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59.7M
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Declan/WallStreetJournal_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 3 | 2022-10-20T21:37:53Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_freeway
type: atari_freeway
metrics:
- type: mean_reward
value: 33.75 +/- 0.43
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_freeway** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Declan/WallStreetJournal_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 7 | 2022-10-20T21:40:10Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: cvt-13-384-22k-fv-finetuned-memes
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8315301391035549
- name: Precision
type: precision
value: 0.8302128280229624
- name: Recall
type: recall
value: 0.8315301391035549
- name: F1
type: f1
value: 0.8292026505769348
---
<!-- 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. -->
# cvt-13-384-22k-fv-finetuned-memes
This model is a fine-tuned version of [microsoft/cvt-13-384-22k](https://huggingface.co/microsoft/cvt-13-384-22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5761
- Accuracy: 0.8315
- Precision: 0.8302
- Recall: 0.8315
- F1: 0.8292
## 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: 0.00012
- 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.3821 | 0.99 | 20 | 1.2780 | 0.4969 | 0.5083 | 0.4969 | 0.4458 |
| 1.0785 | 1.99 | 40 | 0.8633 | 0.6669 | 0.6658 | 0.6669 | 0.6500 |
| 0.8862 | 2.99 | 60 | 0.7110 | 0.7218 | 0.7258 | 0.7218 | 0.7013 |
| 0.665 | 3.99 | 80 | 0.5515 | 0.8045 | 0.8137 | 0.8045 | 0.8050 |
| 0.6056 | 4.99 | 100 | 0.5956 | 0.7960 | 0.8041 | 0.7960 | 0.7846 |
| 0.4779 | 5.99 | 120 | 0.6229 | 0.7937 | 0.7945 | 0.7937 | 0.7857 |
| 0.4554 | 6.99 | 140 | 0.5355 | 0.8099 | 0.8126 | 0.8099 | 0.8086 |
| 0.4249 | 7.99 | 160 | 0.5447 | 0.8269 | 0.8275 | 0.8269 | 0.8236 |
| 0.4313 | 8.99 | 180 | 0.5530 | 0.8153 | 0.8140 | 0.8153 | 0.8132 |
| 0.423 | 9.99 | 200 | 0.5346 | 0.8238 | 0.8230 | 0.8238 | 0.8223 |
| 0.3997 | 10.99 | 220 | 0.5413 | 0.8338 | 0.8347 | 0.8338 | 0.8338 |
| 0.4095 | 11.99 | 240 | 0.5999 | 0.8207 | 0.8231 | 0.8207 | 0.8177 |
| 0.3979 | 12.99 | 260 | 0.5632 | 0.8284 | 0.8255 | 0.8284 | 0.8250 |
| 0.3408 | 13.99 | 280 | 0.5725 | 0.8207 | 0.8198 | 0.8207 | 0.8196 |
| 0.3828 | 14.99 | 300 | 0.5631 | 0.8277 | 0.8258 | 0.8277 | 0.8260 |
| 0.3595 | 15.99 | 320 | 0.6005 | 0.8308 | 0.8297 | 0.8308 | 0.8275 |
| 0.3789 | 16.99 | 340 | 0.5840 | 0.8300 | 0.8271 | 0.8300 | 0.8273 |
| 0.3545 | 17.99 | 360 | 0.5983 | 0.8246 | 0.8226 | 0.8246 | 0.8222 |
| 0.3472 | 18.99 | 380 | 0.5795 | 0.8416 | 0.8382 | 0.8416 | 0.8390 |
| 0.355 | 19.99 | 400 | 0.5761 | 0.8315 | 0.8302 | 0.8315 | 0.8292 |
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1.dev0
- Tokenizers 0.13.1
|
Declan/WallStreetJournal_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 9 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_frostbite
type: atari_frostbite
metrics:
- type: mean_reward
value: 11911.00 +/- 5618.22
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_frostbite** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Declan/WallStreetJournal_model_v6 | []
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} | 0 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_gopher
type: atari_gopher
metrics:
- type: mean_reward
value: 7470.00 +/- 11268.62
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_gopher** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Declan/WallStreetJournal_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 9 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_gravitar
type: atari_gravitar
metrics:
- type: mean_reward
value: 6142.50 +/- 1000.66
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_gravitar** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Declan/test_model | []
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} | 0 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_hero
type: atari_hero
metrics:
- type: mean_reward
value: 37214.50 +/- 128.78
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_hero** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
DeepBasak/Slack | []
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} | 0 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_icehockey
type: atari_icehockey
metrics:
- type: mean_reward
value: 22.60 +/- 5.19
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_icehockey** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
DeepChem/ChemBERTa-10M-MTR | [
"pytorch",
"roberta",
"arxiv:1910.09700",
"transformers"
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} | 708 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_jamesbond
type: atari_jamesbond
metrics:
- type: mean_reward
value: 57797.50 +/- 34083.89
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_jamesbond** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
DeepChem/ChemBERTa-5M-MTR | [
"pytorch",
"roberta",
"transformers"
]
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} | 13 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_kangaroo
type: atari_kangaroo
metrics:
- type: mean_reward
value: 985.00 +/- 1000.14
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_kangaroo** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
DeepChem/ChemBERTa-77M-MLM | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 2,416 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_krull
type: atari_krull
metrics:
- type: mean_reward
value: 11410.50 +/- 1495.54
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_krull** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
DeepChem/ChemBERTa-77M-MTR | [
"pytorch",
"roberta",
"transformers"
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} | 7,169 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_kongfumaster
type: atari_kongfumaster
metrics:
- type: mean_reward
value: 32440.00 +/- 9438.72
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_kongfumaster** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
DeepChem/SmilesTokenizer_PubChem_1M | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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} | 227 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_montezuma
type: atari_montezuma
metrics:
- type: mean_reward
value: 385.00 +/- 65.38
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_montezuma** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
DeepESP/gpt2-spanish-medium | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"es",
"dataset:ebooks",
"transformers",
"GPT-2",
"Spanish",
"ebooks",
"nlg",
"license:mit"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
} | 340 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_mspacman
type: atari_mspacman
metrics:
- type: mean_reward
value: 6288.00 +/- 1954.58
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_mspacman** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
DeepESP/gpt2-spanish | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"es",
"dataset:ebooks",
"transformers",
"GPT-2",
"Spanish",
"ebooks",
"nlg",
"license:mit",
"has_space"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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"prefix": null
}
}
} | 1,463 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_namethisgame
type: atari_namethisgame
metrics:
- type: mean_reward
value: 23810.50 +/- 2277.79
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_namethisgame** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
DeepPavlov/bert-base-bg-cs-pl-ru-cased | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"bg",
"cs",
"pl",
"ru",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
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},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 1,614 | null | # [Refer]ence-fr[ee] sentence summarization
See [Github repo](https://github.com/msclar/referee) for all details. **DO NOT USE HOSTED INFERENCE API**. Instead, use the appropriate `src/generated_summaries_*.py` script, that specifies the expected delimiters and decoding params.
## Paper citation
If you used this model for your experiments or found it helpful, consider citing the following paper:
```
@inproceedings{sclar-etal-2022-referee,
title = "Referee: Reference-Free Sentence Summarization with Sharper Controllability through Symbolic Knowledge Distillation",
author = "Sclar, Melanie and
West, Peter and
Kumar, Sachin and
Tsvetkov, Yulia and
Choi, Yejin",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.655",
pages = "9649--9668",
abstract = "We present Referee, a novel framework for sentence summarization that can be trained reference-free (i.e., requiring no gold summaries for supervision), while allowing direct control for compression ratio. Our work is the first to demonstrate that reference-free, controlled sentence summarization is feasible via the conceptual framework of Symbolic Knowledge Distillation (West et al., 2022), where latent knowledge in pre-trained language models is distilled via explicit examples sampled from the teacher models, further purified with three types of filters: length, fidelity, and Information Bottleneck. Moreover, we uniquely propose iterative distillation of knowledge, where student models from the previous iteration of distillation serve as teacher models in the next iteration. Starting off from a relatively modest set of GPT3-generated summaries, we demonstrate how iterative knowledge distillation can lead to considerably smaller, but better summarizers with sharper controllability. A useful by-product of this iterative distillation process is a high-quality dataset of sentence-summary pairs with varying degrees of compression ratios. Empirical results demonstrate that the final student models vastly outperform the much larger GPT3-Instruct model in terms of the controllability of compression ratios, without compromising the quality of resulting summarization.",
}
```
|
DeepPavlov/bert-base-cased-conversational | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"en",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 3,009 | null | # [Refer]ence-fr[ee] sentence summarization
See [Github repo](https://github.com/msclar/referee) for all details. **DO NOT USE HOSTED INFERENCE API**. Instead, use the appropriate `src/generated_summaries_*.py` script, that specifies the expected delimiters and decoding params.
## Paper citation
If you used this model for your experiments or found it helpful, consider citing the following paper:
```
@inproceedings{sclar-etal-2022-referee,
title = "Referee: Reference-Free Sentence Summarization with Sharper Controllability through Symbolic Knowledge Distillation",
author = "Sclar, Melanie and
West, Peter and
Kumar, Sachin and
Tsvetkov, Yulia and
Choi, Yejin",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.655",
pages = "9649--9668",
abstract = "We present Referee, a novel framework for sentence summarization that can be trained reference-free (i.e., requiring no gold summaries for supervision), while allowing direct control for compression ratio. Our work is the first to demonstrate that reference-free, controlled sentence summarization is feasible via the conceptual framework of Symbolic Knowledge Distillation (West et al., 2022), where latent knowledge in pre-trained language models is distilled via explicit examples sampled from the teacher models, further purified with three types of filters: length, fidelity, and Information Bottleneck. Moreover, we uniquely propose iterative distillation of knowledge, where student models from the previous iteration of distillation serve as teacher models in the next iteration. Starting off from a relatively modest set of GPT3-generated summaries, we demonstrate how iterative knowledge distillation can lead to considerably smaller, but better summarizers with sharper controllability. A useful by-product of this iterative distillation process is a high-quality dataset of sentence-summary pairs with varying degrees of compression ratios. Empirical results demonstrate that the final student models vastly outperform the much larger GPT3-Instruct model in terms of the controllability of compression ratios, without compromising the quality of resulting summarization.",
}
```
|
DeepPavlov/bert-base-multilingual-cased-sentence | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"multilingual",
"arxiv:1704.05426",
"arxiv:1809.05053",
"arxiv:1908.10084",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 140 | 2022-10-20T22:15:50Z | # [Refer]ence-fr[ee] sentence summarization
See [Github repo](https://github.com/msclar/referee) for all details. **DO NOT USE HOSTED INFERENCE API**. Instead, use the appropriate `src/generated_summaries_*.py` script, that specifies the expected delimiters and decoding params.
## Paper citation
If you used this model for your experiments or found it helpful, consider citing the following paper:
```
@inproceedings{sclar-etal-2022-referee,
title = "Referee: Reference-Free Sentence Summarization with Sharper Controllability through Symbolic Knowledge Distillation",
author = "Sclar, Melanie and
West, Peter and
Kumar, Sachin and
Tsvetkov, Yulia and
Choi, Yejin",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.655",
pages = "9649--9668",
abstract = "We present Referee, a novel framework for sentence summarization that can be trained reference-free (i.e., requiring no gold summaries for supervision), while allowing direct control for compression ratio. Our work is the first to demonstrate that reference-free, controlled sentence summarization is feasible via the conceptual framework of Symbolic Knowledge Distillation (West et al., 2022), where latent knowledge in pre-trained language models is distilled via explicit examples sampled from the teacher models, further purified with three types of filters: length, fidelity, and Information Bottleneck. Moreover, we uniquely propose iterative distillation of knowledge, where student models from the previous iteration of distillation serve as teacher models in the next iteration. Starting off from a relatively modest set of GPT3-generated summaries, we demonstrate how iterative knowledge distillation can lead to considerably smaller, but better summarizers with sharper controllability. A useful by-product of this iterative distillation process is a high-quality dataset of sentence-summary pairs with varying degrees of compression ratios. Empirical results demonstrate that the final student models vastly outperform the much larger GPT3-Instruct model in terms of the controllability of compression ratios, without compromising the quality of resulting summarization.",
}
```
|
DeepPavlov/distilrubert-base-cased-conversational | [
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
]
| null | {
"architectures": null,
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 6,324 | null | # [Refer]ence-fr[ee] sentence summarization
See [Github repo](https://github.com/msclar/referee) for all details. **DO NOT USE HOSTED INFERENCE API**. Instead, use the appropriate `src/generated_summaries_*.py` script, that specifies the expected delimiters and decoding params.
## Paper citation
If you used this model for your experiments or found it helpful, consider citing the following paper:
```
@inproceedings{sclar-etal-2022-referee,
title = "Referee: Reference-Free Sentence Summarization with Sharper Controllability through Symbolic Knowledge Distillation",
author = "Sclar, Melanie and
West, Peter and
Kumar, Sachin and
Tsvetkov, Yulia and
Choi, Yejin",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.655",
pages = "9649--9668",
abstract = "We present Referee, a novel framework for sentence summarization that can be trained reference-free (i.e., requiring no gold summaries for supervision), while allowing direct control for compression ratio. Our work is the first to demonstrate that reference-free, controlled sentence summarization is feasible via the conceptual framework of Symbolic Knowledge Distillation (West et al., 2022), where latent knowledge in pre-trained language models is distilled via explicit examples sampled from the teacher models, further purified with three types of filters: length, fidelity, and Information Bottleneck. Moreover, we uniquely propose iterative distillation of knowledge, where student models from the previous iteration of distillation serve as teacher models in the next iteration. Starting off from a relatively modest set of GPT3-generated summaries, we demonstrate how iterative knowledge distillation can lead to considerably smaller, but better summarizers with sharper controllability. A useful by-product of this iterative distillation process is a high-quality dataset of sentence-summary pairs with varying degrees of compression ratios. Empirical results demonstrate that the final student models vastly outperform the much larger GPT3-Instruct model in terms of the controllability of compression ratios, without compromising the quality of resulting summarization.",
}
```
|
DeepPavlov/distilrubert-tiny-cased-conversational-v1 | [
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
]
| null | {
"architectures": null,
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 9,141 | null | # [Refer]ence-fr[ee] sentence summarization
See [Github repo](https://github.com/msclar/referee) for all details. **DO NOT USE HOSTED INFERENCE API**. Instead, use the appropriate `src/generated_summaries_*.py` script, that specifies the expected delimiters and decoding params.
## Paper citation
If you used this model for your experiments or found it helpful, consider citing the following paper:
```
@inproceedings{sclar-etal-2022-referee,
title = "Referee: Reference-Free Sentence Summarization with Sharper Controllability through Symbolic Knowledge Distillation",
author = "Sclar, Melanie and
West, Peter and
Kumar, Sachin and
Tsvetkov, Yulia and
Choi, Yejin",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.655",
pages = "9649--9668",
abstract = "We present Referee, a novel framework for sentence summarization that can be trained reference-free (i.e., requiring no gold summaries for supervision), while allowing direct control for compression ratio. Our work is the first to demonstrate that reference-free, controlled sentence summarization is feasible via the conceptual framework of Symbolic Knowledge Distillation (West et al., 2022), where latent knowledge in pre-trained language models is distilled via explicit examples sampled from the teacher models, further purified with three types of filters: length, fidelity, and Information Bottleneck. Moreover, we uniquely propose iterative distillation of knowledge, where student models from the previous iteration of distillation serve as teacher models in the next iteration. Starting off from a relatively modest set of GPT3-generated summaries, we demonstrate how iterative knowledge distillation can lead to considerably smaller, but better summarizers with sharper controllability. A useful by-product of this iterative distillation process is a high-quality dataset of sentence-summary pairs with varying degrees of compression ratios. Empirical results demonstrate that the final student models vastly outperform the much larger GPT3-Instruct model in terms of the controllability of compression ratios, without compromising the quality of resulting summarization.",
}
```
|
DeepPavlov/marianmt-tatoeba-enru | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
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"prefix": null
}
}
} | 1 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_phoenix
type: atari_phoenix
metrics:
- type: mean_reward
value: 319850.00 +/- 153652.97
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_phoenix** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
DeepPavlov/marianmt-tatoeba-ruen | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 30 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/jiswooning-the3ammusician/1666304830215/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/1560736534143422465/3oAu6oCD_400x400.jpg')">
</div>
<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/1521185553382883334/fHjvh84L_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>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">TOR Kate & K8 misses KARD</div>
<div style="text-align: center; font-size: 14px;">@jiswooning-the3ammusician</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 TOR Kate & K8 misses KARD.
| Data | TOR Kate | K8 misses KARD |
| --- | --- | --- |
| Tweets downloaded | 3234 | 3193 |
| Retweets | 1038 | 1194 |
| Short tweets | 310 | 208 |
| Tweets kept | 1886 | 1791 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1vcg0753/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 @jiswooning-the3ammusician's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1plbf2ii) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1plbf2ii/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/jiswooning-the3ammusician')
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)
|
DeepPavlov/roberta-large-winogrande | [
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"en",
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"arxiv:1907.11692",
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} | 348 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_pitfall
type: atari_pitfall
metrics:
- type: mean_reward
value: -83.05 +/- 160.20
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_pitfall** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
DeepPavlov/rubert-base-cased-conversational | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"ru",
"transformers",
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]
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} | 17,362 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_pong
type: atari_pong
metrics:
- type: mean_reward
value: 20.95 +/- 0.22
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_pong** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
DeltaHub/lora_t5-base_mrpc | [
"pytorch",
"transformers"
]
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}
} | 3 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_solaris
type: atari_solaris
metrics:
- type: mean_reward
value: 3494.00 +/- 2422.19
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_solaris** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
DeskDown/MarianMix_en-zh_to_vi-ms-hi-ja | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
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} | 5 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_videopinball
type: atari_videopinball
metrics:
- type: mean_reward
value: 372372.91 +/- 274249.66
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_videopinball** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Dizoid/Lll | []
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} | 0 | null | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
inference: false
extra_gated_prompt: |-
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
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 v1-5 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 blog](https://huggingface.co/blog/stable_diffusion).
The **Stable-Diffusion-v1-5** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2)
checkpoint and subsequently fine-tuned on 595k 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).
You can use this both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [RunwayML GitHub repository](https://github.com/runwayml/stable-diffusion).
### Diffusers
```py
from diffusers import StableDiffusionPipeline
import torch
model_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16")
pipe = pipe.to(device)
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]
image.save("astronaut_rides_horse.png")
```
For more detailed instructions, use-cases and examples in JAX follow the instructions [here](https://github.com/huggingface/diffusers#text-to-image-generation-with-stable-diffusion)
### Original GitHub Repository
1. Download the weights
- [v1-5-pruned-emaonly.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt) - 4.27GB, ema-only weight. uses less VRAM - suitable for inference
- [v1-5-pruned.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned.ckpt) - 7.7GB, ema+non-ema weights. uses more VRAM - suitable for fine-tuning
2. Follow instructions [here](https://github.com/runwayml/stable-diffusion).
## 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}
}
# 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.
### Safety Module
The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers.
This checker works by checking model outputs against known hard-coded NSFW concepts.
The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter.
Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` *after generation* of the images.
The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept.
## 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 v1-5 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.
Currently six Stable Diffusion checkpoints are provided, 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).
- [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) Resumed from `stable-diffusion-v1-2` - 595,000 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).
- [`stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting) Resumed from `stable-diffusion-v1-5` - then 440,000 steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything.
- **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 PNDM/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).* |
Dkwkk/Da | []
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} | 0 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_beamrider
type: atari_beamrider
metrics:
- type: mean_reward
value: 32610.60 +/- 18969.02
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_beamrider** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Dkwkk/W | []
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} | 0 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_berzerk
type: atari_berzerk
metrics:
- type: mean_reward
value: 57896.00 +/- 1685.96
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_berzerk** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Dmitriiserg/Pxd | []
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} | 0 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_bowling
type: atari_bowling
metrics:
- type: mean_reward
value: 35.75 +/- 4.00
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_bowling** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Dmitry12/sber | []
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} | 0 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_boxing
type: atari_boxing
metrics:
- type: mean_reward
value: 98.05 +/- 3.99
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_boxing** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Doiman/DialoGPT-medium-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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} | 13 | null | ---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: pegasus-newsroom_wires_hdwriter42k
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. -->
# pegasus-newsroom_wires_hdwriter42k
This model is a fine-tuned version of [google/pegasus-newsroom](https://huggingface.co/google/pegasus-newsroom) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5521
- Rouge1: 37.5311
- Rouge2: 17.6638
- Rougel: 31.2287
- Rougelsum: 31.7152
- Gen Len: 35.1639
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.662 | 1.0 | 16875 | 1.5702 | 36.8068 | 17.3179 | 30.6952 | 31.1331 | 36.4668 |
| 1.5071 | 2.0 | 33750 | 1.5503 | 37.5627 | 17.6519 | 31.2538 | 31.7985 | 35.3514 |
| 1.4512 | 3.0 | 50625 | 1.5521 | 37.5311 | 17.6638 | 31.2287 | 31.7152 | 35.1639 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
DongHai/DialoGPT-small-rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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} | 9 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_breakout
type: atari_breakout
metrics:
- type: mean_reward
value: 724.50 +/- 208.42
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_breakout** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
DongHyoungLee/distilbert-base-uncased-finetuned-cola | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | {
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} | 27 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_centipede
type: atari_centipede
metrics:
- type: mean_reward
value: 14345.55 +/- 5572.37
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_centipede** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
DongHyoungLee/kogpt2-base-v2-finetuned-kogpt2_nsmc_single_sentence_classification | []
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} | 0 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_choppercommand
type: atari_choppercommand
metrics:
- type: mean_reward
value: 61585.00 +/- 214116.50
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_choppercommand** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Donghyun/L2_BERT | []
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} | 0 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_crazyclimber
type: atari_crazyclimber
metrics:
- type: mean_reward
value: 179305.00 +/- 31261.92
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_crazyclimber** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Dongjae/mrc2reader | [
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"XLMRobertaForQuestionAnswering"
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}
} | 3 | null | ---
license: other
---
<html>
<body>
<h1>Welcome to Crying-Chopper Model</h1>
<p>This is a Stable Diffusion 1.4 based model that adds the ability to make any character you would like into a Crying Chopper meme as seen in the below picture. This model was trained on about 20 different versions aka characters of this art style, thanks to the wonderful artists over at the OnePieceCock discord server. To get the best result do a prompt like this 'NAME as cryingchopper, ...' make sure to keep crying chopper with no space because that was how it trained. </p>
<img alt="cleanchooper.jpg" src="https://s3.amazonaws.com/moonup/production/uploads/1666321853612-631ba03acf39db4b171a0877.jpeg" title="cleanchooper.jpg">
<a href="https://s3.amazonaws.com/moonup/production/uploads/1666321853612-631ba03acf39db4b171a0877.jpeg" download>Download: Crying-Chopper_model-v1</a>
</body>
</html>
|
Dongmin/testmodel | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
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},
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},
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}
}
} | 11 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_defender
type: atari_defender
metrics:
- type: mean_reward
value: 327717.50 +/- 106134.53
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_defender** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Waynehillsdev/Waynehills_summary_tensorflow | [
"tf",
"t5",
"text2text-generation",
"transformers",
"generated_from_keras_callback",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
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}
} | 5 | 2022-10-21T01:47:30Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_demonattack
type: atari_demonattack
metrics:
- type: mean_reward
value: 125889.00 +/- 28853.55
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_demonattack** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Waynehillsdev/wav2vec2-base-timit-demo-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
| automatic-speech-recognition | {
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"Wav2Vec2ForCTC"
],
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}
} | 5 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_doubledunk
type: atari_doubledunk
metrics:
- type: mean_reward
value: 22.30 +/- 1.31
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_doubledunk** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Waynehillsdev/waynehills_sentimental_kor | [
"pytorch",
"electra",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"ElectraForSequenceClassification"
],
"model_type": "electra",
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}
} | 33 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_enduro
type: atari_enduro
metrics:
- type: mean_reward
value: 1877.90 +/- 371.71
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_enduro** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Doohae/p_encoder | [
"pytorch"
]
| null | {
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}
} | 3 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_fishingderby
type: atari_fishingderby
metrics:
- type: mean_reward
value: 25.95 +/- 24.64
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_fishingderby** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Doohae/q_encoder | [
"pytorch"
]
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} | 3 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_freeway
type: atari_freeway
metrics:
- type: mean_reward
value: 33.80 +/- 0.40
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_freeway** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Doohae/roberta | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
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} | 3 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_frostbite
type: atari_frostbite
metrics:
- type: mean_reward
value: 13981.50 +/- 2869.03
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_frostbite** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Doquey/DialoGPT-small-Luisbot1 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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} | 7 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_gopher
type: atari_gopher
metrics:
- type: mean_reward
value: 1627.00 +/- 1758.38
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_gopher** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Doquey/DialoGPT-small-Michaelbot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
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"task_specific_params": {
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} | 10 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_gravitar
type: atari_gravitar
metrics:
- type: mean_reward
value: 3737.50 +/- 847.48
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_gravitar** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Doxophobia/DialoGPT-medium-celeste | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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} | 11 | 2022-10-21T02:15:35Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_icehockey
type: atari_icehockey
metrics:
- type: mean_reward
value: 26.00 +/- 6.99
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_icehockey** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
DoyyingFace/bert-COVID-HATE-finetuned-test | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
} | 29 | null | ---
tags:
- Question(s) Generation
metrics:
- rouge
model-index:
- name: consciousAI/question-generation-auto-t5-v1-base-s
results: []
---
# Auto Question Generation
The model is intended to be used for Auto Question Generation task i.e. no hint are required as input. The model is expected to produce one or possibly more than one question from the provided context.
[Live Demo: Question Generation](https://huggingface.co/spaces/consciousAI/question_generation)
Including this there are five models trained with different training sets, demo provide comparison to all in one go. However, you can reach individual projects at below links:
[Auto Question Generation v2](https://huggingface.co/consciousAI/question-generation-auto-t5-v1-base-s-q)
[Auto Question Generation v3](https://huggingface.co/consciousAI/question-generation-auto-t5-v1-base-s-q-c)
[Auto/Hints based Question Generation v1](https://huggingface.co/consciousAI/question-generation-auto-hints-t5-v1-base-s-q)
[Auto/Hints based Question Generation v2](https://huggingface.co/consciousAI/question-generation-auto-hints-t5-v1-base-s-q-c)
This model can be used as below:
```
from transformers import (
AutoModelForSeq2SeqLM,
AutoTokenizer
)
model_checkpoint = "consciousAI/question-generation-auto-t5-v1-base-s"
model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
## Input with prompt
context="question_context: <context>"
encodings = tokenizer.encode(context, return_tensors='pt', truncation=True, padding='max_length').to(device)
## You can play with many hyperparams to condition the output, look at demo
output = model.generate(encodings,
#max_length=300,
#min_length=20,
#length_penalty=2.0,
num_beams=4,
#early_stopping=True,
#do_sample=True,
#temperature=1.1
)
## Multiple questions are expected to be delimited by '?' You can write a small wrapper to elegantly format. Look at the demo.
questions = [tokenizer.decode(id, clean_up_tokenization_spaces=False, skip_special_tokens=False) for id in output]
```
## Training and evaluation data
SQUAD split.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 2
- eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
Rouge metrics is heavily penalized because of multiple questions in target sample space,
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|
| 2.0146 | 1.0 | 4758 | 1.6980 | 0.143 | 0.0705 | 0.1257 | 0.1384 |
...
| 1.1733 | 9.0 | 23790 | 1.6319 | 0.1404 | 0.0718 | 0.1239 | 0.1351 |
| 1.1225 | 10.0 | 28548 | 1.6476 | 0.1407 | 0.0716 | 0.1245 | 0.1356 |
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.0
|
DoyyingFace/bert-asian-hate-tweets-asian-clean-with-unclean-valid | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
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} | 29 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_jamesbond
type: atari_jamesbond
metrics:
- type: mean_reward
value: 27870.00 +/- 17865.83
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_jamesbond** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-12 | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
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}
} | 29 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_kangaroo
type: atari_kangaroo
metrics:
- type: mean_reward
value: 1520.00 +/- 781.41
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_kangaroo** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-8 | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
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} | 30 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_krull
type: atari_krull
metrics:
- type: mean_reward
value: 11747.00 +/- 884.18
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_krull** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-100 | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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}
} | 28 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_kongfumaster
type: atari_kongfumaster
metrics:
- type: mean_reward
value: 24405.00 +/- 8855.48
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_kongfumaster** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-25 | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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}
} | 30 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_montezuma
type: atari_montezuma
metrics:
- type: mean_reward
value: 5.00 +/- 21.79
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_montezuma** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-50 | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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} | 28 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_mspacman
type: atari_mspacman
metrics:
- type: mean_reward
value: 9056.50 +/- 2544.32
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_mspacman** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-with-clean-valid | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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} | 33 | null | Access to model aaa3131/aaa3131 is restricted and you are not in the authorized list. Visit https://huggingface.co/aaa3131/aaa3131 to ask for access. |
DoyyingFace/bert-asian-hate-tweets-concat-clean-with-unclean-valid | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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} | 25 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_namethisgame
type: atari_namethisgame
metrics:
- type: mean_reward
value: 22827.50 +/- 3187.38
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_namethisgame** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
DoyyingFace/bert-asian-hate-tweets-concat-clean | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
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"BertForSequenceClassification"
],
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} | 25 | 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 40 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": 40,
"warmup_steps": 4,
"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 --> |
albert-base-v1 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
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],
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} | 38,156 | 2022-10-21T02:50:49Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_phoenix
type: atari_phoenix
metrics:
- type: mean_reward
value: 427935.00 +/- 129673.56
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_phoenix** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
albert-base-v2 | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
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} | 4,785,283 | 2022-10-21T02:52:39Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_pitfall
type: atari_pitfall
metrics:
- type: mean_reward
value: -119.35 +/- 193.77
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_pitfall** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
albert-large-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
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}
} | 687 | 2022-10-21T02:54:44Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_pong
type: atari_pong
metrics:
- type: mean_reward
value: 21.00 +/- 0.00
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_pong** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
albert-large-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
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],
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}
} | 26,792 | 2022-10-21T02:57:17Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_privateye
type: atari_privateye
metrics:
- type: mean_reward
value: 100.00 +/- 0.00
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_privateye** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
albert-xxlarge-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
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} | 7,091 | 2022-10-21T03:00:47Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_qbert
type: atari_qbert
metrics:
- type: mean_reward
value: 40277.50 +/- 9491.60
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_qbert** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
albert-xxlarge-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
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} | 42,640 | 2022-10-21T03:03:20Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_riverraid
type: atari_riverraid
metrics:
- type: mean_reward
value: 22423.50 +/- 9564.01
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_riverraid** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
bert-base-cased-finetuned-mrpc | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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} | 11,644 | 2022-10-21T03:05:31Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_roadrunner
type: atari_roadrunner
metrics:
- type: mean_reward
value: 70320.00 +/- 7419.61
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_roadrunner** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
bert-base-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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} | 8,621,271 | 2022-10-21T03:08:16Z | ---
tags:
- generated_from_trainer
datasets:
- funsd
model-index:
- name: layoutlm-funsd-pytorch
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. -->
# layoutlm-funsd-pytorch
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7042
- Answer: {'precision': 0.712403951701427, 'recall': 0.8022249690976514, 'f1': 0.7546511627906977, 'number': 809}
- Header: {'precision': 0.3203125, 'recall': 0.3445378151260504, 'f1': 0.33198380566801616, 'number': 119}
- Question: {'precision': 0.7747589833479404, 'recall': 0.8300469483568075, 'f1': 0.8014505893019038, 'number': 1065}
- Overall Precision: 0.7220
- Overall Recall: 0.7898
- Overall F1: 0.7544
- Overall Accuracy: 0.8078
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7641 | 1.0 | 10 | 1.5569 | {'precision': 0.01979045401629802, 'recall': 0.021013597033374538, 'f1': 0.02038369304556355, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.20930232558139536, 'recall': 0.15211267605633802, 'f1': 0.1761827079934747, 'number': 1065} | 0.1096 | 0.0898 | 0.0987 | 0.3917 |
| 1.4096 | 2.0 | 20 | 1.1718 | {'precision': 0.18729096989966554, 'recall': 0.138442521631644, 'f1': 0.15920398009950248, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4800601956358164, 'recall': 0.5990610328638498, 'f1': 0.5329991645781119, 'number': 1065} | 0.3892 | 0.3763 | 0.3827 | 0.6045 |
| 1.0362 | 3.0 | 30 | 0.9322 | {'precision': 0.5212620027434842, 'recall': 0.46971569839307786, 'f1': 0.494148244473342, 'number': 809} | {'precision': 0.10344827586206896, 'recall': 0.025210084033613446, 'f1': 0.040540540540540536, 'number': 119} | {'precision': 0.6362847222222222, 'recall': 0.6882629107981221, 'f1': 0.661253946774921, 'number': 1065} | 0.5843 | 0.5600 | 0.5719 | 0.7091 |
| 0.8024 | 4.0 | 40 | 0.7725 | {'precision': 0.6457858769931663, 'recall': 0.7008652657601978, 'f1': 0.6721991701244814, 'number': 809} | {'precision': 0.1791044776119403, 'recall': 0.10084033613445378, 'f1': 0.12903225806451613, 'number': 119} | {'precision': 0.6911130284728214, 'recall': 0.752112676056338, 'f1': 0.7203237410071942, 'number': 1065} | 0.6559 | 0.6924 | 0.6737 | 0.7700 |
| 0.6483 | 5.0 | 50 | 0.7035 | {'precision': 0.6575790621592148, 'recall': 0.7453646477132262, 'f1': 0.6987253765932794, 'number': 809} | {'precision': 0.26881720430107525, 'recall': 0.21008403361344538, 'f1': 0.2358490566037736, 'number': 119} | {'precision': 0.7120067170445005, 'recall': 0.7962441314553991, 'f1': 0.75177304964539, 'number': 1065} | 0.6706 | 0.7406 | 0.7039 | 0.7857 |
| 0.5298 | 6.0 | 60 | 0.6747 | {'precision': 0.6925601750547046, 'recall': 0.7824474660074165, 'f1': 0.73476494486361, 'number': 809} | {'precision': 0.3472222222222222, 'recall': 0.21008403361344538, 'f1': 0.2617801047120419, 'number': 119} | {'precision': 0.7333333333333333, 'recall': 0.8366197183098592, 'f1': 0.7815789473684212, 'number': 1065} | 0.7038 | 0.7772 | 0.7387 | 0.7984 |
| 0.4644 | 7.0 | 70 | 0.6752 | {'precision': 0.6750261233019854, 'recall': 0.7985166872682324, 'f1': 0.7315968289920726, 'number': 809} | {'precision': 0.29357798165137616, 'recall': 0.2689075630252101, 'f1': 0.28070175438596495, 'number': 119} | {'precision': 0.7529812606473595, 'recall': 0.8300469483568075, 'f1': 0.7896382313532827, 'number': 1065} | 0.6973 | 0.7837 | 0.7380 | 0.8010 |
| 0.4253 | 8.0 | 80 | 0.6664 | {'precision': 0.699666295884316, 'recall': 0.7775030902348579, 'f1': 0.7365339578454333, 'number': 809} | {'precision': 0.3106796116504854, 'recall': 0.2689075630252101, 'f1': 0.28828828828828823, 'number': 119} | {'precision': 0.7704485488126649, 'recall': 0.8225352112676056, 'f1': 0.7956403269754768, 'number': 1065} | 0.7186 | 0.7712 | 0.7439 | 0.8017 |
| 0.3815 | 9.0 | 90 | 0.6658 | {'precision': 0.6973684210526315, 'recall': 0.7861557478368356, 'f1': 0.7391051714119697, 'number': 809} | {'precision': 0.3228346456692913, 'recall': 0.3445378151260504, 'f1': 0.3333333333333333, 'number': 119} | {'precision': 0.7474916387959866, 'recall': 0.8394366197183099, 'f1': 0.7908005307386111, 'number': 1065} | 0.7029 | 0.7883 | 0.7431 | 0.8053 |
| 0.3391 | 10.0 | 100 | 0.6736 | {'precision': 0.7022900763358778, 'recall': 0.796044499381953, 'f1': 0.7462340672074159, 'number': 809} | {'precision': 0.3252032520325203, 'recall': 0.33613445378151263, 'f1': 0.3305785123966942, 'number': 119} | {'precision': 0.7681034482758621, 'recall': 0.8366197183098592, 'f1': 0.8008988764044945, 'number': 1065} | 0.7159 | 0.7903 | 0.7513 | 0.8073 |
| 0.3117 | 11.0 | 110 | 0.6947 | {'precision': 0.7086956521739131, 'recall': 0.8059332509270705, 'f1': 0.7541931752458069, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.3445378151260504, 'f1': 0.33884297520661155, 'number': 119} | {'precision': 0.7992667277726856, 'recall': 0.8187793427230047, 'f1': 0.8089053803339518, 'number': 1065} | 0.7334 | 0.7852 | 0.7584 | 0.8083 |
| 0.2991 | 12.0 | 120 | 0.6963 | {'precision': 0.7058823529411765, 'recall': 0.8009888751545118, 'f1': 0.7504342790966995, 'number': 809} | {'precision': 0.33064516129032256, 'recall': 0.3445378151260504, 'f1': 0.33744855967078186, 'number': 119} | {'precision': 0.7716262975778547, 'recall': 0.8375586854460094, 'f1': 0.8032417829806394, 'number': 1065} | 0.7193 | 0.7933 | 0.7545 | 0.8076 |
| 0.282 | 13.0 | 130 | 0.6991 | {'precision': 0.7153846153846154, 'recall': 0.8046971569839307, 'f1': 0.7574171029668412, 'number': 809} | {'precision': 0.336, 'recall': 0.35294117647058826, 'f1': 0.3442622950819672, 'number': 119} | {'precision': 0.7898032200357782, 'recall': 0.8291079812206573, 'f1': 0.8089784699954191, 'number': 1065} | 0.7320 | 0.7908 | 0.7603 | 0.8102 |
| 0.2722 | 14.0 | 140 | 0.7044 | {'precision': 0.712253829321663, 'recall': 0.8046971569839307, 'f1': 0.7556587347649449, 'number': 809} | {'precision': 0.3228346456692913, 'recall': 0.3445378151260504, 'f1': 0.3333333333333333, 'number': 119} | {'precision': 0.7811120917917035, 'recall': 0.8309859154929577, 'f1': 0.8052775250227479, 'number': 1065} | 0.7254 | 0.7913 | 0.7569 | 0.8081 |
| 0.2634 | 15.0 | 150 | 0.7042 | {'precision': 0.712403951701427, 'recall': 0.8022249690976514, 'f1': 0.7546511627906977, 'number': 809} | {'precision': 0.3203125, 'recall': 0.3445378151260504, 'f1': 0.33198380566801616, 'number': 119} | {'precision': 0.7747589833479404, 'recall': 0.8300469483568075, 'f1': 0.8014505893019038, 'number': 1065} | 0.7220 | 0.7898 | 0.7544 | 0.8078 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1
|
bert-base-chinese | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"zh",
"arxiv:1810.04805",
"transformers",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"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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}
}
} | 3,377,486 | 2022-10-21T03:10:28Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_robotank
type: atari_robotank
metrics:
- type: mean_reward
value: 73.50 +/- 11.60
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_robotank** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
bert-base-german-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 175,983 | 2022-10-21T03:12:48Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_seaquest
type: atari_seaquest
metrics:
- type: mean_reward
value: 2371.00 +/- 462.41
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_seaquest** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
bert-base-german-dbmdz-cased | [
"pytorch",
"jax",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 1,814 | 2022-10-21T03:14:01Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_skiing
type: atari_skiing
metrics:
- type: mean_reward
value: -12798.15 +/- 193.45
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_skiing** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
bert-base-german-dbmdz-uncased | [
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 68,305 | 2022-10-21T03:18:04Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_solaris
type: atari_solaris
metrics:
- type: mean_reward
value: 1673.00 +/- 800.23
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_solaris** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
bert-base-multilingual-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
"hi",
"hu",
"is",
"io",
"id",
"ga",
"it",
"ja",
"jv",
"kn",
"kk",
"ky",
"ko",
"la",
"lv",
"lt",
"roa",
"nds",
"lm",
"mk",
"mg",
"ms",
"ml",
"mr",
"mn",
"min",
"ne",
"new",
"nb",
"nn",
"oc",
"fa",
"pms",
"pl",
"pt",
"pa",
"ro",
"ru",
"sco",
"sr",
"scn",
"sk",
"sl",
"aze",
"es",
"su",
"sw",
"sv",
"tl",
"tg",
"th",
"ta",
"tt",
"te",
"tr",
"uk",
"ud",
"uz",
"vi",
"vo",
"war",
"cy",
"fry",
"pnb",
"yo",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
"do_sample": null,
"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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}
}
} | 4,749,504 | 2022-10-21T03:21:04Z | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
inference: false
extra_gated_prompt: |-
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
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 v1-5 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 blog](https://huggingface.co/blog/stable_diffusion).
The **Stable-Diffusion-v1-5** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2)
checkpoint and subsequently fine-tuned on 595k 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).
You can use this both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [RunwayML GitHub repository](https://github.com/runwayml/stable-diffusion).
### Diffusers
```py
from diffusers import StableDiffusionPipeline
import torch
model_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16")
pipe = pipe.to(device)
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]
image.save("astronaut_rides_horse.png")
```
For more detailed instructions, use-cases and examples in JAX follow the instructions [here](https://github.com/huggingface/diffusers#text-to-image-generation-with-stable-diffusion)
### Original GitHub Repository
1. Download the weights
- [v1-5-pruned-emaonly.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt) - 4.27GB, ema-only weight. uses less VRAM - suitable for inference
- [v1-5-pruned.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned.ckpt) - 7.7GB, ema+non-ema weights. uses more VRAM - suitable for fine-tuning
2. Follow instructions [here](https://github.com/runwayml/stable-diffusion).
## 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}
}
# 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.
### Safety Module
The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers.
This checker works by checking model outputs against known hard-coded NSFW concepts.
The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter.
Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` *after generation* of the images.
The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept.
## 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 v1-5 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.
Currently six Stable Diffusion checkpoints are provided, 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).
- [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) Resumed from `stable-diffusion-v1-2` - 595,000 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).
- [`stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting) Resumed from `stable-diffusion-v1-5` - then 440,000 steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything.
- **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 PNDM/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).* |
bert-base-multilingual-uncased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
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"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
"hi",
"hu",
"is",
"io",
"id",
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"it",
"ja",
"jv",
"kn",
"kk",
"ky",
"ko",
"la",
"lv",
"lt",
"roa",
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"ms",
"ml",
"mr",
"min",
"ne",
"new",
"nb",
"nn",
"oc",
"fa",
"pms",
"pl",
"pt",
"pa",
"ro",
"ru",
"sco",
"sr",
"scn",
"sk",
"sl",
"aze",
"es",
"su",
"sw",
"sv",
"tl",
"tg",
"ta",
"tt",
"te",
"tr",
"uk",
"ud",
"uz",
"vi",
"vo",
"war",
"cy",
"fry",
"pnb",
"yo",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
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"BertForMaskedLM"
],
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}
}
} | 328,585 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_spaceinvaders
type: atari_spaceinvaders
metrics:
- type: mean_reward
value: 14799.75 +/- 20276.64
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_spaceinvaders** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
bert-base-uncased | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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},
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}
} | 59,663,489 | 2022-10-21T03:27:24Z | ---
language: en
thumbnail: http://www.huggingtweets.com/levelsio/1666322920443/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/1562107516066095106/IUccJ78Y_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">@levelsio</div>
<div style="text-align: center; font-size: 14px;">@levelsio</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 @levelsio.
| Data | @levelsio |
| --- | --- |
| Tweets downloaded | 3243 |
| Retweets | 173 |
| Short tweets | 535 |
| Tweets kept | 2535 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/tof4zha8/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 @levelsio's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/lcpeawur) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/lcpeawur/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/levelsio')
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)
|
bert-large-cased-whole-word-masking-finetuned-squad | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
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},
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}
}
} | 8,214 | 2022-10-21T03:29:28Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_stargunner
type: atari_stargunner
metrics:
- type: mean_reward
value: 248390.00 +/- 33929.53
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_stargunner** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
bert-large-cased-whole-word-masking | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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"task_specific_params": {
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},
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}
} | 2,316 | 2022-10-21T03:31:19Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9255
- name: F1
type: f1
value: 0.9253582087556043
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2244
- Accuracy: 0.9255
- F1: 0.9254
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8602 | 1.0 | 250 | 0.3344 | 0.901 | 0.8979 |
| 0.263 | 2.0 | 500 | 0.2244 | 0.9255 | 0.9254 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu102
- Datasets 1.16.1
- Tokenizers 0.10.3
|
bert-large-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
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} | 388,769 | 2022-10-21T03:31:38Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_tennis
type: atari_tennis
metrics:
- type: mean_reward
value: 23.00 +/- 1.10
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_tennis** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
bert-large-uncased-whole-word-masking-finetuned-squad | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
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}
} | 480,510 | 2022-10-21T03:37:51Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_timepilot
type: atari_timepilot
metrics:
- type: mean_reward
value: 88855.00 +/- 25100.17
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_timepilot** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
bert-large-uncased-whole-word-masking | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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},
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}
} | 76,685 | 2022-10-21T03:40:37Z | ---
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 125 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": 125,
"warmup_steps": 13,
"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 --> |
camembert-base | [
"pytorch",
"tf",
"safetensors",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"arxiv:1911.03894",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
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"CamembertForMaskedLM"
],
"model_type": "camembert",
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"prefix": null
}
}
} | 1,440,898 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_tutankham
type: atari_tutankham
metrics:
- type: mean_reward
value: 253.90 +/- 34.44
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_tutankham** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
distilbert-base-cased | [
"pytorch",
"tf",
"onnx",
"distilbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1910.01108",
"transformers",
"license:apache-2.0",
"has_space"
]
| null | {
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},
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}
}
} | 574,859 | 2022-10-21T04:06:32Z | ---
language: en
thumbnail: http://www.huggingtweets.com/elonmusk-mar15sa-sergiorocks/1666325239514/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/1580062742693699584/RJ5EI7PS_400x400.jpg')">
</div>
<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/1142324885550751744/wVNatx7J_400x400.png')">
</div>
<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/566329118489194496/f_ALTi7v_400x400.jpeg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & Sergio Pereira 🚀 & Marissa Goldberg</div>
<div style="text-align: center; font-size: 14px;">@elonmusk-mar15sa-sergiorocks</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 Musk & Sergio Pereira 🚀 & Marissa Goldberg.
| Data | Elon Musk | Sergio Pereira 🚀 | Marissa Goldberg |
| --- | --- | --- | --- |
| Tweets downloaded | 3200 | 3250 | 3248 |
| Retweets | 133 | 18 | 301 |
| Short tweets | 949 | 54 | 110 |
| Tweets kept | 2118 | 3178 | 2837 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ahul38aq/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 @elonmusk-mar15sa-sergiorocks's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1r3916r2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1r3916r2/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/elonmusk-mar15sa-sergiorocks')
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)
|
distilbert-base-german-cased | [
"pytorch",
"safetensors",
"distilbert",
"fill-mask",
"de",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
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}
}
} | 43,667 | 2022-10-21T04:07:10Z | ---
tags:
- conversational
---
# CAiFE DialoGPT2 Model |
distilbert-base-uncased-distilled-squad | [
"pytorch",
"tf",
"tflite",
"coreml",
"safetensors",
"distilbert",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| question-answering | {
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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}
}
} | 100,097 | 2022-10-21T04:17:27Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_videopinball
type: atari_videopinball
metrics:
- type: mean_reward
value: 312181.56 +/- 273369.94
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_videopinball** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
distilbert-base-uncased | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"distilbert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1910.01108",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
}
} | 10,887,471 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-custom-mail
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. -->
# distilgpt2-finetuned-custom-mail
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1905
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 7 | 3.5915 |
| No log | 2.0 | 14 | 3.4986 |
| No log | 3.0 | 21 | 3.4418 |
| No log | 4.0 | 28 | 3.3970 |
| No log | 5.0 | 35 | 3.3569 |
| No log | 6.0 | 42 | 3.3207 |
| No log | 7.0 | 49 | 3.2972 |
| No log | 8.0 | 56 | 3.2806 |
| No log | 9.0 | 63 | 3.2620 |
| No log | 10.0 | 70 | 3.2451 |
| No log | 11.0 | 77 | 3.2302 |
| No log | 12.0 | 84 | 3.2177 |
| No log | 13.0 | 91 | 3.2083 |
| No log | 14.0 | 98 | 3.2024 |
| No log | 15.0 | 105 | 3.1984 |
| No log | 16.0 | 112 | 3.1962 |
| No log | 17.0 | 119 | 3.1938 |
| No log | 18.0 | 126 | 3.1920 |
| No log | 19.0 | 133 | 3.1913 |
| No log | 20.0 | 140 | 3.1905 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
gpt2-large | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"gpt2",
"text-generation",
"en",
"arxiv:1910.09700",
"transformers",
"license:mit",
"has_space"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
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}
}
} | 1,454,819 | 2022-10-21T04:30:42Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: VM-Fast_Check
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9101123809814453
---
# VM-Fast_Check
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### person drinking
#### person smoking
#### swimsuit boy
#### swimsuit girl
 |
xlm-roberta-base | [
"pytorch",
"tf",
"jax",
"onnx",
"safetensors",
"xlm-roberta",
"fill-mask",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"arxiv:1911.02116",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
} | 25,485,905 | 2022-10-21T05:38:20Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_crazyclimber
type: atari_crazyclimber
metrics:
- type: mean_reward
value: 179445.00 +/- 33017.64
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_crazyclimber** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
123abhiALFLKFO/distilbert-base-uncased-finetuned-cola | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
| text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
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},
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} | 57 | 2022-10-21T06:22:19Z | ---
license: cc-by-nc-sa-4.0
datasets:
- race
language:
- en
---
# How to use
This is a model used in the EMNLP 2022 work, "Evaluating the Knowledge Dependency of Questions". <br>
We fine-tuned the [roberta-large](https://huggingface.co/roberta-large) model on RACE dataset. <br>
Please refer to our publication to check how to use the model to evaluate the question.
RACE finetune:
- validation_loss: 0.488
- validation_acc : 0.836
Code | https://github.com/riiid/question-score
Paper | https://aclanthology.org/2022.emnlp-main.718/
# How to cite
```
@inproceedings{moon-etal-2022-evaluating,
title = "Evaluating the Knowledge Dependency of Questions",
author = "Moon, Hyeongdon and
Yang, Yoonseok and
Yu, Hangyeol and
Lee, Seunghyun and
Jeong, Myeongho and
Park, Juneyoung and
Shin, Jamin and
Kim, Minsam and
Choi, Seungtaek",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.718",
pages = "10512--10526",
abstract = "The automatic generation of Multiple Choice Questions (MCQ) has the potential to reduce the time educators spend on student assessment significantly. However, existing evaluation metrics for MCQ generation, such as BLEU, ROUGE, and METEOR, focus on the n-gram based similarity of the generated MCQ to the gold sample in the dataset and disregard their educational value.They fail to evaluate the MCQ{'}s ability to assess the student{'}s knowledge of the corresponding target fact. To tackle this issue, we propose a novel automatic evaluation metric, coined Knowledge Dependent Answerability (KDA), which measures the MCQ{'}s answerability given knowledge of the target fact. Specifically, we first show how to measure KDA based on student responses from a human survey.Then, we propose two automatic evaluation metrics, KDA{\_}disc and KDA{\_}cont, that approximate KDA by leveraging pre-trained language models to imitate students{'} problem-solving behavior.Through our human studies, we show that KDA{\_}disc and KDA{\_}soft have strong correlations with both (1) KDA and (2) usability in an actual classroom setting, labeled by experts. Furthermore, when combined with n-gram based similarity metrics, KDA{\_}disc and KDA{\_}cont are shown to have a strong predictive power for various expert-labeled MCQ quality measures.",
}
``` |
Ab2021/bookst5 | []
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} | 0 | 2022-10-21T13:24:29Z | ('---\ndatasets:\n- ctu-aic/ctkfacts_nli\nlanguages:\n- cs\nlicense: cc-by-sa-4.0\ntags:\n- natural-language-inference\n\n---',)
# 🦾 xlm-roberta-large-squad2-ctkfacts_nli
Transformer model for **Natural Language Inference** in ['cs'] languages finetuned on ['ctu-aic/ctkfacts_nli'] datasets.
## 🧰 Usage
### 👾 Using UKPLab `sentence_transformers` `CrossEncoder`
The model was trained using the `CrossEncoder` API and we recommend it for its usage.
```python
from sentence_transformers.cross_encoder import CrossEncoder
model = CrossEncoder('ctu-aic/xlm-roberta-large-squad2-ctkfacts_nli')
scores = model.predict([["My first context.", "My first hypothesis."],
["Second context.", "Hypothesis."]])
```
### 🤗 Using Huggingface `transformers`
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("ctu-aic/xlm-roberta-large-squad2-ctkfacts_nli")
tokenizer = AutoTokenizer.from_pretrained("ctu-aic/xlm-roberta-large-squad2-ctkfacts_nli")
```
## 🌳 Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
## 👬 Authors
The model was trained and uploaded by **[ullriher](https://udb.fel.cvut.cz/?uid=ullriher&sn=&givenname=&_cmd=Hledat&_reqn=1&_type=user&setlang=en)** (e-mail: [[email protected]](mailto:[email protected]))
The code was codeveloped by the NLP team at Artificial Intelligence Center of CTU in Prague ([AIC](https://www.aic.fel.cvut.cz/)).
## 🔐 License
[cc-by-sa-4.0](https://choosealicense.com/licenses/cc-by-sa-4.0)
## 💬 Citation
If you find this repository helpful, feel free to cite our publication:
```
@article{DBLP:journals/corr/abs-2201-11115,
author = {Herbert Ullrich and
Jan Drchal and
Martin R{'{y}}par and
Hana Vincourov{'{a}} and
V{'{a}}clav Moravec},
title = {CsFEVER and CTKFacts: Acquiring Czech Data for Fact Verification},
journal = {CoRR},
volume = {abs/2201.11115},
year = {2022},
url = {https://arxiv.org/abs/2201.11115},
eprinttype = {arXiv},
eprint = {2201.11115},
timestamp = {Tue, 01 Feb 2022 14:59:01 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-11115.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
AdapterHub/bert-base-uncased-pf-winogrande | [
"bert",
"en",
"dataset:winogrande",
"arxiv:2104.08247",
"adapter-transformers",
"adapterhub:comsense/winogrande"
]
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} | 1 | null | ---
license: creativeml-openrail-m
---
Model trained with the SD 1.5: runwayml/stable-diffusion-v1-5
Dreambooth google colab: https://colab.research.google.com/github/ShivamShrirao/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb
youtube video of the training: https://www.youtube.com/watch?v=uzmJXDSxoRk&ab_channel=InversiaImages
|
AdapterHub/roberta-base-pf-conll2003 | [
"roberta",
"en",
"dataset:conll2003",
"arxiv:2104.08247",
"adapter-transformers",
"token-classification",
"adapterhub:ner/conll2003"
]
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} | 13 | null | ---
license: mit
library_name: keras
tags:
- audio
- music
- generation
- tensorflow
---
# Musika Techno Model
Pretrained Techno GAN model for the [Musika system](https://github.com/marcoppasini/musika) for fast infinite waveform music generation.
Introduced in [this paper](https://arxiv.org/abs/2208.08706).
## Model description
This pretrained GAN system consists of a ResNet-style generator and discriminator. During training, stability is controlled by adapting the strength of gradient penalty regularization on-the-fly. The gradient penalty weighting term is contained in *switch.npy*. The generator is conditioned on a latent coordinate system to produce samples of arbitrary length. The latent representations produced by the generator are then passed to a decoder which converts them into waveform audio.
The generator has a context window of about 12 seconds of audio.
### How to use
This pretrained Techno GAN system is automatically downloaded at the first execution of the system. Try Musika [here](https://github.com/marcoppasini/musika)!
## Training data
The Techno GAN system was trained on 1000 hours of music with the *techno* tag from *jamendo.com*. |
AdapterHub/roberta-base-pf-imdb | [
"roberta",
"en",
"dataset:imdb",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:sentiment/imdb"
]
| text-classification | {
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} | 0 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: longformer_openbook
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. -->
# longformer_openbook
This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7773
- Accuracy: 0.71
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 25
- total_train_batch_size: 100
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.99 | 49 | 0.8618 | 0.662 |
| No log | 1.99 | 98 | 0.7773 | 0.71 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.11.0
|
AdapterHub/roberta-base-pf-swag | [
"roberta",
"en",
"dataset:swag",
"arxiv:2104.08247",
"adapter-transformers"
]
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} | 1 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: distilbartv2
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. -->
# distilbartv2
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- label_smoothing_factor: 0.1
### Training results
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Adharsh2608/DialoGPT-small-harrypotter | []
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} | 0 | 2022-10-22T02:37:33Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: rahul77/t5-small-finetuned-thehindu1
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. -->
# rahul77/t5-small-finetuned-thehindu1
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4672
- Validation Loss: 0.7612
- Train Rouge1: 29.6559
- Train Rouge2: 24.0992
- Train Rougel: 27.7417
- Train Rougelsum: 28.4408
- Train Gen Len: 19.0
- Epoch: 49
## 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 Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 1.2252 | 0.9927 | 25.8031 | 17.7261 | 23.4483 | 25.0648 | 19.0 | 0 |
| 1.0509 | 0.9137 | 28.0482 | 20.6823 | 25.5396 | 27.0125 | 19.0 | 1 |
| 0.9961 | 0.8638 | 28.2964 | 22.1783 | 26.4157 | 27.4368 | 19.0 | 2 |
| 0.9266 | 0.8321 | 27.7054 | 21.8853 | 26.0306 | 26.9068 | 19.0 | 3 |
| 0.8851 | 0.8117 | 28.3740 | 22.8198 | 26.8479 | 27.5047 | 19.0 | 4 |
| 0.8505 | 0.7975 | 28.7979 | 23.1437 | 27.0745 | 27.7887 | 19.0 | 5 |
| 0.8247 | 0.7890 | 28.9634 | 23.3567 | 27.3117 | 28.0320 | 19.0 | 6 |
| 0.8154 | 0.7827 | 28.8667 | 23.4468 | 27.1404 | 27.8453 | 19.0 | 7 |
| 0.7889 | 0.7813 | 29.0498 | 23.6403 | 27.5662 | 28.1518 | 19.0 | 8 |
| 0.7676 | 0.7774 | 29.1829 | 23.5778 | 27.7014 | 28.3268 | 19.0 | 9 |
| 0.7832 | 0.7714 | 29.1040 | 23.3700 | 27.6605 | 28.2650 | 19.0 | 10 |
| 0.7398 | 0.7676 | 29.1040 | 23.3700 | 27.6605 | 28.2650 | 19.0 | 11 |
| 0.7473 | 0.7644 | 29.4387 | 24.1983 | 27.9842 | 28.5700 | 19.0 | 12 |
| 0.7270 | 0.7628 | 29.3128 | 24.1484 | 27.8565 | 28.4215 | 19.0 | 13 |
| 0.7174 | 0.7615 | 29.3128 | 24.1484 | 27.8565 | 28.4215 | 19.0 | 14 |
| 0.7231 | 0.7577 | 29.3838 | 23.9483 | 27.6550 | 28.3416 | 19.0 | 15 |
| 0.7099 | 0.7558 | 29.4866 | 24.1703 | 27.8649 | 28.4404 | 19.0 | 16 |
| 0.7060 | 0.7548 | 29.4866 | 24.1703 | 27.8649 | 28.4404 | 19.0 | 17 |
| 0.6884 | 0.7539 | 29.4866 | 24.1703 | 27.8649 | 28.4404 | 19.0 | 18 |
| 0.6778 | 0.7546 | 29.4866 | 24.1703 | 27.8649 | 28.4404 | 19.0 | 19 |
| 0.6586 | 0.7519 | 29.4866 | 24.1703 | 27.8649 | 28.4404 | 19.0 | 20 |
| 0.6474 | 0.7521 | 29.4866 | 24.1703 | 27.8649 | 28.4404 | 19.0 | 21 |
| 0.6392 | 0.7527 | 29.4866 | 24.1703 | 27.8649 | 28.4404 | 19.0 | 22 |
| 0.6424 | 0.7537 | 29.4866 | 24.1703 | 27.8649 | 28.4404 | 19.0 | 23 |
| 0.6184 | 0.7536 | 29.4866 | 24.1703 | 27.8649 | 28.4404 | 19.0 | 24 |
| 0.6164 | 0.7520 | 29.4866 | 24.0547 | 27.7388 | 28.3416 | 19.0 | 25 |
| 0.6115 | 0.7502 | 29.4866 | 23.9746 | 27.8232 | 28.4227 | 19.0 | 26 |
| 0.6056 | 0.7498 | 29.4866 | 23.9746 | 27.8232 | 28.4227 | 19.0 | 27 |
| 0.6004 | 0.7488 | 29.4451 | 23.7671 | 27.5435 | 28.2982 | 19.0 | 28 |
| 0.5851 | 0.7478 | 29.4451 | 23.7671 | 27.5435 | 28.2982 | 19.0 | 29 |
| 0.5777 | 0.7496 | 29.4866 | 23.9746 | 27.8232 | 28.4227 | 19.0 | 30 |
| 0.5751 | 0.7486 | 29.4866 | 23.9746 | 27.8232 | 28.4227 | 19.0 | 31 |
| 0.5730 | 0.7485 | 29.4866 | 23.9746 | 27.8232 | 28.4227 | 19.0 | 32 |
| 0.5487 | 0.7499 | 29.4962 | 24.0563 | 27.8422 | 28.4356 | 19.0 | 33 |
| 0.5585 | 0.7517 | 29.4962 | 24.0563 | 27.8422 | 28.4356 | 19.0 | 34 |
| 0.5450 | 0.7538 | 29.4962 | 24.0563 | 27.8422 | 28.4356 | 19.0 | 35 |
| 0.5427 | 0.7509 | 29.4962 | 24.0563 | 27.8422 | 28.4356 | 19.0 | 36 |
| 0.5287 | 0.7500 | 29.4962 | 24.0563 | 27.8422 | 28.4356 | 19.0 | 37 |
| 0.5231 | 0.7486 | 29.4962 | 24.0563 | 27.8422 | 28.4356 | 19.0 | 38 |
| 0.5155 | 0.7523 | 29.4962 | 24.0563 | 27.8422 | 28.4356 | 19.0 | 39 |
| 0.5105 | 0.7550 | 29.4962 | 24.0563 | 27.8422 | 28.4356 | 19.0 | 40 |
| 0.5175 | 0.7557 | 29.6736 | 24.3120 | 28.0332 | 28.5828 | 19.0 | 41 |
| 0.5053 | 0.7560 | 29.6736 | 24.3120 | 28.0332 | 28.5828 | 19.0 | 42 |
| 0.4928 | 0.7548 | 29.6736 | 24.3120 | 28.0332 | 28.5828 | 19.0 | 43 |
| 0.4913 | 0.7568 | 29.6559 | 24.0992 | 27.7417 | 28.4408 | 19.0 | 44 |
| 0.4841 | 0.7574 | 29.6559 | 24.0992 | 27.7417 | 28.4408 | 19.0 | 45 |
| 0.4770 | 0.7583 | 29.6736 | 24.3120 | 28.0332 | 28.5828 | 19.0 | 46 |
| 0.4727 | 0.7581 | 29.6736 | 24.3120 | 28.0332 | 28.5828 | 19.0 | 47 |
| 0.4612 | 0.7623 | 29.6736 | 24.3120 | 28.0332 | 28.5828 | 19.0 | 48 |
| 0.4672 | 0.7612 | 29.6559 | 24.0992 | 27.7417 | 28.4408 | 19.0 | 49 |
### Framework versions
- Transformers 4.23.1
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Ajteks/Chatbot | []
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}
} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.fr
split: train
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.8346456692913387
---
<!-- 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-fr
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: 0.2763
- F1: 0.8346
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5779 | 1.0 | 191 | 0.3701 | 0.7701 |
| 0.2735 | 2.0 | 382 | 0.2908 | 0.8254 |
| 0.1769 | 3.0 | 573 | 0.2763 | 0.8346 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Akame/Vi | []
| null | {
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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 28000 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": 28000,
"warmup_steps": 2800,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
(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 --> |
Akashpb13/Kabyle_xlsr | [
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"kab",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"sw",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
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},
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},
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}
}
} | 3 | 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: 0.1745
- F1: 0.8505
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3055 | 1.0 | 835 | 0.1842 | 0.8099 |
| 0.1561 | 2.0 | 1670 | 0.1711 | 0.8452 |
| 0.1016 | 3.0 | 2505 | 0.1745 | 0.8505 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Akashpb13/Swahili_xlsr | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"sw",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
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},
"text-generation": {
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},
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},
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},
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}
}
} | 10 | null | ---
license: apache-2.0
language: zh
tags:
- Text2Text Generation
- T5
- chinese
inference: true
widget:
- text: "情感分析任务：【房间还是比较舒适的,酒店服务良好】这篇文章的情感态度是什么？正面/负面"
- type: "text-generation"
---
# Randeng-T5-Char-57M-MultiTask-Chinese
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
- Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/)
## 简介 Brief Introduction
在Randeng-T5-Char-57M-Chinese的基础上，收集了100个左右的中文数据集，进行Text2Text统一范式的有监督任务预训练。
On the basis of Randeng-T5-Char-57M-Chinese, about 100 Chinese datasets were collected and pre-trained for the supervised task of Text2Text unified paradigm.
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 通用 General | 自然语言转换 NLT | 燃灯 Randeng | MultiTask | 57M | 多任务-中文 MultiTask-Chinese |
## 模型信息 Model Information
参考论文：[Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](http://jmlr.org/papers/v21/20-074.html)
基于[Randeng-T5-Char-57M-Chinese](https://huggingface.co/IDEA-CCNL/Randeng-T5-Char-57M-Chinese)，我们在收集的100+个中文领域的多任务数据集（从中采样了30w+个样本）上微调了它，得到了此多任务版本。这些多任务包括：情感分析，新闻分类，文本分类，意图识别，自然语言推理，多项选择，指代消解，抽取式阅读理解，实体识别，关键词抽取，生成式摘要。
Based on [Randeng-T5-Char-57M-Chinese](https://huggingface.co/IDEA-CCNL/Randeng-T5-Char-57M-Chinese), we fine-tuned it on a collection of 100+ multitasking datasets in Chinese domains (from which 30w+ samples were sampled) to obtain this multitasking version. These multitasks include: sentiment analysis, news classification, text classification, intention recognition, natural language inference, multiple choice, denotational disambiguation, extractive reading comprehension, entity recognition, keyword extraction, and generative summarization.
## 使用 Usage
```python
import torch
from transformers import BertTokenizer, T5Config, T5ForConditionalGeneration
# load tokenizer and model
pretrained_model = "IDEA-CCNL/Randeng-T5-Char-57M-MultiTask-Chinese"
tokenizer = BertTokenizer.from_pretrained(pretrained_model)
config = T5Config.from_pretrained(pretrained_model)
model = T5ForConditionalGeneration.from_pretrained(pretrained_model, config=config)
model.resize_token_embeddings(len(tokenizer))
model.eval()
# tokenize
text = "情感分析任务：【房间还是比较舒适的,酒店服务良好】这篇文章的情感态度是什么？正面/负面"
encode_dict = tokenizer(text, max_length=512, padding='max_length',truncation=True)
inputs = {
"input_ids": torch.tensor([encode_dict['input_ids']]).long(),
"attention_mask": torch.tensor([encode_dict['attention_mask']]).long(),
}
# generate answer
logits = model.generate(
input_ids = inputs['input_ids'],
max_length=10,
early_stopping=True,
)
logits=logits[:,1:]
predict_label = [tokenizer.decode(i,skip_special_tokens=True) for i in logits]
print(predict_label)
# model output: 正面
```
除了分类任务，其他任务的数据构造例子如下：
In addition to classification tasks, data construction examples of other tasks are as follows:
```python
example_dict={
"文本分类":{"text_a":"钢琴块3别踩白块儿3钢琴块3是一款简洁的钢琴模拟软件,在Android平台上,类似的软件还是比较多的。","choices":["相机","影视娱乐","棋牌中心","新闻","财经","策略","休闲益智","教育"]},
'新闻分类':{"text_a":"微软披露拓扑量子计算机计划！","choices":["故事","文化","娱乐","体育","财经","房产","汽车","教育","科技"]},
'情感分析':{"text_a":"刚买iphone13 pro 还不到一个月，天天死机最差的一次购物体验","choices":["好评","差评"]},
'意图识别':{"text_a":"打电话给吴小军。","choices":["放音乐","播放下一首","打电话","退出导航","开始导航","其他","暂停音乐","导航","开导航"]},
'语义匹配':{"text_a":"今天心情不好","text_b":"我很不开心","choices":["相似","不相似"]},
'自然语言推理':{"text_a":"小明正在上高中","text_b":"小明是一个初中生","choices":["无关","矛盾","蕴含"]},
'多项选择':{"text_a":"这大家千万不能着急，我们现在只是暂时输了7分。距离比赛结束还有20多分钟呢，我们是完全有机会转败为赢的，大家加油!","question":"说话人希望大家：","choices":["别得意","冷静一些","加快速度","提前预习"]},
'指代消解':{"text_a":"李鸣觉得董客这人，踏实得叫人难受。可因为孟野和森森太疯，他只好去找董客聊天，但在董客眼里，李鸣也是不正常，他竟然放着现成的大学不愿上。","question":"【他】指的是【李鸣】吗？","choices":["是","不是"]},
'实体识别':{"text_a":"北京大学是我国的一座历史名校，坐落在海淀区，蔡元培曾经担任校长","question":"机构"},
'抽取式阅读理解':{"text_a":"《H》正式定档3月7日下午两点整在京东商城独家平台开启第一批5000份预售,定价230元人民币,回馈最忠实的火星歌迷,意在用精品回馈三年来跟随华晨宇音乐不离不弃的粉丝们的支持与厚爱","question":"华晨宇专辑h预售价格是多少？"},
'关键词抽取':{"text_a":"今儿在大众点评，找到了口碑不错的老茶故事私房菜。"},
"生成式摘要":{"text_a":"针对传统的流量分类管理系统存在不稳定、结果反馈不及时、分类结果显示不直观等问题,设计一个基于web的在线的流量分类管理系统.该系统采用流中前5个包(排除3次握手包)所含信息作为特征值计算资源,集成一种或多种分类算法用于在线网络流量分类,应用数据可视化技术处理分类结果.实验表明:在采用适应在线分类的特征集和c4.5决策树算法做分类时,系统能快速做出分类,且精度达到94％以上;数据可视化有助于人机交互,改善分类指导."}
}
# 构造prompt的过程中，verbalizer这个占位key的内容，是通过 "/".join(choices) 拼接起来
dataset2instruction = {
"情感分析": {
"prompt": "{}任务：【{}】这篇文章的情感态度是什么？{}",
"keys_order": ["subtask_type","text_a", "verbalizer"],
"data_type": "classification",
},
"文本分类": {
"prompt": "{}任务：【{}】这篇文章的类别是什么？{}",
"keys_order": ["subtask_type","text_a", "verbalizer"],
"data_type": "classification",
},
"新闻分类": {
"prompt": "{}任务：【{}】这篇文章的类别是什么？{}",
"keys_order": ["subtask_type","text_a", "verbalizer"],
"data_type": "classification",
},
"意图识别": {
"prompt": "{}任务：【{}】这句话的意图是什么？{}",
"keys_order": ["subtask_type","text_a", "verbalizer"],
"data_type": "classification",
},
# --------------------
"自然语言推理": {
"prompt": "{}任务：【{}】和【{}】，以上两句话的逻辑关系是什么？{}",
"keys_order": ["subtask_type","text_a", "text_b", "verbalizer"],
"data_type": "classification",
},
"语义匹配": {
"prompt": "{}任务：【{}】和【{}】，以上两句话的内容是否相似？{}",
"keys_order": ["subtask_type","text_a", "text_b", "verbalizer"],
"data_type": "classification",
},
# -----------------------
"指代消解": {
"prompt": "{}任务：文章【{}】中{}{}",
"keys_order": ["subtask_type","text_a", "question", "verbalizer"],
"data_type": "classification",
},
"多项选择": {
"prompt": "{}任务：阅读文章【{}】问题【{}】？{}",
"keys_order": ["subtask_type","text_a", "question", "verbalizer"],
"data_type": "classification",
},
# ------------------------
"抽取式阅读理解": {
"prompt": "{}任务：阅读文章【{}】问题【{}】的答案是什么？",
"keys_order": ["subtask_type","text_a", "question"],
"data_type": "mrc",
},
"实体识别": {
"prompt": "{}任务：找出【{}】这篇文章中所有【{}】类型的实体？",
"keys_order": ["subtask_type","text_a", "question"],
"data_type": "ner",
},
# ------------------------
"关键词抽取": {
"prompt": "{}任务：【{}】这篇文章的关键词是什么？",
"keys_order": ["subtask_type","text_a"],
"data_type": "keys",
},
"关键词识别":{
"prompt": "{}任务：阅读文章【{}】问题【{}】{}",
"keys_order": ["subtask_type","text_a","question","verbalizer"],
"data_type": "classification",
},
"生成式摘要": {
"prompt": "{}任务：【{}】这篇文章的摘要是什么？",
"keys_order": ["subtask_type","text_a"],
"data_type": "summ",
},
}
def get_instruction(sample):
template = dataset2instruction[sample["subtask_type"]]
# print(template)
# print(sample)
sample["instruction"] = template["prompt"].format(*[
sample[k] for k in template["keys_order"]
])
print(sample["instruction"])
return sample["instruction"]
```
## 预训练或微调 prtrain or finetune
如果您对于怎么预训练Randeng-T5模型或者想在自己的下游任务中微调Randeng模型，欢迎使用[Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM/)项目，这里提供了完整的示例：
- [预训练](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/pretrain_t5)
- [微调](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/mt5_summary)
If you want to pre train the Randeng T5 model or fine tune the Randeng model in your downstream tasks, welcome to use [Fengshenbang LM]（ https://github.com/IDEA-CCNL/Fengshenbang-LM/ ）A complete example of the project is provided here:
- [Pre training](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/pretrain_t5)
- [Fine tune](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/mt5_summary)
## 引用 Citation
如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2209.02970)：
If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2209.02970):
```text
@article{fengshenbang,
author = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen},
title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
journal = {CoRR},
volume = {abs/2209.02970},
year = {2022}
}
```
也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
``` |
Akashpb13/xlsr_maltese_wav2vec2 | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"mt",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
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},
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},
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},
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"prefix": null
}
}
} | 8 | 2022-10-22T09:13:13Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: tomthekkan/mt5-small-finetuned-amazon-en-es
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. -->
# tomthekkan/mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.1138
- Validation Loss: 3.3816
- Epoch: 7
## 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': 5.6e-05, 'decay_steps': 9672, '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 | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 9.9822 | 4.2802 | 0 |
| 5.9654 | 3.7811 | 1 |
| 5.2343 | 3.6557 | 2 |
| 4.8190 | 3.5433 | 3 |
| 4.5149 | 3.4695 | 4 |
| 4.3105 | 3.4202 | 5 |
| 4.1907 | 3.3909 | 6 |
| 4.1138 | 3.3816 | 7 |
### Framework versions
- Transformers 4.23.1
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Akbarariza/Anjar | []
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}
}
} | 0 | 2022-10-22T09:33:50Z | ---
language: en
thumbnail: http://www.huggingtweets.com/ouvessvit/1666431286897/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/1539686183927795712/_V9skTmk_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">Natalie Godec 🇺🇦</div>
<div style="text-align: center; font-size: 14px;">@ouvessvit</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 Natalie Godec 🇺🇦.
| Data | Natalie Godec 🇺🇦 |
| --- | --- |
| Tweets downloaded | 1043 |
| Retweets | 74 |
| Short tweets | 83 |
| Tweets kept | 886 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2yoysr8v/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 @ouvessvit's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3q5y5xzk) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3q5y5xzk/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/ouvessvit')
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)
|
AkshaySg/LanguageIdentification | [
"multilingual",
"dataset:VoxLingua107",
"LID",
"spoken language recognition",
"license:apache-2.0"
]
| null | {
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}
} | 0 | null | ---
language:
- ne
license: apache-2.0
tags:
- generated_from_trainer
- automatic-speech-recognition
- speech
- openslr
- nepali
datasets:
- spktsagar/openslr-nepali-asr-cleaned
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-nepali-openslr
results:
- task:
type: automatic-speech-recognition
name: Nepali Speech Recognition
dataset:
type: spktsagar/openslr-nepali-asr-cleaned
name: OpenSLR Nepali ASR
config: original
split: train
metrics:
- type: were
value: 21.27
name: Test WER
verified: false
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-nepali-openslr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an [OpenSLR Nepali ASR](https://huggingface.co/datasets/spktsagar/openslr-nepali-asr-cleaned) dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.1767
- eval_wer: 0.2127
- eval_runtime: 595.3962
- eval_samples_per_second: 36.273
- eval_steps_per_second: 4.535
- epoch: 6.07
- step: 23200
## Model description
Wav2Vec2 is a pretrained model for Automatic Speech Recognition (ASR) and was released in September 2020 by Alexei Baevski, Michael Auli, and Alex Conneau. Soon after the superior performance of Wav2Vec2 was demonstrated on one of the most popular English datasets for ASR, called LibriSpeech, Facebook AI presented a multi-lingual version of Wav2Vec2, called XLSR. XLSR stands for cross-lingual speech representations and refers to model's ability to learn speech representations that are useful across multiple languages.
## How to use?
1. Install transformers and librosa
```
pip install librosa, transformers
```
2. Run the following code which loads your audio file, preprocessor, models, and returns your prediction
```python
import librosa
from transformers import pipeline
audio, sample_rate = librosa.load("<path to your audio file>", sr=16000)
recognizer = pipeline("automatic-speech-recognition", model="spktsagar/wav2vec2-large-xls-r-300m-nepali-openslr")
prediction = recognizer(audio)
```
## Intended uses & limitations
The model is trained on the OpenSLR Nepali ASR dataset, which in itself has some incorrect transcriptions, so it is obvious that the model will not have perfect predictions for your transcript. Similarly, due to colab's resource limit utterances longer than 5 sec are filtered out from the dataset during training and evaluation. Hence, the model might not perform as expected when given audio input longer than 5 sec.
## Training and evaluation data and Training procedure
For dataset preparation and training code, please consult [my blog](https://sagar-spkt.github.io/posts/2022/08/finetune-xlsr-nepali/).
### 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: 10
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.23.1
- Pytorch 1.11.0+cu113
- Datasets 2.6.0
- Tokenizers 0.13.1
|
AkshaySg/gramCorrection | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"early_stopping": true,
"length_penalty": 2,
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"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
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},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 4 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: khynnah94/testpyramidsrnd
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Akuva2001/SocialGraph | [
"has_space"
]
| null | {
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}
}
} | 0 | null | Access to model Deep199807/dj is restricted and you are not in the authorized list. Visit https://huggingface.co/Deep199807/dj to ask for access. |
Al/mymodel | []
| null | {
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}
} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: marshmallow-finetuned-cord-v2
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. -->
# marshmallow-finetuned-cord-v2
This model is a fine-tuned version of [naver-clova-ix/donut-base-finetuned-cord-v2](https://huggingface.co/naver-clova-ix/donut-base-finetuned-cord-v2) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- 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
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
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