modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
aujer/autotrain-not_interested_2-1213045881
|
aujer
| 2022-08-02T21:15:40Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:aujer/autotrain-data-not_interested_2",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-02T21:14:05Z
|
---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- aujer/autotrain-data-not_interested_2
co2_eq_emissions:
emissions: 1.695519133475222
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1213045881
- CO2 Emissions (in grams): 1.6955
## Validation Metrics
- Loss: 1.607
- Accuracy: 0.535
- Macro F1: 0.306
- Micro F1: 0.535
- Weighted F1: 0.440
- Macro Precision: 0.346
- Micro Precision: 0.535
- Weighted Precision: 0.435
- Macro Recall: 0.345
- Micro Recall: 0.535
- Weighted Recall: 0.535
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/aujer/autotrain-not_interested_2-1213045881
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("aujer/autotrain-not_interested_2-1213045881", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("aujer/autotrain-not_interested_2-1213045881", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
srcocotero/tiny-bert-qa
|
srcocotero
| 2022-08-02T19:58:09Z
| 6
| 2
|
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-07-27T19:12:14Z
|
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: mini_model
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. -->
# mini_model
This model is a fine-tuned version of [nreimers/BERT-Tiny_L-2_H-128_A-2](https://huggingface.co/nreimers/BERT-Tiny_L-2_H-128_A-2) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 5
- 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
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
bhadi26/hadi-rebecca-test-model-public
|
bhadi26
| 2022-08-02T19:37:35Z
| 0
| 0
| null |
[
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1907.11692",
"arxiv:1806.02847",
"license:mit",
"region:us"
] | null | 2022-08-02T19:37:11Z
|
---
language: en
tags:
- exbert
license: mit
datasets:
- bookcorpus
- wikipedia
---
# RoBERTa base model
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1907.11692) and first released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). This model is case-sensitive: it
makes a difference between english and English.
Disclaimer: The team releasing RoBERTa did not write a model card for this model so this model card has been written by
the Hugging Face team.
## Model description
RoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means
it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model
randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict
the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one
after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to
learn a bidirectional representation of the sentence.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
## Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
See the [model hub](https://huggingface.co/models?filter=roberta) to look for fine-tuned versions on a task that
interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='roberta-base')
>>> unmasker("Hello I'm a <mask> model.")
[{'sequence': "<s>Hello I'm a male model.</s>",
'score': 0.3306540250778198,
'token': 2943,
'token_str': 'Ġmale'},
{'sequence': "<s>Hello I'm a female model.</s>",
'score': 0.04655390977859497,
'token': 2182,
'token_str': 'Ġfemale'},
{'sequence': "<s>Hello I'm a professional model.</s>",
'score': 0.04232972860336304,
'token': 2038,
'token_str': 'Ġprofessional'},
{'sequence': "<s>Hello I'm a fashion model.</s>",
'score': 0.037216778844594955,
'token': 2734,
'token_str': 'Ġfashion'},
{'sequence': "<s>Hello I'm a Russian model.</s>",
'score': 0.03253649175167084,
'token': 1083,
'token_str': 'ĠRussian'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaModel.from_pretrained('roberta-base')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = TFRobertaModel.from_pretrained('roberta-base')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
### Limitations and bias
The training data used for this model contains a lot of unfiltered content from the internet, which is far from
neutral. Therefore, the model can have biased predictions:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='roberta-base')
>>> unmasker("The man worked as a <mask>.")
[{'sequence': '<s>The man worked as a mechanic.</s>',
'score': 0.08702439814805984,
'token': 25682,
'token_str': 'Ġmechanic'},
{'sequence': '<s>The man worked as a waiter.</s>',
'score': 0.0819653645157814,
'token': 38233,
'token_str': 'Ġwaiter'},
{'sequence': '<s>The man worked as a butcher.</s>',
'score': 0.073323555290699,
'token': 32364,
'token_str': 'Ġbutcher'},
{'sequence': '<s>The man worked as a miner.</s>',
'score': 0.046322137117385864,
'token': 18678,
'token_str': 'Ġminer'},
{'sequence': '<s>The man worked as a guard.</s>',
'score': 0.040150221437215805,
'token': 2510,
'token_str': 'Ġguard'}]
>>> unmasker("The Black woman worked as a <mask>.")
[{'sequence': '<s>The Black woman worked as a waitress.</s>',
'score': 0.22177888453006744,
'token': 35698,
'token_str': 'Ġwaitress'},
{'sequence': '<s>The Black woman worked as a prostitute.</s>',
'score': 0.19288744032382965,
'token': 36289,
'token_str': 'Ġprostitute'},
{'sequence': '<s>The Black woman worked as a maid.</s>',
'score': 0.06498628109693527,
'token': 29754,
'token_str': 'Ġmaid'},
{'sequence': '<s>The Black woman worked as a secretary.</s>',
'score': 0.05375480651855469,
'token': 2971,
'token_str': 'Ġsecretary'},
{'sequence': '<s>The Black woman worked as a nurse.</s>',
'score': 0.05245552211999893,
'token': 9008,
'token_str': 'Ġnurse'}]
```
This bias will also affect all fine-tuned versions of this model.
## Training data
The RoBERTa model was pretrained on the reunion of five datasets:
- [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books;
- [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers) ;
- [CC-News](https://commoncrawl.org/2016/10/news-dataset-available/), a dataset containing 63 millions English news
articles crawled between September 2016 and February 2019.
- [OpenWebText](https://github.com/jcpeterson/openwebtext), an opensource recreation of the WebText dataset used to
train GPT-2,
- [Stories](https://arxiv.org/abs/1806.02847) a dataset containing a subset of CommonCrawl data filtered to match the
story-like style of Winograd schemas.
Together theses datasets weight 160GB of text.
## Training procedure
### Preprocessing
The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50,000. The inputs of
the model take pieces of 512 contiguous token that may span over documents. The beginning of a new document is marked
with `<s>` and the end of one by `</s>`
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `<mask>`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
Contrary to BERT, the masking is done dynamically during pretraining (e.g., it changes at each epoch and is not fixed).
### Pretraining
The model was trained on 1024 V100 GPUs for 500K steps with a batch size of 8K and a sequence length of 512. The
optimizer used is Adam with a learning rate of 6e-4, \\(\beta_{1} = 0.9\\), \\(\beta_{2} = 0.98\\) and
\\(\epsilon = 1e-6\\), a weight decay of 0.01, learning rate warmup for 24,000 steps and linear decay of the learning
rate after.
## Evaluation results
When fine-tuned on downstream tasks, this model achieves the following results:
Glue test results:
| Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE |
|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|
| | 87.6 | 91.9 | 92.8 | 94.8 | 63.6 | 91.2 | 90.2 | 78.7 |
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-1907-11692,
author = {Yinhan Liu and
Myle Ott and
Naman Goyal and
Jingfei Du and
Mandar Joshi and
Danqi Chen and
Omer Levy and
Mike Lewis and
Luke Zettlemoyer and
Veselin Stoyanov},
title = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach},
journal = {CoRR},
volume = {abs/1907.11692},
year = {2019},
url = {http://arxiv.org/abs/1907.11692},
archivePrefix = {arXiv},
eprint = {1907.11692},
timestamp = {Thu, 01 Aug 2019 08:59:33 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
<a href="https://huggingface.co/exbert/?model=roberta-base">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
Rifky/indobert-hoax-classification
|
Rifky
| 2022-08-02T19:32:31Z
| 18
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-02T16:42:51Z
|
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: indobert-hoax-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. -->
# indobert-hoax-classification
This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6230
- Accuracy: 0.8059
## 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: 4.2173070213315e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 30
- 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 | 85 | 0.5540 | 0.7029 |
| No log | 2.0 | 170 | 0.5432 | 0.7029 |
| No log | 3.0 | 255 | 0.4963 | 0.7441 |
| No log | 4.0 | 340 | 0.5791 | 0.7971 |
| No log | 5.0 | 425 | 0.6230 | 0.8059 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
liujxing/pegassus-samsum
|
liujxing
| 2022-08-02T19:03:10Z
| 12
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-08-01T14:37:11Z
|
---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegassus-samsum
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. -->
# pegassus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5463
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7619 | 0.54 | 500 | 1.5463 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.11.0
- Datasets 2.4.0
- Tokenizers 0.12.1
|
osanseviero/CleanRLTest
|
osanseviero
| 2022-08-02T18:32:15Z
| 0
| 0
| null |
[
"CartPole-v1",
"ppo",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-08-02T17:45:02Z
|
---
tags:
- CartPole-v1
- ppo
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: CleanRLTest
results:
- metrics:
- type: mean_reward
value: 232.20 +/- 102.47
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
---
# **PPO** Agent playing
This is a trained model of a ***PPO** agent playing CartPole-v1.
To learn to use this model and train yours check Unit 8 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
{'exp_name': 'ppo', 'seed': 1, 'torch_deterministic': True, 'cuda': True, 'track': False, 'wandb_project_name': 'cleanRL', 'wandb_entity': None, 'capture_video': False, 'env_id': 'CartPole-v1', 'total_timesteps': 50000, 'learning_rate': 0.00025, 'num_envs': 4, 'num_steps': 128, 'anneal_lr': True, 'gae': True, 'gamma': 0.99, 'gae_lambda': 0.95, 'num_minibatches': 4, 'update_epochs': 4, 'norm_adv': True, 'clip_coef': 0.2, 'clip_vloss': True, 'ent_coef': 0.01, 'vf_coef': 0.5, 'max_grad_norm': 0.5, 'target_kl': None, 'batch_size': 512, 'minibatch_size': 128}
Boom
|
elopezlopez/distilbert-base-uncased_fold_10_ternary_v1
|
elopezlopez
| 2022-08-02T18:22:45Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-02T18:09:19Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilbert-base-uncased_fold_10_ternary_v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_10_ternary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9887
- F1: 0.7797
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 290 | 0.5701 | 0.7463 |
| 0.5651 | 2.0 | 580 | 0.5359 | 0.7748 |
| 0.5651 | 3.0 | 870 | 0.6043 | 0.7847 |
| 0.2605 | 4.0 | 1160 | 1.0124 | 0.7587 |
| 0.2605 | 5.0 | 1450 | 1.1140 | 0.7599 |
| 0.1223 | 6.0 | 1740 | 1.2713 | 0.7859 |
| 0.0469 | 7.0 | 2030 | 1.3188 | 0.7822 |
| 0.0469 | 8.0 | 2320 | 1.3819 | 0.7946 |
| 0.0279 | 9.0 | 2610 | 1.5444 | 0.7847 |
| 0.0279 | 10.0 | 2900 | 1.5851 | 0.7908 |
| 0.0084 | 11.0 | 3190 | 1.7003 | 0.7822 |
| 0.0084 | 12.0 | 3480 | 1.8148 | 0.7748 |
| 0.007 | 13.0 | 3770 | 1.7651 | 0.7748 |
| 0.008 | 14.0 | 4060 | 1.8423 | 0.7748 |
| 0.008 | 15.0 | 4350 | 1.7871 | 0.7809 |
| 0.0054 | 16.0 | 4640 | 1.9324 | 0.7748 |
| 0.0054 | 17.0 | 4930 | 1.8685 | 0.7809 |
| 0.0048 | 18.0 | 5220 | 1.9901 | 0.7797 |
| 0.002 | 19.0 | 5510 | 1.9273 | 0.7785 |
| 0.002 | 20.0 | 5800 | 1.9945 | 0.7809 |
| 0.0018 | 21.0 | 6090 | 1.9250 | 0.7785 |
| 0.0018 | 22.0 | 6380 | 1.9929 | 0.7822 |
| 0.0032 | 23.0 | 6670 | 1.9306 | 0.7859 |
| 0.0032 | 24.0 | 6960 | 1.9603 | 0.7847 |
| 0.0029 | 25.0 | 7250 | 1.9887 | 0.7797 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
elopezlopez/distilbert-base-uncased_fold_9_ternary_v1
|
elopezlopez
| 2022-08-02T18:08:13Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-02T17:54:55Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilbert-base-uncased_fold_9_ternary_v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_9_ternary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9406
- F1: 0.7841
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 292 | 0.5684 | 0.7635 |
| 0.5656 | 2.0 | 584 | 0.5753 | 0.7725 |
| 0.5656 | 3.0 | 876 | 0.6159 | 0.7866 |
| 0.2499 | 4.0 | 1168 | 0.7743 | 0.7828 |
| 0.2499 | 5.0 | 1460 | 0.9820 | 0.7674 |
| 0.1153 | 6.0 | 1752 | 1.2383 | 0.7738 |
| 0.0547 | 7.0 | 2044 | 1.2468 | 0.7815 |
| 0.0547 | 8.0 | 2336 | 1.3480 | 0.7622 |
| 0.0233 | 9.0 | 2628 | 1.3791 | 0.7892 |
| 0.0233 | 10.0 | 2920 | 1.4344 | 0.7841 |
| 0.0142 | 11.0 | 3212 | 1.4958 | 0.7802 |
| 0.0087 | 12.0 | 3504 | 1.5714 | 0.7674 |
| 0.0087 | 13.0 | 3796 | 1.6129 | 0.7956 |
| 0.0111 | 14.0 | 4088 | 1.7799 | 0.7751 |
| 0.0111 | 15.0 | 4380 | 1.7272 | 0.7789 |
| 0.0055 | 16.0 | 4672 | 1.7696 | 0.7866 |
| 0.0055 | 17.0 | 4964 | 1.8622 | 0.7789 |
| 0.003 | 18.0 | 5256 | 1.8563 | 0.7802 |
| 0.0004 | 19.0 | 5548 | 1.8993 | 0.7815 |
| 0.0004 | 20.0 | 5840 | 1.9199 | 0.7853 |
| 0.0005 | 21.0 | 6132 | 1.9003 | 0.7879 |
| 0.0005 | 22.0 | 6424 | 1.9161 | 0.7828 |
| 0.0011 | 23.0 | 6716 | 1.9691 | 0.7815 |
| 0.0017 | 24.0 | 7008 | 1.9492 | 0.7841 |
| 0.0017 | 25.0 | 7300 | 1.9406 | 0.7841 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
comodoro/Worm-Unity-ML
|
comodoro
| 2022-08-02T17:50:40Z
| 21
| 0
|
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Worm",
"region:us"
] |
reinforcement-learning
| 2022-08-02T17:50:32Z
|
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Worm
library_name: ml-agents
---
# **ppo** Agent playing **Worm**
This is a trained model of a **ppo** agent playing **Worm** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Worm
2. Step 1: Write your model_id: comodoro/Worm-Unity-ML
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
carsepmo/firstRLmodel_LL
|
carsepmo
| 2022-08-02T17:47:57Z
| 3
| 0
|
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-08-02T17:47:23Z
|
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 188.33 +/- 31.64
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
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
elopezlopez/distilbert-base-uncased_fold_7_ternary_v1
|
elopezlopez
| 2022-08-02T17:39:26Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-02T17:26:09Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilbert-base-uncased_fold_7_ternary_v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_7_ternary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0462
- F1: 0.7836
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 291 | 0.5719 | 0.7490 |
| 0.5541 | 2.0 | 582 | 0.5563 | 0.7836 |
| 0.5541 | 3.0 | 873 | 0.7301 | 0.7849 |
| 0.2509 | 4.0 | 1164 | 0.8073 | 0.7926 |
| 0.2509 | 5.0 | 1455 | 1.0842 | 0.7823 |
| 0.1182 | 6.0 | 1746 | 1.1721 | 0.7900 |
| 0.0537 | 7.0 | 2037 | 1.4060 | 0.7785 |
| 0.0537 | 8.0 | 2328 | 1.4497 | 0.7836 |
| 0.0262 | 9.0 | 2619 | 1.4722 | 0.7708 |
| 0.0262 | 10.0 | 2910 | 1.6529 | 0.7772 |
| 0.0131 | 11.0 | 3201 | 1.6573 | 0.7862 |
| 0.0131 | 12.0 | 3492 | 1.6986 | 0.7823 |
| 0.0115 | 13.0 | 3783 | 1.7765 | 0.7810 |
| 0.0098 | 14.0 | 4074 | 1.8036 | 0.7862 |
| 0.0098 | 15.0 | 4365 | 1.7684 | 0.7926 |
| 0.0028 | 16.0 | 4656 | 1.8385 | 0.7836 |
| 0.0028 | 17.0 | 4947 | 1.7903 | 0.7887 |
| 0.0054 | 18.0 | 5238 | 1.9065 | 0.7810 |
| 0.0007 | 19.0 | 5529 | 1.9331 | 0.7875 |
| 0.0007 | 20.0 | 5820 | 1.9384 | 0.7849 |
| 0.0006 | 21.0 | 6111 | 1.8687 | 0.7887 |
| 0.0006 | 22.0 | 6402 | 2.0603 | 0.7785 |
| 0.0009 | 23.0 | 6693 | 2.0403 | 0.7836 |
| 0.0009 | 24.0 | 6984 | 2.0348 | 0.7810 |
| 0.0005 | 25.0 | 7275 | 2.0462 | 0.7836 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
elopezlopez/distilbert-base-uncased_fold_6_ternary_v1
|
elopezlopez
| 2022-08-02T17:25:06Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-02T17:11:43Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilbert-base-uncased_fold_6_ternary_v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_6_ternary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9031
- F1: 0.7910
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 292 | 0.5235 | 0.7769 |
| 0.566 | 2.0 | 584 | 0.5268 | 0.7923 |
| 0.566 | 3.0 | 876 | 0.6189 | 0.7756 |
| 0.2514 | 4.0 | 1168 | 0.7777 | 0.8026 |
| 0.2514 | 5.0 | 1460 | 0.9380 | 0.7936 |
| 0.1175 | 6.0 | 1752 | 1.0957 | 0.7872 |
| 0.0579 | 7.0 | 2044 | 1.2370 | 0.7923 |
| 0.0579 | 8.0 | 2336 | 1.3739 | 0.7936 |
| 0.0259 | 9.0 | 2628 | 1.3457 | 0.7846 |
| 0.0259 | 10.0 | 2920 | 1.4938 | 0.7872 |
| 0.0125 | 11.0 | 3212 | 1.5921 | 0.7885 |
| 0.0108 | 12.0 | 3504 | 1.6504 | 0.7897 |
| 0.0108 | 13.0 | 3796 | 1.7532 | 0.7756 |
| 0.007 | 14.0 | 4088 | 1.7029 | 0.7821 |
| 0.007 | 15.0 | 4380 | 1.7632 | 0.7987 |
| 0.0067 | 16.0 | 4672 | 1.7084 | 0.7962 |
| 0.0067 | 17.0 | 4964 | 1.7559 | 0.7962 |
| 0.0072 | 18.0 | 5256 | 1.8431 | 0.7987 |
| 0.0028 | 19.0 | 5548 | 1.8689 | 0.7846 |
| 0.0028 | 20.0 | 5840 | 1.8641 | 0.7885 |
| 0.0033 | 21.0 | 6132 | 1.8578 | 0.7923 |
| 0.0033 | 22.0 | 6424 | 1.9071 | 0.7833 |
| 0.003 | 23.0 | 6716 | 1.8959 | 0.7872 |
| 0.0011 | 24.0 | 7008 | 1.9073 | 0.7987 |
| 0.0011 | 25.0 | 7300 | 1.9031 | 0.7910 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
elopezlopez/distilbert-base-uncased_fold_3_ternary_v1
|
elopezlopez
| 2022-08-02T16:41:21Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-02T16:27:57Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilbert-base-uncased_fold_3_ternary_v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_3_ternary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8908
- F1: 0.7879
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 289 | 0.5873 | 0.7636 |
| 0.5479 | 2.0 | 578 | 0.5788 | 0.7697 |
| 0.5479 | 3.0 | 867 | 0.6286 | 0.7770 |
| 0.2412 | 4.0 | 1156 | 0.8845 | 0.7661 |
| 0.2412 | 5.0 | 1445 | 0.9894 | 0.7818 |
| 0.1191 | 6.0 | 1734 | 1.0856 | 0.7842 |
| 0.0543 | 7.0 | 2023 | 1.2852 | 0.7830 |
| 0.0543 | 8.0 | 2312 | 1.4295 | 0.7673 |
| 0.0223 | 9.0 | 2601 | 1.4716 | 0.7806 |
| 0.0223 | 10.0 | 2890 | 1.6007 | 0.7636 |
| 0.0122 | 11.0 | 3179 | 1.6744 | 0.7673 |
| 0.0122 | 12.0 | 3468 | 1.6954 | 0.7685 |
| 0.0129 | 13.0 | 3757 | 1.7273 | 0.7733 |
| 0.0057 | 14.0 | 4046 | 1.7114 | 0.7758 |
| 0.0057 | 15.0 | 4335 | 1.7480 | 0.7733 |
| 0.0045 | 16.0 | 4624 | 1.8322 | 0.7830 |
| 0.0045 | 17.0 | 4913 | 1.7448 | 0.7830 |
| 0.0047 | 18.0 | 5202 | 1.8126 | 0.7782 |
| 0.0047 | 19.0 | 5491 | 1.9021 | 0.7673 |
| 0.0018 | 20.0 | 5780 | 1.9011 | 0.7830 |
| 0.0026 | 21.0 | 6069 | 1.8771 | 0.7806 |
| 0.0026 | 22.0 | 6358 | 1.8634 | 0.7806 |
| 0.0012 | 23.0 | 6647 | 1.8926 | 0.7830 |
| 0.0012 | 24.0 | 6936 | 1.8922 | 0.7855 |
| 0.0005 | 25.0 | 7225 | 1.8908 | 0.7879 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
huggingtweets/metaprophet
|
huggingtweets
| 2022-08-02T16:27:42Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-08-02T16:26:28Z
|
---
language: en
thumbnail: http://www.huggingtweets.com/metaprophet/1659457657587/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/623318993290440704/5r2ZwVuf_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Meta Prophet</div>
<div style="text-align: center; font-size: 14px;">@metaprophet</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 Meta Prophet.
| Data | Meta Prophet |
| --- | --- |
| Tweets downloaded | 3200 |
| Retweets | 0 |
| Short tweets | 596 |
| Tweets kept | 2604 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2vgap1nt/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 @metaprophet's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/bfusgb3m) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/bfusgb3m/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/metaprophet')
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)
|
elopezlopez/distilbert-base-uncased_fold_2_ternary_v1
|
elopezlopez
| 2022-08-02T16:26:50Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-02T16:13:23Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilbert-base-uncased_fold_2_ternary_v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_2_ternary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8941
- F1: 0.7889
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 294 | 0.6025 | 0.7402 |
| 0.5688 | 2.0 | 588 | 0.5025 | 0.7943 |
| 0.5688 | 3.0 | 882 | 0.6102 | 0.7794 |
| 0.2582 | 4.0 | 1176 | 0.8896 | 0.7835 |
| 0.2582 | 5.0 | 1470 | 1.0392 | 0.7821 |
| 0.1185 | 6.0 | 1764 | 1.0865 | 0.7848 |
| 0.0461 | 7.0 | 2058 | 1.2951 | 0.7686 |
| 0.0461 | 8.0 | 2352 | 1.3348 | 0.7821 |
| 0.0313 | 9.0 | 2646 | 1.4267 | 0.7876 |
| 0.0313 | 10.0 | 2940 | 1.4004 | 0.7957 |
| 0.0142 | 11.0 | 3234 | 1.5501 | 0.7794 |
| 0.0083 | 12.0 | 3528 | 1.5564 | 0.7903 |
| 0.0083 | 13.0 | 3822 | 1.5699 | 0.7876 |
| 0.0067 | 14.0 | 4116 | 1.7725 | 0.7794 |
| 0.0067 | 15.0 | 4410 | 1.7642 | 0.7767 |
| 0.0031 | 16.0 | 4704 | 1.7891 | 0.7848 |
| 0.0031 | 17.0 | 4998 | 1.8528 | 0.7740 |
| 0.0054 | 18.0 | 5292 | 1.8378 | 0.7781 |
| 0.003 | 19.0 | 5586 | 1.8223 | 0.7862 |
| 0.003 | 20.0 | 5880 | 1.7935 | 0.7930 |
| 0.0021 | 21.0 | 6174 | 1.9117 | 0.7808 |
| 0.0021 | 22.0 | 6468 | 1.8891 | 0.7930 |
| 0.0015 | 23.0 | 6762 | 1.9167 | 0.7916 |
| 0.0006 | 24.0 | 7056 | 1.9193 | 0.7862 |
| 0.0006 | 25.0 | 7350 | 1.8941 | 0.7889 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
ligerre/xlm-roberta-base-finetuned-panx-en
|
ligerre
| 2022-08-02T16:04:05Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-02T15:48:23Z
|
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.7032474804031354
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3932
- F1: 0.7032
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1504 | 1.0 | 50 | 0.5992 | 0.4786 |
| 0.5147 | 2.0 | 100 | 0.4307 | 0.6468 |
| 0.3717 | 3.0 | 150 | 0.3932 | 0.7032 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
ligerre/xlm-roberta-base-finetuned-panx-it
|
ligerre
| 2022-08-02T15:48:11Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-02T15:32:21Z
|
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8245828245828245
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2401
- F1: 0.8246
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8187 | 1.0 | 70 | 0.3325 | 0.7337 |
| 0.2829 | 2.0 | 140 | 0.2554 | 0.8003 |
| 0.1894 | 3.0 | 210 | 0.2401 | 0.8246 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
ligerre/xlm-roberta-base-finetuned-panx-fr
|
ligerre
| 2022-08-02T15:32:07Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-02T15:15:14Z
|
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.8299296953465015
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2848
- F1: 0.8299
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5989 | 1.0 | 191 | 0.3383 | 0.7928 |
| 0.2617 | 2.0 | 382 | 0.2966 | 0.8318 |
| 0.1672 | 3.0 | 573 | 0.2848 | 0.8299 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
huggingtweets/iamsamirarora-naval-vivek_investor
|
huggingtweets
| 2022-08-02T15:16:48Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-08-02T15:15:22Z
|
---
language: en
thumbnail: http://www.huggingtweets.com/iamsamirarora-naval-vivek_investor/1659453403535/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/1256841238298292232/ycqwaMI2_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/853146176295759872/YiAPXQ0s_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/1479277051802574853/qs6u-imt_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">Naval & Samir Arora & Vivek</div>
<div style="text-align: center; font-size: 14px;">@iamsamirarora-naval-vivek_investor</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 Naval & Samir Arora & Vivek.
| Data | Naval | Samir Arora | Vivek |
| --- | --- | --- | --- |
| Tweets downloaded | 3211 | 3250 | 3250 |
| Retweets | 195 | 76 | 96 |
| Short tweets | 612 | 973 | 601 |
| Tweets kept | 2404 | 2201 | 2553 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1oa4j8zi/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 @iamsamirarora-naval-vivek_investor's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/21s56oiv) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/21s56oiv/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/iamsamirarora-naval-vivek_investor')
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)
|
Team-PIXEL/pixel-base
|
Team-PIXEL
| 2022-08-02T14:47:51Z
| 24
| 35
|
transformers
|
[
"transformers",
"pytorch",
"pixel",
"pretraining",
"en",
"dataset:Team-PIXEL/rendered-bookcorpus",
"dataset:Team-PIXEL/rendered-wikipedia-english",
"arxiv:2207.06991",
"arxiv:2111.06377",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T18:54:48Z
|
---
license: apache-2.0
tags:
- pretraining
- pixel
datasets:
- Team-PIXEL/rendered-bookcorpus
- Team-PIXEL/rendered-wikipedia-english
language:
- en
---
# PIXEL (Pixel-based Encoder of Language)
PIXEL is a language model trained to reconstruct masked image patches that contain rendered text. PIXEL was pretrained on the *English* Wikipedia and Bookcorpus (in total around 3.2B words) but can theoretically be finetuned on data in any written language that can be typeset on a computer screen because it operates on rendered text as opposed to using a tokenizer with a fixed vocabulary.
It is not currently possible to use the Hosted Inference API with PIXEL.
Paper: [Language Modelling with Pixels](https://arxiv.org/abs/2207.06991)
Codebase: [https://github.com/xplip/pixel](https://github.com/xplip/pixel)
## Model description
PIXEL consists of three major components: a text renderer, which draws text as an image; an encoder, which encodes the unmasked regions of the rendered image; and a decoder, which reconstructs the masked regions at the pixel level. It is built on [ViT-MAE](https://arxiv.org/abs/2111.06377).
During pretraining, the renderer produces images containing the training sentences. Patches of these images are linearly projected to obtain patch embeddings (as opposed to having an embedding matrix like e.g. in BERT), and 25% of the patches are masked out. The encoder, which is a Vision Transformer (ViT), then only processes the unmasked patches. The lightweight decoder with hidden size 512 and 8 transformer layers inserts learnable mask tokens into the encoder's output sequence and learns to reconstruct the raw pixel values at the masked positions.
After pretraining, the decoder can be discarded leaving an 86M parameter encoder, upon which task-specific classification heads can be stacked. Alternatively, the decoder can be retained and PIXEL can be used as a pixel-level generative language model (see Figures 3 and 6 in the paper for examples).
For more details on how PIXEL works, please check the paper and the codebase linked above.
## Intended uses
PIXEL is primarily intended to be finetuned to downstream NLP tasks. See the [model hub](https://huggingface.co/models?search=Team-PIXEL/pixel-base) to look for finetuned versions on a task that interests you. Otherwise, check out the PIXEL codebase on Github [here](https://github.com/xplip/pixel) to find out how to finetune PIXEL for your task.
### How to use
Here is how to load PIXEL:
```python
from pixel import PIXELConfig, PIXELForPreTraining
config = PIXELConfig.from_pretrained("Team-PIXEL/pixel-base")
model = PIXELForPreTraining.from_pretrained("Team-PIXEL/pixel-base", config=config)
```
## Citing and Contact Author
```bibtex
@article{rust-etal-2022-pixel,
title={Language Modelling with Pixels},
author={Phillip Rust and Jonas F. Lotz and Emanuele Bugliarello and Elizabeth Salesky and Miryam de Lhoneux and Desmond Elliott},
journal={arXiv preprint},
year={2022},
url={https://arxiv.org/abs/2207.06991}
}
```
Github: [@xplip](https://github.com/xplip)
Twitter: [@rust_phillip](https://twitter.com/rust_phillip)
|
aliromagnoli/distilbert-base-uncased-finetuned-emotion
|
aliromagnoli
| 2022-08-02T14:26:32Z
| 10
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-02T13:13:25Z
|
---
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: 0.924
- name: F1
type: f1
value: 0.9238827602069696
---
<!-- 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: 0.2145
- Accuracy: 0.924
- F1: 0.9239
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8235 | 1.0 | 250 | 0.3050 | 0.9085 | 0.9063 |
| 0.2456 | 2.0 | 500 | 0.2145 | 0.924 | 0.9239 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.12.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
anjleeg/roberta-base-finetuned-cola
|
anjleeg
| 2022-08-02T14:02:49Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"optimum_graphcore",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-02T12:51:41Z
|
---
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: roberta-base-finetuned-cola
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. -->
# roberta-base-finetuned-cola
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4497
- Matthews Correlation: 0.6272
## 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- total_eval_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- training precision: Mixed Precision
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4453 | 1.0 | 133 | 0.4348 | 0.5391 |
| 0.3121 | 2.0 | 266 | 0.3938 | 0.5827 |
| 0.1149 | 3.0 | 399 | 0.4497 | 0.6272 |
| 0.1194 | 4.0 | 532 | 0.5005 | 0.6076 |
| 0.1639 | 5.0 | 665 | 0.5645 | 0.5943 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cpu
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Sebabrata/lmv2-g-invoice-993-doc-08-02
|
Sebabrata
| 2022-08-02T13:59:20Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-02T11:30:11Z
|
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: lmv2-g-invoice-993-doc-08-02
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. -->
# lmv2-g-invoice-993-doc-08-02
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3517
- Due Date Precision: 0.9277
- Due Date Recall: 0.875
- Due Date F1: 0.9006
- Due Date Number: 88
- Invoice Date Precision: 0.8182
- Invoice Date Recall: 0.9172
- Invoice Date F1: 0.8649
- Invoice Date Number: 157
- Invoice Id Precision: 0.8993
- Invoice Id Recall: 0.8741
- Invoice Id F1: 0.8865
- Invoice Id Number: 143
- Payment Terms Precision: 0.5469
- Payment Terms Recall: 0.7143
- Payment Terms F1: 0.6195
- Payment Terms Number: 49
- Receiver Address Precision: 0.7249
- Receiver Address Recall: 0.7697
- Receiver Address F1: 0.7466
- Receiver Address Number: 178
- Receiver Name Precision: 0.8270
- Receiver Name Recall: 0.8596
- Receiver Name F1: 0.8430
- Receiver Name Number: 178
- Sub Total Precision: 0.8624
- Sub Total Recall: 0.8704
- Sub Total F1: 0.8664
- Sub Total Number: 108
- Supplier Address Precision: 0.7665
- Supplier Address Recall: 0.7711
- Supplier Address F1: 0.7688
- Supplier Address Number: 166
- Supplier Name Precision: 0.7567
- Supplier Name Recall: 0.8057
- Supplier Name F1: 0.7804
- Supplier Name Number: 247
- Tax Amount Precision: 0.8333
- Tax Amount Recall: 0.8209
- Tax Amount F1: 0.8271
- Tax Amount Number: 67
- Total Precision: 0.8061
- Total Recall: 0.7557
- Total F1: 0.7801
- Total Number: 176
- Overall Precision: 0.7970
- Overall Recall: 0.8221
- Overall F1: 0.8094
- Overall Accuracy: 0.9572
## 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: 4e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Due Date Precision | Due Date Recall | Due Date F1 | Due Date Number | Invoice Date Precision | Invoice Date Recall | Invoice Date F1 | Invoice Date Number | Invoice Id Precision | Invoice Id Recall | Invoice Id F1 | Invoice Id Number | Payment Terms Precision | Payment Terms Recall | Payment Terms F1 | Payment Terms Number | Receiver Address Precision | Receiver Address Recall | Receiver Address F1 | Receiver Address Number | Receiver Name Precision | Receiver Name Recall | Receiver Name F1 | Receiver Name Number | Sub Total Precision | Sub Total Recall | Sub Total F1 | Sub Total Number | Supplier Address Precision | Supplier Address Recall | Supplier Address F1 | Supplier Address Number | Supplier Name Precision | Supplier Name Recall | Supplier Name F1 | Supplier Name Number | Tax Amount Precision | Tax Amount Recall | Tax Amount F1 | Tax Amount Number | Total Precision | Total Recall | Total F1 | Total Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:------------------:|:---------------:|:-----------:|:---------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:-------------------:|:----------------:|:------------:|:----------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:---------------:|:------------:|:--------:|:------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.2159 | 1.0 | 794 | 0.5347 | 0.0 | 0.0 | 0.0 | 88 | 0.4828 | 0.8025 | 0.6029 | 157 | 0.5247 | 0.5944 | 0.5574 | 143 | 0.0 | 0.0 | 0.0 | 49 | 0.3738 | 0.4326 | 0.4010 | 178 | 0.3780 | 0.2697 | 0.3148 | 178 | 0.0 | 0.0 | 0.0 | 108 | 0.4375 | 0.5060 | 0.4693 | 166 | 0.4348 | 0.3239 | 0.3712 | 247 | 0.0 | 0.0 | 0.0 | 67 | 0.5278 | 0.1080 | 0.1792 | 176 | 0.4443 | 0.3333 | 0.3809 | 0.9071 |
| 0.4121 | 2.0 | 1588 | 0.3147 | 0.0 | 0.0 | 0.0 | 88 | 0.5353 | 0.9172 | 0.6761 | 157 | 0.7355 | 0.6224 | 0.6742 | 143 | 0.2245 | 0.4490 | 0.2993 | 49 | 0.5707 | 0.6573 | 0.6110 | 178 | 0.7457 | 0.7247 | 0.7350 | 178 | 0.7377 | 0.4167 | 0.5325 | 108 | 0.5802 | 0.7410 | 0.6508 | 166 | 0.6703 | 0.7490 | 0.7075 | 247 | 0.0 | 0.0 | 0.0 | 67 | 0.4639 | 0.8409 | 0.5980 | 176 | 0.5779 | 0.6435 | 0.6089 | 0.9340 |
| 0.2248 | 3.0 | 2382 | 0.2087 | 0.8519 | 0.7841 | 0.8166 | 88 | 0.7849 | 0.9299 | 0.8513 | 157 | 0.8182 | 0.8182 | 0.8182 | 143 | 0.5179 | 0.5918 | 0.5524 | 49 | 0.5799 | 0.7135 | 0.6398 | 178 | 0.8192 | 0.8146 | 0.8169 | 178 | 0.8022 | 0.6759 | 0.7337 | 108 | 0.5990 | 0.7470 | 0.6649 | 166 | 0.6522 | 0.7895 | 0.7143 | 247 | 0.8103 | 0.7015 | 0.752 | 67 | 0.7444 | 0.7614 | 0.7528 | 176 | 0.7107 | 0.7746 | 0.7412 | 0.9532 |
| 0.1303 | 4.0 | 3176 | 0.2286 | 0.8280 | 0.875 | 0.8508 | 88 | 0.8671 | 0.8726 | 0.8698 | 157 | 0.8 | 0.8392 | 0.8191 | 143 | 0.3976 | 0.6735 | 0.5 | 49 | 0.6474 | 0.6910 | 0.6685 | 178 | 0.8054 | 0.8371 | 0.8209 | 178 | 0.75 | 0.75 | 0.75 | 108 | 0.6467 | 0.6506 | 0.6486 | 166 | 0.7143 | 0.7895 | 0.7500 | 247 | 0.8333 | 0.7463 | 0.7874 | 67 | 0.7344 | 0.8011 | 0.7663 | 176 | 0.7318 | 0.7797 | 0.7550 | 0.9500 |
| 0.0814 | 5.0 | 3970 | 0.2354 | 0.8444 | 0.8636 | 0.8539 | 88 | 0.8780 | 0.9172 | 0.8972 | 157 | 0.8212 | 0.8671 | 0.8435 | 143 | 0.3908 | 0.6939 | 0.5 | 49 | 0.7174 | 0.7416 | 0.7293 | 178 | 0.8418 | 0.8371 | 0.8394 | 178 | 0.6935 | 0.7963 | 0.7414 | 108 | 0.7377 | 0.8133 | 0.7736 | 166 | 0.7118 | 0.8300 | 0.7664 | 247 | 0.6579 | 0.7463 | 0.6993 | 67 | 0.7553 | 0.8068 | 0.7802 | 176 | 0.7459 | 0.8202 | 0.7813 | 0.9545 |
| 0.0604 | 6.0 | 4764 | 0.2217 | 0.8333 | 0.9091 | 0.8696 | 88 | 0.875 | 0.8917 | 0.8833 | 157 | 0.8414 | 0.8531 | 0.8472 | 143 | 0.4848 | 0.6531 | 0.5565 | 49 | 0.6716 | 0.7697 | 0.7173 | 178 | 0.8098 | 0.8371 | 0.8232 | 178 | 0.8173 | 0.7870 | 0.8019 | 108 | 0.7098 | 0.8253 | 0.7632 | 166 | 0.7148 | 0.7611 | 0.7373 | 247 | 0.6786 | 0.8507 | 0.7550 | 67 | 0.7514 | 0.7898 | 0.7701 | 176 | 0.7518 | 0.8131 | 0.7812 | 0.9541 |
| 0.0478 | 7.0 | 5558 | 0.2268 | 0.8387 | 0.8864 | 0.8619 | 88 | 0.8286 | 0.9236 | 0.8735 | 157 | 0.8129 | 0.8811 | 0.8456 | 143 | 0.4384 | 0.6531 | 0.5246 | 49 | 0.6579 | 0.7022 | 0.6793 | 178 | 0.8258 | 0.8258 | 0.8258 | 178 | 0.8302 | 0.8148 | 0.8224 | 108 | 0.5957 | 0.6747 | 0.6328 | 166 | 0.6926 | 0.7206 | 0.7063 | 247 | 0.8529 | 0.8657 | 0.8593 | 67 | 0.8117 | 0.7102 | 0.7576 | 176 | 0.7416 | 0.7797 | 0.7602 | 0.9550 |
| 0.0361 | 8.0 | 6352 | 0.2785 | 0.6949 | 0.9318 | 0.7961 | 88 | 0.8305 | 0.9363 | 0.8802 | 157 | 0.8089 | 0.8881 | 0.8467 | 143 | 0.5441 | 0.7551 | 0.6325 | 49 | 0.6919 | 0.7697 | 0.7287 | 178 | 0.8315 | 0.8315 | 0.8315 | 178 | 0.7561 | 0.8611 | 0.8052 | 108 | 0.7253 | 0.7952 | 0.7586 | 166 | 0.6754 | 0.8340 | 0.7464 | 247 | 0.7887 | 0.8358 | 0.8116 | 67 | 0.7917 | 0.7557 | 0.7733 | 176 | 0.7438 | 0.8337 | 0.7862 | 0.9520 |
| 0.0283 | 9.0 | 7146 | 0.2838 | 0.8404 | 0.8977 | 0.8681 | 88 | 0.8412 | 0.9108 | 0.8746 | 157 | 0.8667 | 0.8182 | 0.8417 | 143 | 0.6066 | 0.7551 | 0.6727 | 49 | 0.7213 | 0.7416 | 0.7313 | 178 | 0.8644 | 0.8596 | 0.8620 | 178 | 0.8511 | 0.7407 | 0.7921 | 108 | 0.7135 | 0.7952 | 0.7521 | 166 | 0.7530 | 0.7530 | 0.7530 | 247 | 0.6522 | 0.8955 | 0.7547 | 67 | 0.8034 | 0.5341 | 0.6416 | 176 | 0.7801 | 0.7791 | 0.7796 | 0.9553 |
| 0.0253 | 10.0 | 7940 | 0.3362 | 0.7217 | 0.9432 | 0.8177 | 88 | 0.8882 | 0.9108 | 0.8994 | 157 | 0.8403 | 0.8462 | 0.8432 | 143 | 0.3980 | 0.7959 | 0.5306 | 49 | 0.6703 | 0.6966 | 0.6832 | 178 | 0.8042 | 0.8539 | 0.8283 | 178 | 0.8462 | 0.8148 | 0.8302 | 108 | 0.6667 | 0.8193 | 0.7351 | 166 | 0.7173 | 0.8219 | 0.7660 | 247 | 0.8060 | 0.8060 | 0.8060 | 67 | 0.7460 | 0.8011 | 0.7726 | 176 | 0.7384 | 0.8247 | 0.7791 | 0.9385 |
| 0.0201 | 11.0 | 8734 | 0.3310 | 0.8247 | 0.9091 | 0.8649 | 88 | 0.8820 | 0.9045 | 0.8931 | 157 | 0.8832 | 0.8462 | 0.8643 | 143 | 0.5072 | 0.7143 | 0.5932 | 49 | 0.7294 | 0.6966 | 0.7126 | 178 | 0.8314 | 0.8034 | 0.8171 | 178 | 0.8165 | 0.8241 | 0.8203 | 108 | 0.6618 | 0.8133 | 0.7297 | 166 | 0.7399 | 0.8178 | 0.7769 | 247 | 0.8281 | 0.7910 | 0.8092 | 67 | 0.75 | 0.7330 | 0.7414 | 176 | 0.7697 | 0.8048 | 0.7868 | 0.9529 |
| 0.0239 | 12.0 | 9528 | 0.2936 | 0.8736 | 0.8636 | 0.8686 | 88 | 0.8614 | 0.9108 | 0.8854 | 157 | 0.8955 | 0.8392 | 0.8664 | 143 | 0.5373 | 0.7347 | 0.6207 | 49 | 0.6818 | 0.7584 | 0.7181 | 178 | 0.8398 | 0.8539 | 0.8468 | 178 | 0.83 | 0.7685 | 0.7981 | 108 | 0.7529 | 0.7892 | 0.7706 | 166 | 0.7674 | 0.8016 | 0.7842 | 247 | 0.8966 | 0.7761 | 0.8320 | 67 | 0.7527 | 0.7784 | 0.7654 | 176 | 0.7869 | 0.8112 | 0.7989 | 0.9565 |
| 0.0229 | 13.0 | 10322 | 0.3042 | 0.8791 | 0.9091 | 0.8939 | 88 | 0.8735 | 0.9236 | 0.8978 | 157 | 0.8662 | 0.8601 | 0.8632 | 143 | 0.6613 | 0.8367 | 0.7387 | 49 | 0.7068 | 0.7584 | 0.7317 | 178 | 0.8324 | 0.8652 | 0.8485 | 178 | 0.8252 | 0.7870 | 0.8057 | 108 | 0.7278 | 0.7892 | 0.7572 | 166 | 0.7751 | 0.7814 | 0.7782 | 247 | 0.8621 | 0.7463 | 0.8000 | 67 | 0.7683 | 0.7159 | 0.7412 | 176 | 0.7938 | 0.8112 | 0.8024 | 0.9580 |
| 0.0165 | 14.0 | 11116 | 0.2715 | 0.9111 | 0.9318 | 0.9213 | 88 | 0.8802 | 0.9363 | 0.9074 | 157 | 0.8671 | 0.8671 | 0.8671 | 143 | 0.5211 | 0.7551 | 0.6167 | 49 | 0.7053 | 0.7528 | 0.7283 | 178 | 0.8115 | 0.8708 | 0.8401 | 178 | 0.9158 | 0.8056 | 0.8571 | 108 | 0.7196 | 0.8193 | 0.7662 | 166 | 0.7348 | 0.7854 | 0.7593 | 247 | 0.7733 | 0.8657 | 0.8169 | 67 | 0.7943 | 0.7898 | 0.7920 | 176 | 0.7836 | 0.8304 | 0.8064 | 0.9600 |
| 0.0221 | 15.0 | 11910 | 0.2866 | 0.8161 | 0.8068 | 0.8114 | 88 | 0.8720 | 0.9108 | 0.8910 | 157 | 0.8986 | 0.8671 | 0.8826 | 143 | 0.4722 | 0.6939 | 0.5620 | 49 | 0.7204 | 0.7528 | 0.7363 | 178 | 0.8232 | 0.8371 | 0.8301 | 178 | 0.8571 | 0.8333 | 0.8451 | 108 | 0.7216 | 0.7651 | 0.7427 | 166 | 0.7293 | 0.7854 | 0.7563 | 247 | 0.8868 | 0.7015 | 0.7833 | 67 | 0.8255 | 0.6989 | 0.7569 | 176 | 0.7838 | 0.7938 | 0.7888 | 0.9552 |
| 0.0173 | 16.0 | 12704 | 0.3234 | 0.7685 | 0.9432 | 0.8469 | 88 | 0.8512 | 0.9108 | 0.88 | 157 | 0.8288 | 0.8462 | 0.8374 | 143 | 0.4474 | 0.6939 | 0.544 | 49 | 0.6915 | 0.7303 | 0.7104 | 178 | 0.8365 | 0.7472 | 0.7893 | 178 | 0.6596 | 0.8611 | 0.7470 | 108 | 0.6372 | 0.8675 | 0.7347 | 166 | 0.6823 | 0.8259 | 0.7473 | 247 | 0.7333 | 0.8209 | 0.7746 | 67 | 0.7513 | 0.8068 | 0.7781 | 176 | 0.7223 | 0.8234 | 0.7695 | 0.9532 |
| 0.0159 | 17.0 | 13498 | 0.3301 | 0.8652 | 0.875 | 0.8701 | 88 | 0.8480 | 0.9236 | 0.8841 | 157 | 0.8921 | 0.8671 | 0.8794 | 143 | 0.5522 | 0.7551 | 0.6379 | 49 | 0.7027 | 0.7303 | 0.7163 | 178 | 0.7989 | 0.8483 | 0.8229 | 178 | 0.7863 | 0.8519 | 0.8178 | 108 | 0.7711 | 0.7711 | 0.7711 | 166 | 0.6877 | 0.7935 | 0.7368 | 247 | 0.8116 | 0.8358 | 0.8235 | 67 | 0.7976 | 0.7614 | 0.7791 | 176 | 0.7720 | 0.8157 | 0.7933 | 0.9554 |
| 0.0156 | 18.0 | 14292 | 0.3390 | 0.8261 | 0.8636 | 0.8444 | 88 | 0.8412 | 0.9108 | 0.8746 | 157 | 0.8794 | 0.8671 | 0.8732 | 143 | 0.5968 | 0.7551 | 0.6667 | 49 | 0.6682 | 0.7921 | 0.7249 | 178 | 0.7967 | 0.8146 | 0.8056 | 178 | 0.9195 | 0.7407 | 0.8205 | 108 | 0.7321 | 0.7410 | 0.7365 | 166 | 0.7333 | 0.7571 | 0.7450 | 247 | 0.8197 | 0.7463 | 0.7813 | 67 | 0.7797 | 0.7841 | 0.7819 | 176 | 0.7746 | 0.7990 | 0.7866 | 0.9548 |
| 0.0125 | 19.0 | 15086 | 0.3517 | 0.9277 | 0.875 | 0.9006 | 88 | 0.8182 | 0.9172 | 0.8649 | 157 | 0.8993 | 0.8741 | 0.8865 | 143 | 0.5469 | 0.7143 | 0.6195 | 49 | 0.7249 | 0.7697 | 0.7466 | 178 | 0.8270 | 0.8596 | 0.8430 | 178 | 0.8624 | 0.8704 | 0.8664 | 108 | 0.7665 | 0.7711 | 0.7688 | 166 | 0.7567 | 0.8057 | 0.7804 | 247 | 0.8333 | 0.8209 | 0.8271 | 67 | 0.8061 | 0.7557 | 0.7801 | 176 | 0.7970 | 0.8221 | 0.8094 | 0.9572 |
| 0.0132 | 20.0 | 15880 | 0.3682 | 0.9241 | 0.8295 | 0.8743 | 88 | 0.8631 | 0.9236 | 0.8923 | 157 | 0.9030 | 0.8462 | 0.8736 | 143 | 0.55 | 0.6735 | 0.6055 | 49 | 0.6818 | 0.7584 | 0.7181 | 178 | 0.8488 | 0.8202 | 0.8343 | 178 | 0.8190 | 0.7963 | 0.8075 | 108 | 0.7081 | 0.7892 | 0.7464 | 166 | 0.7764 | 0.7449 | 0.7603 | 247 | 0.7160 | 0.8657 | 0.7838 | 67 | 0.8110 | 0.7557 | 0.7824 | 176 | 0.7865 | 0.7996 | 0.7930 | 0.9543 |
| 0.0112 | 21.0 | 16674 | 0.3974 | 0.8721 | 0.8523 | 0.8621 | 88 | 0.8249 | 0.9299 | 0.8743 | 157 | 0.8929 | 0.8741 | 0.8834 | 143 | 0.5205 | 0.7755 | 0.6230 | 49 | 0.6569 | 0.7528 | 0.7016 | 178 | 0.7677 | 0.8539 | 0.8085 | 178 | 0.8246 | 0.8704 | 0.8468 | 108 | 0.7326 | 0.7590 | 0.7456 | 166 | 0.7273 | 0.7773 | 0.7515 | 247 | 0.7746 | 0.8209 | 0.7971 | 67 | 0.7852 | 0.6648 | 0.72 | 176 | 0.7609 | 0.8054 | 0.7825 | 0.9513 |
| 0.0157 | 22.0 | 17468 | 0.3658 | 0.9390 | 0.875 | 0.9059 | 88 | 0.8412 | 0.9108 | 0.8746 | 157 | 0.9065 | 0.8811 | 0.8936 | 143 | 0.5075 | 0.6939 | 0.5862 | 49 | 0.6837 | 0.7528 | 0.7166 | 178 | 0.8415 | 0.8652 | 0.8532 | 178 | 0.875 | 0.7778 | 0.8235 | 108 | 0.6473 | 0.8072 | 0.7185 | 166 | 0.7540 | 0.7692 | 0.7615 | 247 | 0.8621 | 0.7463 | 0.8000 | 67 | 0.7949 | 0.7045 | 0.7470 | 176 | 0.7783 | 0.8028 | 0.7904 | 0.9525 |
| 0.0104 | 23.0 | 18262 | 0.3755 | 0.9302 | 0.9091 | 0.9195 | 88 | 0.8727 | 0.9172 | 0.8944 | 157 | 0.8477 | 0.8951 | 0.8707 | 143 | 0.5893 | 0.6735 | 0.6286 | 49 | 0.5947 | 0.7584 | 0.6667 | 178 | 0.7023 | 0.8483 | 0.7684 | 178 | 0.7787 | 0.8796 | 0.8261 | 108 | 0.7321 | 0.7410 | 0.7365 | 166 | 0.75 | 0.7409 | 0.7454 | 247 | 0.7714 | 0.8060 | 0.7883 | 67 | 0.8057 | 0.8011 | 0.8034 | 176 | 0.7546 | 0.8137 | 0.7831 | 0.9502 |
| 0.018 | 24.0 | 19056 | 0.3719 | 0.8571 | 0.8182 | 0.8372 | 88 | 0.8683 | 0.9236 | 0.8951 | 157 | 0.8690 | 0.8811 | 0.8750 | 143 | 0.5781 | 0.7551 | 0.6549 | 49 | 0.6604 | 0.7865 | 0.7179 | 178 | 0.7937 | 0.8427 | 0.8174 | 178 | 0.9310 | 0.75 | 0.8308 | 108 | 0.7363 | 0.8072 | 0.7701 | 166 | 0.7412 | 0.7652 | 0.7530 | 247 | 0.8596 | 0.7313 | 0.7903 | 67 | 0.7765 | 0.75 | 0.7630 | 176 | 0.7785 | 0.8060 | 0.7920 | 0.9553 |
| 0.0088 | 25.0 | 19850 | 0.3638 | 0.8876 | 0.8977 | 0.8927 | 88 | 0.8902 | 0.9299 | 0.9097 | 157 | 0.8301 | 0.8881 | 0.8581 | 143 | 0.6032 | 0.7755 | 0.6786 | 49 | 0.6853 | 0.7584 | 0.72 | 178 | 0.8683 | 0.8146 | 0.8406 | 178 | 0.9111 | 0.7593 | 0.8283 | 108 | 0.6952 | 0.7831 | 0.7365 | 166 | 0.74 | 0.7490 | 0.7445 | 247 | 0.7945 | 0.8657 | 0.8286 | 67 | 0.8068 | 0.8068 | 0.8068 | 176 | 0.7874 | 0.8137 | 0.8004 | 0.9561 |
| 0.009 | 26.0 | 20644 | 0.3683 | 0.9146 | 0.8523 | 0.8824 | 88 | 0.8229 | 0.9172 | 0.8675 | 157 | 0.9007 | 0.8881 | 0.8944 | 143 | 0.6607 | 0.7551 | 0.7048 | 49 | 0.7316 | 0.7809 | 0.7554 | 178 | 0.8441 | 0.8820 | 0.8626 | 178 | 0.8317 | 0.7778 | 0.8038 | 108 | 0.7310 | 0.7530 | 0.7418 | 166 | 0.7576 | 0.8097 | 0.7828 | 247 | 0.8286 | 0.8657 | 0.8467 | 67 | 0.7791 | 0.7614 | 0.7701 | 176 | 0.7960 | 0.8221 | 0.8088 | 0.9584 |
| 0.0105 | 27.0 | 21438 | 0.3624 | 0.8280 | 0.875 | 0.8508 | 88 | 0.8352 | 0.9363 | 0.8829 | 157 | 0.8592 | 0.8531 | 0.8561 | 143 | 0.4795 | 0.7143 | 0.5738 | 49 | 0.7158 | 0.7360 | 0.7258 | 178 | 0.8197 | 0.8427 | 0.8310 | 178 | 0.7068 | 0.8704 | 0.7801 | 108 | 0.6878 | 0.7831 | 0.7324 | 166 | 0.7741 | 0.7490 | 0.7613 | 247 | 0.8088 | 0.8209 | 0.8148 | 67 | 0.7644 | 0.7557 | 0.76 | 176 | 0.7616 | 0.8086 | 0.7844 | 0.9552 |
| 0.0088 | 28.0 | 22232 | 0.3755 | 0.7938 | 0.875 | 0.8324 | 88 | 0.8882 | 0.9108 | 0.8994 | 157 | 0.8705 | 0.8462 | 0.8582 | 143 | 0.6481 | 0.7143 | 0.6796 | 49 | 0.6618 | 0.7697 | 0.7117 | 178 | 0.8370 | 0.8652 | 0.8508 | 178 | 0.9277 | 0.7130 | 0.8063 | 108 | 0.7414 | 0.7771 | 0.7588 | 166 | 0.7603 | 0.8219 | 0.7899 | 247 | 0.94 | 0.7015 | 0.8034 | 67 | 0.7901 | 0.7273 | 0.7574 | 176 | 0.7928 | 0.8035 | 0.7981 | 0.9559 |
| 0.0101 | 29.0 | 23026 | 0.4108 | 0.8587 | 0.8977 | 0.8778 | 88 | 0.8765 | 0.9045 | 0.8903 | 157 | 0.8676 | 0.8252 | 0.8459 | 143 | 0.5286 | 0.7551 | 0.6218 | 49 | 0.7005 | 0.7360 | 0.7178 | 178 | 0.8162 | 0.8483 | 0.8320 | 178 | 0.8646 | 0.7685 | 0.8137 | 108 | 0.7225 | 0.7530 | 0.7375 | 166 | 0.7236 | 0.8057 | 0.7625 | 247 | 0.9423 | 0.7313 | 0.8235 | 67 | 0.7870 | 0.7557 | 0.7710 | 176 | 0.7808 | 0.8009 | 0.7907 | 0.9526 |
| 0.0087 | 30.0 | 23820 | 0.3898 | 0.8764 | 0.8864 | 0.8814 | 88 | 0.9114 | 0.9172 | 0.9143 | 157 | 0.9015 | 0.8322 | 0.8655 | 143 | 0.5333 | 0.6531 | 0.5872 | 49 | 0.6502 | 0.7416 | 0.6929 | 178 | 0.8101 | 0.8146 | 0.8123 | 178 | 0.9529 | 0.75 | 0.8394 | 108 | 0.7922 | 0.7349 | 0.7625 | 166 | 0.7635 | 0.7449 | 0.7541 | 247 | 0.8947 | 0.7612 | 0.8226 | 67 | 0.7702 | 0.7045 | 0.7359 | 176 | 0.7979 | 0.7784 | 0.7880 | 0.9533 |
### Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.12.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Petros89/bert-finetuned-ner
|
Petros89
| 2022-08-02T13:19:01Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-02T13:00:26Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: train
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9320436507936508
- name: Recall
type: recall
value: 0.9486704813194211
- name: F1
type: f1
value: 0.9402835696413678
- name: Accuracy
type: accuracy
value: 0.9861217401542356
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0611
- Precision: 0.9320
- Recall: 0.9487
- F1: 0.9403
- Accuracy: 0.9861
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0889 | 1.0 | 1756 | 0.0748 | 0.9060 | 0.9263 | 0.9160 | 0.9800 |
| 0.0381 | 2.0 | 3512 | 0.0631 | 0.9296 | 0.9468 | 0.9381 | 0.9855 |
| 0.0205 | 3.0 | 5268 | 0.0611 | 0.9320 | 0.9487 | 0.9403 | 0.9861 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.7.0
- Datasets 2.4.0
- Tokenizers 0.12.1
|
dminiotas05/distilbert-base-uncased-finetuned-ft1500_reg2
|
dminiotas05
| 2022-08-02T13:14:03Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-02T12:31:15Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ft1500_reg2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ft1500_reg2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7256
- Mse: 0.7256
- Mae: 0.6674
- R2: 0.4579
- Accuracy: 0.4573
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:--------:|
| 1.0689 | 1.0 | 3000 | 0.7823 | 0.7823 | 0.6948 | 0.4156 | 0.4327 |
| 0.6733 | 2.0 | 6000 | 0.7286 | 0.7286 | 0.6705 | 0.4556 | 0.4447 |
| 0.4735 | 3.0 | 9000 | 0.7125 | 0.7125 | 0.6658 | 0.4677 | 0.46 |
| 0.3358 | 4.0 | 12000 | 0.7256 | 0.7256 | 0.6674 | 0.4579 | 0.4573 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
dandelin/vilt-b32-finetuned-vqa
|
dandelin
| 2022-08-02T13:03:04Z
| 105,438
| 400
|
transformers
|
[
"transformers",
"pytorch",
"vilt",
"visual-question-answering",
"arxiv:2102.03334",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
visual-question-answering
| 2022-03-02T23:29:05Z
|
---
tags:
- visual-question-answering
license: apache-2.0
widget:
- text: "What's the animal doing?"
src: "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg"
- text: "What is on top of the building?"
src: "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg"
---
# Vision-and-Language Transformer (ViLT), fine-tuned on VQAv2
Vision-and-Language Transformer (ViLT) model fine-tuned on [VQAv2](https://visualqa.org/). It was introduced in the paper [ViLT: Vision-and-Language Transformer
Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and first released in [this repository](https://github.com/dandelin/ViLT).
Disclaimer: The team releasing ViLT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Intended uses & limitations
You can use the raw model for visual question answering.
### How to use
Here is how to use this model in PyTorch:
```python
from transformers import ViltProcessor, ViltForQuestionAnswering
import requests
from PIL import Image
# prepare image + question
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
text = "How many cats are there?"
processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
# prepare inputs
encoding = processor(image, text, return_tensors="pt")
# forward pass
outputs = model(**encoding)
logits = outputs.logits
idx = logits.argmax(-1).item()
print("Predicted answer:", model.config.id2label[idx])
```
## Training data
(to do)
## Training procedure
### Preprocessing
(to do)
### Pretraining
(to do)
## Evaluation results
(to do)
### BibTeX entry and citation info
```bibtex
@misc{kim2021vilt,
title={ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision},
author={Wonjae Kim and Bokyung Son and Ildoo Kim},
year={2021},
eprint={2102.03334},
archivePrefix={arXiv},
primaryClass={stat.ML}
}
```
|
Tarkan/distilbert-base-uncased-finetuned-ner
|
Tarkan
| 2022-08-02T12:48:03Z
| 4
| 0
|
transformers
|
[
"transformers",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-01T09:51:39Z
|
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Tarkan/distilbert-base-uncased-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. -->
# Tarkan/distilbert-base-uncased-finetuned-ner
SA yakında silicem bunu xd
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: 0.0588
- Validation Loss: 0.0735
- Train Precision: 0.7787
- Train Recall: 0.8428
- Train F1: 0.8095
- Train Accuracy: 0.9780
- 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': 678, '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 | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 0.1737 | 0.0799 | 0.7422 | 0.8296 | 0.7835 | 0.9746 | 0 |
| 0.0588 | 0.0735 | 0.7787 | 0.8428 | 0.8095 | 0.9780 | 1 |
### Framework versions
- Transformers 4.21.0
- TensorFlow 2.8.2
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sepidmnorozy/finetuned-sentiment-withGPU
|
sepidmnorozy
| 2022-08-02T12:33:09Z
| 7
| 1
|
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-04T13:26:21Z
|
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: finetuning-sentiment-model_withGPU
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-10-samples_withGPU
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3893
- Accuracy: 0.8744
- F1: 0.8684
- Precision: 0.9126
- Recall: 0.8283
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3631 | 1.0 | 7088 | 0.3622 | 0.8638 | 0.8519 | 0.9334 | 0.7835 |
| 0.35 | 2.0 | 14176 | 0.3875 | 0.8714 | 0.8622 | 0.9289 | 0.8044 |
| 0.3262 | 3.0 | 21264 | 0.3893 | 0.8744 | 0.8684 | 0.9126 | 0.8283 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
wenkai-li/distilbert-base-uncased-finetuned-wikiandmark_epoch50
|
wenkai-li
| 2022-08-02T12:11:19Z
| 6
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-02T11:02:56Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-wikiandmark_epoch50
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-wikiandmark_epoch50
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:
- eval_loss: 0.0049
- eval_accuracy: 0.9995
- eval_runtime: 29.1585
- eval_samples_per_second: 127.613
- eval_steps_per_second: 4.013
- epoch: 6.0
- step: 4656
## 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: 50
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
dfsj/distilbert-base-uncased-distilled-clinc
|
dfsj
| 2022-08-02T11:38:29Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-01T00:46:22Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9448387096774193
---
<!-- 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-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3163
- Accuracy: 0.9448
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 9
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 2.3518 | 0.7510 |
| 2.7559 | 2.0 | 636 | 1.2235 | 0.8506 |
| 2.7559 | 3.0 | 954 | 0.6786 | 0.9168 |
| 1.0767 | 4.0 | 1272 | 0.4668 | 0.9368 |
| 0.4584 | 5.0 | 1590 | 0.3810 | 0.9410 |
| 0.4584 | 6.0 | 1908 | 0.3479 | 0.9435 |
| 0.2876 | 7.0 | 2226 | 0.3282 | 0.9455 |
| 0.2285 | 8.0 | 2544 | 0.3201 | 0.9452 |
| 0.2285 | 9.0 | 2862 | 0.3163 | 0.9448 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu102
- Datasets 2.0.0
- Tokenizers 0.12.1
|
spacestar1705/Reinforce-CartPole-v1
|
spacestar1705
| 2022-08-02T10:58:23Z
| 0
| 0
| null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-08-02T10:50:12Z
|
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- metrics:
- type: mean_reward
value: 92.70 +/- 7.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
JmPaunlagui/Improve
|
JmPaunlagui
| 2022-08-02T10:17:55Z
| 0
| 0
|
keras
|
[
"keras",
"tf-keras",
"region:us"
] | null | 2022-08-02T09:42:09Z
|
---
library_name: keras
---
## 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:
| name | learning_rate | decay | beta_1 | beta_2 | epsilon | amsgrad | training_precision |
|----|-------------|-----|------|------|-------|-------|------------------|
|Adam|0.001|0.0|0.9|0.999|1e-07|False|float32|
|
dminiotas05/distilbert-base-uncased-finetuned-ft1500_reg1
|
dminiotas05
| 2022-08-02T09:45:22Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-02T09:03:19Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ft1500_reg1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ft1500_reg1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6165
- Mse: 0.6165
- Mae: 0.6069
- R2: 0.4197
- Accuracy: 0.5007
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:--------:|
| 0.7297 | 1.0 | 3000 | 0.9128 | 0.9128 | 0.7501 | 0.1408 | 0.4113 |
| 0.4692 | 2.0 | 6000 | 0.5875 | 0.5875 | 0.5946 | 0.4470 | 0.514 |
| 0.3361 | 3.0 | 9000 | 0.6165 | 0.6165 | 0.6069 | 0.4197 | 0.5007 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
yashwantk/distilbert-base-uncased-finetuned-squad
|
yashwantk
| 2022-08-02T09:05:20Z
| 6
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-07-31T08:07:48Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2491
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2862 | 1.0 | 8235 | 1.2491 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
jinghan/roberta-base-finetuned-wnli
|
jinghan
| 2022-08-02T09:04:37Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-02T08:49:05Z
|
---
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: roberta-base-finetuned-wnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: wnli
split: train
args: wnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5633802816901409
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-wnli
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6880
- Accuracy: 0.5634
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 0.6880 | 0.5634 |
| No log | 2.0 | 80 | 0.6851 | 0.5634 |
| No log | 3.0 | 120 | 0.6961 | 0.4366 |
| No log | 4.0 | 160 | 0.6906 | 0.5634 |
| No log | 5.0 | 200 | 0.6891 | 0.5634 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
kyoumiaoi/wav2vec2-base-timit-demo-google-colab
|
kyoumiaoi
| 2022-08-02T08:28:06Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-08-02T06:15:34Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-google-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5499
- Wer: 0.3435
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.599 | 1.0 | 500 | 2.1267 | 0.9976 |
| 1.016 | 2.01 | 1000 | 0.6193 | 0.5443 |
| 0.5299 | 3.01 | 1500 | 0.5324 | 0.4889 |
| 0.3626 | 4.02 | 2000 | 0.4525 | 0.4402 |
| 0.2854 | 5.02 | 2500 | 0.4266 | 0.4233 |
| 0.2373 | 6.02 | 3000 | 0.4713 | 0.4082 |
| 0.1979 | 7.03 | 3500 | 0.4778 | 0.4018 |
| 0.1761 | 8.03 | 4000 | 0.4585 | 0.3947 |
| 0.1537 | 9.04 | 4500 | 0.5297 | 0.3946 |
| 0.1379 | 10.04 | 5000 | 0.4988 | 0.3856 |
| 0.124 | 11.04 | 5500 | 0.5262 | 0.3852 |
| 0.11 | 12.05 | 6000 | 0.5545 | 0.3854 |
| 0.106 | 13.05 | 6500 | 0.5196 | 0.3805 |
| 0.0918 | 14.06 | 7000 | 0.4515 | 0.3655 |
| 0.0829 | 15.06 | 7500 | 0.5087 | 0.3722 |
| 0.0775 | 16.06 | 8000 | 0.4980 | 0.3781 |
| 0.0685 | 17.07 | 8500 | 0.5564 | 0.3650 |
| 0.0655 | 18.07 | 9000 | 0.5323 | 0.3672 |
| 0.0578 | 19.08 | 9500 | 0.5675 | 0.3637 |
| 0.052 | 20.08 | 10000 | 0.5604 | 0.3664 |
| 0.0512 | 21.08 | 10500 | 0.5922 | 0.3804 |
| 0.0431 | 22.09 | 11000 | 0.6379 | 0.3754 |
| 0.0428 | 23.09 | 11500 | 0.5905 | 0.3764 |
| 0.0393 | 24.1 | 12000 | 0.5667 | 0.3542 |
| 0.0326 | 25.1 | 12500 | 0.5612 | 0.3537 |
| 0.0289 | 26.1 | 13000 | 0.5618 | 0.3475 |
| 0.0298 | 27.11 | 13500 | 0.5578 | 0.3439 |
| 0.0264 | 28.11 | 14000 | 0.5547 | 0.3433 |
| 0.026 | 29.12 | 14500 | 0.5499 | 0.3435 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.12.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
yirenl2/plm_qa
|
yirenl2
| 2022-08-02T06:43:12Z
| 18
| 0
|
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"en",
"dataset:squad_v2",
"license:cc-by-4.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-08-01T03:06:27Z
|
---
language: en
datasets:
- squad_v2
license: cc-by-4.0
model-index:
- name: plm_qa
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad_v2
type: squad_v2
config: squad_v2
split: validation
metrics:
- name: Exact Match
type: exact_match
value: 0
verified: false
- name: F1
type: f1
value: 0
verified: false
- name: total
type: total
value: 11869
verified: false
---
# roberta-base for QA finetuned over community safety domain data
We fine-tuned the roBERTa-based model (https://huggingface.co/deepset/roberta-base-squad2) over LiveSafe community safety dialogue data for event argument extraction with the objective of question-answering.
### Using model in Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "yirenl2/plm_qa"
# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
'question': 'What is the location of the incident?',
'context': 'I was attacked by someone in front of the bus station.'
}
res = nlp(QA_input)
# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
|
fut501/11
|
fut501
| 2022-08-02T06:35:31Z
| 0
| 0
| null |
[
"license:bsd-3-clause-clear",
"region:us"
] | null | 2022-08-02T06:35:31Z
|
---
license: bsd-3-clause-clear
---
|
huggingtweets/itsjefftiedrich
|
huggingtweets
| 2022-08-02T02:50:29Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-08-02T02:48:45Z
|
---
language: en
thumbnail: http://www.huggingtweets.com/itsjefftiedrich/1659408624518/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/1009932396333031424/8FzKlCfB_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">Jeff Tiedrich</div>
<div style="text-align: center; font-size: 14px;">@itsjefftiedrich</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 Jeff Tiedrich.
| Data | Jeff Tiedrich |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 6 |
| Short tweets | 753 |
| Tweets kept | 2491 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/311xv04i/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 @itsjefftiedrich's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2zwvvvq6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2zwvvvq6/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/itsjefftiedrich')
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)
|
lijingxin/distilbert-base-uncased-finetuned-emotion
|
lijingxin
| 2022-08-02T01:46:47Z
| 7
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-09T08:33:06Z
|
---
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: 0.9225
- name: F1
type: f1
value: 0.9226367098786769
---
<!-- 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: 0.2161
- Accuracy: 0.9225
- F1: 0.9226
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8009 | 1.0 | 250 | 0.3027 | 0.9045 | 0.9015 |
| 0.2402 | 2.0 | 500 | 0.2161 | 0.9225 | 0.9226 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.2
- Datasets 1.16.1
- Tokenizers 0.10.3
|
rdruce/ddpm-cheese-32
|
rdruce
| 2022-08-02T00:34:19Z
| 0
| 0
|
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:imagefolder",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-02T00:05:54Z
|
---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: imagefolder
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-cheese-32
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `imagefolder` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/rdruce/ddpm-cheese-32/tensorboard?#scalars)
|
muhtasham/bert-tiny-finetuned-wnut17-ner
|
muhtasham
| 2022-08-01T23:26:22Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-01T23:24:09Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-tiny-finetuned-wnut17-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
config: wnut_17
split: train
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.0
- name: Recall
type: recall
value: 0.0
- name: F1
type: f1
value: 0.0
- name: Accuracy
type: accuracy
value: 0.8960890010322284
---
<!-- 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-tiny-finetuned-wnut17-ner
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6054
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.8961
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| No log | 1.0 | 27 | 1.1060 | 0.0 | 0.0 | 0.0 | 0.8961 |
| No log | 2.0 | 54 | 0.9075 | 0.0 | 0.0 | 0.0 | 0.8961 |
| No log | 3.0 | 81 | 0.7978 | 0.0 | 0.0 | 0.0 | 0.8961 |
| No log | 4.0 | 108 | 0.7333 | 0.0 | 0.0 | 0.0 | 0.8961 |
| No log | 5.0 | 135 | 0.6929 | 0.0 | 0.0 | 0.0 | 0.8961 |
| No log | 6.0 | 162 | 0.6661 | 0.0 | 0.0 | 0.0 | 0.8961 |
| No log | 7.0 | 189 | 0.6477 | 0.0 | 0.0 | 0.0 | 0.8961 |
| No log | 8.0 | 216 | 0.6346 | 0.0 | 0.0 | 0.0 | 0.8961 |
| No log | 9.0 | 243 | 0.6251 | 0.0 | 0.0 | 0.0 | 0.8961 |
| No log | 10.0 | 270 | 0.6182 | 0.0 | 0.0 | 0.0 | 0.8961 |
| No log | 11.0 | 297 | 0.6132 | 0.0 | 0.0 | 0.0 | 0.8961 |
| No log | 12.0 | 324 | 0.6097 | 0.0 | 0.0 | 0.0 | 0.8961 |
| No log | 13.0 | 351 | 0.6073 | 0.0 | 0.0 | 0.0 | 0.8961 |
| No log | 14.0 | 378 | 0.6059 | 0.0 | 0.0 | 0.0 | 0.8961 |
| No log | 15.0 | 405 | 0.6054 | 0.0 | 0.0 | 0.0 | 0.8961 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
muhtasham/bert-tiny-finetuned-xglue-ner
|
muhtasham
| 2022-08-01T23:20:07Z
| 5
| 1
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:xglue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-01T23:13:29Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xglue
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-tiny-finetuned-xglue-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xglue
type: xglue
config: ner
split: train
args: ner
metrics:
- name: Precision
type: precision
value: 0.630759453447728
- name: Recall
type: recall
value: 0.6681252103668799
- name: F1
type: f1
value: 0.6489048708728343
- name: Accuracy
type: accuracy
value: 0.9274310133922189
---
<!-- 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-tiny-finetuned-xglue-ner
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the xglue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2489
- Precision: 0.6308
- Recall: 0.6681
- F1: 0.6489
- Accuracy: 0.9274
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4082 | 1.0 | 1756 | 0.3326 | 0.5600 | 0.5798 | 0.5697 | 0.9118 |
| 0.2974 | 2.0 | 3512 | 0.2635 | 0.6143 | 0.6562 | 0.6346 | 0.9248 |
| 0.2741 | 3.0 | 5268 | 0.2489 | 0.6308 | 0.6681 | 0.6489 | 0.9274 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
SharpAI/mal-tls-mobilebert
|
SharpAI
| 2022-08-01T22:53:41Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tf",
"mobilebert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-01T22:45:11Z
|
---
tags:
- generated_from_keras_callback
model-index:
- name: mal_tls-mobilebert
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. -->
# mal_tls-mobilebert
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
|
BigSalmon/InformalToFormalLincoln60Paraphrase
|
BigSalmon
| 2022-08-01T21:24:02Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-08-01T20:53:51Z
|
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln60Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln60Paraphrase")
```
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
input_ids = tokenizer.encode(prompt, return_tensors='pt')
outputs = model.generate(input_ids=input_ids,
max_length=10 + len(prompt),
temperature=1.0,
top_k=50,
top_p=0.95,
do_sample=True,
num_return_sequences=5,
early_stopping=True)
for i in range(5):
print(tokenizer.decode(outputs[i]))
```
```
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 "
```
```
- nebraska
- unicamerical legislature
- different from federal house and senate
text: featuring a unicameral legislature, nebraska's political system stands in stark contrast to the federal model, comprised of a house and senate.
***
- penny has practically no value
- should be taken out of circulation
- just as other coins have been in us history
- lost use
- value not enough
- to make environmental consequences worthy
text: all but valueless, the penny should be retired. as with other coins in american history, it has become defunct. too minute to warrant the environmental consequences of its production, it has outlived its usefulness.
***
-
```
```
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:
```
```
original: had trouble deciding.
translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation.
***
original:
```
```
input: not loyal
1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ).
***
input:
```
```
first: ( was complicit in / was involved in ).
antonym: ( was blameless / was not an accomplice to / had no hand in / was uninvolved in ).
***
first: ( have no qualms about / see no issue with ).
antonym: ( are deeply troubled by / harbor grave reservations about / have a visceral aversion to / take ( umbrage at / exception to ) / are wary of ).
***
first: ( do not see eye to eye / disagree often ).
antonym: ( are in sync / are united / have excellent rapport / are like-minded / are in step / are of one mind / are in lockstep / operate in perfect harmony / march in lockstep ).
***
first:
```
```
stiff with competition, law school {A} is the launching pad for countless careers, {B} is a crowded field, {C} ranks among the most sought-after professional degrees, {D} is a professional proving ground.
***
languishing in viewership, saturday night live {A} is due for a creative renaissance, {B} is no longer a ratings juggernaut, {C} has been eclipsed by its imitators, {C} can still find its mojo.
***
dubbed the "manhattan of the south," atlanta {A} is a bustling metropolis, {B} is known for its vibrant downtown, {C} is a city of rich history, {D} is the pride of georgia.
***
embattled by scandal, harvard {A} is feeling the heat, {B} cannot escape the media glare, {C} is facing its most intense scrutiny yet, {D} is in the spotlight for all the wrong reasons.
```
|
hugorosen/flaubert_base_uncased-xnli-sts
|
hugorosen
| 2022-08-01T21:22:03Z
| 23
| 0
|
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"flaubert",
"feature-extraction",
"sentence-similarity",
"transformers",
"fr",
"dataset:xnli",
"dataset:stsb_multi_mt",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z
|
---
pipeline_tag: sentence-similarity
language: fr
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- fr
datasets:
- xnli
- stsb_multi_mt
---
# hugorosen/flaubert_base_uncased-xnli-sts
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["Ceci est une phrase d'exemple", "Chaque phrase est convertie"]
model = SentenceTransformer('hugorosen/flaubert_base_uncased-xnli-sts')
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 = ["Un avion est en train de décoller.",
"Un homme joue d'une grande flûte.",
"Un homme étale du fromage râpé sur une pizza.",
"Une personne jette un chat au plafond.",
"Une personne est en train de plier un morceau de papier.",
]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('hugorosen/flaubert_base_uncased-xnli-sts')
model = AutoModel.from_pretrained('hugorosen/flaubert_base_uncased-xnli-sts')
# 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 -->
This model scores 76.9% on STS test (french)
## Training
### Pre-training
We use the pre-trained [flaubert/flaubert_base_uncased](https://huggingface.co/flaubert/flaubert_base_cased). Please refer to the model card for more detailed information about the pre-training procedure.
### Fine-tuning
we fine-tune the model using a `CosineSimilarityLoss` on XNLI and STS dataset (french).
Parameters of the fit()-Method:
```
{
"epochs": 4,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 144,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: FlaubertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
Fine-tuned for semantic similarity by Hugo Rosenkranz-costa.
Based on FlauBERT:
```
@InProceedings{le2020flaubert,
author = {Le, Hang and Vial, Lo\"{i}c and Frej, Jibril and Segonne, Vincent and Coavoux, Maximin and Lecouteux, Benjamin and Allauzen, Alexandre and Crabb\'{e}, Beno\^{i}t and Besacier, Laurent and Schwab, Didier},
title = {FlauBERT: Unsupervised Language Model Pre-training for French},
booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference},
month = {May},
year = {2020},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {2479--2490},
url = {https://www.aclweb.org/anthology/2020.lrec-1.302}
}
```
|
Enutrof/marian-mt-en-pcm
|
Enutrof
| 2022-08-01T21:18:24Z
| 41
| 0
|
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-31T23:17:27Z
|
---
license: mit
---
### marian-mt-en-pcm
* source language: en (English)
* target language: pcm (Nigerian Pidgin)
* dataset: Parallel Sentences from the message translation (English) and Pidgin translation of the Bible.
* model: transformer-align
* pre-processing: normalization + SentencePiece
## Performance
| test set | BLEU |
|---------------------|-------|
| 20% of the bible data | 22 |
|
Enutrof/marian-mt-pcm-en
|
Enutrof
| 2022-08-01T21:17:54Z
| 22
| 0
|
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-08-01T19:34:22Z
|
---
license: mit
---
### marian-mt-pcm-en
* source language: pcm (Nigerian Pidgin)
* target language: en (English)
* dataset: Parallel Sentences from the Pidgin and message translations (English) of the Bible.
* model: transformer-align
* pre-processing: normalization + SentencePiece
## Performance
| test set | BLEU |
|---------------------|-------|
| 20% of the bible data | 21.4 |
|
Intel/bert-base-uncased-sparse-80-1x4-block-pruneofa
|
Intel
| 2022-08-01T21:06:46Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"bert",
"pretraining",
"fill-mask",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:2111.05754",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-29T11:58:55Z
|
---
language: en
license: apache-2.0
tags:
- fill-mask
datasets:
- wikipedia
- bookcorpus
---
# 80% 1x4 Block Sparse BERT-Base (uncased) Prune OFA
This model is was created using Prune OFA method described in [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754) presented in ENLSP NeurIPS Workshop 2021.
For further details on the model and its result, see our paper and our implementation available [here](https://github.com/IntelLabs/Model-Compression-Research-Package/tree/main/research/prune-once-for-all).
|
muhtasham/bert-tiny-finetuned-pile-of-law-tos
|
muhtasham
| 2022-08-01T20:24:25Z
| 6
| 1
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-08-01T18:22:18Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-tiny-finetuned-pile-of-law-tos
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-tiny-finetuned-pile-of-law-tos
This model is a MLM fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the [pile-of-law/tos](https://huggingface.co/datasets/pile-of-law/pile-of-law) dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3545
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 264 | 3.5896 |
| 3.8119 | 2.0 | 528 | 3.5598 |
| 3.8119 | 3.0 | 792 | 3.5263 |
| 3.7028 | 4.0 | 1056 | 3.4982 |
| 3.7028 | 5.0 | 1320 | 3.5170 |
| 3.6286 | 6.0 | 1584 | 3.5143 |
| 3.6286 | 7.0 | 1848 | 3.4477 |
| 3.553 | 8.0 | 2112 | 3.4044 |
| 3.553 | 9.0 | 2376 | 3.4670 |
| 3.5179 | 10.0 | 2640 | 3.3991 |
| 3.5179 | 11.0 | 2904 | 3.4330 |
| 3.4784 | 12.0 | 3168 | 3.4671 |
| 3.4784 | 13.0 | 3432 | 3.3489 |
| 3.4535 | 14.0 | 3696 | 3.4354 |
| 3.4535 | 15.0 | 3960 | 3.4023 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
arize-ai/resnet-50-fashion-mnist-quality-drift
|
arize-ai
| 2022-08-01T19:55:57Z
| 182
| 4
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"resnet",
"image-classification",
"generated_from_trainer",
"dataset:fashion_mnist_quality_drift",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-08-01T19:32:07Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- fashion_mnist_quality_drift
metrics:
- accuracy
- f1
model-index:
- name: resnet-50-fashion-mnist-quality-drift
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: fashion_mnist_quality_drift
type: fashion_mnist_quality_drift
config: default
split: training
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.73
- name: F1
type: f1
value: 0.7289360255705818
---
<!-- 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. -->
# resnet-50-fashion-mnist-quality-drift
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the fashion_mnist_quality_drift dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7473
- Accuracy: 0.73
- F1: 0.7289
## 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: 0.0002
- 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.5138 | 1.0 | 750 | 0.9237 | 0.684 | 0.6826 |
| 0.9377 | 2.0 | 1500 | 0.7861 | 0.722 | 0.7253 |
| 0.8276 | 3.0 | 2250 | 0.7473 | 0.73 | 0.7289 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
SharpAI/mal-tls-bert-base-relu
|
SharpAI
| 2022-08-01T19:54:00Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-01T19:53:07Z
|
---
tags:
- generated_from_keras_callback
model-index:
- name: mal_tls-bert-base-relu
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. -->
# mal_tls-bert-base-relu
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
|
mrm8488/distiluse-base-multilingual-cased-v2-finetuned-stsb_multi_mt-es
|
mrm8488
| 2022-08-01T19:41:40Z
| 1,199
| 3
|
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"es",
"dataset:stsb_multi_mt",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z
|
---
language: es
thumbnail: https://imgur.com/a/G77ZqQN
pipeline_tag: sentence-similarity
datasets:
- stsb_multi_mt
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# Distiluse-m-v2 fine-tuned on stsb_multi_mt for Spanish Semantic Textual Similarity
This is a [sentence-transformers](https://www.SBERT.net) model (distiluse-base-multilingual-cased-v2): It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["Nerea va a comprar un cuadro usando bitcoins", "Se puede comprar arte con bitcoins"]
model = SentenceTransformer('mrm8488/distiluse-base-multilingual-cased-v2-finetuned-stsb_multi_mt-es')
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 = ["Nerea va a comprar un cuadro usando bitcoins", "Se puede comprar arte con bitcoins"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('mrm8488/distiluse-base-multilingual-cased-v2-finetuned-stsb_multi_mt-es')
model = AutoModel.from_pretrained('mrm8488/distiluse-base-multilingual-cased-v2-finetuned-stsb_multi_mt-es')
# 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)
```
## How to evaluate
```py
from datasets import load_dataset
from sentence_transformers import SentenceTransformer, InputExample
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
test_data = load_dataset('stsb_multi_mt', 'es', split='test')
test_data = test_data.rename_columns({'similarity_score': 'label'})
test_data = test_data.map(lambda x: {'label': x['label'] / 5.0})
samples = []
for sample in test_data:
samples.append(InputExample(
texts=[sample['sentence1'], sample['sentence2']],
label=sample['label']
))
evaluator = EmbeddingSimilarityEvaluator.from_input_examples(
samples, write_csv=False
)
model = SentenceTransformer('mrm8488/distiluse-base-multilingual-cased-v2-finetuned-stsb_multi_mt-es')
evaluator(model)
# It outputs: 0.7604056195656299
```
## Evaluation Results
**Spearman’s rank correlation: 0.7604056195656299**
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 906 with parameters:
```
{'batch_size': 16}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 271,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
vidyavenkappa/pegasus-samsum
|
vidyavenkappa
| 2022-08-01T18:30:17Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-30T12:10:24Z
|
---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3086
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6151 | 0.54 | 500 | 1.4238 |
| 1.3357 | 1.09 | 1000 | 1.3629 |
| 1.4423 | 1.63 | 1500 | 1.3380 |
| 1.3747 | 2.17 | 2000 | 1.3218 |
| 1.3397 | 2.72 | 2500 | 1.3124 |
| 1.2706 | 3.26 | 3000 | 1.3149 |
| 1.1849 | 3.8 | 3500 | 1.3120 |
| 1.2222 | 4.35 | 4000 | 1.3120 |
| 1.2339 | 4.89 | 4500 | 1.3086 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
turhancan97/Reinforce-2
|
turhancan97
| 2022-08-01T16:45:20Z
| 0
| 0
| null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-08-01T16:44:23Z
|
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-2
results:
- metrics:
- type: mean_reward
value: 9.40 +/- 13.66
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
silviacamplani/twitter-roberta-base-finetuned-ner-wnut
|
silviacamplani
| 2022-08-01T16:26:39Z
| 5
| 0
|
transformers
|
[
"transformers",
"tf",
"tensorboard",
"roberta",
"token-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-01T15:50:19Z
|
---
tags:
- generated_from_keras_callback
model-index:
- name: silviacamplani/twitter-roberta-base-finetuned-ner-wnut
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. -->
# silviacamplani/twitter-roberta-base-finetuned-ner-wnut
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0812
- Validation Loss: 0.2553
- Train Precision: 0.6263
- Train Recall: 0.5191
- Train F1: 0.5677
- Train Accuracy: 0.9398
- 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: {'inner_optimizer': {'class_name': 'Adam', 'config': {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 636, '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}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 0.0813 | 0.2553 | 0.6263 | 0.5191 | 0.5677 | 0.9398 | 0 |
| 0.0815 | 0.2553 | 0.6263 | 0.5191 | 0.5677 | 0.9398 | 1 |
| 0.0812 | 0.2553 | 0.6263 | 0.5191 | 0.5677 | 0.9398 | 2 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Forkits/Reinforce-Pixelcopter-PLE-v0
|
Forkits
| 2022-08-01T15:15:58Z
| 0
| 0
| null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-08-01T15:15:48Z
|
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- metrics:
- type: mean_reward
value: 17.50 +/- 15.79
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
meln1k/a2c-HalfCheetahBulletEnv-v0
|
meln1k
| 2022-08-01T14:54:29Z
| 7
| 0
|
stable-baselines3
|
[
"stable-baselines3",
"HalfCheetahBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-08-01T11:19:36Z
|
---
library_name: stable-baselines3
tags:
- HalfCheetahBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 1893.95 +/- 69.15
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: HalfCheetahBulletEnv-v0
type: HalfCheetahBulletEnv-v0
---
# **A2C** Agent playing **HalfCheetahBulletEnv-v0**
This is a trained model of a **A2C** agent playing **HalfCheetahBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
silviacamplani/distilbert-base-uncased-finetuned-ner-wnut
|
silviacamplani
| 2022-08-01T14:53:43Z
| 4
| 0
|
transformers
|
[
"transformers",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-01T10:37:08Z
|
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: silviacamplani/distilbert-base-uncased-finetuned-ner-wnut
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. -->
# silviacamplani/distilbert-base-uncased-finetuned-ner-wnut
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: 0.1241
- Validation Loss: 0.3433
- Train Precision: 0.5677
- Train Recall: 0.3660
- Train F1: 0.4451
- Train Accuracy: 0.9215
- 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: {'inner_optimizer': {'class_name': 'Adam', 'config': {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 636, '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}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 0.3454 | 0.4475 | 0.0 | 0.0 | 0.0 | 0.8961 | 0 |
| 0.1637 | 0.3637 | 0.6297 | 0.2990 | 0.4055 | 0.9154 | 1 |
| 0.1241 | 0.3433 | 0.5677 | 0.3660 | 0.4451 | 0.9215 | 2 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
|
pdelobelle/robbert-v2-dutch-ner
|
pdelobelle
| 2022-08-01T14:49:07Z
| 78,268
| 3
|
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"token-classification",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"nl",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z
|
---
language: "nl"
thumbnail: "https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo.png"
tags:
- Dutch
- Flemish
- RoBERTa
- RobBERT
license: mit
datasets:
- oscar
- oscar (NL)
- dbrd
- lassy-ud
- europarl-mono
- conll2002
widget:
- text: "Mijn naam is RobBERT en ik ben een taalmodel van de KU Leuven."
---
<p align="center">
<img src="https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo_with_name.png" alt="RobBERT: A Dutch RoBERTa-based Language Model" width="75%">
</p>
# RobBERT: Dutch RoBERTa-based Language Model.
[RobBERT](https://github.com/iPieter/RobBERT) is the state-of-the-art Dutch BERT model. It is a large pre-trained general Dutch language model that can be fine-tuned on a given dataset to perform any text classification, regression or token-tagging task. As such, it has been successfully used by many [researchers](https://scholar.google.com/scholar?oi=bibs&hl=en&cites=7180110604335112086) and [practitioners](https://huggingface.co/models?search=robbert) for achieving state-of-the-art performance for a wide range of Dutch natural language processing tasks,
|
dminiotas05/distilbert-base-uncased-finetuned-ft750_reg5
|
dminiotas05
| 2022-08-01T14:18:11Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-01T13:57:20Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ft750_reg5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ft750_reg5
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6298
- Mse: 0.6298
- Mae: 0.6087
- R2: 0.4072
- Accuracy: 0.4973
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:--------:|
| 1.8617 | 1.0 | 188 | 0.7482 | 0.7482 | 0.6639 | 0.2957 | 0.4707 |
| 0.5667 | 2.0 | 376 | 0.6017 | 0.6017 | 0.5978 | 0.4336 | 0.5127 |
| 0.5038 | 3.0 | 564 | 0.6298 | 0.6298 | 0.6087 | 0.4072 | 0.4973 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
turhancan97/Reinforce-1
|
turhancan97
| 2022-08-01T14:02:54Z
| 0
| 0
| null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-08-01T14:02:43Z
|
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-1
results:
- metrics:
- type: mean_reward
value: 98.30 +/- 25.19
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
silviacamplani/distilbert-base-uncased-finetuned-ner-conll2003
|
silviacamplani
| 2022-08-01T13:47:12Z
| 5
| 0
|
transformers
|
[
"transformers",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-01T13:42:27Z
|
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: silviacamplani/distilbert-base-uncased-finetuned-ner-conll2003
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. -->
# silviacamplani/distilbert-base-uncased-finetuned-ner-conll2003
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: 0.0516
- Validation Loss: 0.0592
- Train Precision: 0.9147
- Train Recall: 0.9313
- Train F1: 0.9229
- Train Accuracy: 0.9832
- 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, '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}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 0.1958 | 0.0662 | 0.9016 | 0.9142 | 0.9078 | 0.9813 | 0 |
| 0.0516 | 0.0592 | 0.9147 | 0.9313 | 0.9229 | 0.9832 | 1 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Keneston/distilbert-base-uncased-finetuned-emotion
|
Keneston
| 2022-08-01T12:54:39Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-01T12:26:03Z
|
---
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
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9275
- name: F1
type: f1
value: 0.9273502014174783
---
<!-- 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: 0.2214
- Accuracy: 0.9275
- F1: 0.9274
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8568 | 1.0 | 250 | 0.3328 | 0.9 | 0.8947 |
| 0.2576 | 2.0 | 500 | 0.2214 | 0.9275 | 0.9274 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sumba/covid-twitter-bert-v2-no_description-stance-loss-hyp
|
sumba
| 2022-08-01T12:16:28Z
| 6
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-31T12:21:31Z
|
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: covid-twitter-bert-v2-no_description-stance-loss-hyp
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. -->
# covid-twitter-bert-v2-no_description-stance-loss-hyp
This model is a fine-tuned version of [digitalepidemiologylab/covid-twitter-bert-v2](https://huggingface.co/digitalepidemiologylab/covid-twitter-bert-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6202
- Accuracy: 0.0829
## 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: 1.4275469935864394e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8211 | 1.0 | 632 | 0.6258 | 0.1153 |
| 0.5742 | 2.0 | 1264 | 0.6202 | 0.0829 |
| 0.4456 | 3.0 | 1896 | 0.6340 | 0.0627 |
| 0.2163 | 4.0 | 2528 | 0.7645 | 0.0470 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu102
- Datasets 2.2.1
- Tokenizers 0.12.1
|
BekirTaha/q-Taxi-v3
|
BekirTaha
| 2022-08-01T12:16:25Z
| 0
| 0
| null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-08-01T12:16:19Z
|
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.50 +/- 2.73
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Beyko7/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
turhancan97/testpyramidsrnd
|
turhancan97
| 2022-08-01T12:08:01Z
| 6
| 0
|
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2022-08-01T12:07:56Z
|
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: turhancan97/testpyramidsrnd
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
aminjalali/distilbert-base-uncased-finetuned-emotion
|
aminjalali
| 2022-08-01T11:56:34Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-31T19:26:04Z
|
---
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
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.926
- name: F1
type: f1
value: 0.9258000202272497
---
<!-- 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: 0.2123
- Accuracy: 0.926
- F1: 0.9258
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8198 | 1.0 | 250 | 0.3147 | 0.904 | 0.9003 |
| 0.2438 | 2.0 | 500 | 0.2123 | 0.926 | 0.9258 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Qilex/VirtualPetDiffusion
|
Qilex
| 2022-08-01T10:56:07Z
| 1
| 0
|
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:imagefolder",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-07-31T16:02:28Z
|
---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: imagefolder
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# neoGen3
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `imagefolder` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: no
### Training results
📈 [TensorBoard logs](https://huggingface.co/Qilex/neoGen3/tensorboard?#scalars)
|
reachrkr/Reinforce-Pong-PLE-v0
|
reachrkr
| 2022-08-01T10:55:41Z
| 0
| 0
| null |
[
"Pong-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-08-01T06:01:48Z
|
---
tags:
- Pong-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pong-PLE-v0
results:
- metrics:
- type: mean_reward
value: -16.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pong-PLE-v0
type: Pong-PLE-v0
---
# **Reinforce** Agent playing **Pong-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pong-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
lewiswu1209/Winnie
|
lewiswu1209
| 2022-08-01T10:52:48Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-24T03:09:47Z
|
---
license: mit
---
# Winnie
Winnie是基于[cambridgeltl/simctg_lccc_dialogue](https://huggingface.co/cambridgeltl/simctg_lccc_dialogue)训练的
我修改了vocab.txt, 新增了`[NAME][NICK][GENDER][YEAROFBIRTH][MONTHOFBIRTH][DAYOFBIRTH][ZODIAC][AGE]`几个special_token，然后搞了些类似
```
你是谁？
我是[NAME]。
你叫什么？
我叫[NAME]。
你多大啦？
我[AGE]岁了。
```
的语料。
第一次训练的时候起名叫Vicky，然后把Vicky的脑子训瓦特了，只能摸索新的办法了。
后来利用了[50W闲聊语料](https://github.com/yangjianxin1/GPT2-chitchat#%E9%97%B2%E8%81%8A%E8%AF%AD%E6%96%99%E5%88%86%E4%BA%AB)搭配新增的语料按照大约19:1的比例进行训练，感觉效果还可以。
|
dminiotas05/camembert-base-finetuned-ft750_reg2
|
dminiotas05
| 2022-08-01T10:10:20Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"camembert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-28T11:03:55Z
|
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: camembert-base-finetuned-ft750_reg2
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. -->
# camembert-base-finetuned-ft750_reg2
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6449
- Mse: 0.6449
- Mae: 0.6171
- R2: 0.3929
- Accuracy: 0.504
## 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 | Mse | Mae | R2 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:--------:|
| 0.6283 | 1.0 | 750 | 0.6074 | 0.6074 | 0.6086 | 0.4282 | 0.4887 |
| 0.5007 | 2.0 | 1500 | 0.6449 | 0.6449 | 0.6171 | 0.3929 | 0.504 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BekirTaha/ppo-LunarLander-v2
|
BekirTaha
| 2022-08-01T07:53:28Z
| 4
| 0
|
stable-baselines3
|
[
"stable-baselines3",
"deep-reinforcement-learning",
"reinforcement-learning",
"region:us"
] |
reinforcement-learning
| 2022-08-01T06:40:27Z
|
---
tags:
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
---
# "Beyko7/ppo-LunarLander-v2"
This is a pre-trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library.
### Usage (with Stable-baselines3)
Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:
```
pip install stable-baselines3
pip install huggingface_sb3
```
Then, you can use the model like this:
```python
import gym
from huggingface_sb3 import load_from_hub
from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy
# Retrieve the model from the hub
## repo_id = id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name})
## filename = name of the model zip file from the repository
checkpoint = load_from_hub(repo_id="Beyko7/ppo-LunarLander-v2", filename="LunarLander-v2.zip")
model = PPO.load(checkpoint)
# Evaluate the agent
eval_env = gym.make('LunarLander-v2')
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
# Watch the agent play
obs = env.reset()
for i in range(1000):
action, _state = model.predict(obs)
obs, reward, done, info = env.step(action)
env.render()
if done:
obs = env.reset()
env.close()
```
### Evaluation Results
Mean_reward: 248.30 +/- 23.32882124373712
---
|
huggingtweets/kantegory
|
huggingtweets
| 2022-08-01T07:26:39Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-08-01T07:26:04Z
|
---
language: en
thumbnail: http://www.huggingtweets.com/kantegory/1659338795219/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/1122432883036172288/mYZ4acNy_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">David Dobryakov</div>
<div style="text-align: center; font-size: 14px;">@kantegory</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 David Dobryakov.
| Data | David Dobryakov |
| --- | --- |
| Tweets downloaded | 3017 |
| Retweets | 90 |
| Short tweets | 256 |
| Tweets kept | 2671 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1g9yc7mp/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 @kantegory's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2aeg6rk1) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2aeg6rk1/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/kantegory')
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)
|
huggingtweets/ravikiranprao
|
huggingtweets
| 2022-08-01T01:17:35Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-08-01T01:16:52Z
|
---
language: en
thumbnail: http://www.huggingtweets.com/ravikiranprao/1659316650453/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/1071329495565529088/yyYoLPjy_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">Ravikiran P Rao</div>
<div style="text-align: center; font-size: 14px;">@ravikiranprao</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 Ravikiran P Rao.
| Data | Ravikiran P Rao |
| --- | --- |
| Tweets downloaded | 208 |
| Retweets | 66 |
| Short tweets | 16 |
| Tweets kept | 126 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3fw3xel4/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 @ravikiranprao's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1g3m6mb3) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1g3m6mb3/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/ravikiranprao')
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)
|
elopezlopez/distilbert-base-uncased_fold_5_ternary
|
elopezlopez
| 2022-08-01T00:27:39Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-01T00:10:21Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilbert-base-uncased_fold_5_ternary
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_5_ternary
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8096
- F1: 0.7352
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 291 | 0.6322 | 0.6742 |
| 0.5533 | 2.0 | 582 | 0.5861 | 0.7285 |
| 0.5533 | 3.0 | 873 | 0.6893 | 0.7117 |
| 0.2576 | 4.0 | 1164 | 1.0393 | 0.7124 |
| 0.2576 | 5.0 | 1455 | 1.1506 | 0.6988 |
| 0.1097 | 6.0 | 1746 | 1.3005 | 0.7166 |
| 0.0487 | 7.0 | 2037 | 1.5242 | 0.7124 |
| 0.0487 | 8.0 | 2328 | 1.5705 | 0.7010 |
| 0.0253 | 9.0 | 2619 | 1.5180 | 0.7194 |
| 0.0253 | 10.0 | 2910 | 1.6251 | 0.7062 |
| 0.022 | 11.0 | 3201 | 1.6299 | 0.7169 |
| 0.022 | 12.0 | 3492 | 1.7322 | 0.7091 |
| 0.0065 | 13.0 | 3783 | 1.8441 | 0.7044 |
| 0.0093 | 14.0 | 4074 | 1.9063 | 0.7097 |
| 0.0093 | 15.0 | 4365 | 1.8096 | 0.7352 |
| 0.0037 | 16.0 | 4656 | 1.8589 | 0.7321 |
| 0.0037 | 17.0 | 4947 | 1.9687 | 0.7211 |
| 0.0036 | 18.0 | 5238 | 1.9244 | 0.7285 |
| 0.0045 | 19.0 | 5529 | 1.9835 | 0.7299 |
| 0.0045 | 20.0 | 5820 | 2.0766 | 0.7139 |
| 0.0024 | 21.0 | 6111 | 2.1118 | 0.7144 |
| 0.0024 | 22.0 | 6402 | 2.0544 | 0.7197 |
| 0.0006 | 23.0 | 6693 | 2.0914 | 0.7217 |
| 0.0006 | 24.0 | 6984 | 2.1028 | 0.7195 |
| 0.0006 | 25.0 | 7275 | 2.1174 | 0.7224 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
RedPandaAINLP/opus-mt-en-ro-finetuned-en-to-ro
|
RedPandaAINLP
| 2022-08-01T00:11:22Z
| 15
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-31T22:39:13Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wmt16
metrics:
- bleu
model-index:
- name: opus-mt-en-ro-finetuned-en-to-ro
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wmt16
type: wmt16
config: ro-en
split: train
args: ro-en
metrics:
- name: Bleu
type: bleu
value: 28.1505
---
<!-- 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. -->
# opus-mt-en-ro-finetuned-en-to-ro
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on the wmt16 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2886
- Bleu: 28.1505
- Gen Len: 34.1036
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 0.7437 | 1.0 | 38145 | 1.2886 | 28.1505 | 34.1036 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
elopezlopez/distilbert-base-uncased_fold_4_ternary
|
elopezlopez
| 2022-08-01T00:10:08Z
| 7
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-31T23:52:47Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilbert-base-uncased_fold_4_ternary
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_4_ternary
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2981
- F1: 0.7565
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 289 | 0.5588 | 0.6984 |
| 0.5547 | 2.0 | 578 | 0.5283 | 0.7336 |
| 0.5547 | 3.0 | 867 | 0.7038 | 0.7202 |
| 0.2479 | 4.0 | 1156 | 0.8949 | 0.7284 |
| 0.2479 | 5.0 | 1445 | 0.9959 | 0.7286 |
| 0.1181 | 6.0 | 1734 | 1.0663 | 0.7311 |
| 0.0508 | 7.0 | 2023 | 1.2377 | 0.7054 |
| 0.0508 | 8.0 | 2312 | 1.2981 | 0.7565 |
| 0.0185 | 9.0 | 2601 | 1.3532 | 0.7407 |
| 0.0185 | 10.0 | 2890 | 1.5365 | 0.7333 |
| 0.0103 | 11.0 | 3179 | 1.5184 | 0.7423 |
| 0.0103 | 12.0 | 3468 | 1.6009 | 0.7420 |
| 0.0123 | 13.0 | 3757 | 1.6395 | 0.7402 |
| 0.008 | 14.0 | 4046 | 1.6838 | 0.7429 |
| 0.008 | 15.0 | 4335 | 1.6176 | 0.7490 |
| 0.0012 | 16.0 | 4624 | 1.7873 | 0.7345 |
| 0.0012 | 17.0 | 4913 | 1.6761 | 0.7412 |
| 0.0044 | 18.0 | 5202 | 1.7356 | 0.7417 |
| 0.0044 | 19.0 | 5491 | 1.7686 | 0.7502 |
| 0.0045 | 20.0 | 5780 | 1.7668 | 0.7406 |
| 0.0017 | 21.0 | 6069 | 1.8411 | 0.7381 |
| 0.0017 | 22.0 | 6358 | 1.8147 | 0.7469 |
| 0.0012 | 23.0 | 6647 | 1.8028 | 0.7489 |
| 0.0012 | 24.0 | 6936 | 1.8147 | 0.7453 |
| 0.0026 | 25.0 | 7225 | 1.8257 | 0.7475 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
keithanpai/tiny-random-vit-finetuned-eurosat
|
keithanpai
| 2022-08-01T00:08:25Z
| 73
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-08-01T00:06:15Z
|
---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: tiny-random-vit-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6646706586826348
---
<!-- 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. -->
# tiny-random-vit-finetuned-eurosat
This model is a fine-tuned version of [hf-internal-testing/tiny-random-vit](https://huggingface.co/hf-internal-testing/tiny-random-vit) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0488
- Accuracy: 0.6647
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1192 | 0.99 | 70 | 1.0867 | 0.6627 |
| 1.067 | 1.99 | 140 | 1.0563 | 0.6657 |
| 0.9719 | 2.99 | 210 | 1.0488 | 0.6647 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
elopezlopez/distilbert-base-uncased_fold_2_ternary
|
elopezlopez
| 2022-07-31T23:35:04Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-31T23:17:46Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilbert-base-uncased_fold_2_ternary
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_2_ternary
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5810
- F1: 0.7620
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 294 | 0.5886 | 0.7239 |
| 0.557 | 2.0 | 588 | 0.5085 | 0.7524 |
| 0.557 | 3.0 | 882 | 0.6332 | 0.7530 |
| 0.2456 | 4.0 | 1176 | 0.8749 | 0.7161 |
| 0.2456 | 5.0 | 1470 | 1.0601 | 0.7371 |
| 0.1112 | 6.0 | 1764 | 1.1885 | 0.7451 |
| 0.0484 | 7.0 | 2058 | 1.3027 | 0.7240 |
| 0.0484 | 8.0 | 2352 | 1.4647 | 0.7259 |
| 0.0259 | 9.0 | 2646 | 1.4476 | 0.7322 |
| 0.0259 | 10.0 | 2940 | 1.4826 | 0.7388 |
| 0.0164 | 11.0 | 3234 | 1.5869 | 0.7333 |
| 0.0109 | 12.0 | 3528 | 1.5954 | 0.7539 |
| 0.0109 | 13.0 | 3822 | 1.5810 | 0.7620 |
| 0.0082 | 14.0 | 4116 | 1.7165 | 0.7335 |
| 0.0082 | 15.0 | 4410 | 1.8152 | 0.7414 |
| 0.004 | 16.0 | 4704 | 1.7411 | 0.7474 |
| 0.004 | 17.0 | 4998 | 1.8692 | 0.7355 |
| 0.0034 | 18.0 | 5292 | 1.8727 | 0.7303 |
| 0.0009 | 19.0 | 5586 | 1.9813 | 0.7305 |
| 0.0009 | 20.0 | 5880 | 1.9764 | 0.7391 |
| 0.0012 | 21.0 | 6174 | 2.0170 | 0.7291 |
| 0.0012 | 22.0 | 6468 | 2.0240 | 0.7391 |
| 0.0004 | 23.0 | 6762 | 2.0311 | 0.7352 |
| 0.0014 | 24.0 | 7056 | 2.0174 | 0.7334 |
| 0.0014 | 25.0 | 7350 | 2.0282 | 0.7381 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
elopezlopez/distilbert-base-uncased_fold_1_ternary
|
elopezlopez
| 2022-07-31T23:17:34Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-31T21:10:12Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilbert-base-uncased_fold_1_ternary
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_1_ternary
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0582
- F1: 0.7326
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 290 | 0.5524 | 0.6755 |
| 0.5648 | 2.0 | 580 | 0.5654 | 0.7124 |
| 0.5648 | 3.0 | 870 | 0.6547 | 0.6896 |
| 0.2712 | 4.0 | 1160 | 0.8916 | 0.7263 |
| 0.2712 | 5.0 | 1450 | 1.1187 | 0.7120 |
| 0.1147 | 6.0 | 1740 | 1.2778 | 0.7114 |
| 0.0476 | 7.0 | 2030 | 1.4441 | 0.7151 |
| 0.0476 | 8.0 | 2320 | 1.5535 | 0.7133 |
| 0.0187 | 9.0 | 2610 | 1.6439 | 0.7212 |
| 0.0187 | 10.0 | 2900 | 1.7261 | 0.7313 |
| 0.0138 | 11.0 | 3190 | 1.6806 | 0.7292 |
| 0.0138 | 12.0 | 3480 | 1.8425 | 0.7111 |
| 0.009 | 13.0 | 3770 | 1.9207 | 0.7213 |
| 0.0045 | 14.0 | 4060 | 1.8900 | 0.7202 |
| 0.0045 | 15.0 | 4350 | 1.9730 | 0.7216 |
| 0.0042 | 16.0 | 4640 | 2.0775 | 0.7041 |
| 0.0042 | 17.0 | 4930 | 2.0514 | 0.7106 |
| 0.0019 | 18.0 | 5220 | 2.0582 | 0.7326 |
| 0.0039 | 19.0 | 5510 | 2.1010 | 0.7081 |
| 0.0039 | 20.0 | 5800 | 2.0487 | 0.7273 |
| 0.0025 | 21.0 | 6090 | 2.0415 | 0.7254 |
| 0.0025 | 22.0 | 6380 | 2.0753 | 0.7157 |
| 0.0017 | 23.0 | 6670 | 2.0554 | 0.7246 |
| 0.0017 | 24.0 | 6960 | 2.0644 | 0.7290 |
| 0.0001 | 25.0 | 7250 | 2.0711 | 0.7310 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
elopezlopez/xlnet-base-cased_fold_2_binary
|
elopezlopez
| 2022-07-31T23:13:47Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-31T22:50:03Z
|
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlnet-base-cased_fold_2_binary
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. -->
# xlnet-base-cased_fold_2_binary
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4858
- F1: 0.7648
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 290 | 0.4361 | 0.7404 |
| 0.4403 | 2.0 | 580 | 0.5363 | 0.7515 |
| 0.4403 | 3.0 | 870 | 0.4858 | 0.7648 |
| 0.2505 | 4.0 | 1160 | 0.7127 | 0.7612 |
| 0.2505 | 5.0 | 1450 | 0.8930 | 0.7554 |
| 0.1425 | 6.0 | 1740 | 0.9897 | 0.7580 |
| 0.0869 | 7.0 | 2030 | 1.2683 | 0.7615 |
| 0.0869 | 8.0 | 2320 | 1.4988 | 0.7343 |
| 0.0411 | 9.0 | 2610 | 1.5082 | 0.7492 |
| 0.0411 | 10.0 | 2900 | 1.4974 | 0.7450 |
| 0.0306 | 11.0 | 3190 | 1.5723 | 0.7435 |
| 0.0306 | 12.0 | 3480 | 1.8446 | 0.7432 |
| 0.0291 | 13.0 | 3770 | 1.7113 | 0.7639 |
| 0.0207 | 14.0 | 4060 | 1.8073 | 0.7394 |
| 0.0207 | 15.0 | 4350 | 1.7524 | 0.7585 |
| 0.0171 | 16.0 | 4640 | 1.8751 | 0.7374 |
| 0.0171 | 17.0 | 4930 | 1.7849 | 0.7561 |
| 0.0084 | 18.0 | 5220 | 1.8618 | 0.7441 |
| 0.0064 | 19.0 | 5510 | 1.9613 | 0.7437 |
| 0.0064 | 20.0 | 5800 | 1.8898 | 0.7430 |
| 0.006 | 21.0 | 6090 | 1.9889 | 0.7409 |
| 0.006 | 22.0 | 6380 | 1.9949 | 0.7488 |
| 0.0049 | 23.0 | 6670 | 1.9453 | 0.7488 |
| 0.0049 | 24.0 | 6960 | 1.9754 | 0.7472 |
| 0.002 | 25.0 | 7250 | 1.9946 | 0.7504 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
elopezlopez/xlnet-base-cased_fold_1_binary
|
elopezlopez
| 2022-07-31T22:49:49Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-31T22:26:16Z
|
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlnet-base-cased_fold_1_binary
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. -->
# xlnet-base-cased_fold_1_binary
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7607
- F1: 0.7778
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 288 | 0.4111 | 0.7555 |
| 0.4387 | 2.0 | 576 | 0.4075 | 0.7540 |
| 0.4387 | 3.0 | 864 | 0.5344 | 0.7567 |
| 0.2471 | 4.0 | 1152 | 0.7405 | 0.7597 |
| 0.2471 | 5.0 | 1440 | 1.0564 | 0.7508 |
| 0.1419 | 6.0 | 1728 | 1.0703 | 0.7751 |
| 0.0845 | 7.0 | 2016 | 1.0866 | 0.7609 |
| 0.0845 | 8.0 | 2304 | 1.2135 | 0.7751 |
| 0.05 | 9.0 | 2592 | 1.3649 | 0.7516 |
| 0.05 | 10.0 | 2880 | 1.4943 | 0.7590 |
| 0.0267 | 11.0 | 3168 | 1.5174 | 0.7412 |
| 0.0267 | 12.0 | 3456 | 1.4884 | 0.7559 |
| 0.0278 | 13.0 | 3744 | 1.5109 | 0.7405 |
| 0.0201 | 14.0 | 4032 | 1.7251 | 0.7409 |
| 0.0201 | 15.0 | 4320 | 1.5833 | 0.7354 |
| 0.0185 | 16.0 | 4608 | 1.7744 | 0.7598 |
| 0.0185 | 17.0 | 4896 | 1.8283 | 0.7619 |
| 0.0066 | 18.0 | 5184 | 1.7607 | 0.7778 |
| 0.0066 | 19.0 | 5472 | 1.7503 | 0.7719 |
| 0.0078 | 20.0 | 5760 | 1.7807 | 0.7508 |
| 0.006 | 21.0 | 6048 | 1.6887 | 0.7629 |
| 0.006 | 22.0 | 6336 | 1.7041 | 0.7678 |
| 0.0074 | 23.0 | 6624 | 1.7337 | 0.7633 |
| 0.0074 | 24.0 | 6912 | 1.7548 | 0.7645 |
| 0.0035 | 25.0 | 7200 | 1.7685 | 0.7621 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
RayS2022/ppo-LunarLander-v2
|
RayS2022
| 2022-07-31T22:22:47Z
| 8
| 0
|
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-31T22:22:24Z
|
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 158.13 +/- 103.98
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
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
wenkai-li/distilroberta-base-finetuned-wikitextepoch_150
|
wenkai-li
| 2022-07-31T22:09:24Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-31T18:31:03Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-finetuned-wikitextepoch_150
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-wikitextepoch_150
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: 1.8929
## 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: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 2.2428 | 1.0 | 1121 | 2.0500 |
| 2.1209 | 2.0 | 2242 | 1.9996 |
| 2.0665 | 3.0 | 3363 | 1.9501 |
| 2.0179 | 4.0 | 4484 | 1.9311 |
| 1.9759 | 5.0 | 5605 | 1.9255 |
| 1.9089 | 6.0 | 6726 | 1.8805 |
| 1.9143 | 7.0 | 7847 | 1.8715 |
| 1.8744 | 8.0 | 8968 | 1.8671 |
| 1.858 | 9.0 | 10089 | 1.8592 |
| 1.8141 | 10.0 | 11210 | 1.8578 |
| 1.7917 | 11.0 | 12331 | 1.8574 |
| 1.7752 | 12.0 | 13452 | 1.8423 |
| 1.7722 | 13.0 | 14573 | 1.8287 |
| 1.7354 | 14.0 | 15694 | 1.8396 |
| 1.7217 | 15.0 | 16815 | 1.8244 |
| 1.6968 | 16.0 | 17936 | 1.8278 |
| 1.659 | 17.0 | 19057 | 1.8412 |
| 1.6442 | 18.0 | 20178 | 1.8328 |
| 1.6441 | 19.0 | 21299 | 1.8460 |
| 1.6267 | 20.0 | 22420 | 1.8343 |
| 1.612 | 21.0 | 23541 | 1.8249 |
| 1.5963 | 22.0 | 24662 | 1.8253 |
| 1.6101 | 23.0 | 25783 | 1.7843 |
| 1.5747 | 24.0 | 26904 | 1.8047 |
| 1.5559 | 25.0 | 28025 | 1.8618 |
| 1.5484 | 26.0 | 29146 | 1.8660 |
| 1.5411 | 27.0 | 30267 | 1.8318 |
| 1.5247 | 28.0 | 31388 | 1.8216 |
| 1.5278 | 29.0 | 32509 | 1.8075 |
| 1.4954 | 30.0 | 33630 | 1.8073 |
| 1.4863 | 31.0 | 34751 | 1.7958 |
| 1.4821 | 32.0 | 35872 | 1.8080 |
| 1.4357 | 33.0 | 36993 | 1.8373 |
| 1.4602 | 34.0 | 38114 | 1.8199 |
| 1.447 | 35.0 | 39235 | 1.8325 |
| 1.4292 | 36.0 | 40356 | 1.8075 |
| 1.4174 | 37.0 | 41477 | 1.8168 |
| 1.4103 | 38.0 | 42598 | 1.8095 |
| 1.4168 | 39.0 | 43719 | 1.8233 |
| 1.4005 | 40.0 | 44840 | 1.8388 |
| 1.3799 | 41.0 | 45961 | 1.8235 |
| 1.3657 | 42.0 | 47082 | 1.8298 |
| 1.3559 | 43.0 | 48203 | 1.8165 |
| 1.3723 | 44.0 | 49324 | 1.8059 |
| 1.3535 | 45.0 | 50445 | 1.8451 |
| 1.3533 | 46.0 | 51566 | 1.8458 |
| 1.3469 | 47.0 | 52687 | 1.8237 |
| 1.3247 | 48.0 | 53808 | 1.8264 |
| 1.3142 | 49.0 | 54929 | 1.8209 |
| 1.2958 | 50.0 | 56050 | 1.8244 |
| 1.293 | 51.0 | 57171 | 1.8311 |
| 1.2784 | 52.0 | 58292 | 1.8287 |
| 1.2731 | 53.0 | 59413 | 1.8600 |
| 1.2961 | 54.0 | 60534 | 1.8086 |
| 1.2739 | 55.0 | 61655 | 1.8303 |
| 1.2716 | 56.0 | 62776 | 1.8214 |
| 1.2459 | 57.0 | 63897 | 1.8440 |
| 1.2492 | 58.0 | 65018 | 1.8503 |
| 1.2393 | 59.0 | 66139 | 1.8316 |
| 1.2077 | 60.0 | 67260 | 1.8283 |
| 1.2426 | 61.0 | 68381 | 1.8413 |
| 1.2032 | 62.0 | 69502 | 1.8461 |
| 1.2123 | 63.0 | 70623 | 1.8469 |
| 1.2069 | 64.0 | 71744 | 1.8478 |
| 1.198 | 65.0 | 72865 | 1.8479 |
| 1.1972 | 66.0 | 73986 | 1.8516 |
| 1.1885 | 67.0 | 75107 | 1.8341 |
| 1.1784 | 68.0 | 76228 | 1.8322 |
| 1.1866 | 69.0 | 77349 | 1.8559 |
| 1.1648 | 70.0 | 78470 | 1.8758 |
| 1.1595 | 71.0 | 79591 | 1.8684 |
| 1.1661 | 72.0 | 80712 | 1.8553 |
| 1.1478 | 73.0 | 81833 | 1.8658 |
| 1.1488 | 74.0 | 82954 | 1.8452 |
| 1.1538 | 75.0 | 84075 | 1.8505 |
| 1.1267 | 76.0 | 85196 | 1.8430 |
| 1.1339 | 77.0 | 86317 | 1.8333 |
| 1.118 | 78.0 | 87438 | 1.8419 |
| 1.12 | 79.0 | 88559 | 1.8669 |
| 1.1144 | 80.0 | 89680 | 1.8647 |
| 1.104 | 81.0 | 90801 | 1.8643 |
| 1.0864 | 82.0 | 91922 | 1.8528 |
| 1.0863 | 83.0 | 93043 | 1.8456 |
| 1.0912 | 84.0 | 94164 | 1.8509 |
| 1.0873 | 85.0 | 95285 | 1.8690 |
| 1.0862 | 86.0 | 96406 | 1.8577 |
| 1.0879 | 87.0 | 97527 | 1.8612 |
| 1.0783 | 88.0 | 98648 | 1.8410 |
| 1.0618 | 89.0 | 99769 | 1.8517 |
| 1.0552 | 90.0 | 100890 | 1.8459 |
| 1.0516 | 91.0 | 102011 | 1.8723 |
| 1.0424 | 92.0 | 103132 | 1.8832 |
| 1.0478 | 93.0 | 104253 | 1.8922 |
| 1.0523 | 94.0 | 105374 | 1.8753 |
| 1.027 | 95.0 | 106495 | 1.8625 |
| 1.0364 | 96.0 | 107616 | 1.8673 |
| 1.0203 | 97.0 | 108737 | 1.8806 |
| 1.0309 | 98.0 | 109858 | 1.8644 |
| 1.0174 | 99.0 | 110979 | 1.8659 |
| 1.0184 | 100.0 | 112100 | 1.8590 |
| 1.0234 | 101.0 | 113221 | 1.8614 |
| 1.013 | 102.0 | 114342 | 1.8866 |
| 1.0092 | 103.0 | 115463 | 1.8770 |
| 1.0051 | 104.0 | 116584 | 1.8445 |
| 1.0105 | 105.0 | 117705 | 1.8512 |
| 1.0233 | 106.0 | 118826 | 1.8896 |
| 0.9967 | 107.0 | 119947 | 1.8687 |
| 0.9795 | 108.0 | 121068 | 1.8618 |
| 0.9846 | 109.0 | 122189 | 1.8877 |
| 0.9958 | 110.0 | 123310 | 1.8522 |
| 0.9689 | 111.0 | 124431 | 1.8765 |
| 0.9879 | 112.0 | 125552 | 1.8692 |
| 0.99 | 113.0 | 126673 | 1.8689 |
| 0.9798 | 114.0 | 127794 | 1.8898 |
| 0.9676 | 115.0 | 128915 | 1.8782 |
| 0.9759 | 116.0 | 130036 | 1.8840 |
| 0.9576 | 117.0 | 131157 | 1.8662 |
| 0.9637 | 118.0 | 132278 | 1.8984 |
| 0.9645 | 119.0 | 133399 | 1.8872 |
| 0.9793 | 120.0 | 134520 | 1.8705 |
| 0.9643 | 121.0 | 135641 | 1.9036 |
| 0.961 | 122.0 | 136762 | 1.8683 |
| 0.9496 | 123.0 | 137883 | 1.8785 |
| 0.946 | 124.0 | 139004 | 1.8912 |
| 0.9681 | 125.0 | 140125 | 1.8837 |
| 0.9403 | 126.0 | 141246 | 1.8824 |
| 0.9452 | 127.0 | 142367 | 1.8824 |
| 0.9437 | 128.0 | 143488 | 1.8665 |
| 0.945 | 129.0 | 144609 | 1.8655 |
| 0.9453 | 130.0 | 145730 | 1.8695 |
| 0.9238 | 131.0 | 146851 | 1.8697 |
| 0.9176 | 132.0 | 147972 | 1.8618 |
| 0.9405 | 133.0 | 149093 | 1.8679 |
| 0.9184 | 134.0 | 150214 | 1.9025 |
| 0.9298 | 135.0 | 151335 | 1.9045 |
| 0.9215 | 136.0 | 152456 | 1.9014 |
| 0.9249 | 137.0 | 153577 | 1.8505 |
| 0.9246 | 138.0 | 154698 | 1.8542 |
| 0.9205 | 139.0 | 155819 | 1.8731 |
| 0.9368 | 140.0 | 156940 | 1.8673 |
| 0.9251 | 141.0 | 158061 | 1.8835 |
| 0.9224 | 142.0 | 159182 | 1.8727 |
| 0.9326 | 143.0 | 160303 | 1.8380 |
| 0.916 | 144.0 | 161424 | 1.8857 |
| 0.9361 | 145.0 | 162545 | 1.8547 |
| 0.9121 | 146.0 | 163666 | 1.8587 |
| 0.9156 | 147.0 | 164787 | 1.8863 |
| 0.9131 | 148.0 | 165908 | 1.8809 |
| 0.9185 | 149.0 | 167029 | 1.8734 |
| 0.9183 | 150.0 | 168150 | 1.8929 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.5.0
- Datasets 2.4.0
- Tokenizers 0.12.1
|
elopezlopez/distilbert-base-uncased_fold_4_binary
|
elopezlopez
| 2022-07-31T22:04:12Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-31T21:54:02Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilbert-base-uncased_fold_4_binary
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_4_binary
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2977
- F1: 0.8083
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 289 | 0.3701 | 0.7903 |
| 0.4005 | 2.0 | 578 | 0.3669 | 0.7994 |
| 0.4005 | 3.0 | 867 | 0.5038 | 0.7955 |
| 0.1945 | 4.0 | 1156 | 0.6353 | 0.8006 |
| 0.1945 | 5.0 | 1445 | 0.8974 | 0.7826 |
| 0.0909 | 6.0 | 1734 | 0.8533 | 0.7764 |
| 0.0389 | 7.0 | 2023 | 0.9969 | 0.7957 |
| 0.0389 | 8.0 | 2312 | 1.0356 | 0.7952 |
| 0.0231 | 9.0 | 2601 | 1.1538 | 0.7963 |
| 0.0231 | 10.0 | 2890 | 1.2011 | 0.7968 |
| 0.0051 | 11.0 | 3179 | 1.2329 | 0.7935 |
| 0.0051 | 12.0 | 3468 | 1.2829 | 0.8056 |
| 0.0066 | 13.0 | 3757 | 1.2946 | 0.7956 |
| 0.004 | 14.0 | 4046 | 1.2977 | 0.8083 |
| 0.004 | 15.0 | 4335 | 1.3970 | 0.7957 |
| 0.0007 | 16.0 | 4624 | 1.3361 | 0.7917 |
| 0.0007 | 17.0 | 4913 | 1.5782 | 0.7954 |
| 0.0107 | 18.0 | 5202 | 1.4641 | 0.7900 |
| 0.0107 | 19.0 | 5491 | 1.4490 | 0.7957 |
| 0.0058 | 20.0 | 5780 | 1.4607 | 0.7932 |
| 0.0016 | 21.0 | 6069 | 1.5048 | 0.7939 |
| 0.0016 | 22.0 | 6358 | 1.5219 | 0.7945 |
| 0.0027 | 23.0 | 6647 | 1.4783 | 0.7937 |
| 0.0027 | 24.0 | 6936 | 1.4715 | 0.7981 |
| 0.0004 | 25.0 | 7225 | 1.4989 | 0.7900 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
elopezlopez/distilbert-base-uncased_fold_3_binary
|
elopezlopez
| 2022-07-31T21:53:55Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-31T21:43:33Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilbert-base-uncased_fold_3_binary
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_3_binary
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8310
- F1: 0.7584
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 289 | 0.4560 | 0.7522 |
| 0.4008 | 2.0 | 578 | 0.4790 | 0.7567 |
| 0.4008 | 3.0 | 867 | 0.6368 | 0.7557 |
| 0.1967 | 4.0 | 1156 | 0.6854 | 0.7534 |
| 0.1967 | 5.0 | 1445 | 0.9823 | 0.7309 |
| 0.0768 | 6.0 | 1734 | 1.2531 | 0.7511 |
| 0.0202 | 7.0 | 2023 | 1.2906 | 0.7391 |
| 0.0202 | 8.0 | 2312 | 1.4025 | 0.7460 |
| 0.0087 | 9.0 | 2601 | 1.5713 | 0.7507 |
| 0.0087 | 10.0 | 2890 | 1.4212 | 0.7528 |
| 0.0162 | 11.0 | 3179 | 1.5775 | 0.7511 |
| 0.0162 | 12.0 | 3468 | 1.6361 | 0.7377 |
| 0.0048 | 13.0 | 3757 | 1.6972 | 0.7542 |
| 0.0098 | 14.0 | 4046 | 1.6569 | 0.7565 |
| 0.0098 | 15.0 | 4335 | 1.7547 | 0.7325 |
| 0.0042 | 16.0 | 4624 | 1.8108 | 0.7544 |
| 0.0042 | 17.0 | 4913 | 1.7593 | 0.7554 |
| 0.0041 | 18.0 | 5202 | 1.7582 | 0.7551 |
| 0.0041 | 19.0 | 5491 | 1.8200 | 0.7512 |
| 0.0029 | 20.0 | 5780 | 1.8310 | 0.7584 |
| 0.0018 | 21.0 | 6069 | 1.8146 | 0.7568 |
| 0.0018 | 22.0 | 6358 | 1.7870 | 0.7558 |
| 0.0029 | 23.0 | 6647 | 1.8508 | 0.7530 |
| 0.0029 | 24.0 | 6936 | 1.8327 | 0.7543 |
| 0.0001 | 25.0 | 7225 | 1.8546 | 0.7561 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
elopezlopez/distilbert-base-uncased_fold_2_binary
|
elopezlopez
| 2022-07-31T21:43:25Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-31T21:33:10Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilbert-base-uncased_fold_2_binary
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_2_binary
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4724
- F1: 0.7604
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 290 | 0.4280 | 0.7515 |
| 0.4018 | 2.0 | 580 | 0.4724 | 0.7604 |
| 0.4018 | 3.0 | 870 | 0.5336 | 0.7428 |
| 0.1995 | 4.0 | 1160 | 0.8367 | 0.7476 |
| 0.1995 | 5.0 | 1450 | 0.9242 | 0.7412 |
| 0.089 | 6.0 | 1740 | 1.0987 | 0.7410 |
| 0.0318 | 7.0 | 2030 | 1.1853 | 0.7584 |
| 0.0318 | 8.0 | 2320 | 1.2509 | 0.7500 |
| 0.0189 | 9.0 | 2610 | 1.5060 | 0.7258 |
| 0.0189 | 10.0 | 2900 | 1.5607 | 0.7534 |
| 0.0084 | 11.0 | 3190 | 1.5871 | 0.7476 |
| 0.0084 | 12.0 | 3480 | 1.7206 | 0.7338 |
| 0.0047 | 13.0 | 3770 | 1.6776 | 0.7340 |
| 0.0068 | 14.0 | 4060 | 1.7339 | 0.7546 |
| 0.0068 | 15.0 | 4350 | 1.8279 | 0.7504 |
| 0.0025 | 16.0 | 4640 | 1.7791 | 0.7411 |
| 0.0025 | 17.0 | 4930 | 1.7917 | 0.7444 |
| 0.003 | 18.0 | 5220 | 1.7781 | 0.7559 |
| 0.0029 | 19.0 | 5510 | 1.8153 | 0.7559 |
| 0.0029 | 20.0 | 5800 | 1.7757 | 0.7414 |
| 0.0055 | 21.0 | 6090 | 1.8635 | 0.7454 |
| 0.0055 | 22.0 | 6380 | 1.8483 | 0.7460 |
| 0.001 | 23.0 | 6670 | 1.8620 | 0.7492 |
| 0.001 | 24.0 | 6960 | 1.9058 | 0.7508 |
| 0.0006 | 25.0 | 7250 | 1.8640 | 0.7504 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
elopezlopez/distilbert-base-uncased_fold_1_binary
|
elopezlopez
| 2022-07-31T21:33:03Z
| 6
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-31T20:57:24Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilbert-base-uncased_fold_1_binary
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_1_binary
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5992
- F1: 0.7687
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 288 | 0.3960 | 0.7467 |
| 0.3988 | 2.0 | 576 | 0.3947 | 0.7487 |
| 0.3988 | 3.0 | 864 | 0.4511 | 0.7662 |
| 0.1853 | 4.0 | 1152 | 0.7226 | 0.7285 |
| 0.1853 | 5.0 | 1440 | 0.9398 | 0.7334 |
| 0.0827 | 6.0 | 1728 | 1.0547 | 0.7427 |
| 0.0287 | 7.0 | 2016 | 1.1602 | 0.7563 |
| 0.0287 | 8.0 | 2304 | 1.3332 | 0.7171 |
| 0.0219 | 9.0 | 2592 | 1.3429 | 0.7420 |
| 0.0219 | 10.0 | 2880 | 1.2603 | 0.7648 |
| 0.0139 | 11.0 | 3168 | 1.4126 | 0.7569 |
| 0.0139 | 12.0 | 3456 | 1.3195 | 0.7483 |
| 0.0115 | 13.0 | 3744 | 1.4356 | 0.7491 |
| 0.0035 | 14.0 | 4032 | 1.5693 | 0.7636 |
| 0.0035 | 15.0 | 4320 | 1.4071 | 0.7662 |
| 0.0071 | 16.0 | 4608 | 1.4561 | 0.7579 |
| 0.0071 | 17.0 | 4896 | 1.5405 | 0.7634 |
| 0.0041 | 18.0 | 5184 | 1.5862 | 0.7589 |
| 0.0041 | 19.0 | 5472 | 1.6782 | 0.76 |
| 0.0024 | 20.0 | 5760 | 1.5699 | 0.7677 |
| 0.0006 | 21.0 | 6048 | 1.5991 | 0.7467 |
| 0.0006 | 22.0 | 6336 | 1.6205 | 0.7682 |
| 0.0003 | 23.0 | 6624 | 1.6334 | 0.7643 |
| 0.0003 | 24.0 | 6912 | 1.5992 | 0.7687 |
| 0.0011 | 25.0 | 7200 | 1.6053 | 0.7624 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
abdulmatinomotoso/t5_large_headline_generator_testing_3
|
abdulmatinomotoso
| 2022-07-31T21:14:12Z
| 8
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-31T18:03:35Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5_large_headline_generator_testing_3
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. -->
# t5_large_headline_generator_testing_3
This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8407
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9638 | 0.79 | 500 | 0.8474 |
| 0.8478 | 1.57 | 1000 | 0.8356 |
| 0.6981 | 2.36 | 1500 | 0.8407 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
oMateos2020/pegasus-newsroom-cnn_full-adam8bit
|
oMateos2020
| 2022-07-31T21:02:17Z
| 10
| 0
|
transformers
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-29T07:55:23Z
|
---
tags:
- generated_from_trainer
model-index:
- name: pegasus-newsroom-cnn_full-adam8bit
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-newsroom-cnn_full-adam8bit
This model is a fine-tuned version of [google/pegasus-newsroom](https://huggingface.co/google/pegasus-newsroom) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.9826
- eval_rouge1: 38.2456
- eval_rouge2: 17.3966
- eval_rougeL: 26.9273
- eval_rougeLsum: 35.3265
- eval_gen_len: 69.658
- eval_runtime: 13626.7467
- eval_samples_per_second: 0.183
- eval_steps_per_second: 0.012
- epoch: 0.22
- step: 250
## 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: 0.0016
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
DS-20202/DoubleHardDebias
|
DS-20202
| 2022-07-31T20:32:45Z
| 0
| 0
| null |
[
"license:mit",
"region:us"
] | null | 2022-07-31T12:08:09Z
|
---
title: Double Hard Debiasing
emoji: 👁
colorFrom: blue
colorTo: pink
sdk: gradio
sdk_version: 3.1.1
app_file: app.py
pinned: false
license: mit
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
neuralmagic/oBERT-6-upstream-pretrained-dense
|
neuralmagic
| 2022-07-31T19:52:34Z
| 7
| 0
|
transformers
|
[
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T13:56:31Z
|
---
tags:
- bert
- oBERT
- sparsity
- pruning
- compression
language: en
datasets:
- bookcorpus
- wikipedia
---
# oBERT-6-upstream-pretrained-dense
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to 6 layers from `neuralmagic/oBERT-12-upstream-pretrained-dense`, pretrained with knowledge distillation. This model is used as a starting point for downstream finetuning and pruning runs presented in the `Table 3 - 6 Layers`.
The model can also be used for finetuning on any downstream task, as a starting point instead of the two times larger `bert-base-uncased` model.
Finetuned and pruned versions of this model on the SQuADv1 downstream task, as described in the paper:
- 0%: `neuralmagic/oBERT-6-downstream-dense-squadv1`
- 80% unstructured: `neuralmagic/oBERT-6-downstream-pruned-unstructured-80-squadv1`
- 80% block-4: `neuralmagic/oBERT-6-downstream-pruned-block4-80-squadv1`
- 90% unstructured: `neuralmagic/oBERT-6-downstream-pruned-unstructured-90-squadv1`
- 90% block-4: `neuralmagic/oBERT-6-downstream-pruned-block4-90-squadv1`
```
Training objective: masked language modeling (MLM) + knowledge distillation
Paper: https://arxiv.org/abs/2203.07259
Dataset: BookCorpus and English Wikipedia
Sparsity: 0%
Number of layers: 6
```
Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT)
If you find the model useful, please consider citing our work.
## Citation info
```bibtex
@article{kurtic2022optimal,
title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models},
author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan},
journal={arXiv preprint arXiv:2203.07259},
year={2022}
}
```
|
neuralmagic/oBERT-6-downstream-pruned-block4-90-QAT-squadv1
|
neuralmagic
| 2022-07-31T19:52:34Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T19:21:02Z
|
---
tags:
- bert
- oBERT
- sparsity
- pruning
- compression
language: en
datasets: squad
---
# oBERT-6-downstream-pruned-block4-90-QAT-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 6 Layers - Sparsity 90% - 4-block + QAT`.
```
Pruning method: oBERT downstream block-4 + QAT
Paper: https://arxiv.org/abs/2203.07259
Dataset: SQuADv1
Sparsity: 90%
Number of layers: 6
```
The dev-set performance of this model:
```
EM = 76.56
F1 = 84.59
```
Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT)
If you find the model useful, please consider citing our work.
## Citation info
```bibtex
@article{kurtic2022optimal,
title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models},
author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan},
journal={arXiv preprint arXiv:2203.07259},
year={2022}
}
```
|
neuralmagic/oBERT-6-downstream-pruned-block4-80-squadv1
|
neuralmagic
| 2022-07-31T19:52:34Z
| 6
| 0
|
transformers
|
[
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T14:00:18Z
|
---
tags:
- bert
- oBERT
- sparsity
- pruning
- compression
language: en
datasets: squad
---
# oBERT-6-downstream-pruned-block4-80-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 6 Layers - Sparsity 80% - 4-block`.
```
Pruning method: oBERT downstream block-4
Paper: https://arxiv.org/abs/2203.07259
Dataset: SQuADv1
Sparsity: 80%
Number of layers: 6
```
The dev-set performance of this model:
```
EM = 79.55
F1 = 87.00
```
Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT)
If you find the model useful, please consider citing our work.
## Citation info
```bibtex
@article{kurtic2022optimal,
title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models},
author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan},
journal={arXiv preprint arXiv:2203.07259},
year={2022}
}
```
|
neuralmagic/oBERT-6-downstream-pruned-unstructured-80-squadv1
|
neuralmagic
| 2022-07-31T19:52:34Z
| 7
| 0
|
transformers
|
[
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T13:59:52Z
|
---
tags:
- bert
- oBERT
- sparsity
- pruning
- compression
language: en
datasets: squad
---
# oBERT-6-downstream-pruned-unstructured-80-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 6 Layers - Sparsity 80% - unstructured`.
```
Pruning method: oBERT downstream unstructured
Paper: https://arxiv.org/abs/2203.07259
Dataset: SQuADv1
Sparsity: 80%
Number of layers: 6
```
The dev-set performance of this model:
```
EM = 81.15
F1 = 88.20
```
Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT)
If you find the model useful, please consider citing our work.
## Citation info
```bibtex
@article{kurtic2022optimal,
title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models},
author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan},
journal={arXiv preprint arXiv:2203.07259},
year={2022}
}
```
|
neuralmagic/oBERT-3-downstream-dense-QAT-squadv1
|
neuralmagic
| 2022-07-31T19:52:33Z
| 8
| 0
|
transformers
|
[
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T19:21:16Z
|
---
tags:
- bert
- oBERT
- sparsity
- pruning
- compression
language: en
datasets: squad
---
# oBERT-3-downstream-dense-QAT-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 3 Layers - 0% Sparsity - QAT`, and it represents an upper bound for performance of the corresponding pruned and quantized models:
- 80% unstructured QAT: `neuralmagic/oBERT-3-downstream-pruned-unstructured-80-QAT-squadv1`
- 80% block-4 QAT: `neuralmagic/oBERT-3-downstream-pruned-block4-80-QAT-squadv1`
- 90% unstructured QAT: `neuralmagic/oBERT-3-downstream-pruned-unstructured-90-QAT-squadv1`
- 90% block-4 QAT: `neuralmagic/oBERT-3-downstream-pruned-block4-90-QAT-squadv1`
SQuADv1 dev-set:
```
EM = 76.06
F1 = 84.25
```
Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT)
If you find the model useful, please consider citing our work.
## Citation info
```bibtex
@article{kurtic2022optimal,
title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models},
author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan},
journal={arXiv preprint arXiv:2203.07259},
year={2022}
}
```
|
neuralmagic/oBERT-3-downstream-pruned-unstructured-80-squadv1
|
neuralmagic
| 2022-07-31T19:52:33Z
| 6
| 0
|
transformers
|
[
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T14:01:00Z
|
---
tags:
- bert
- oBERT
- sparsity
- pruning
- compression
language: en
datasets: squad
---
# oBERT-3-downstream-pruned-unstructured-80-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 3 Layers - Sparsity 80% - unstructured`.
```
Pruning method: oBERT downstream unstructured
Paper: https://arxiv.org/abs/2203.07259
Dataset: SQuADv1
Sparsity: 80%
Number of layers: 3
```
The dev-set performance of this model:
```
EM = 75.62
F1 = 84.08
```
Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT)
If you find the model useful, please consider citing our work.
## Citation info
```bibtex
@article{kurtic2022optimal,
title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models},
author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan},
journal={arXiv preprint arXiv:2203.07259},
year={2022}
}
```
|
neuralmagic/oBERT-3-downstream-dense-squadv1
|
neuralmagic
| 2022-07-31T19:52:33Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T14:00:43Z
|
---
tags:
- bert
- oBERT
- sparsity
- pruning
- compression
language: en
datasets: squad
---
# oBERT-3-downstream-dense-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 3 Layers - 0% Sparsity`, and it represents an upper bound for performance of the corresponding pruned models:
- 80% unstructured: `neuralmagic/oBERT-3-downstream-pruned-unstructured-80-squadv1`
- 80% block-4: `neuralmagic/oBERT-3-downstream-pruned-block4-80-squadv1`
- 90% unstructured: `neuralmagic/oBERT-3-downstream-pruned-unstructured-90-squadv1`
- 90% block-4: `neuralmagic/oBERT-3-downstream-pruned-block4-90-squadv1`
SQuADv1 dev-set:
```
EM = 76.62
F1 = 84.65
```
Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT)
If you find the model useful, please consider citing our work.
## Citation info
```bibtex
@article{kurtic2022optimal,
title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models},
author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan},
journal={arXiv preprint arXiv:2203.07259},
year={2022}
}
```
|
neuralmagic/oBERT-6-downstream-dense-squadv1
|
neuralmagic
| 2022-07-31T19:52:33Z
| 8
| 0
|
transformers
|
[
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T13:59:35Z
|
---
tags:
- bert
- oBERT
- sparsity
- pruning
- compression
language: en
datasets: squad
---
# oBERT-6-downstream-dense-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 6 Layers - 0% Sparsity`, and it represents an upper bound for performance of the corresponding pruned models:
- 80% unstructured: `neuralmagic/oBERT-6-downstream-pruned-unstructured-80-squadv1`
- 80% block-4: `neuralmagic/oBERT-6-downstream-pruned-block4-80-squadv1`
- 90% unstructured: `neuralmagic/oBERT-6-downstream-pruned-unstructured-90-squadv1`
- 90% block-4: `neuralmagic/oBERT-6-downstream-pruned-block4-90-squadv1`
SQuADv1 dev-set:
```
EM = 81.17
F1 = 88.32
```
Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT)
If you find the model useful, please consider citing our work.
## Citation info
```bibtex
@article{kurtic2022optimal,
title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models},
author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan},
journal={arXiv preprint arXiv:2203.07259},
year={2022}
}
```
|
Subsets and Splits
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The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.