modelId
stringlengths 4
81
| tags
sequence | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
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|---|---|---|---|---|---|---|
bert-base-multilingual-uncased | [
"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",
"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 | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
}
} | 328,585 | 2022-05-03T23:52:57Z | ---
language:
- en
datasets:
- pubmed
metrics:
- f1
pipeline_tag: text-classification
widget:
- text: "many pathogenic processes and diseases are the result of an erroneous activation of the complement cascade and a number of inhibitors of complement have thus been examined for anti-inflammatory actions."
example_title: "background example"
- text: "a total of 192 mi patients and 140 control persons were included."
example_title: "methods example"
- text: "mi patients had 18 % higher plasma levels of map44 (iqr 11-25 %) as compared to the healthy control group (p < 0. 001.)"
example_title: "results example"
- text: "the finding that a brief cb group intervention delivered by real-world providers significantly reduced mdd onset relative to both brochure control and bibliotherapy is very encouraging, although effects on continuous outcome measures were small or nonsignificant and approximately half the magnitude of those found in efficacy research, potentially because the present sample reported lower initial depression."
example_title: "conclusions example"
- text: "in order to understand and update the prevalence of myopia in taiwan, a nationwide survey was performed in 1995."
example_title: "objective example"
---
# bluebert-pubmed-uncased-L-12-H-768-A-12_pub_section
- original model file name: textclassifer_bluebert_pubmed_uncased_L-12_H-768_A-12_pubmed_20k
- This is a fine-tuned checkpoint of `bionlp/bluebert_pubmed_uncased_L-12_H-768_A-12` for document section text classification
- possible document section classes are:BACKGROUND, CONCLUSIONS, METHODS, OBJECTIVE, RESULTS,
## metadata
### training_metrics
- val_accuracy: 0.8367536067962646
- val_matthewscorrcoef: 0.779039740562439
- val_f1score: 0.834040641784668
- val_cross_entropy: 0.5102494359016418
- epoch: 18.0
- train_accuracy_step: 0.7890625
- train_matthewscorrcoef_step: 0.7113237380981445
- train_f1score_step: 0.7884777784347534
- train_cross_entropy_step: 0.5615811944007874
- train_accuracy_epoch: 0.7955580949783325
- train_matthewscorrcoef_epoch: 0.7233519554138184
- train_f1score_epoch: 0.7916122078895569
- train_cross_entropy_epoch: 0.6050205230712891
- test_accuracy: 0.8310602307319641
- test_matthewscorrcoef: 0.7718994617462158
- test_f1score: 0.8283351063728333
- test_cross_entropy: 0.5230290293693542
- date_run: Apr-22-2022_t-05
- huggingface_tag: bionlp/bluebert_pubmed_uncased_L-12_H-768_A-12
|
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"
],
"model_type": "bert",
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}
} | 8,214 | 2022-05-04T00:14:18Z | ---
language:
- en
datasets:
- pubmed
- ml4pubmed/pubmed-classification-20k
metrics:
- f1
tags:
- text-classification
- document sections
- sentence classification
- document classification
- medical
- health
- biomedical
pipeline_tag: text-classification
widget:
- text: >-
many pathogenic processes and diseases are the result of an erroneous
activation of the complement cascade and a number of inhibitors of
complement have thus been examined for anti-inflammatory actions.
example_title: background example
- text: a total of 192 mi patients and 140 control persons were included.
example_title: methods example
- text: >-
mi patients had 18 % higher plasma levels of map44 (iqr 11-25 %) as compared
to the healthy control group (p < 0. 001.)
example_title: results example
- text: >-
the finding that a brief cb group intervention delivered by real-world
providers significantly reduced mdd onset relative to both brochure control
and bibliotherapy is very encouraging, although effects on continuous
outcome measures were small or nonsignificant and approximately half the
magnitude of those found in efficacy research, potentially because the
present sample reported lower initial depression.
example_title: conclusions example
- text: >-
in order to understand and update the prevalence of myopia in taiwan, a
nationwide survey was performed in 1995.
example_title: objective example
license: apache-2.0
---
# BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext_pub_section
- original model file name: textclassifer_BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext_pubmed_20k
- This is a fine-tuned checkpoint of `microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext` for document section text classification
- possible document section classes are:BACKGROUND, CONCLUSIONS, METHODS, OBJECTIVE, RESULTS,
## usage in python
install transformers as needed:
```bash
pip install -U transformers
```
Run the following, changing the example text to your use case:
```python
from transformers import pipeline
model_tag = "ml4pubmed/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext_pub_section"
classifier = pipeline(
'text-classification',
model=model_tag,
)
prompt = """
Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train.
"""
classifier(
prompt,
) # classify the sentence
```
## metadata
### training_metrics
- val_accuracy: 0.8678670525550842
- val_matthewscorrcoef: 0.8222037553787231
- val_f1score: 0.866841197013855
- val_cross_entropy: 0.3674609065055847
- epoch: 8.0
- train_accuracy_step: 0.83984375
- train_matthewscorrcoef_step: 0.7790813446044922
- train_f1score_step: 0.837363600730896
- train_cross_entropy_step: 0.39843088388442993
- train_accuracy_epoch: 0.8538406491279602
- train_matthewscorrcoef_epoch: 0.8031334280967712
- train_f1score_epoch: 0.8521654605865479
- train_cross_entropy_epoch: 0.4116102457046509
- test_accuracy: 0.8578397035598755
- test_matthewscorrcoef: 0.8091378808021545
- test_f1score: 0.8566917181015015
- test_cross_entropy: 0.3963385224342346
- date_run: Apr-22-2022_t-19
- huggingface_tag: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext |
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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}
} | 2,316 | 2022-05-04T00:23:49Z | ---
language:
- en
datasets:
- pubmed
metrics:
- f1
pipeline_tag: text-classification
widget:
- text: "Many pathogenic processes and diseases are the result of an erroneous activation of the complement cascade and a number of inhibitors of complement have thus been examined for anti-inflammatory actions."
example_title: "BACKGROUND example"
- text: "A total of 192 MI patients and 140 control persons were included."
example_title: "METHODS example"
- text: "MI patients had 18 % higher plasma levels of MAp44 (IQR 11-25 %) as compared to the healthy control group (p < 0. 001.)"
example_title: "RESULTS example"
- text: "The finding that a brief CB group intervention delivered by real-world providers significantly reduced MDD onset relative to both brochure control and bibliotherapy is very encouraging, although effects on continuous outcome measures were small or nonsignificant and approximately half the magnitude of those found in efficacy research, potentially because the present sample reported lower initial depression."
example_title: "CONCLUSIONS example"
- text: "In order to understand and update the prevalence of myopia in Taiwan, a nationwide survey was performed in 1995."
example_title: "OBJECTIVE example"
---
# BioM-BERT-PubMed-PMC-Large_pub_section
- original model file name: textclassifer_BioM-BERT-PubMed-PMC-Large_pubmed_20k
- This is a fine-tuned checkpoint of `sultan/BioM-BERT-PubMed-PMC-Large` for document section text classification
- possible document section classes are:BACKGROUND, CONCLUSIONS, METHODS, OBJECTIVE, RESULTS,
## metadata
### training_metrics
- date_run: Apr-23-2022_t-04
- huggingface_tag: sultan/BioM-BERT-PubMed-PMC-Large
### training_parameters
- date_run: Apr-23-2022_t-04
- huggingface_tag: sultan/BioM-BERT-PubMed-PMC-Large
|
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"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
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},
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},
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"translation_en_to_fr": {
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}
}
} | 480,510 | 2022-05-04T01:20:24Z | ---
tags:
- conversational
---
# Mandy Bot DialoGPT Model |
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"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
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"prefix": null
}
}
} | 76,685 | 2022-05-04T01:32:45Z | ---
language:
- en
datasets:
- pubmed
metrics:
- f1
tags:
- text-classification
- document sections
- sentence classification
- document classification
- medical
- health
- biomedical
pipeline_tag: text-classification
widget:
- text: "many pathogenic processes and diseases are the result of an erroneous activation of the complement cascade and a number of inhibitors of complement have thus been examined for anti-inflammatory actions."
example_title: "background example"
- text: "a total of 192 mi patients and 140 control persons were included."
example_title: "methods example"
- text: "mi patients had 18 % higher plasma levels of map44 (iqr 11-25 %) as compared to the healthy control group (p < 0. 001.)"
example_title: "results example"
- text: "the finding that a brief cb group intervention delivered by real-world providers significantly reduced mdd onset relative to both brochure control and bibliotherapy is very encouraging, although effects on continuous outcome measures were small or nonsignificant and approximately half the magnitude of those found in efficacy research, potentially because the present sample reported lower initial depression."
example_title: "conclusions example"
- text: "in order to understand and update the prevalence of myopia in taiwan, a nationwide survey was performed in 1995."
example_title: "objective example"
---
# xtremedistil-l12-h384-uncased_pub_section
- original model file name: textclassifer_xtremedistil-l12-h384-uncased_pubmed_20k
- This is a fine-tuned checkpoint of `microsoft/xtremedistil-l12-h384-uncased` for document section text classification
- possible document section classes are:BACKGROUND, CONCLUSIONS, METHODS, OBJECTIVE, RESULTS,
## usage in python
install transformers as needed: `pip install -U transformers`
run the following, changing the example text to your use case:
```
from transformers import pipeline
model_tag = "ml4pubmed/xtremedistil-l12-h384-uncased_pub_section"
classifier = pipeline(
'text-classification',
model=model_tag,
)
prompt = """
Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train.
"""
classifier(
prompt,
) # classify the sentence
```
## metadata
### training_parameters
- date_run: Apr-24-2022_t-12
- huggingface_tag: microsoft/xtremedistil-l12-h384-uncased
|
bert-large-uncased | [
"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"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"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": {
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"translation_en_to_ro": {
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}
}
} | 1,058,496 | 2022-05-04T01:44:12Z | ---
language: en
thumbnail: http://www.huggingtweets.com/dril-nycguidovoice-senn_spud/1651629321136/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/1510917391533830145/XW-zSFDJ_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/1503095773059244036/xof9dI-A_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/1387151448203358209/HKNuKY7L_400x400.jpg')">
</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">wint & Nick Mullen & Will Sennett</div>
<div style="text-align: center; font-size: 14px;">@dril-nycguidovoice-senn_spud</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 wint & Nick Mullen & Will Sennett.
| Data | wint | Nick Mullen | Will Sennett |
| --- | --- | --- | --- |
| Tweets downloaded | 3229 | 1007 | 3231 |
| Retweets | 486 | 71 | 314 |
| Short tweets | 300 | 41 | 631 |
| Tweets kept | 2443 | 895 | 2286 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3dcek2rh/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 @dril-nycguidovoice-senn_spud's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2f1xmo4s) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2f1xmo4s/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/dril-nycguidovoice-senn_spud')
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)
|
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"
],
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} | 1,440,898 | 2022-05-04T01:48:49Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Xzt/bert-finetuned-ner
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. -->
# Xzt/bert-finetuned-ner
This model is a fine-tuned version of [hfl/chinese-bert-wwm-ext](https://huggingface.co/hfl/chinese-bert-wwm-ext) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0622
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7039, '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: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 0.0622 | 0 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.6.0
- Datasets 1.18.0
- Tokenizers 0.12.1
|
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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} | 574,859 | 2022-05-04T02:46:12Z | ---
license: apache-2.0
---
# Taiyi-vit-87M-D
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
- Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/)
## 简介 Brief Introduction
COCO和VG上特殊预训练的，英文版的MAP（名称暂定）的视觉端ViT-base。
Special pre-training on COCO and VG, the visual encoder for MAP (temporary) in English, ViT-base.
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 特殊 Special | 多模态 Multimodal | 太乙 Taiyi | 待定 TBD | 89M | 特殊预训练方法 D |
## 模型信息 Model Information
基于clip-vit-base (patch 16, resolution 224x224)，我们使用特殊的训练任务引入一些多模态信息。"D"表示这是一种新的预训练方法。对于特殊的多模态表征，在论文中我们设计了集中不同的训练目标。预训练数据集为MSCOCO和VG。我们的代码和预训练任务的细节将在论文接受后公开。
Based on pre-trained clip-vit-base (patch 16, resolution 224x224), we apply some multimodal information with special pre-training tasks. "D" implies a special training method. For special multimodal representations, we design several special training objectives in our paper. The pre-training datasets are MSCOCO and VG. Our code and details of pre-training tasks will be made publicly available upon paper acceptance.
### 下游任务 Performance
| | CIFAR10 | ImageNet1k |
|--------------------------------------|:-------:|:----------:|
| clip-vit-base-patch16-224 (official) | 96.2 | 80.2 |
| Taiyi-vit-87M-D (local) | 98.7 | 82.4 |
The local test settings are:
learning rate=2e-5,
batch size=128,
num train epochs=5,
weight decay=0.01
## 使用 Usage
```python
from transformers import ViTFeatureExtractor, ViTForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = ViTFeatureExtractor.from_pretrained('IDEA-CCNL/Taiyi-vit-87M-D')
model = ViTForImageClassification.from_pretrained('IDEA-CCNL/Taiyi-vit-87M-D')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
# Predicted class: Egyptian cat
```
## 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}},
}
```
|
distilbert-base-uncased-finetuned-sst-2-english | [
"pytorch",
"tf",
"rust",
"safetensors",
"distilbert",
"text-classification",
"en",
"dataset:sst2",
"dataset:glue",
"arxiv:1910.01108",
"doi:10.57967/hf/0181",
"transformers",
"license:apache-2.0",
"model-index",
"has_space"
] | text-classification | {
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} | 3,060,704 | 2022-05-04T03:52:17Z | ---
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: 0.9245
- name: F1
type: f1
value: 0.9247463289719563
---
<!-- 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: 0.2223
- Accuracy: 0.9245
- F1: 0.9247
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8296 | 1.0 | 250 | 0.3200 | 0.902 | 0.9002 |
| 0.2522 | 2.0 | 500 | 0.2223 | 0.9245 | 0.9247 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
distilgpt2 | [
"pytorch",
"tf",
"jax",
"tflite",
"rust",
"coreml",
"safetensors",
"gpt2",
"text-generation",
"en",
"dataset:openwebtext",
"arxiv:1910.01108",
"arxiv:2201.08542",
"arxiv:2203.12574",
"arxiv:1910.09700",
"arxiv:1503.02531",
"transformers",
"exbert",
"license:apache-2.0",
"model-index",
"co2_eq_emissions",
"has_space"
] | text-generation | {
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} | 1,611,668 | 2022-05-04T04:07:58Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: datauma/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. -->
# datauma/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: 4.2505
- Validation Loss: 3.4530
- 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 |
|:----------:|:---------------:|:-----:|
| 11.9288 | 5.8713 | 0 |
| 6.6821 | 4.3246 | 1 |
| 5.6453 | 3.8715 | 2 |
| 5.0908 | 3.6368 | 3 |
| 4.7348 | 3.5496 | 4 |
| 4.5106 | 3.4939 | 5 |
| 4.3261 | 3.4659 | 6 |
| 4.2505 | 3.4530 | 7 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
xlm-mlm-ende-1024 | [
"pytorch",
"tf",
"xlm",
"fill-mask",
"multilingual",
"en",
"de",
"arxiv:1901.07291",
"arxiv:1910.09700",
"transformers",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"XLMWithLMHeadModel"
],
"model_type": "xlm",
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} | 287 | 2022-05-04T08:55:56Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: data2vec-text-base-finetuned-rte
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: rte
metrics:
- name: Accuracy
type: accuracy
value: 0.6209386281588448
---
<!-- 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. -->
# data2vec-text-base-finetuned-rte
This model is a fine-tuned version of [facebook/data2vec-text-base](https://huggingface.co/facebook/data2vec-text-base) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6670
- Accuracy: 0.6209
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 156 | 0.7091 | 0.4729 |
| No log | 2.0 | 312 | 0.6893 | 0.5271 |
| No log | 3.0 | 468 | 0.6670 | 0.6209 |
| 0.6919 | 4.0 | 624 | 0.6740 | 0.5921 |
| 0.6919 | 5.0 | 780 | 0.6644 | 0.6101 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
007J/smile | [] | null | {
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} | 0 | 2022-05-04T10:10:10Z | ---
license: mit
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a Vietnamese [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like questions answering 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 -->
The thesis will be available on [https://github.com/ncthuan/uet-qa](https://github.com/ncthuan/uet-qa) with evaluation results in chapter 4.
paraphrase-multilingual-minilm: 75 recall@10, 49 MRR@10
this model: 85 recall@10, 58 MRR@10
## Training
It was distilled using English-Vietnamese parallel data with this [training script](https://github.com/ncthuan/uet-qa/blob/main/scripts/train/make_multilingual.py)
that follows the work of [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://www.sbert.net/examples/training/multilingual/README.html)
teacher: msmarco-MiniLM-L12-cos-v5
student: paraphrase-multilingual-minilm-L12-v2
Data: PhoMT, MKQA, MLQA, XQuAD
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 40148 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MSELoss.MSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 2000,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"correct_bias": false,
"eps": 1e-06,
"lr": 1e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 2000,
"weight_decay": 0.005
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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 -->
```
@inproceedings{reimers-2020-multilingual-sentence-bert,
title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2020",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2004.09813",
}
@article{thuan2022-uetqa,
title={{Extractive question answering system on regulations for University of Engineering and Technology}},
author={Nguyen, Thuan},
journal={Undergraduate Thesis, University of Engineering and Technology, Vietnam National University Hanoi},
year={2022}
}
``` |
0xDEADBEA7/DialoGPT-small-rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 9 | 2022-05-04T10:29:07Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-tcrs-runtest
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-tcrs-runtest
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1370
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 22.437 | 1.43 | 10 | 36.3252 | 1.0 |
| 14.7939 | 2.86 | 20 | 10.7441 | 1.0 |
| 4.1824 | 4.29 | 30 | 3.7354 | 1.0 |
| 3.289 | 5.71 | 40 | 3.5265 | 1.0 |
| 3.1639 | 7.14 | 50 | 3.2868 | 1.0 |
| 3.1107 | 8.57 | 60 | 3.3268 | 1.0 |
| 3.0737 | 10.0 | 70 | 3.1149 | 1.0 |
| 3.0273 | 11.43 | 80 | 3.2031 | 1.0 |
| 3.0422 | 12.86 | 90 | 3.0771 | 1.0 |
| 2.9957 | 14.29 | 100 | 3.0418 | 1.0 |
| 2.9894 | 15.71 | 110 | 3.0321 | 1.0 |
| 2.9997 | 17.14 | 120 | 3.0545 | 1.0 |
| 2.9806 | 18.57 | 130 | 2.9936 | 1.0 |
| 2.969 | 20.0 | 140 | 3.0322 | 1.0 |
| 2.9692 | 21.43 | 150 | 3.0238 | 1.0 |
| 2.9638 | 22.86 | 160 | 3.0407 | 1.0 |
| 2.969 | 24.29 | 170 | 3.2487 | 1.0 |
| 2.9783 | 25.71 | 180 | 3.1248 | 1.0 |
| 2.9576 | 27.14 | 190 | 3.0880 | 1.0 |
| 2.968 | 28.57 | 200 | 3.0962 | 1.0 |
| 2.9784 | 30.0 | 210 | 3.1370 | 1.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu102
- Datasets 1.18.3
- Tokenizers 0.10.3
|
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} | 0 | 2022-05-04T16:08:55Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 286.36 +/- 12.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
AK270802/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
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} | 10 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 267.56 +/- 15.74
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
AKulk/wav2vec2-base-timit-demo-colab | [] | null | {
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 215.15 +/- 49.08
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
AKulk/wav2vec2-base-timit-epochs10 | [
"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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} | 4 | 2022-05-04T17:56:16Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 210.82 +/- 19.82
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
|
AKulk/wav2vec2-base-timit-epochs5 | [
"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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} | 4 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 265.98 +/- 9.31
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
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} | 0 | 2022-05-04T18:27:29Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 279.01 +/- 16.25
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
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} | 0 | 2022-05-04T18:30:43Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -212.34 +/- 46.89
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
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} | 0 | 2022-05-04T18:43:43Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 198.92 +/- 36.84
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
ARTeLab/it5-summarization-fanpage | [
"pytorch",
"t5",
"text2text-generation",
"it",
"dataset:ARTeLab/fanpage",
"transformers",
"summarization",
"autotrain_compatible",
"has_space"
] | summarization | {
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} | 698 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: HomayounSadri/bert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# HomayounSadri/bert-base-uncased-finetuned-squad
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6196
- Validation Loss: 1.0521
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16596, '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: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.3747 | 1.0166 | 0 |
| 0.8290 | 0.9963 | 1 |
| 0.6196 | 1.0521 | 2 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
ARTeLab/it5-summarization-ilpost | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"it",
"dataset:ARTeLab/ilpost",
"transformers",
"summarization",
"autotrain_compatible",
"has_space"
] | summarization | {
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} | 16 | 2022-05-04T18:47:25Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ppo-LunarLander-v2
results:
- metrics:
- type: mean_reward
value: 159.70 +/- 97.42
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **ppo-LunarLander-v2** Agent playing **LunarLander-v2**
This is a trained model of a **ppo-LunarLander-v2** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
ARTeLab/it5-summarization-mlsum | [
"pytorch",
"t5",
"text2text-generation",
"it",
"dataset:ARTeLab/mlsum-it",
"transformers",
"summarization",
"autotrain_compatible",
"has_space"
] | summarization | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
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} | 16 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 270.00 +/- 22.67
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
ARTeLab/mbart-summarization-mlsum | [
"pytorch",
"mbart",
"text2text-generation",
"it",
"dataset:ARTeLab/mlsum-it",
"transformers",
"summarization",
"autotrain_compatible",
"has_space"
] | summarization | {
"architectures": [
"MBartForConditionalGeneration"
],
"model_type": "mbart",
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} | 111 | 2022-05-04T18:54:19Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 284.37 +/- 17.27
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
ASCCCCCCCC/PENGMENGJIE-finetuned-emotion | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | {
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} | 35 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: reaprtripr/distillRobertaMLM-csnPython-finetuned-imdb
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. -->
# reaprtripr/distillRobertaMLM-csnPython-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.1703
- Validation Loss: 2.6914
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -688, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.1703 | 2.6914 | 0 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
ASCCCCCCCC/PENGMENGJIE | [
"license:apache-2.0"
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} | 0 | 2022-05-04T19:00:47Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -230.00 +/- 76.52
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
ASCCCCCCCC/PMJ | [] | null | {
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 261.24 +/- 15.44
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
ASCCCCCCCC/bert-base-chinese-finetuned-amazon_zh | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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} | 39 | 2022-05-04T19:03:25Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ppo-LunarLander-v2
results:
- metrics:
- type: mean_reward
value: 203.94 +/- 26.92
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **ppo-LunarLander-v2** Agent playing **LunarLander-v2**
This is a trained model of a **ppo-LunarLander-v2** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
ASCCCCCCCC/distilbert-base-multilingual-cased-amazon_zh_20000 | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | {
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} | 39 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO_v2
results:
- metrics:
- type: mean_reward
value: 254.01 +/- 15.18
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO_v1** Agent playing **LunarLander-v2**
This is a trained model of a **PPO_v1** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
ASCCCCCCCC/distilbert-base-uncased-finetuned-amazon_zh_20000 | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | {
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} | 39 | 2022-05-04T19:28:11Z | ---
language: en
thumbnail: http://www.huggingtweets.com/kanyewest-usmnt-zlisto/1651692574685/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/1410587808666955776/mWkKWw1U_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/1276461929934942210/cqNhNk6v_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/1486104199763013632/uC8Ujhgj_400x400.jpg')">
</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">USMNT & ye & Tauhid R. Zaman</div>
<div style="text-align: center; font-size: 14px;">@kanyewest-usmnt-zlisto</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 USMNT & ye & Tauhid R. Zaman.
| Data | USMNT | ye | Tauhid R. Zaman |
| --- | --- | --- | --- |
| Tweets downloaded | 3247 | 1858 | 3098 |
| Retweets | 600 | 188 | 1232 |
| Short tweets | 215 | 573 | 106 |
| Tweets kept | 2432 | 1097 | 1760 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/gvuccyzi/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 @kanyewest-usmnt-zlisto's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1no8s780) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1no8s780/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/kanyewest-usmnt-zlisto')
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)
|
ASCCCCCCCC/distilbert-base-uncased-finetuned-clinc | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
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} | 35 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 76.51 +/- 127.92
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
AT/bert-base-uncased-finetuned-wikitext2 | [] | null | {
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} | 0 | 2022-05-04T19:48:30Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-roundup-2-8
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. -->
# bart-large-cnn-finetuned-roundup-2-8
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1855
- Rouge1: 53.552
- Rouge2: 34.9077
- Rougel: 38.0158
- Rougelsum: 50.7179
- Gen Len: 142.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| No log | 1.0 | 167 | 1.2085 | 50.8706 | 32.0069 | 32.9241 | 47.9805 | 142.0 |
| No log | 2.0 | 334 | 1.0897 | 53.2218 | 34.1317 | 34.4827 | 50.4795 | 139.4286 |
| 1.0256 | 3.0 | 501 | 1.0535 | 50.8882 | 30.2514 | 31.5051 | 47.9856 | 141.9048 |
| 1.0256 | 4.0 | 668 | 1.0515 | 54.9414 | 35.2309 | 36.006 | 52.0331 | 142.0 |
| 1.0256 | 5.0 | 835 | 1.0829 | 53.0709 | 33.4587 | 36.4223 | 50.1627 | 140.7619 |
| 0.4579 | 6.0 | 1002 | 1.1310 | 51.5274 | 30.7069 | 32.4146 | 48.8851 | 142.0 |
| 0.4579 | 7.0 | 1169 | 1.1670 | 52.1536 | 31.7158 | 35.7483 | 49.2678 | 142.0 |
| 0.4579 | 8.0 | 1336 | 1.1855 | 53.552 | 34.9077 | 38.0158 | 50.7179 | 142.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 273.85 +/- 20.83
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
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} | 0 | 2022-05-04T20:03:20Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- germa_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-german-cased-fine-tuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: germa_ner
type: germa_ner
args: default
metrics:
- name: Precision
type: precision
value: 0.8089260808926081
- name: Recall
type: recall
value: 0.872836719337848
- name: F1
type: f1
value: 0.8396670285921101
- name: Accuracy
type: accuracy
value: 0.9748511630761677
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-german-cased-fine-tuned-ner
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the germa_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0966
- Precision: 0.8089
- Recall: 0.8728
- F1: 0.8397
- Accuracy: 0.9749
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.159 | 1.0 | 737 | 0.0922 | 0.7472 | 0.8461 | 0.7936 | 0.9703 |
| 0.0714 | 2.0 | 1474 | 0.0916 | 0.7886 | 0.8713 | 0.8279 | 0.9731 |
| 0.0319 | 3.0 | 2211 | 0.0966 | 0.8089 | 0.8728 | 0.8397 | 0.9749 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.9.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
|
ATGdev/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 16 | 2022-05-04T20:12:04Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -2071.55 +/- 941.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
ATGdev/ai_ironman | [] | null | {
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 295.94 +/- 13.13
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
AUBMC-AIM/MammoGANesis | [
"license:cc-by-nc-4.0",
"has_space"
] | null | {
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} | 0 | 2022-05-04T20:18:06Z | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/GPT2InformalToFormalLincoln42")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPT2InformalToFormalLincoln42")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
``` |
AUBMC-AIM/OCTaGAN | [
"license:cc-by-nc-4.0",
"has_space"
] | null | {
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 282.72 +/- 23.16
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
AVSilva/bertimbau-large-fine-tuned-sd | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:mit",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
"model_type": "bert",
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} | 10 | 2022-05-04T20:51:51Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ppo
results:
- metrics:
- type: mean_reward
value: 231.79 +/- 19.49
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **ppo** Agent playing **LunarLander-v2**
This is a trained model of a **ppo** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
AdapterHub/bert-base-uncased-pf-cosmos_qa | [
"bert",
"en",
"dataset:cosmos_qa",
"arxiv:2104.08247",
"adapter-transformers",
"adapterhub:comsense/cosmosqa"
] | null | {
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}
} | 2 | null | ---
tags:
- generated_from_trainer
model-index:
- name: gpt2-fr-eos-paco-cheese
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. -->
# gpt2-fr-eos-paco-cheese
This model is a fine-tuned version of [dbddv01/gpt2-french-small](https://huggingface.co/dbddv01/gpt2-french-small) 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 65
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
AethiQs-Max/cross_encoder | [] | null | {
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 248.52 +/- 19.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
AiPorter/DialoGPT-small-Back_to_the_future | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-rater
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-rater
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.11.6
|
Akiva/Joke | [] | null | {
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 215.27 +/- 12.72
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
AkshaySg/langid | [
"multilingual",
"dataset:VoxLingua107",
"speechbrain",
"audio-classification",
"embeddings",
"Language",
"Identification",
"pytorch",
"ECAPA-TDNN",
"TDNN",
"VoxLingua107",
"license:apache-2.0"
] | audio-classification | {
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}
} | 2 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ppo-LunarLander-v2
results:
- metrics:
- type: mean_reward
value: 197.02 +/- 73.63
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **ppo-LunarLander-v2** Agent playing **LunarLander-v2**
This is a trained model of a **ppo-LunarLander-v2** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
Akuva2001/SocialGraph | [
"has_space"
] | null | {
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 168.17 +/- 105.80
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
Aleksandar/bert-srb-ner-setimes-lr | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- filipino_voice
model-index:
- name: english-filipino-wav2vec2-l-xls-r-test-02
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. -->
# english-filipino-wav2vec2-l-xls-r-test-02
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the filipino_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4561
- Wer: 0.2632
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.1707 | 2.09 | 400 | 0.8006 | 0.8224 |
| 0.4801 | 4.19 | 800 | 0.3363 | 0.4329 |
| 0.2541 | 6.28 | 1200 | 0.3365 | 0.3676 |
| 0.1851 | 8.38 | 1600 | 0.3485 | 0.3739 |
| 0.1408 | 10.47 | 2000 | 0.3628 | 0.3420 |
| 0.1098 | 12.57 | 2400 | 0.3979 | 0.3277 |
| 0.1019 | 14.66 | 2800 | 0.4031 | 0.2896 |
| 0.0887 | 16.75 | 3200 | 0.3977 | 0.3024 |
| 0.0798 | 18.85 | 3600 | 0.3959 | 0.3129 |
| 0.0671 | 20.94 | 4000 | 0.4489 | 0.3241 |
| 0.0633 | 23.04 | 4400 | 0.4455 | 0.3026 |
| 0.055 | 25.13 | 4800 | 0.4668 | 0.2910 |
| 0.0523 | 27.23 | 5200 | 0.4670 | 0.2960 |
| 0.0468 | 29.32 | 5600 | 0.4536 | 0.2781 |
| 0.0392 | 31.41 | 6000 | 0.4612 | 0.2860 |
| 0.0381 | 33.51 | 6400 | 0.4651 | 0.2841 |
| 0.034 | 35.6 | 6800 | 0.4723 | 0.2716 |
| 0.0315 | 37.7 | 7200 | 0.4546 | 0.2642 |
| 0.0294 | 39.79 | 7600 | 0.4561 | 0.2632 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Aleksandar/bert-srb-ner-setimes | [
"pytorch",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | token-classification | {
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} | 8 | null | ---
language: en
license: apache-2.0
datasets:
- Super-NaturalInstructions
---
# Model description
Tk-Instruct is a series of encoder-decoder Transformer models that are trained to solve various NLP tasks by following in-context instructions (plain language task definitions, k-shot examples, explanations, etc). Built upon the pre-trained [T5 models](https://arxiv.org/abs/1910.10683), they are fine-tuned on a large number of tasks & instructions that are collected in the [Natural Instructions benchmark](https://github.com/allenai/natural-instructions), which contains 1600+ tasks in 70+ broach categories in total. This enables the model to not only process the training tasks, but also generalize to many unseen tasks without further parameter update.
More resources for using the model:
- **Paper**: [link](https://arxiv.org/abs/2204.07705)
- **Code repository**: [Tk-Instruct](https://github.com/yizhongw/Tk-Instruct)
- **Official Website**: [Natural Instructions](https://instructions.apps.allenai.org/)
- **All released models**: [allenai/tk-instruct](https://huggingface.co/models?search=allenai/tk-instruct)
## Intended uses & limitations
Tk-Instruct can be used to do many NLP tasks by following instructions.
### How to use
When instructing the model, task definition or demonstration examples or explanations should be prepended to the original input and fed into the model. You can easily try Tk-Instruct models as follows:
```python
>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
>>> tokenizer = AutoTokenizer.from_pretrained("allenai/tk-instruct-3b-def")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("allenai/tk-instruct-3b-def")
>>> input_ids = tokenizer.encode(
"Definition: return the currency of the given country. Now complete the following example - Input: India. Output:",
return_tensors="pt")
>>> output = model.generate(input_ids, max_length=10)
>>> output = tokenizer.decode(output[0], skip_special_tokens=True) # model should output 'Indian Rupee'
>>> input_ids = tokenizer.encode(
"Definition: negate the following sentence. Input: John went to school. Output:",
return_tensors="pt")
>>> output = model.generate(input_ids, max_length=10)
>>> output = tokenizer.decode(output[0], skip_special_tokens=True) # model should output 'John did not go to shool.'
```
### Limitations
We are still working on understanding the behaviors of these models, but here are several issues we have found:
- Models are generally sensitive to the instruction. Sometimes rewording the instruction can lead to very different output.
- Models are not always compliant to the instruction. Sometimes the model don't follow your instruction (e.g., when you ask the model to generate one sentence, it might still generate one word or a long story).
- Models might totally fail on some tasks.
If you find serious issues or any interesting result, you are welcome to share with us!
## Training data
Tk-Instruct is trained using the tasks & instructions in [Natural Instructions benchmark](https://github.com/allenai/natural-instructions), which contains 1600+ tasks in 70+ broach categories in total. We follow the official train/test split. Tk-Instruct model series were trained using 757 tasks, and mTk-Instruct series were trained using 1271 tasks (including some non-English tasks).
The training tasks are in 64 broad categories, such as text categorization / question answering / sentiment analysis / summarization / grammar error detection / dialogue generation / etc. The other 12 categories are selected for evaluation.
## Training procedure
All our models are initialized from either T5 models or mT5 models. Because generating the output can be regarded as a form of language modeling, we used their [LM adapted version](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-t511lm100k). All data is converted into a text-to-text format, and models are fine-tuned to maximize the likelihood of the output sequence.
Our [released models](https://huggingface.co/models?search=allenai/tk-instruct) are in different sizes, and each of them was trained with a specific type of instruction encoding. For instance, `tk-instruct-3b-def-pos` was initialized from [t5-xl-lm-adapt](https://huggingface.co/google/t5-xl-lm-adapt), and it saw task definition & 2 positive examples as the instruction during training time.
Although they are trained with only one type of instruction encodings, we found they can usually work with other type of encodings at test time (see more in our paper).
### BibTeX entry and citation info
```bibtex
@article{wang2022benchmarking,
title={Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and A. Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and M. Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddharth Deepak Mishra and Sujan C. Reddy and Sumanta Patro and Tanay Dixit and Xu-dong Shen and Chitta Baral and Yejin Choi and Hannaneh Hajishirzi and Noah A. Smith and Daniel Khashabi},
year={2022},
archivePrefix={arXiv},
eprint={2204.07705},
primaryClass={cs.CL},
}
``` |
Aleksandar/bert-srb-ner | [
"pytorch",
"bert",
"token-classification",
"dataset:wikiann",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | token-classification | {
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} | 4 | null | ---
license: mit
language: de
widget:
- text: "In einer schockierenden Entdeckung fanden Wissenschaftler eine Herde Einhörner, die in einem abgelegenen, zuvor unerforschten Tal in den Anden lebten."
---
# Replication of [gpt2-wechsel-german](https://huggingface.co/benjamin/gpt2-wechsel-german)
- trained with [BigScience's DeepSpeed-Megatron-LM code base](https://github.com/bigscience-workshop/Megatron-DeepSpeed)
- 22hrs on 4xA100 GPUs (~ 80 TFLOPs / GPU)
- stopped after 100k steps
- less than a single epoch on `oscar_unshuffled_deduplicated_de` (excluding validation set; original model was trained for 75 epochs on less data)
- bf16
- zero stage 1
- tp/pp = 1
## Evaluation
| Model | PPL |
|---|---|
| `gpt2-wechsel-german-ds-meg` | **26.4** |
| `gpt2-wechsel-german` | 26.8 |
| `gpt2` (retrained from scratch) | 27.63 |
## License
MIT
|
Aleksandar/distilbert-srb-base-cased-oscar | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | fill-mask | {
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} | 4 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 239.95 +/- 17.78
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
Aleksandar1932/gpt2-hip-hop | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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"GPT2LMHeadModel"
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} | 8 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/theovalpawffice/1651782387551/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/1346560834613469184/LJVlGDRS_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">The Oval Pawffice® 🇺🇸 DOTUS Fans</div>
<div style="text-align: center; font-size: 14px;">@theovalpawffice</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 The Oval Pawffice® 🇺🇸 DOTUS Fans.
| Data | The Oval Pawffice® 🇺🇸 DOTUS Fans |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 68 |
| Short tweets | 106 |
| Tweets kept | 3076 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/uraeqzqr/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 @theovalpawffice's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3jrqovr7) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3jrqovr7/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/theovalpawffice')
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)
|
Aleksandar1932/gpt2-pop | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
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library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 254.31 +/- 23.37
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
Aleksandar1932/gpt2-rock-124439808 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
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} | 11 | null | ---
tags:
- conversational
---
# RickBot built for [Chai](https://chai.ml/)
Make your own [here](https://colab.research.google.com/drive/1o5LxBspm-C28HQvXN-PRQavapDbm5WjG?usp=sharing)
|
Aleksandra/distilbert-base-uncased-finetuned-squad | [] | null | {
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} | 0 | 2022-05-05T20:43:34Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 273.94 +/- 11.64
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
AlekseyKulnevich/Pegasus-Summarization | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
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} | 7 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 272.58 +/- 19.42
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
Alerosae/SocratesGPT-2 | [
"pytorch",
"gpt2",
"feature-extraction",
"en",
"transformers",
"text-generation"
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} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-reviews-128
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-reviews-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) 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: 48
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1
|
AlexN/xls-r-300m-fr-0 | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
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} | 4 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 285.97 +/- 19.96
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
AlexaRyck/KEITH | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: HomayounSadri/distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# HomayounSadri/distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.9777
- Validation Loss: 1.1221
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 11064, '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: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.5153 | 1.1668 | 0 |
| 0.9777 | 1.1221 | 1 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Alexander-Learn/bert-finetuned-ner-accelerate | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
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} | 4 | null | ---
tags:
- keras
- image-to-image
- pixelwise-segmentation
datasets:
- DIBCO
- H-DIBCO
license: apache-2.0
---
# Model Card for sbb_binarization
<!-- Provide a quick summary of what the model is/does. [Optional] -->
This is a pixelwise segmentation model for document image binarization.
The model is a hybrid CNN-Transformer encoder-decoder model (Resnet50-Unet) developed by the Berlin State Library (SBB) in the [QURATOR](https://staatsbibliothek-berlin.de/die-staatsbibliothek/projekte/project-id-1060-2018) project. It can be used to convert all pixels in a color or grayscale document image to only black or white pixels.
The main aim is to improve the contrast between foreground (text) and background (paper) for purposes of Optical Character Recognition (OCR).
# Table of Contents
- [Model Card for sbb_binarization](#model-card-for-sbb_binarization)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [Model Description](#model-description)
- [Uses](#uses)
- [Direct Use](#direct-use)
- [Downstream Use](#downstream-use)
- [Out-of-Scope Use](#out-of-scope-use)
- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
- [Recommendations](#recommendations)
- [Training Details](#training-details)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Preprocessing](#preprocessing)
- [Speeds, Sizes, Times](#speeds-sizes-times)
- [Evaluation](#evaluation)
- [Testing Data, Factors & Metrics](#testing-data-factors--metrics)
- [Testing Data](#testing-data)
- [Factors](#factors)
- [Metrics](#metrics)
- [Results](#results)
- [Model Examination](#model-examination)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications](#technical-specifications)
- [Model Architecture and Objective](#model-architecture-and-objective)
- [Compute Infrastructure](#compute-infrastructure)
- [Hardware](#hardware)
- [Software](#software)
- [Citation](#citation)
- [Glossary [optional]](#glossary-optional)
- [More Information [optional]](#more-information-optional)
- [Model Card Authors](#model-card-authors)
- [Model Card Contact](#model-card-contact)
- [How to Get Started with the Model](#how-to-get-started-with-the-model)
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is/does. -->
Document image binarization is one of the main pre-processing steps for text recognition in document image analysis.
Noise, faint characters, bad scanning conditions, uneven light exposure or paper aging can cause artifacts that negatively impact text recognition algorithms.
The task of binarization is to segment the foreground (text) from these degradations in order to improve Optical Character Recognition (OCR) results.
Convolutional neural networks (CNNs) are one popular method for binarization, while Vision Transformers are gaining performance.
The sbb_binarization model therefore applies a hybrid CNN-Transformer encoder-decoder model architecture.
- **Developed by:** [Vahid Rezanezhad]([email protected])
- **Shared by [Optional]:** [Staatsbibliothek zu Berlin / Berlin State Library](https://huggingface.co/SBB)
- **Model type:** Neural Network
- **Language(s) (NLP):** Irrelevant; works on all languages
- **License:** apache-2.0
- **Parent Model:** [ResNet-50, see the paper by Zhang et al](https://arxiv.org/abs/1512.03385)
- **Resources for more information:** More information needed
- [GitHub Repo](https://github.com/qurator-spk/sbb_binarization)
- Associated Paper 1 [Time-Quality Binarization Competition](https://dib.cin.ufpe.br/docs/DocEng21_bin_competition_report.pdf)
- Associated Paper 2 [Time-Quality Document Image Binarization](https://dib.cin.ufpe.br/docs/papers/ICDAR2021-TQDIB_final_published.pdf)
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
Document image binarization is the main use case of this model. The architecture of this model alongside with training techniques like model weights ensembling can reach or outperform state-of-the-art results on standard Document Binarization Competition (DIBCO) datasets in the both machine-printed and handwritten documents.
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
The intended use is the binarization of document images, particularly of historical documents, understood as one of the main pre-processing steps for text recognition.
## Downstream Use
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
A possible downstream use of this model might lie with the binarization of illustrative elements contained in document images such as digitized newspapers, magazines or books. In such cases, binarization might support analysis of creator attribution, artistic style (e.g., in line drawings), or analysis of image similarity. Furthermore, the model can be used or trained for any other image enhancement use cases too.
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
This model does **NOT** perform any Optical Character Recognition (OCR), it is an image-to-image model only.
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
The aim of the development of this model was to improve document image binarization as a necessary pre-processing step. Since the content of the document images is not touched, ethical challenges cannot be identified. The endeavor of developing the model was not undertaken for profit; though a product based on this model might be developed in the future, it will always remain openly accessible without any commercial interest.
This algorithm performs a pixelwise segmentation which is done in patches. Therefore, one technical limitation of this model is that it is unable to capture and see long range dependencies.
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
The application of machine learning models to convert a document image into a binary output is a process which can still be improved. We have used many pseudo-labeled images to train our model, so any improvement or ground truth extension would probably lead to better results.
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
The dataset used for training is a combination of training sets from previous [DIBCO](https://dib.cin.ufpe.br/#!/datasets) binarization competitions alongside with the [Palm Leaf dataset](https://ieeexplore.ieee.org/abstract/document/7814130) and the Persian Heritage Image Binarization Competition [PHIBC](https://arxiv.org/abs/1306.6263) dataset, with additional pseudo-labeled images from the Berlin State Library (SBB; datasets to be published). Furthermore, a dataset for very dark or very bright images has been produced for training.
## Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
We have used a batch size of 8 with learning rate of 1e − 4 for 20 epochs. A soft dice is applied as loss function. In the training we have taken advantage of dataset augmentation. The augmentation includes flipping, scaling and blurring. The best model weights are chosen based on some problematic documents from the SBB dataset. The final model results of the ensemble of the best weights.
### Preprocessing
In order to use this model for binarization no preprocessing is needed for the input image.
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
More information needed
### Training hyperparameters
In the training process, the hyperparameters were patch size, learning rate, number of epochs and depth of encoder part.
### Training results
See the two papers listed below in the evaluation section.
# Evaluation
In the DocEng’2021 [Time-Quality Binarization Competition](https://dib.cin.ufpe.br/docs/DocEng21_bin_competition_report.pdf), the model ranked twelve times under the top 8 of 63 methods, winning 2 tasks.
In the ICDAR 2021 Competition on [Time-Quality Document Image Binarization](https://dib.cin.ufpe.br/docs/papers/ICDAR2021-TQDIB_final_published.pdf), the model ranked two times under the top 20 of 61 methods, winning 1 task.
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
The testing data are the ones used in the [Time-Quality Binarization Competition](https://dib.cin.ufpe.br/docs/DocEng21_bin_competition_report.pdf) and listed in the paper on [Time-Quality Document Image Binarization](https://dib.cin.ufpe.br/docs/papers/ICDAR2021-TQDIB_final_published.pdf).
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
More information needed.
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
The model has been evaluated both based on OCR and pixelwise segmentation results. The metrics which have been used in the case of visual evaluation are pixel proportion error and Cohen's Kappa value, and Levenshtein distance error in the case of OCR.
## Results
See the two papers listed above in the evaluation section.
# Model Examination
More information needed.
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** Nvidia 2080.
- **Hours used:** Two days.
- **Cloud Provider:** No cloud.
- **Compute Region:** Germany.
- **Carbon Emitted:** More information needed.
# Technical Specifications
## Model Architecture and Objective
The proposed model is a hybrid CNN-Transformer encoder-decoder model. The encoder part consists of a ResNet-50 model. The ResNet-50 includes convolutional neural networks and is responsible for extracting as many features as possible from the input image. After that the input image goes through the CNN part, then the output undergoes upsampling convolutional layers until the same output size as in the input image is reached.
## Compute Infrastructure
Training has been performed on a single Nvidia 2080 GPU.
### Hardware
See above.
### Software
See the code published on [GitHub](https://github.com/qurator-spk/sbb_binarization).
# Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
Coming soon.
**BibTeX:**
More information needed.
**APA:**
More information needed.
# Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
More information needed
# More Information [optional]
More information needed.
# Model Card Authors
<!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. -->
[Vahid Rezanezhad]([email protected]), [Clemens Neudecker](https://huggingface.co/cneud), [Konstantin Baierer]([email protected]) and [Jörg Lehmann]([email protected])
# Model Card Contact
Questions and comments about the model can be directed to Clemens Neudecker at [email protected], questions and comments about the model card can be directed to Jörg Lehmann at [email protected]
# How to Get Started with the Model
Use the code below to get started with the model.
sbb_binarize \
-m <from_pretrained_keras("sbb_binarization")> \
<input image> \
<output image>
<details>
How to get started with this model is explained in the ReadMe file of the GitHub repository [over here](https://github.com/qurator-spk/sbb_binarization).
</details>
|
Alexander-Learn/bert-finetuned-ner | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
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} | 8 | null | ---
language: en
tags:
- exbert
license: mit
widget:
- text: "Left pleural effusion with adjacent [MASK]."
example_title: "Radiology 1"
- text: "Heart size normal and lungs are [MASK]."
example_title: "Radiology 2"
- text: "[MASK] is a tumor suppressor gene."
example_title: "Biomedical"
- text: "The patient was on [MASK] for chronic atrial fibrillation"
example_title: "Medication"
---
# CXR-BERT-general
[CXR-BERT](https://arxiv.org/abs/2204.09817) is a chest X-ray (CXR) domain-specific language model that makes use of an improved vocabulary, novel pretraining procedure, weight regularization, and text augmentations. The resulting model demonstrates improved performance on radiology natural language inference, radiology masked language model token prediction, and downstream vision-language processing tasks such as zero-shot phrase grounding and image classification.
First, we pretrain **CXR-BERT-general** from a randomly initialized BERT model via Masked Language Modeling (MLM) on abstracts [PubMed](https://pubmed.ncbi.nlm.nih.gov/) and clinical notes from the publicly-available [MIMIC-III](https://physionet.org/content/mimiciii/1.4/) and [MIMIC-CXR](https://physionet.org/content/mimic-cxr/). In that regard, the general model is expected be applicable for research in clinical domains other than the chest radiology through domain specific fine-tuning.
**CXR-BERT-specialized** is continually pretrained from CXR-BERT-general to further specialize in the chest X-ray domain. At the final stage, CXR-BERT is trained in a multi-modal contrastive learning framework, similar to the [CLIP](https://arxiv.org/abs/2103.00020) framework. The latent representation of [CLS] token is utilized to align text/image embeddings.
## Model variations
| Model | Model identifier on HuggingFace | Vocabulary | Note |
| ------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- | -------------- | --------------------------------------------------------- |
| CXR-BERT-general | [microsoft/BiomedVLP-CXR-BERT-general](https://huggingface.co/microsoft/BiomedVLP-CXR-BERT-general) | PubMed & MIMIC | Pretrained for biomedical literature and clinical domains |
| CXR-BERT-specialized (after multi-modal training) | [microsoft/BiomedVLP-CXR-BERT-specialized](https://huggingface.co/microsoft/BiomedVLP-CXR-BERT-specialized) | PubMed & MIMIC | Pretrained for chest X-ray domain |
## Citation
The corresponding manuscript is accepted to be presented at the [**European Conference on Computer Vision (ECCV) 2022**](https://eccv2022.ecva.net/)
```bibtex
@misc{https://doi.org/10.48550/arxiv.2204.09817,
doi = {10.48550/ARXIV.2204.09817},
url = {https://arxiv.org/abs/2204.09817},
author = {Boecking, Benedikt and Usuyama, Naoto and Bannur, Shruthi and Castro, Daniel C. and Schwaighofer, Anton and Hyland, Stephanie and Wetscherek, Maria and Naumann, Tristan and Nori, Aditya and Alvarez-Valle, Javier and Poon, Hoifung and Oktay, Ozan},
title = {Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing},
publisher = {arXiv},
year = {2022},
}
```
## Model Use
### Intended Use
This model is intended to be used solely for (I) future research on visual-language processing and (II) reproducibility of the experimental results reported in the reference paper.
#### Primary Intended Use
The primary intended use is to support AI researchers building on top of this work. CXR-BERT and its associated models should be helpful for exploring various clinical NLP & VLP research questions, especially in the radiology domain.
#### Out-of-Scope Use
**Any** deployed use case of the model --- commercial or otherwise --- is currently out of scope. Although we evaluated the models using a broad set of publicly-available research benchmarks, the models and evaluations are not intended for deployed use cases. Please refer to [the associated paper](https://arxiv.org/abs/2204.09817) for more details.
## Data
This model builds upon existing publicly-available datasets:
- [PubMed](https://pubmed.ncbi.nlm.nih.gov/)
- [MIMIC-III](https://physionet.org/content/mimiciii/)
- [MIMIC-CXR](https://physionet.org/content/mimic-cxr/)
These datasets reflect a broad variety of sources ranging from biomedical abstracts to intensive care unit notes to chest X-ray radiology notes. The radiology notes are accompanied with their associated chest x-ray DICOM images in MIMIC-CXR dataset.
## Performance
We demonstrate that this language model achieves state-of-the-art results in radiology natural language inference through its improved vocabulary and novel language pretraining objective leveraging semantics and discourse characteristics in radiology reports.
A highlight of comparison to other common models, including [ClinicalBERT](https://aka.ms/clinicalbert) and [PubMedBERT](https://aka.ms/pubmedbert):
| | RadNLI accuracy (MedNLI transfer) | Mask prediction accuracy | Avg. # tokens after tokenization | Vocabulary size |
| ----------------------------------------------- | :-------------------------------: | :----------------------: | :------------------------------: | :-------------: |
| RadNLI baseline | 53.30 | - | - | - |
| ClinicalBERT | 47.67 | 39.84 | 78.98 (+38.15%) | 28,996 |
| PubMedBERT | 57.71 | 35.24 | 63.55 (+11.16%) | 28,895 |
| CXR-BERT (after Phase-III) | 60.46 | 77.72 | 58.07 (+1.59%) | 30,522 |
| **CXR-BERT (after Phase-III + Joint Training)** | **65.21** | **81.58** | **58.07 (+1.59%)** | 30,522 |
CXR-BERT also contributes to better vision-language representation learning through its improved text encoding capability. Below is the zero-shot phrase grounding performance on the **MS-CXR** dataset, which evaluates the quality of image-text latent representations.
| Vision–Language Pretraining Method | Text Encoder | MS-CXR Phrase Grounding (Avg. CNR Score) |
| ---------------------------------- | ------------ | :--------------------------------------: |
| Baseline | ClinicalBERT | 0.769 |
| Baseline | PubMedBERT | 0.773 |
| ConVIRT | ClinicalBERT | 0.818 |
| GLoRIA | ClinicalBERT | 0.930 |
| **BioViL** | **CXR-BERT** | **1.027** |
| **BioViL-L** | **CXR-BERT** | **1.142** |
Additional details about performance can be found in the corresponding paper, [Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing](https://arxiv.org/abs/2204.09817).
## Limitations
This model was developed using English corpora, and thus can be considered English-only.
## Further information
Please refer to the corresponding paper, ["Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing", ECCV'22](https://arxiv.org/abs/2204.09817) for additional details on the model training and evaluation.
For additional inference pipelines with CXR-BERT, please refer to the [HI-ML GitHub](https://aka.ms/biovil-code) repository. The associated source files will soon be accessible through this link.
|
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -357.14 +/- 83.17
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
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} | 0 | null | ---
language: en
license: apache-2.0
datasets:
- Super-NaturalInstructions
---
# Model description
Tk-Instruct is a series of encoder-decoder Transformer models that are trained to solve various NLP tasks by following in-context instructions (plain language task definitions, k-shot examples, explanations, etc). Built upon the pre-trained [T5 models](https://arxiv.org/abs/1910.10683), they are fine-tuned on a large number of tasks & instructions that are collected in the [Natural Instructions benchmark](https://github.com/allenai/natural-instructions), which contains 1600+ tasks in 70+ broach categories in total. This enables the model to not only process the training tasks, but also generalize to many unseen tasks without further parameter update.
More resources for using the model:
- **Paper**: [link](https://arxiv.org/abs/2204.07705)
- **Code repository**: [Tk-Instruct](https://github.com/yizhongw/Tk-Instruct)
- **Official Website**: [Natural Instructions](https://instructions.apps.allenai.org/)
- **All released models**: [allenai/tk-instruct](https://huggingface.co/models?search=allenai/tk-instruct)
## Intended uses & limitations
Tk-Instruct can be used to do many NLP tasks by following instructions.
### How to use
When instructing the model, task definition or demonstration examples or explanations should be prepended to the original input and fed into the model. You can easily try Tk-Instruct models as follows:
```python
>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
>>> tokenizer = AutoTokenizer.from_pretrained("allenai/tk-instruct-3b-def")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("allenai/tk-instruct-3b-def")
>>> input_ids = tokenizer.encode(
"Definition: return the currency of the given country. Now complete the following example - Input: India. Output:",
return_tensors="pt")
>>> output = model.generate(input_ids, max_length=10)
>>> output = tokenizer.decode(output[0], skip_special_tokens=True) # model should output 'Indian Rupee'
>>> input_ids = tokenizer.encode(
"Definition: negate the following sentence. Input: John went to school. Output:",
return_tensors="pt")
>>> output = model.generate(input_ids, max_length=10)
>>> output = tokenizer.decode(output[0], skip_special_tokens=True) # model should output 'John did not go to shool.'
```
### Limitations
We are still working on understanding the behaviors of these models, but here are several issues we have found:
- Models are generally sensitive to the instruction. Sometimes rewording the instruction can lead to very different output.
- Models are not always compliant to the instruction. Sometimes the model don't follow your instruction (e.g., when you ask the model to generate one sentence, it might still generate one word or a long story).
- Models might totally fail on some tasks.
If you find serious issues or any interesting result, you are welcome to share with us!
## Training data
Tk-Instruct is trained using the tasks & instructions in [Natural Instructions benchmark](https://github.com/allenai/natural-instructions), which contains 1600+ tasks in 70+ broach categories in total. We follow the official train/test split. Tk-Instruct model series were trained using 757 tasks, and mTk-Instruct series were trained using 1271 tasks (including some non-English tasks).
The training tasks are in 64 broad categories, such as text categorization / question answering / sentiment analysis / summarization / grammar error detection / dialogue generation / etc. The other 12 categories are selected for evaluation.
## Training procedure
All our models are initialized from either T5 models or mT5 models. Because generating the output can be regarded as a form of language modeling, we used their [LM adapted version](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-t511lm100k). All data is converted into a text-to-text format, and models are fine-tuned to maximize the likelihood of the output sequence.
Our [released models](https://huggingface.co/models?search=allenai/tk-instruct) are in different sizes, and each of them was trained with a specific type of instruction encoding. For instance, `tk-instruct-3b-def-pos` was initialized from [t5-xl-lm-adapt](https://huggingface.co/google/t5-xl-lm-adapt), and it saw task definition & 2 positive examples as the instruction during training time.
Although they are trained with only one type of instruction encodings, we found they can usually work with other type of encodings at test time (see more in our paper).
### BibTeX entry and citation info
```bibtex
@article{wang2022benchmarking,
title={Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and A. Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and M. Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddharth Deepak Mishra and Sujan C. Reddy and Sumanta Patro and Tanay Dixit and Xu-dong Shen and Chitta Baral and Yejin Choi and Hannaneh Hajishirzi and Noah A. Smith and Daniel Khashabi},
year={2022},
archivePrefix={arXiv},
eprint={2204.07705},
primaryClass={cs.CL},
}
``` |
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 267.99 +/- 15.88
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 269.56 +/- 26.62
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AliReza/distilbert-emotion | [] | null | {
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} | 0 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: ALL-test
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9572474360466003
---
# ALL-test
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 |
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- filipino_voice
model-index:
- name: english-filipino-wav2vec2-l-xls-r-test-04
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. -->
# english-filipino-wav2vec2-l-xls-r-test-04
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the filipino_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0713
- Wer: 0.5078
## 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: 0.002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.2131 | 2.09 | 400 | 0.7100 | 0.6832 |
| 0.6539 | 4.19 | 800 | 0.8307 | 0.6602 |
| 0.5081 | 6.28 | 1200 | 0.7120 | 0.6297 |
| 0.42 | 8.38 | 1600 | 0.7309 | 0.6299 |
| 0.3482 | 10.47 | 2000 | 0.7665 | 0.6148 |
| 0.293 | 12.57 | 2400 | 0.7091 | 0.5840 |
| 0.265 | 14.66 | 2800 | 0.8170 | 0.6102 |
| 0.2294 | 16.75 | 3200 | 0.9715 | 0.6216 |
| 0.1872 | 18.85 | 3600 | 0.8516 | 0.5837 |
| 0.1644 | 20.94 | 4000 | 0.8408 | 0.5767 |
| 0.1495 | 23.04 | 4400 | 0.9188 | 0.5717 |
| 0.1276 | 25.13 | 4800 | 1.0149 | 0.5451 |
| 0.116 | 27.23 | 5200 | 1.0220 | 0.5683 |
| 0.1017 | 29.32 | 5600 | 0.9319 | 0.5253 |
| 0.0899 | 31.41 | 6000 | 0.9949 | 0.5435 |
| 0.0861 | 33.51 | 6400 | 1.1029 | 0.5467 |
| 0.0766 | 35.6 | 6800 | 1.0219 | 0.5193 |
| 0.065 | 37.7 | 7200 | 1.0836 | 0.5214 |
| 0.0588 | 39.79 | 7600 | 1.0713 | 0.5078 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Alireza1044/albert-base-v2-cola | [
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
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}
} | 32 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: ALL-94.5
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9452415704727173
---
# ALL-94.5
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 |
Alireza1044/albert-base-v2-mnli | [
"pytorch",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
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}
} | 235 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO MLP Policy Architecture
results:
- metrics:
- type: mean_reward
value: 143.60 +/- 115.75
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO MLP Policy Architecture** Agent playing **LunarLander-v2**
This is a trained model of a **PPO MLP Policy Architecture** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
Alireza1044/albert-base-v2-mrpc | [
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
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},
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}
}
} | 204 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 278.81 +/- 19.74
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
Altidore/DuggFace | [] | null | {
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 277.15 +/- 21.48
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
Amalq/distilroberta-base-finetuned-MentalHealth | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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} | 0 | 2022-05-06T03:42:01Z | ---
license: apache-2.0
---
The ch-w2v-conformer model uses following datasets to pretrain:
ISML datasets (6 languages,70k hours): internal dataset contains 40k hours Chinese, Cantonese, Tibetan, Inner Mongolian, Inner Kazakh, Uighur.
Babel datasets (17 languages, 2k hours): Assamese, Bengali, Cantonese, Cebuano, Georgian, Haitian, Kazakh, Kurmanji, Lao, Pashto, Swahili, Tagalog, Tamil, Tok, Turkish, Vietnamese, Zulu
After pretraining, we build ASR system based on CTC-Attention structure. In very low resource task, we find that if too many initialization network structures are constructed in the upper layer of pre-training conformer encoder, the migration performance of the pre-training model will be destroyed, so we only build a single-layer transformer decoder for joint training.
pretrained model link:
## constrained-plus Task Performance
* Languages: Cantonese,mongolian,kazakh
* config: conf/train_conformer_large_10h.yaml
* Feature info: using mfcc feature, with dither 1.0, without cmvn
* Training info: lr 0.001, batch size 10, 4 gpus on V100, acc_grad 1, 80 epochs
* Decoding info: ctc_weight 0.5, average_num 35
dev set results trained only with 10 hours training set
## w2v-Conformer
| decoding_method | Cantonese(CER) | mongolian(WER) |
|:-------------------:|:----:|:----:|
| ctc_greedy_search | 31.46 | 53.64 |
| ctc_prefix_search | 31.47 | 53.50 |
| attention_rescoring | 31.45 | 52.96 |
## Conformer （train from scartch）
| decoding_method | Cantonese(CER) | mongolian(WER) |
|:-------------------:|----:|:----:|
| ctc_greedy_search | 61.43 | 89.38 |
| ctc_prefix_search | 61.37 | 89.53|
| attention_rescoring | 60.61 | 89.60| |
Amalq/distilroberta-base-finetuned-anxiety-depression | [] | null | {
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"model_type": null,
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}
} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 286.01 +/- 23.09
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
Andrija/SRoBERTa-base | [
"pytorch",
"roberta",
"fill-mask",
"hr",
"sr",
"multilingual",
"dataset:oscar",
"dataset:leipzig",
"transformers",
"masked-lm",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
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}
} | 80 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 265.94 +/- 12.97
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
AndyyyCai/bert-base-uncased-finetuned-copa | [
"pytorch",
"bert",
"multiple-choice",
"transformers"
] | multiple-choice | {
"architectures": [
"BertForMultipleChoice"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
"summarization": {
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}
} | 4 | null | ---
language:
- nl
- en
datasets:
- yhavinga/mc4_nl_cleaned
tags:
- t5
- seq2seq
inference: false
license: apache-2.0
---
# t5-eff-large-8l-dutch-english-cased
A [T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) sequence to sequence model
pre-trained from scratch on [cleaned Dutch 🇳🇱🇧🇪 mC4 and cleaned English 🇬🇧 C4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned).
This **t5 eff** model has **334M** parameters.
It was pre-trained with masked language modeling (denoise token span corruption) objective on the dataset
`mc4_nl_cleaned` config `large_en_nl` for **1** epoch(s) and a duration of **3d 23h**,
with a sequence length of **512**, batch size **128** and **851850** total steps (**56B** tokens).
Pre-training evaluation loss and accuracy are **1,15** and **0,74**.
Refer to the evaluation section below for a comparison of the pre-trained models on summarization and translation.
* Pre-trained T5 models need to be finetuned before they can be used for downstream tasks, therefore the inference widget on the right has been turned off.
* For a demo of the Dutch CNN summarization models, head over to the Hugging Face Spaces for
the **[Netherformer 📰](https://huggingface.co/spaces/flax-community/netherformer)** example application!
Please refer to the original T5 papers and Scale Efficiently papers for more information about the T5 architecture
and configs, though it must be noted that this model (t5-eff-large-8l-dutch-english-cased) is unrelated to these projects and not an 'official' checkpoint.
* **[Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf)** by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu*.
* **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*.
## Tokenizer
The model uses a cased SentencePiece tokenizer configured with the `Nmt, NFKC, Replace multi-space to single-space` normalizers
and has 32003 tokens.
It was trained on Dutch and English with scripts from the Huggingface Transformers [Flax examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling).
See [./raw/main/tokenizer.json](tokenizer.json) for details.
## Dataset(s)
All models listed below are pre-trained on
[cleaned Dutch mC4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned),
which is the original mC4, except
* Documents that contained words from a selection of the Dutch and English [List of Dirty Naught Obscene and Otherwise Bad Words](https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words) are removed
* Sentences with less than 3 words are removed
* Sentences with a word of more than 1000 characters are removed
* Documents with less than 5 sentences are removed
* Documents with "javascript", "lorum ipsum", "terms of use", "privacy policy", "cookie policy", "uses cookies",
"use of cookies", "use cookies", "elementen ontbreken", "deze printversie" are removed.
The Dutch and English models are pre-trained on a 50/50% mix of Dutch mC4 and English C4.
The translation models are fine-tuned on [CCMatrix](https://huggingface.co/datasets/yhavinga/ccmatrix).
## Dutch T5 Models
Three types of [Dutch T5 models have been trained (blog)](https://huggingface.co/spaces/yhavinga/pre-training-dutch-t5-models).
`t5-base-dutch` is the only model with an original T5 config.
The other model types t5-v1.1 and t5-eff have `gated-relu` instead of `relu` as activation function,
and trained with a drop-out of `0.0` unless training would diverge (`t5-v1.1-large-dutch-cased`).
The T5-eff models are models that differ in their number of layers. The table will list
the several dimensions of these models. Not all t5-eff models are efficient, the best example being the inefficient
`t5-xl-4L-dutch-english-cased`.
| | [t5-base-dutch](https://huggingface.co/yhavinga/t5-base-dutch) | [t5-v1.1-base-dutch-uncased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-uncased) | [t5-v1.1-base-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cased) | [t5-v1.1-large-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-large-dutch-cased) | [t5-v1_1-base-dutch-english-cased](https://huggingface.co/yhavinga/t5-v1_1-base-dutch-english-cased) | [t5-v1_1-base-dutch-english-cased-1024](https://huggingface.co/yhavinga/t5-v1_1-base-dutch-english-cased-1024) | [t5-small-24L-dutch-english](https://huggingface.co/yhavinga/t5-small-24L-dutch-english) | [t5-xl-4L-dutch-english-cased](https://huggingface.co/yhavinga/t5-xl-4L-dutch-english-cased) | [t5-base-36L-dutch-english-cased](https://huggingface.co/yhavinga/t5-base-36L-dutch-english-cased) | [t5-eff-xl-8l-dutch-english-cased](https://huggingface.co/yhavinga/t5-eff-xl-8l-dutch-english-cased) | [t5-eff-large-8l-dutch-english-cased](https://huggingface.co/yhavinga/t5-eff-large-8l-dutch-english-cased) |
|:------------------|:----------------|:-----------------------------|:---------------------------|:----------------------------|:-----------------------------------|:----------------------------------------|:-----------------------------|:-------------------------------|:----------------------------------|:-----------------------------------|:--------------------------------------|
| *type* | t5 | t5-v1.1 | t5-v1.1 | t5-v1.1 | t5-v1.1 | t5-v1.1 | t5 eff | t5 eff | t5 eff | t5 eff | t5 eff |
| *d_model* | 768 | 768 | 768 | 1024 | 768 | 768 | 512 | 2048 | 768 | 1024 | 1024 |
| *d_ff* | 3072 | 2048 | 2048 | 2816 | 2048 | 2048 | 1920 | 5120 | 2560 | 16384 | 4096 |
| *num_heads* | 12 | 12 | 12 | 16 | 12 | 12 | 8 | 32 | 12 | 32 | 16 |
| *d_kv* | 64 | 64 | 64 | 64 | 64 | 64 | 64 | 64 | 64 | 128 | 64 |
| *num_layers* | 12 | 12 | 12 | 24 | 12 | 12 | 24 | 4 | 36 | 8 | 8 |
| *num parameters* | 223M | 248M | 248M | 783M | 248M | 248M | 250M | 585M | 729M | 1241M | 335M |
| *feed_forward_proj* | relu | gated-gelu | gated-gelu | gated-gelu | gated-gelu | gated-gelu | gated-gelu | gated-gelu | gated-gelu | gated-gelu | gated-gelu |
| *dropout* | 0.1 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 |
| *dataset* | mc4_nl_cleaned | mc4_nl_cleaned full | mc4_nl_cleaned full | mc4_nl_cleaned | mc4_nl_cleaned small_en_nl | mc4_nl_cleaned large_en_nl | mc4_nl_cleaned large_en_nl | mc4_nl_cleaned large_en_nl | mc4_nl_cleaned large_en_nl | mc4_nl_cleaned large_en_nl | mc4_nl_cleaned large_en_nl |
| *tr. seq len* | 512 | 1024 | 1024 | 512 | 512 | 1024 | 512 | 512 | 512 | 512 | 512 |
| *batch size* | 128 | 64 | 64 | 64 | 128 | 64 | 128 | 512 | 512 | 64 | 128 |
| *total steps* | 527500 | 1014525 | 1210154 | 1120k/2427498 | 2839630 | 1520k/3397024 | 851852 | 212963 | 212963 | 538k/1703705 | 851850 |
| *epochs* | 1 | 2 | 2 | 2 | 10 | 4 | 1 | 1 | 1 | 1 | 1 |
| *duration* | 2d9h | 5d5h | 6d6h | 8d13h | 11d18h | 9d1h | 4d10h | 6d1h | 17d15h | 4d 19h | 3d 23h |
| *optimizer* | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor |
| *lr* | 0.005 | 0.005 | 0.005 | 0.005 | 0.005 | 0.005 | 0.005 | 0.005 | 0.009 | 0.005 | 0.005 |
| *warmup* | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 5000.0 | 20000.0 | 2500.0 | 1000.0 | 1500.0 | 1500.0 |
| *eval loss* | 1,38 | 1,20 | 0,96 | 1,07 | 1,11 | 1,13 | 1,18 | 1,27 | 1,05 | 1,3019 | 1,15 |
| *eval acc* | 0,70 | 0,73 | 0,78 | 0,76 | 0,75 | 0,74 | 0,74 | 0,72 | 0,76 | 0,71 | 0,74 |
## Evaluation
Most models from the list above have been fine-tuned for summarization and translation.
The figure below shows the evaluation scores, where the x-axis shows the translation Bleu score (higher is better)
and y-axis the summarization Rouge1 translation score (higher is better).
Point size is proportional to the model size. Models with faster inference speed are green, slower inference speed is
plotted as bleu.
Evaluation was run on fine-tuned models trained with the following settings:
| | Summarization | Translation |
|---------------:|------------------|-------------------|
| Dataset | CNN Dailymail NL | CCMatrix en -> nl |
| #train samples | 50K | 50K |
| Optimizer | Adam | Adam |
| learning rate | 0.001 | 0.0005 |
| source length | 1024 | 128 |
| target length | 142 | 128 |
|label smoothing | 0.05 | 0.1 |
| #eval samples | 1000 | 1000 |
Note that the amount of training data is limited to a fraction of the total dataset sizes, therefore the scores
below can only be used to compare the 'transfer-learning' strength. The fine-tuned checkpoints for this evaluation
are not saved, since they were trained for comparison of pre-trained models only.
The numbers for summarization are the Rouge scores on 1000 documents from the test split.
| | [t5-base-dutch](https://huggingface.co/yhavinga/t5-base-dutch) | [t5-v1.1-base-dutch-uncased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-uncased) | [t5-v1.1-base-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cased) | [t5-v1_1-base-dutch-english-cased](https://huggingface.co/yhavinga/t5-v1_1-base-dutch-english-cased) | [t5-v1_1-base-dutch-english-cased-1024](https://huggingface.co/yhavinga/t5-v1_1-base-dutch-english-cased-1024) | [t5-small-24L-dutch-english](https://huggingface.co/yhavinga/t5-small-24L-dutch-english) | [t5-xl-4L-dutch-english-cased](https://huggingface.co/yhavinga/t5-xl-4L-dutch-english-cased) | [t5-base-36L-dutch-english-cased](https://huggingface.co/yhavinga/t5-base-36L-dutch-english-cased) | [t5-eff-large-8l-dutch-english-cased](https://huggingface.co/yhavinga/t5-eff-large-8l-dutch-english-cased) | mt5-base |
|:------------------------|----------------:|-----------------------------:|---------------------------:|-----------------------------------:|----------------------------------------:|-----------------------------:|-------------------------------:|----------------------------------:|--------------------------------------:|-----------:|
| *rouge1* | 33.38 | 33.97 | 34.39 | 33.38 | 34.97 | 34.38 | 30.35 | **35.04** | 34.04 | 33.25 |
| *rouge2* | 13.32 | 13.85 | 13.98 | 13.47 | 14.01 | 13.89 | 11.57 | **14.23** | 13.76 | 12.74 |
| *rougeL* | 24.22 | 24.72 | 25.1 | 24.34 | 24.99 | **25.25** | 22.69 | 25.05 | 24.75 | 23.5 |
| *rougeLsum* | 30.23 | 30.9 | 31.44 | 30.51 | 32.01 | 31.38 | 27.5 | **32.12** | 31.12 | 30.15 |
| *samples_per_second* | 3.18 | 3.02 | 2.99 | 3.22 | 2.97 | 1.57 | 2.8 | 0.61 | **3.27** | 1.22 |
The models below have been evaluated for English to Dutch translation.
Note that the first four models are pre-trained on Dutch only. That they still perform adequate is probably because
the translation direction is English to Dutch.
The numbers reported are the Bleu scores on 1000 documents from the test split.
| | [t5-base-dutch](https://huggingface.co/yhavinga/t5-base-dutch) | [t5-v1.1-base-dutch-uncased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-uncased) | [t5-v1.1-base-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cased) | [t5-v1.1-large-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-large-dutch-cased) | [t5-v1_1-base-dutch-english-cased](https://huggingface.co/yhavinga/t5-v1_1-base-dutch-english-cased) | [t5-v1_1-base-dutch-english-cased-1024](https://huggingface.co/yhavinga/t5-v1_1-base-dutch-english-cased-1024) | [t5-small-24L-dutch-english](https://huggingface.co/yhavinga/t5-small-24L-dutch-english) | [t5-xl-4L-dutch-english-cased](https://huggingface.co/yhavinga/t5-xl-4L-dutch-english-cased) | [t5-base-36L-dutch-english-cased](https://huggingface.co/yhavinga/t5-base-36L-dutch-english-cased) | [t5-eff-large-8l-dutch-english-cased](https://huggingface.co/yhavinga/t5-eff-large-8l-dutch-english-cased) | mt5-base |
|:-------------------------------|----------------:|-----------------------------:|---------------------------:|----------------------------:|-----------------------------------:|----------------------------------------:|-----------------------------:|-------------------------------:|----------------------------------:|--------------------------------------:|-----------:|
| *precision_ng1* | 74.17 | 78.09 | 77.08 | 72.12 | 77.19 | 78.76 | 78.59 | 77.3 | **79.75** | 78.88 | 73.47 |
| *precision_ng2* | 52.42 | 57.52 | 55.31 | 48.7 | 55.39 | 58.01 | 57.83 | 55.27 | **59.89** | 58.27 | 50.12 |
| *precision_ng3* | 39.55 | 45.2 | 42.54 | 35.54 | 42.25 | 45.13 | 45.02 | 42.06 | **47.4** | 45.95 | 36.59 |
| *precision_ng4* | 30.23 | 36.04 | 33.26 | 26.27 | 32.74 | 35.72 | 35.41 | 32.61 | **38.1** | 36.91 | 27.26 |
| *bp* | 0.99 | 0.98 | 0.97 | 0.98 | 0.98 | 0.98 | 0.98 | 0.97 | 0.98 | 0.98 | 0.98 |
| *score* | 45.88 | 51.21 | 48.31 | 41.59 | 48.17 | 51.31 | 50.82 | 47.83 | **53** | 51.79 | 42.74 |
| *samples_per_second* | **45.19** | 45.05 | 38.67 | 10.12 | 42.19 | 42.61 | 12.85 | 33.74 | 9.07 | 37.86 | 9.03 |
## Translation models
The models `t5-small-24L-dutch-english` and `t5-base-36L-dutch-english` have been fine-tuned for both language
directions on the first 25M samples from CCMatrix, giving a total of 50M training samples.
Evaluation is performed on out-of-sample CCMatrix and also on Tatoeba and Opus Books.
The `_bp` columns list the *brevity penalty*. The `avg_bleu` score is the bleu score
averaged over all three evaluation datasets. The best scores displayed in bold for both translation directions.
| | [t5-base-36L-ccmatrix-multi](https://huggingface.co/yhavinga/t5-base-36L-ccmatrix-multi) | [t5-base-36L-ccmatrix-multi](https://huggingface.co/yhavinga/t5-base-36L-ccmatrix-multi) | [t5-small-24L-ccmatrix-multi](https://huggingface.co/yhavinga/t5-small-24L-ccmatrix-multi) | [t5-small-24L-ccmatrix-multi](https://huggingface.co/yhavinga/t5-small-24L-ccmatrix-multi) |
|:-----------------------|:-----------------------------|:-----------------------------|:------------------------------|:------------------------------|
| *source_lang* | en | nl | en | nl |
| *target_lang* | nl | en | nl | en |
| *source_prefix* | translate English to Dutch: | translate Dutch to English: | translate English to Dutch: | translate Dutch to English: |
| *ccmatrix_bleu* | **56.8** | 62.8 | 57.4 | **63.1** |
| *tatoeba_bleu* | **46.6** | **52.8** | 46.4 | 51.7 |
| *opus_books_bleu* | **13.5** | **24.9** | 12.9 | 23.4 |
| *ccmatrix_bp* | 0.95 | 0.96 | 0.95 | 0.96 |
| *tatoeba_bp* | 0.97 | 0.94 | 0.98 | 0.94 |
| *opus_books_bp* | 0.8 | 0.94 | 0.77 | 0.89 |
| *avg_bleu* | **38.96** | **46.86** | 38.92 | 46.06 |
| *max_source_length* | 128 | 128 | 128 | 128 |
| *max_target_length* | 128 | 128 | 128 | 128 |
| *adam_beta1* | 0.9 | 0.9 | 0.9 | 0.9 |
| *adam_beta2* | 0.997 | 0.997 | 0.997 | 0.997 |
| *weight_decay* | 0.05 | 0.05 | 0.002 | 0.002 |
| *lr* | 5e-05 | 5e-05 | 0.0005 | 0.0005 |
| *label_smoothing_factor* | 0.15 | 0.15 | 0.1 | 0.1 |
| *train_batch_size* | 128 | 128 | 128 | 128 |
| *warmup_steps* | 2000 | 2000 | 2000 | 2000 |
| *total steps* | 390625 | 390625 | 390625 | 390625 |
| *duration* | 4d 5h | 4d 5h | 3d 2h | 3d 2h |
| *num parameters* | 729M | 729M | 250M | 250M |
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/). The HuggingFace 🤗 ecosystem was instrumental in all parts
of the training. Weights & Biases made it possible to keep track of many training sessions
and orchestrate hyper-parameter sweeps with insightful visualizations.
The following repositories where helpful in setting up the TPU-VM,
and getting an idea what sensible hyper-parameters are for training gpt2 from scratch:
* [Gsarti's Pretrain and Fine-tune a T5 model with Flax on GCP](https://github.com/gsarti/t5-flax-gcp)
* [Flax/Jax Community week t5-base-dutch](https://huggingface.co/flax-community/t5-base-dutch)
Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/)
|
Ani123/Ani | [] | null | {
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 300.17 +/- 14.49
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
Anirbanbhk/Hate-speech-Pretrained-movies | [
"tf",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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"BertForSequenceClassification"
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}
}
} | 20 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 281.35 +/- 15.31
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
Ankit-11/distilbert-base-uncased-finetuned-toxic | [] | null | {
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}
} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-roundup-4-8
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. -->
# bart-large-cnn-finetuned-roundup-4-8
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7882
- Rouge1: 54.2292
- Rouge2: 37.3874
- Rougel: 40.3261
- Rougelsum: 52.2155
- Gen Len: 141.8889
## 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: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| No log | 1.0 | 398 | 0.9442 | 53.1634 | 33.7744 | 35.5688 | 50.7523 | 142.0 |
| 1.1285 | 2.0 | 796 | 0.8305 | 54.0713 | 35.7079 | 37.5147 | 51.6285 | 142.0 |
| 0.6796 | 3.0 | 1194 | 0.7735 | 52.6656 | 34.0198 | 36.8075 | 50.1502 | 142.0 |
| 0.4572 | 4.0 | 1592 | 0.7759 | 53.6269 | 35.4308 | 38.3735 | 51.1369 | 141.7222 |
| 0.4572 | 5.0 | 1990 | 0.7527 | 54.4206 | 36.0907 | 38.0818 | 51.7885 | 142.0 |
| 0.3171 | 6.0 | 2388 | 0.7755 | 54.9642 | 38.0459 | 41.6383 | 52.8847 | 142.0 |
| 0.2269 | 7.0 | 2786 | 0.7801 | 54.1637 | 35.9853 | 39.5262 | 51.6562 | 142.0 |
| 0.1686 | 8.0 | 3184 | 0.7882 | 54.2292 | 37.3874 | 40.3261 | 52.2155 | 141.8889 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Ankitha/DialoGPT-small-harrypottery | [] | null | {
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-roundup-4-4
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. -->
# bart-large-cnn-finetuned-roundup-4-4
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7912
- Rouge1: 53.8175
- Rouge2: 35.1335
- Rougel: 38.0823
- Rougelsum: 51.2925
- Gen Len: 142.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 398 | 0.9455 | 52.8137 | 33.4924 | 35.5866 | 50.7208 | 142.0 |
| 1.1309 | 2.0 | 796 | 0.8397 | 54.0923 | 35.0799 | 37.4609 | 51.5914 | 142.0 |
| 0.6902 | 3.0 | 1194 | 0.7932 | 53.5752 | 35.0842 | 37.9295 | 51.0356 | 142.0 |
| 0.4951 | 4.0 | 1592 | 0.7912 | 53.8175 | 35.1335 | 38.0823 | 51.2925 | 142.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
AnonymousSub/AR_EManuals-RoBERTa | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
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} | 6 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 230.18 +/- 16.20
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
AnonymousSub/AR_rule_based_roberta_hier_triplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
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"RobertaModel"
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}
} | 4 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: distilbart-cnn-12-6-finetuned-roundup-4-8
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. -->
# distilbart-cnn-12-6-finetuned-roundup-4-8
This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8447
- Rouge1: 54.3326
- Rouge2: 36.1031
- Rougel: 38.842
- Rougelsum: 51.7632
- Gen Len: 142.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| No log | 1.0 | 398 | 1.1572 | 51.6618 | 32.7542 | 34.8631 | 49.2691 | 141.3333 |
| 1.405 | 2.0 | 796 | 1.0039 | 52.2029 | 32.6704 | 34.4948 | 50.1141 | 142.0 |
| 0.9039 | 3.0 | 1194 | 0.9300 | 53.2839 | 34.3928 | 36.8971 | 51.1148 | 142.0 |
| 0.6705 | 4.0 | 1592 | 0.8708 | 52.5229 | 33.8116 | 36.9664 | 50.0067 | 142.0 |
| 0.6705 | 5.0 | 1990 | 0.8508 | 53.4468 | 35.1394 | 38.4144 | 50.794 | 142.0 |
| 0.5205 | 6.0 | 2388 | 0.8347 | 53.8859 | 35.1182 | 38.1126 | 51.3089 | 142.0 |
| 0.3898 | 7.0 | 2786 | 0.8406 | 54.2293 | 36.1189 | 38.7127 | 51.6878 | 142.0 |
| 0.3468 | 8.0 | 3184 | 0.8447 | 54.3326 | 36.1031 | 38.842 | 51.7632 | 142.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
AnonymousSub/AR_rule_based_roberta_only_classfn_twostage_epochs_1_shard_1 | [
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} | 2 | null | ---
license: cc-by-4.0
---
# Aurora SDG AI
This model is able to classify texts related to SDG's in multiple languages. |
AnonymousSub/AR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
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}
} | 6 | null | ---
language: en
tags:
- scibert
license: mit
---
# MultiCite: Multi-label Citation Intent Classification with SciBERT (NAACL 2022)
This model has been trained on the data available here: https://github.com/allenai/multicite |
AnonymousSub/AR_rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
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} | 5 | null | ---
tags:
- generated_from_trainer
model-index:
- name: albert-base-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. -->
# albert-base-v2
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0075
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.024 | 1.0 | 4000 | 0.0300 |
| 0.0049 | 2.0 | 8000 | 0.0075 |
| 0.0 | 3.0 | 12000 | 0.0125 |
| 0.0 | 4.0 | 16000 | 0.0101 |
| 0.0056 | 5.0 | 20000 | 0.0104 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.9.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
AnonymousSub/EManuals_RoBERTa_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
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} | 29 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -113.24 +/- 33.86
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
AnonymousSub/SR_EManuals-RoBERTa | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
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"RobertaModel"
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} | 1 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 288.54 +/- 20.64
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
AnonymousSub/SR_rule_based_roberta_bert_quadruplet_epochs_1_shard_1 | [
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"roberta",
"feature-extraction",
"transformers"
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} | 2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-finetuned-bruno-mars
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. -->
# distilroberta-base-finetuned-bruno-mars
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4055
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 62 | 2.5992 |
| No log | 2.0 | 124 | 2.4069 |
| No log | 3.0 | 186 | 2.4055 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cpu
- Datasets 2.1.0
- Tokenizers 0.12.1
|
AnonymousSub/SR_rule_based_roberta_bert_quadruplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
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} | 2 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-roundup-4-16
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. -->
# bart-large-cnn-finetuned-roundup-4-16
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8760
- Rouge1: 56.3338
- Rouge2: 42.4032
- Rougel: 45.9455
- Rougelsum: 54.6488
- Gen Len: 142.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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: 16
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| No log | 1.0 | 398 | 0.9325 | 52.7796 | 33.0802 | 34.8217 | 50.2211 | 142.0 |
| 1.1317 | 2.0 | 796 | 0.8313 | 53.6274 | 35.3235 | 37.7077 | 51.0888 | 141.2963 |
| 0.6757 | 3.0 | 1194 | 0.7893 | 54.1449 | 34.7532 | 36.3211 | 51.781 | 142.0 |
| 0.4511 | 4.0 | 1592 | 0.7647 | 52.2694 | 34.2286 | 36.5736 | 49.7078 | 142.0 |
| 0.4511 | 5.0 | 1990 | 0.7596 | 55.1986 | 37.5865 | 41.406 | 53.1897 | 141.8333 |
| 0.3037 | 6.0 | 2388 | 0.7688 | 53.9367 | 36.8729 | 39.9456 | 51.5108 | 142.0 |
| 0.209 | 7.0 | 2786 | 0.7590 | 54.6867 | 37.6415 | 41.2602 | 52.746 | 142.0 |
| 0.1452 | 8.0 | 3184 | 0.7744 | 53.5374 | 36.3666 | 40.0432 | 51.3461 | 142.0 |
| 0.11 | 9.0 | 3582 | 0.8042 | 56.6623 | 40.4702 | 44.0028 | 54.5138 | 142.0 |
| 0.11 | 10.0 | 3980 | 0.8105 | 55.6002 | 40.5663 | 43.8119 | 53.9117 | 142.0 |
| 0.0833 | 11.0 | 4378 | 0.8230 | 56.2517 | 40.8567 | 44.0009 | 54.3271 | 142.0 |
| 0.0634 | 12.0 | 4776 | 0.8329 | 55.9228 | 40.6443 | 43.6161 | 54.0975 | 142.0 |
| 0.0474 | 13.0 | 5174 | 0.8570 | 55.4923 | 40.3683 | 43.4675 | 53.404 | 142.0 |
| 0.0349 | 14.0 | 5572 | 0.8658 | 56.4454 | 41.8069 | 44.2922 | 54.464 | 142.0 |
| 0.0349 | 15.0 | 5970 | 0.8754 | 56.3837 | 42.2025 | 45.7817 | 54.4912 | 142.0 |
| 0.0304 | 16.0 | 6368 | 0.8760 | 56.3338 | 42.4032 | 45.9455 | 54.6488 | 142.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
AnonymousSub/SR_rule_based_roberta_bert_triplet_epochs_1_shard_10 | [
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"roberta",
"feature-extraction",
"transformers"
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} | 2 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/trancentrall/1651861073034/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/1439718913286328324/BWMkSlFf_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">jotchua</div>
<div style="text-align: center; font-size: 14px;">@trancentrall</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 jotchua.
| Data | jotchua |
| --- | --- |
| Tweets downloaded | 3197 |
| Retweets | 165 |
| Short tweets | 937 |
| Tweets kept | 2095 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2cfuds5z/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 @trancentrall's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/37rzneux) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/37rzneux/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/trancentrall')
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)
|
AnonymousSub/SR_rule_based_roberta_only_classfn_epochs_1_shard_10 | [
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} | 3 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 295.15 +/- 17.69
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
AnonymousSub/SR_rule_based_roberta_only_classfn_twostage_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
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} | 8 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 270.00 +/- 16.69
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
AnonymousSub/SR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
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} | 4 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: IQA_classification
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. -->
# IQA_classification
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0718
- Accuracy: 0.4862
- Precision: 0.3398
- Recall: 0.4862
- F1: 0.3270
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.3973 | 1.0 | 28 | 1.1588 | 0.4771 | 0.2276 | 0.4771 | 0.3082 |
| 1.1575 | 2.0 | 56 | 1.0718 | 0.4862 | 0.3398 | 0.4862 | 0.3270 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
AnonymousSub/SR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_10 | [
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} | 8 | null | ---
language: en
license: apache-2.0
datasets:
- Super-NaturalInstructions
---
# Model description
Tk-Instruct is a series of encoder-decoder Transformer models that are trained to solve various NLP tasks by following in-context instructions (plain language task definitions, k-shot examples, explanations, etc). Built upon the pre-trained [T5 models](https://arxiv.org/abs/1910.10683), they are fine-tuned on a large number of tasks & instructions that are collected in the [Natural Instructions benchmark](https://github.com/allenai/natural-instructions), which contains 1600+ tasks in 70+ broach categories in total. This enables the model to not only process the training tasks, but also generalize to many unseen tasks without further parameter update.
More resources for using the model:
- **Paper**: [link](https://arxiv.org/abs/2204.07705)
- **Code repository**: [Tk-Instruct](https://github.com/yizhongw/Tk-Instruct)
- **Official Website**: [Natural Instructions](https://instructions.apps.allenai.org/)
- **All released models**: [allenai/tk-instruct](https://huggingface.co/models?search=allenai/tk-instruct)
## Intended uses & limitations
Tk-Instruct can be used to do many NLP tasks by following instructions.
### How to use
When instructing the model, task definition or demonstration examples or explanations should be prepended to the original input and fed into the model. You can easily try Tk-Instruct models as follows:
```python
>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
>>> tokenizer = AutoTokenizer.from_pretrained("allenai/tk-instruct-3b-def")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("allenai/tk-instruct-3b-def")
>>> input_ids = tokenizer.encode(
"Definition: return the currency of the given country. Now complete the following example - Input: India. Output:",
return_tensors="pt")
>>> output = model.generate(input_ids, max_length=10)
>>> output = tokenizer.decode(output[0], skip_special_tokens=True) # model should output 'Indian Rupee'
>>> input_ids = tokenizer.encode(
"Definition: negate the following sentence. Input: John went to school. Output:",
return_tensors="pt")
>>> output = model.generate(input_ids, max_length=10)
>>> output = tokenizer.decode(output[0], skip_special_tokens=True) # model should output 'John did not go to shool.'
```
### Limitations
We are still working on understanding the behaviors of these models, but here are several issues we have found:
- Models are generally sensitive to the instruction. Sometimes rewording the instruction can lead to very different output.
- Models are not always compliant to the instruction. Sometimes the model don't follow your instruction (e.g., when you ask the model to generate one sentence, it might still generate one word or a long story).
- Models might totally fail on some tasks.
If you find serious issues or any interesting result, you are welcome to share with us!
## Training data
Tk-Instruct is trained using the tasks & instructions in [Natural Instructions benchmark](https://github.com/allenai/natural-instructions), which contains 1600+ tasks in 70+ broach categories in total. We follow the official train/test split. Tk-Instruct model series were trained using 757 tasks, and mTk-Instruct series were trained using 1271 tasks (including some non-English tasks).
The training tasks are in 64 broad categories, such as text categorization / question answering / sentiment analysis / summarization / grammar error detection / dialogue generation / etc. The other 12 categories are selected for evaluation.
## Training procedure
All our models are initialized from either T5 models or mT5 models. Because generating the output can be regarded as a form of language modeling, we used their [LM adapted version](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-t511lm100k). All data is converted into a text-to-text format, and models are fine-tuned to maximize the likelihood of the output sequence.
Our [released models](https://huggingface.co/models?search=allenai/tk-instruct) are in different sizes, and each of them was trained with a specific type of instruction encoding. For instance, `tk-instruct-3b-def-pos` was initialized from [t5-xl-lm-adapt](https://huggingface.co/google/t5-xl-lm-adapt), and it saw task definition & 2 positive examples as the instruction during training time.
Although they are trained with only one type of instruction encodings, we found they can usually work with other type of encodings at test time (see more in our paper).
### BibTeX entry and citation info
```bibtex
@article{wang2022benchmarking,
title={Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and A. Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and M. Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddharth Deepak Mishra and Sujan C. Reddy and Sumanta Patro and Tanay Dixit and Xu-dong Shen and Chitta Baral and Yejin Choi and Hannaneh Hajishirzi and Noah A. Smith and Daniel Khashabi},
year={2022},
archivePrefix={arXiv},
eprint={2204.07705},
primaryClass={cs.CL},
}
``` |
AnonymousSub/SR_rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_1 | [
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} | 4 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xlsr-es-col-test
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-es-col-test
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-spanish](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0923
- Wer: 0.0886
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0905 | 18.18 | 400 | 0.0923 | 0.0886 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.1+cu102
- Datasets 1.13.3
- Tokenizers 0.10.3
|
AnonymousSub/SR_rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_10 | [
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} | 4 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 251.39 +/- 19.44
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
AnonymousSub/SR_rule_based_roberta_twostagetriplet_hier_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
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} | 4 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: reaprtripr/distill-bert-csn-python
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. -->
# reaprtripr/distill-bert-csn-python
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: nan
- Validation Loss: nan
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -688, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| nan | nan | 0 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
AnonymousSub/SR_rule_based_roberta_twostagetriplet_hier_epochs_1_shard_10 | [
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} | 7 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 284.45 +/- 12.33
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
AnonymousSub/SR_rule_based_twostagetriplet_hier_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
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} | 2 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: prot_bert_classification_finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# prot_bert_classification_finetuned
This model is a fine-tuned version of [nepp1d0/prot_bert-finetuned-smiles-bindingDB](https://huggingface.co/nepp1d0/prot_bert-finetuned-smiles-bindingDB) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5675
- Accuracy: 0.7299
- F1: 0.7377
- Precision: 0.6995
- Recall: 0.7803
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 3
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4221 | 1.0 | 3332 | 0.6152 | 0.6615 | 0.6711 | 0.6367 | 0.7093 |
| 0.4133 | 2.0 | 6664 | 0.5840 | 0.6845 | 0.6718 | 0.6805 | 0.6634 |
| 0.4293 | 3.0 | 9996 | 0.5727 | 0.7116 | 0.7094 | 0.6961 | 0.7232 |
| 0.3098 | 4.0 | 13328 | 0.5636 | 0.7163 | 0.7220 | 0.6904 | 0.7566 |
| 0.3881 | 5.0 | 16660 | 0.5629 | 0.7265 | 0.7377 | 0.6918 | 0.7900 |
| 0.4943 | 6.0 | 19992 | 0.5675 | 0.7299 | 0.7377 | 0.6995 | 0.7803 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
AnonymousSub/cline-emanuals-techqa | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
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} | 4 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-finetuned-the-beatles
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. -->
# distilroberta-base-finetuned-the-beatles
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5186
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 84 | 2.6517 |
| No log | 2.0 | 168 | 2.6433 |
| No log | 3.0 | 252 | 2.5186 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cpu
- Datasets 2.1.0
- Tokenizers 0.12.1
|
AnonymousSub/cline-papers-biomed-0.618 | [
"pytorch",
"roberta",
"transformers"
] | null | {
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} | 2 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/csbible/1651865198723/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/818891995057946624/2mUjD9A4_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">Christian Standard Bible</div>
<div style="text-align: center; font-size: 14px;">@csbible</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 Christian Standard Bible.
| Data | Christian Standard Bible |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 29 |
| Short tweets | 31 |
| Tweets kept | 3190 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/89bp2qgq/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 @csbible's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/196rw0mt) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/196rw0mt/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/csbible')
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)
|
AnonymousSub/cline-papers-roberta-0.585 | [
"pytorch",
"roberta",
"transformers"
] | null | {
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} | 1 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 257.30 +/- 18.04
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
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