generations look remotely like the solo levelling art style and to maintain consistency.
Positive prompts: anime, manhwa, beautiful, 8k
Negative prompts: chat bubble, chat bubbles, ugly
This model suffers from chat bubbles and added VFX words in its generations, it can often be mitigated by inputting the negative prompts in the Important prompt additions but it is not perfect.
Sampler and CFG settings are identical to Version 1.0.
### Version 1.0 ###
It can be used by modifying the `instance_prompt(s)`: **sololeveling**
This model was trained using 71 training images, 14200 total training steps, model saved every 3550 steps (25%) and text encoder was trained up to 35%. Made using Stable Diffusion v1.5 as the base model.
The final model struggles to do calm / peaceful environments as it was trained on mainly cinematic action scenes - this leads to style bleeding where the ai creates action sequences from seemingly calm and peaceful prompts. Earlier models don't seem to have this problem albeit they are not as sharp and do not reproduce the style as accurately. Negative prompts seem to lessen the effects of action sequences in the final model, however they are not as natural as older models. Another thing to mention is that the model struggles at drawing eyes in action sequences, you may be able to play with the prompt to get eyes to show up though. A comparison between the different model versions can be seen below:
Sampler used: DDIM
CFG: 7
Prompt: man holding a sword, black hair, muscular, in a library, cinematic, full color, fighting a man
(https://i.imgur.com/MBjzUVI.jpg)
man eating food in the subway station, sololeveling, happy, cinematic, golden hour
(https://i.imgur.com/L3MB4Ka.jpg)
In my opinion this model runs best using the DDIM sampler, however I'm still pretty new to experimenting samplers and my opinion about this may change in the future. Please experiment with the different samplers yourself and choose what you believe is best. The model in 106560 steps may be better than the final model.
Here are the images used for training this concept:
sololeveling
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|
Davlan/xlm-roberta-large-masakhaner | [
"pytorch",
"tf",
"xlm-roberta",
"token-classification",
"arxiv:2103.11811",
"transformers",
"autotrain_compatible"
]
| token-classification | {
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"XLMRobertaForTokenClassification"
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} | 1,449 | null | # Introduction
Models in this file are downloaded from
<https://github.com/wenet-e2e/wenet/releases/download/v2.0.1/en.tar.gz>
```bash
wget https://github.com/wenet-e2e/wenet/releases/download/v2.0.1/en.tar.gz
tar xvf en.tar.gz --strip-components=1
rm en.tar.gz
```
|
Davlan/xlm-roberta-large-ner-hrl | [
"pytorch",
"tf",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
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"XLMRobertaForTokenClassification"
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}
} | 1,322 | null | Access to model multimodalart/extra_gated_heading is restricted and you are not in the authorized list. Visit https://huggingface.co/multimodalart/extra_gated_heading to ask for access. |
Dawn576/Dawn | []
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} | 0 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpole0001
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 101.90 +/- 9.57
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
Daymarebait/Discord_BOT_RICK | [
"conversational"
]
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} | 3 | null | ---
language:
- es
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- facebook/multilingual_librispeech
metrics:
- wer
model-index:
- name: Whisper Small Es - Sanchit Gandhi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Multilingual LibriSpeech
type: facebook/multilingual_librispeech
args: 'config: es, split: test'
metrics:
- name: Wer
type: wer
value: 4.988756935106611
---
<!-- 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 Es - Sanchit Gandhi
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Multilingual LibriSpeech dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1252
- Wer: 4.9888
## 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: 2
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- 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: 2500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2346 | 0.2 | 500 | 0.1957 | 8.5131 |
| 0.1252 | 0.4 | 1000 | 0.1448 | 5.7876 |
| 0.2076 | 0.6 | 1500 | 0.1361 | 5.5786 |
| 0.2356 | 0.8 | 2000 | 0.1504 | 6.6611 |
| 0.1893 | 1.0 | 2500 | 0.1252 | 4.9888 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.0
- Datasets 2.6.2.dev0
- Tokenizers 0.12.1
|
Dayout/test | []
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} | 0 | null | ---
license: openrail
---
<h3 align="center">PDF Paragraphs Extraction</h3>
<p align="center">A model for extracting paragraphs from PDFs</p>
This model uses features from the PDF to extract the text and paragraphs from it. It can be used as a service.
The paragraphs contain the page number, the position in the page, the size, and the text.
## Quick Start
Download the service that uses the model:
git clone https://github.com/huridocs/pdf_paragraphs_extraction.git
cd pdf_paragraphs_extraction
Start the service:
./run start
Get the paragraphs from a PDF:
curl -X GET -F 'file=@/PATH/TO/PDF/pdf_name.pdf' localhost:5051
To stop the server:
./run stop
## Performance
Accuracy: 93.9%
Speed: 0.15 seconds per page |
Dazai/Ko | []
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} | 0 | null | ---
license: mit
---
### tyxxxszv on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
#### model by sorupopic
This your the Stable Diffusion model fine-tuned the tyxxxszv concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt(s)`: **tyxxxszv**
You can also train your own concepts and upload them to the library by using [the fast-DremaBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb).
And you can 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), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Here are the images used for training this concept:
tyxxxszv
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.png)
|
Dazai/Ok | []
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} | 0 | null | ---
license: mit
---
# NB-BERT fine-tuned on NoReC
## Description
This model is based on the pre-trained [NB-BERT-large model](https://huggingface.co/NbAiLab/nb-bert-large?text=P%C3%A5+biblioteket+kan+du+l%C3%A5ne+en+%5BMASK%5D.). It is a model for sentiment analysis.
## Data for fine-tuning
This model was fine-tuned on 1000 exemples from the [NoReC train dataset](https://github.com/ltgoslo/norec) that belonged to the screen category. The training lasted 3 epochs with a learning rate of 5e-5. The code used to create this model (and some additional models) can be found on [Github](https://github.com/Karolill/NB-BERT-fine-tuned-on-english). |
Dbluciferm3737/Idk | []
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -711.41 +/- 372.03
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Ddarkros/Test | []
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} | 0 | null | # Introduction
This repo contains torchscript models for Wav2Vec 2.0.
|
DeadBeast/roberta-base-pretrained-mr-2 | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"RobertaForMaskedLM"
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} | 5 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
widget:
source_sentence: "亚丝娜"
sentences:
- "火影忍者"
- "Sword Art Online"
- "结城明日奈"
- "アスナ"
---
# ACGVoc2vec
结构为[sentence-transformers](https://github.com/UKPLab/sentence-transformers)，使用其**distiluse-base-multilingual-cased-v2**预训练权重，以5e-5的学习率在动漫相关语句对数据集下进行微调，损失函数为MultipleNegativesRankingLoss。
数据集主要包括：
* Bangumi
* 动画日文名-动画中文名
* 动画日文名-简介
* 动画中文名-简介
* 动画中文名-标签
* 动画日文名-角色
* 动画中文名-角色
* 声优日文名-声优中文名
* pixiv
* 标签日文名-标签中文名
* AnimeList
* 动画日文名-动画英文名
* 维基百科
* 动画日文名-动画中文名
* 动画日文名-动画英文名
* 中英日详情页h2标题及其对应文本
* 简介多语言对照（中日英）
* 动画名-简介（中日英）
* moegirl
* 动画中文名的简介-简介
* 动画中文名+小标题-对应内容
在进行爬取，清洗，处理后得到8000w对文本对（还在持续增加），batchzise=80训练了20个epoch，使st的权重能够适应该问题空间，生成融合了领域知识的文本特征向量（体现为有关的文本距离更加接近，例如作品与登场人物，或者来自同一作品的登场人物）。
## Usage
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('OysterQAQ/ACGVoc2vec')
embeddings = model.encode(sentences)
print(embeddings)
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Declan/Breitbart_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 7 | null | ---
tags:
- generated_from_trainer
model-index:
- name: SciBERT-WIKI_Life_Form_Finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SciBERT-WIKI_Life_Form_Finetuned
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4427
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4919 | 1.0 | 1175 | 0.4396 |
| 0.3471 | 2.0 | 2350 | 0.4196 |
| 0.2362 | 3.0 | 3525 | 0.4427 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Declan/Breitbart_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"BertForMaskedLM"
],
"model_type": "bert",
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}
} | 3 | null | ---
language:
- es
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- facebook/multilingual_librispeech
metrics:
- wer
model-index:
- name: Whisper Small Es - Sanchit Gandhi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Multilingual LibriSpeech
type: facebook/multilingual_librispeech
args: 'config: es, split: test'
metrics:
- name: Wer
type: wer
value: 4.426038712301834
---
<!-- 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 Es - Sanchit Gandhi
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Multilingual LibriSpeech dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1107
- Wer: 4.4260
## 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: 2
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- 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
- training_steps: 2500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.186 | 0.2 | 500 | 0.1487 | 6.1786 |
| 0.1947 | 0.4 | 1000 | 0.1350 | 5.5910 |
| 0.3566 | 0.6 | 1500 | 0.1242 | 4.9537 |
| 0.1237 | 0.8 | 2000 | 0.1181 | 4.8001 |
| 0.1902 | 1.0 | 2500 | 0.1107 | 4.4260 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.0
- Datasets 2.6.2.dev0
- Tokenizers 0.12.1
|
Declan/Breitbart_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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"task_specific_params": {
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}
} | 3 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pixelcopter0001
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 18.90 +/- 8.83
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Declan/ChicagoTribune_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
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}
} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- gsm8k
model-index:
- name: flan-t5-xl-finetuned-gsm8k
results: []
widget:
- "Please, answer the following question reasoning step-by-step: If Manu eats twice a day, how many meals does he take for a week?"
---
<!-- 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. -->
# flan-t5-xl-finetuned-gsm8k
This model is a fine-tuned version of [google/flan-t5-xl](https://huggingface.co/google/flan-t5-xl) on the gsm8k dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2853
## 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: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.2845 | 1.0 | 1868 | 0.2778 |
| 0.2204 | 2.0 | 3736 | 0.2718 |
| 0.1803 | 3.0 | 5604 | 0.2762 |
| 0.1578 | 4.0 | 7472 | 0.2853 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Declan/FoxNews_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
} | 3 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: train
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8638300289723342
---
<!-- 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: 0.1358
- F1: 0.8638
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2591 | 1.0 | 525 | 0.1621 | 0.8206 |
| 0.1276 | 2.0 | 1050 | 0.1379 | 0.8486 |
| 0.082 | 3.0 | 1575 | 0.1358 | 0.8638 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Declan/HuffPost_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
}
} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
config: default
split: train
args: default
metrics:
- name: Rouge1
type: rouge
value: 28.6501
---
<!-- 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-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4495
- Rouge1: 28.6501
- Rouge2: 7.9821
- Rougel: 22.5657
- Rougelsum: 22.579
- Gen Len: 18.819
## 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.6832 | 1.0 | 25506 | 2.4495 | 28.6501 | 7.9821 | 22.5657 | 22.579 | 18.819 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1
|
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