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} | 0 | 2022-09-27T13:49:20Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-small-few-shot-k-64-finetuned-squad-seed-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-few-shot-k-64-finetuned-squad-seed-4
This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-small-few-shot-k-128-finetuned-squad-seed-0
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-small-few-shot-k-128-finetuned-squad-seed-0
This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-small-few-shot-k-128-finetuned-squad-seed-2
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-small-few-shot-k-128-finetuned-squad-seed-2
This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-base-uncased-emotions-augmented
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-emotions-augmented
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9815
- Accuracy: 0.7539
- F1: 0.7506
## 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8475 | 1.0 | 819 | 0.6336 | 0.7655 | 0.7651 |
| 0.5594 | 2.0 | 1638 | 0.6109 | 0.7695 | 0.7680 |
| 0.4596 | 3.0 | 2457 | 0.6528 | 0.7601 | 0.7556 |
| 0.3663 | 4.0 | 3276 | 0.6992 | 0.7631 | 0.7612 |
| 0.2809 | 5.0 | 4095 | 0.7773 | 0.7571 | 0.7542 |
| 0.2142 | 6.0 | 4914 | 0.8879 | 0.7541 | 0.7504 |
| 0.1671 | 7.0 | 5733 | 0.9476 | 0.7552 | 0.7517 |
| 0.1416 | 8.0 | 6552 | 0.9815 | 0.7539 | 0.7506 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
DeadBeast/mbert-base-cased-finetuned-bengali-fakenews | [
"pytorch",
"bert",
"text-classification",
"bengali",
"dataset:BanFakeNews",
"transformers",
"license:apache-2.0"
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} | 37 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 14.70 +/- 12.44
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-small-few-shot-k-128-finetuned-squad-seed-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-few-shot-k-128-finetuned-squad-seed-4
This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
DecafNosebleed/DialoGPT-small-ScaraBot | [
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"gpt2",
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"conversational"
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} | 15 | null | ---
language:
- ca
license: apache-2.0
tags:
- "catalan"
- "masked-lm"
- "RoBERTa-large-ca-v2"
- "CaText"
- "Catalan Textual Corpus"
widget:
- text: "El Català és una llengua molt <mask>."
- text: "Salvador Dalí va viure a <mask>."
- text: "La Costa Brava té les millors <mask> d'Espanya."
- text: "El cacaolat és un batut de <mask>."
- text: "<mask> és la capital de la Garrotxa."
- text: "Vaig al <mask> a buscar bolets."
- text: "Antoni Gaudí vas ser un <mask> molt important per la ciutat."
- text: "Catalunya és una referència en <mask> a nivell europeu."
---
# Catalan BERTa (roberta-large-ca-v2) large model
## Table of Contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bias)
- [Training](#training)
- [Training data](#training-data)
- [Training procedure](#training-procedure)
- [Evaluation](#evaluation)
- [CLUB benchmark](#club-benchmark)
- [Evaluation results](#evaluation-results)
- [Additional information](#additional-information)
- [Author](#author)
- [Contact information](#contact-information)
- [Copyright](#copyright)
- [Licensing information](#licensing-information)
- [Funding](#funding)
- [Citing information](#citing-information)
- [Disclaimer](#disclaimer)
</details>
## Model description
The **roberta-large-ca-v2** is a transformer-based masked language model for the Catalan language.
It is based on the [RoBERTA](https://github.com/pytorch/fairseq/tree/master/examples/roberta) large model
and has been trained on a medium-size corpus collected from publicly available corpora and crawlers.
## Intended uses and limitations
**roberta-large-ca-v2** model is ready-to-use only for masked language modeling to perform the Fill Mask task (try the inference API or read the next section).
However, it is intended to be fine-tuned on non-generative downstream tasks such as Question Answering, Text Classification, or Named Entity Recognition.
## How to use
Here is how to use this model:
```python
from transformers import AutoModelForMaskedLM
from transformers import AutoTokenizer, FillMaskPipeline
from pprint import pprint
tokenizer_hf = AutoTokenizer.from_pretrained('projecte-aina/roberta-large-ca-v2')
model = AutoModelForMaskedLM.from_pretrained('projecte-aina/roberta-large-ca-v2')
model.eval()
pipeline = FillMaskPipeline(model, tokenizer_hf)
text = f"Em dic <mask>."
res_hf = pipeline(text)
pprint([r['token_str'] for r in res_hf])
```
## Limitations and bias
At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.
## Training
### Training data
The training corpus consists of several corpora gathered from web crawling and public corpora.
| Corpus | Size in GB |
|-------------------------|------------|
| Catalan Crawling | 13.00 |
| Wikipedia | 1.10 |
| DOGC | 0.78 |
| Catalan Open Subtitles | 0.02 |
| Catalan Oscar | 4.00 |
| CaWaC | 3.60 |
| Cat. General Crawling | 2.50 |
| Cat. Goverment Crawling | 0.24 |
| ACN | 0.42 |
| Padicat | 0.63 |
| RacoCatalá | 8.10 |
| Nació Digital | 0.42 |
| Vilaweb | 0.06 |
| Tweets | 0.02 |
### Training procedure
The training corpus has been tokenized using a byte version of [Byte-Pair Encoding (BPE)](https://github.com/openai/gpt-2)
used in the original [RoBERTA](https://github.com/pytorch/fairseq/tree/master/examples/roberta) model with a vocabulary size of 50,262 tokens.
The RoBERTa-large pretraining consists of a masked language model training that follows the approach employed for the RoBERTa large model
with the same hyperparameters as in the original work.
The training lasted a total of 96 hours with 32 NVIDIA V100 GPUs of 16GB DDRAM.
## Evaluation
### CLUB benchmark
The BERTa-large model has been fine-tuned on the downstream tasks of the Catalan Language Understanding Evaluation benchmark (CLUB),
that has been created along with the model.
It contains the following tasks and their related datasets:
1. Named Entity Recognition (NER)
**[NER (AnCora)](https://zenodo.org/record/4762031#.YKaFjqGxWUk)**: extracted named entities from the original [Ancora](https://doi.org/10.5281/zenodo.4762030) version,
filtering out some unconventional ones, like book titles, and transcribed them into a standard CONLL-IOB format
2. Part-of-Speech Tagging (POS)
**[POS (AnCora)](https://zenodo.org/record/4762031#.YKaFjqGxWUk)**: from the [Universal Dependencies treebank](https://github.com/UniversalDependencies/UD_Catalan-AnCora) of the well-known Ancora corpus.
3. Text Classification (TC)
**[TeCla](https://huggingface.co/datasets/projecte-aina/tecla)**: consisting of 137k news pieces from the Catalan News Agency ([ACN](https://www.acn.cat/)) corpus, with 30 labels.
4. Textual Entailment (TE)
**[TE-ca](https://huggingface.co/datasets/projecte-aina/teca)**: consisting of 21,163 pairs of premises and hypotheses, annotated according to the inference relation they have (implication, contradiction, or neutral), extracted from the [Catalan Textual Corpus](https://huggingface.co/datasets/projecte-aina/catalan_textual_corpus).
5. Semantic Textual Similarity (STS)
**[STS-ca](https://huggingface.co/datasets/projecte-aina/sts-ca)**: consisting of more than 3000 sentence pairs, annotated with the semantic similarity between them, scraped from the [Catalan Textual Corpus](https://huggingface.co/datasets/projecte-aina/catalan_textual_corpus).
6. Question Answering (QA):
**[VilaQuAD](https://huggingface.co/datasets/projecte-aina/vilaquad)**: contains 6,282 pairs of questions and answers, outsourced from 2095 Catalan language articles from VilaWeb newswire text.
**[ViquiQuAD](https://huggingface.co/datasets/projecte-aina/viquiquad)**: consisting of more than 15,000 questions outsourced from Catalan Wikipedia randomly chosen from a set of 596 articles that were originally written in Catalan.
**[CatalanQA](https://huggingface.co/datasets/projecte-aina/catalanqa)**: an aggregation of 2 previous datasets (VilaQuAD and ViquiQuAD), 21,427 pairs of Q/A balanced by type of question, containing one question and one answer per context, although the contexts can repeat multiple times.
**[XQuAD-ca](https://huggingface.co/datasets/projecte-aina/xquad-ca)**: the Catalan translation of XQuAD, a multilingual collection of manual translations of 1,190 question-answer pairs from English Wikipedia used only as a _test set_.
Here are the train/dev/test splits of the datasets:
| Task (Dataset) | Total | Train | Dev | Test |
|:--|:--|:--|:--|:--|
| NER (Ancora) |13,581 | 10,628 | 1,427 | 1,526 |
| POS (Ancora)| 16,678 | 13,123 | 1,709 | 1,846 |
| STS (STS-ca) | 3,073 | 2,073 | 500 | 500 |
| TC (TeCla) | 137,775 | 110,203 | 13,786 | 13,786|
| TE (TE-ca) | 21,163 | 16,930 | 2,116 | 2,117
| QA (VilaQuAD) | 6,282 | 3,882 | 1,200 | 1,200 |
| QA (ViquiQuAD) | 14,239 | 11,255 | 1,492 | 1,429 |
| QA (CatalanQA) | 21,427 | 17,135 | 2,157 | 2,135 |
### Evaluation results
| Task | NER (F1) | POS (F1) | STS-ca (Comb) | TeCla (Acc.) | TEca (Acc.) | VilaQuAD (F1/EM)| ViquiQuAD (F1/EM) | CatalanQA (F1/EM) | XQuAD-ca <sup>1</sup> (F1/EM) |
| ------------|:-------------:| -----:|:------|:------|:-------|:------|:----|:----|:----|
| RoBERTa-large-ca-v2 | **89.82** | **99.02** | **83.41** | **75.46** | **83.61** | **89.34/75.50** | **89.20**/75.77 | **90.72/79.06** | **73.79**/55.34 |
| RoBERTa-base-ca-v2 | 89.29 | 98.96 | 79.07 | 74.26 | 83.14 | 87.74/72.58 | 88.72/**75.91** | 89.50/76.63 | 73.64/**55.42** |
| BERTa | 89.76 | 98.96 | 80.19 | 73.65 | 79.26 | 85.93/70.58 | 87.12/73.11 | 89.17/77.14 | 69.20/51.47 |
| mBERT | 86.87 | 98.83 | 74.26 | 69.90 | 74.63 | 82.78/67.33 | 86.89/73.53 | 86.90/74.19 | 68.79/50.80 |
| XLM-RoBERTa | 86.31 | 98.89 | 61.61 | 70.14 | 33.30 | 86.29/71.83 | 86.88/73.11 | 88.17/75.93 | 72.55/54.16 |
<sup>1</sup> : Trained on CatalanQA, tested on XQuAD-ca.
## Additional information
### Author
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected])
### Contact information
For further information, send an email to [email protected]
### Copyright
Copyright (c) 2022 Text Mining Unit at Barcelona Supercomputing Center
### Licensing information
[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
### Funding
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Citation information
If you use any of these resources (datasets or models) in your work, please cite our latest paper:
```bibtex
@inproceedings{armengol-estape-etal-2021-multilingual,
title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
author = "Armengol-Estap{\'e}, Jordi and
Carrino, Casimiro Pio and
Rodriguez-Penagos, Carlos and
de Gibert Bonet, Ona and
Armentano-Oller, Carme and
Gonzalez-Agirre, Aitor and
Melero, Maite and
Villegas, Marta",
booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-acl.437",
doi = "10.18653/v1/2021.findings-acl.437",
pages = "4933--4946",
}
```
### Disclaimer
<details>
<summary>Click to expand</summary>
The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.
When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
In no event shall the owner and creator of the models (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models. |
DecafNosebleed/scarabot-model | [
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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} | 6 | null | ---
license: mit
widget:
- text: "MQIFVKTLTGKTITLEVEPS<mask>TIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG"
---
## ESM-2
ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective. It is suitable for fine-tuning on a wide range of tasks that take protein sequences as input. For detailed information on the model architecture and training data, please refer to the [accompanying paper](https://www.biorxiv.org/content/10.1101/2022.07.20.500902v2). You may also be interested in some demo notebooks ([PyTorch](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling.ipynb), [TensorFlow](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling-tf.ipynb)) which demonstrate how to fine-tune ESM-2 models on your tasks of interest.
Several ESM-2 checkpoints are available in the Hub with varying sizes. Larger sizes generally have somewhat better accuracy, but require much more memory and time to train:
| Checkpoint name | Num layers | Num parameters |
|------------------------------|----|----------|
| [esm2_t48_15B_UR50D](https://huggingface.co/facebook/esm2_t48_15B_UR50D) | 48 | 15B |
| [esm2_t36_3B_UR50D](https://huggingface.co/facebook/esm2_t36_3B_UR50D) | 36 | 3B |
| [esm2_t33_650M_UR50D](https://huggingface.co/facebook/esm2_t33_650M_UR50D) | 33 | 650M |
| [esm2_t30_150M_UR50D](https://huggingface.co/facebook/esm2_t30_150M_UR50D) | 30 | 150M |
| [esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) | 12 | 35M |
| [esm2_t6_8M_UR50D](https://huggingface.co/facebook/esm2_t6_8M_UR50D) | 6 | 8M | |
Declan/Breitbart_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 9 | null | ---
license: mit
widget:
- text: "MQIFVKTLTGKTITLEVEPS<mask>TIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG"
---
## ESM-2
ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective. It is suitable for fine-tuning on a wide range of tasks that take protein sequences as input. For detailed information on the model architecture and training data, please refer to the [accompanying paper](https://www.biorxiv.org/content/10.1101/2022.07.20.500902v2). You may also be interested in some demo notebooks ([PyTorch](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling.ipynb), [TensorFlow](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling-tf.ipynb)) which demonstrate how to fine-tune ESM-2 models on your tasks of interest.
Several ESM-2 checkpoints are available in the Hub with varying sizes. Larger sizes generally have somewhat better accuracy, but require much more memory and time to train:
| Checkpoint name | Num layers | Num parameters |
|------------------------------|----|----------|
| [esm2_t48_15B_UR50D](https://huggingface.co/facebook/esm2_t48_15B_UR50D) | 48 | 15B |
| [esm2_t36_3B_UR50D](https://huggingface.co/facebook/esm2_t36_3B_UR50D) | 36 | 3B |
| [esm2_t33_650M_UR50D](https://huggingface.co/facebook/esm2_t33_650M_UR50D) | 33 | 650M |
| [esm2_t30_150M_UR50D](https://huggingface.co/facebook/esm2_t30_150M_UR50D) | 30 | 150M |
| [esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) | 12 | 35M |
| [esm2_t6_8M_UR50D](https://huggingface.co/facebook/esm2_t6_8M_UR50D) | 6 | 8M | |
Declan/Breitbart_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 7 | null | ---
license: mit
widget:
- text: "MQIFVKTLTGKTITLEVEPS<mask>TIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG"
---
## ESM-2
ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective. It is suitable for fine-tuning on a wide range of tasks that take protein sequences as input. For detailed information on the model architecture and training data, please refer to the [accompanying paper](https://www.biorxiv.org/content/10.1101/2022.07.20.500902v2). You may also be interested in some demo notebooks ([PyTorch](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling.ipynb), [TensorFlow](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling-tf.ipynb)) which demonstrate how to fine-tune ESM-2 models on your tasks of interest.
Several ESM-2 checkpoints are available in the Hub with varying sizes. Larger sizes generally have somewhat better accuracy, but require much more memory and time to train:
| Checkpoint name | Num layers | Num parameters |
|------------------------------|----|----------|
| [esm2_t48_15B_UR50D](https://huggingface.co/facebook/esm2_t48_15B_UR50D) | 48 | 15B |
| [esm2_t36_3B_UR50D](https://huggingface.co/facebook/esm2_t36_3B_UR50D) | 36 | 3B |
| [esm2_t33_650M_UR50D](https://huggingface.co/facebook/esm2_t33_650M_UR50D) | 33 | 650M |
| [esm2_t30_150M_UR50D](https://huggingface.co/facebook/esm2_t30_150M_UR50D) | 30 | 150M |
| [esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) | 12 | 35M |
| [esm2_t6_8M_UR50D](https://huggingface.co/facebook/esm2_t6_8M_UR50D) | 6 | 8M | |
Declan/Breitbart_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-small-few-shot-k-256-finetuned-squad-seed-0
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-small-few-shot-k-256-finetuned-squad-seed-0
This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
Declan/Breitbart_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-small-few-shot-k-256-finetuned-squad-seed-2
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-small-few-shot-k-256-finetuned-squad-seed-2
This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
Declan/Breitbart_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Linksonder/tutorial-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Linksonder/tutorial-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 5.1648
- Validation Loss: 4.7466
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -998, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 5.1648 | 4.7466 | 0 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.5.0
- Datasets 2.4.0
- Tokenizers 0.12.1
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-small-few-shot-k-256-finetuned-squad-seed-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-few-shot-k-256-finetuned-squad-seed-4
This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
Declan/CNN_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
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} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-small-few-shot-k-512-finetuned-squad-seed-0
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-small-few-shot-k-512-finetuned-squad-seed-0
This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
Declan/CNN_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
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} | 3 | null | ---
license: mit
---
### Felps on Stable Diffusion
This is the `<Felps>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
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} | 0 | 2022-09-27T15:12:27Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-small-few-shot-k-512-finetuned-squad-seed-2
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-small-few-shot-k-512-finetuned-squad-seed-2
This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
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"fill-mask",
"transformers",
"autotrain_compatible"
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-small-few-shot-k-512-finetuned-squad-seed-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-few-shot-k-512-finetuned-squad-seed-4
This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
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"fill-mask",
"transformers",
"autotrain_compatible"
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} | 5 | null | ---
license: bigscience-bloom-rail-1.0
tags:
- generated_from_trainer
model-index:
- name: bloom-560m-finetuned-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. -->
# bloom-560m-finetuned-samsum
This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9178
## 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: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7663 | 0.63 | 200 | 2.6934 |
| 2.3769 | 1.26 | 400 | 2.6274 |
| 2.2776 | 1.89 | 600 | 2.5818 |
| 1.873 | 2.52 | 800 | 2.7177 |
| 1.6715 | 3.15 | 1000 | 2.9178 |
| 1.4515 | 3.78 | 1200 | 2.8924 |
| 1.0522 | 4.42 | 1400 | 3.3753 |
| 1.0237 | 5.05 | 1600 | 3.8098 |
| 0.7416 | 5.68 | 1800 | 3.9139 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
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"autotrain_compatible"
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} | 7 | 2022-09-27T15:33:15Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-small-few-shot-k-1024-finetuned-squad-seed-0
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-small-few-shot-k-1024-finetuned-squad-seed-0
This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
Declan/FoxNews_model_v5 | [
"pytorch",
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"transformers",
"autotrain_compatible"
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} | 7 | 2022-09-27T15:51:40Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-small-few-shot-k-1024-finetuned-squad-seed-2
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-small-few-shot-k-1024-finetuned-squad-seed-2
This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
Declan/HuffPost_model_v1 | [
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"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 3 | 2022-09-27T16:05:29Z | Mikki Nylund Art Dali surrealism nature sky berries fruit |
Declan/HuffPost_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 3 | 2022-09-27T16:10:01Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-small-few-shot-k-1024-finetuned-squad-seed-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-few-shot-k-1024-finetuned-squad-seed-4
This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
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"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 3 | 2022-09-28T13:12:27Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned-berttokenizer-shards_ext_
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-berttokenizer-shards_ext_
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0079
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 2.1965 | 1.0 | 43009 | 2.0675 |
| 1.9985 | 2.0 | 86018 | 2.0185 |
| 1.8484 | 3.0 | 129027 | 2.0079 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Declan/HuffPost_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
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} | 7 | 2022-09-27T16:17:59Z | ---
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: 0.947741935483871
---
<!-- 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: 0.2597
- Accuracy: 0.9477
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2126 | 1.0 | 318 | 3.1503 | 0.7529 |
| 2.395 | 2.0 | 636 | 1.5569 | 0.8581 |
| 1.1586 | 3.0 | 954 | 0.7708 | 0.9155 |
| 0.5637 | 4.0 | 1272 | 0.4629 | 0.9342 |
| 0.3005 | 5.0 | 1590 | 0.3397 | 0.9445 |
| 0.183 | 6.0 | 1908 | 0.2937 | 0.9445 |
| 0.1246 | 7.0 | 2226 | 0.2700 | 0.9477 |
| 0.096 | 8.0 | 2544 | 0.2646 | 0.9477 |
| 0.0819 | 9.0 | 2862 | 0.2626 | 0.9471 |
| 0.0767 | 10.0 | 3180 | 0.2597 | 0.9477 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Declan/NPR_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 7 | 2022-09-27T16:28:18Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-base-few-shot-k-16-finetuned-squad-seed-0
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-base-few-shot-k-16-finetuned-squad-seed-0
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
Declan/Politico_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
"model_type": "bert",
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} | 7 | 2022-09-27T17:07:31Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-base-few-shot-k-32-finetuned-squad-seed-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-few-shot-k-32-finetuned-squad-seed-4
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
Declan/WallStreetJournal_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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"translation_en_to_fr": {
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}
} | 3 | null | ---
license: mit
---
### Plen-Ki-Mun on Stable Diffusion
This is the `<plen-ki-mun>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
DeepBasak/Slack | [] | null | {
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}
} | 0 | null | ---
license: mit
---
# Mona (Genshin Impact) on Stable Diffusion
This is the Mona concept taught to Stable Diffusion via Textual Inversion. You can load this concept into a Stable Diffusion fork such as this [repo](https://github.com/AUTOMATIC1111/stable-diffusion-webui) (Instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui-feature-showcase#textual-inversion)).
You can invoke the concept with the keyword mona_genshin. Note that this should be used in conjunction with the [Waifu-Diffusion](https://huggingface.co/hakurei/waifu-diffusion#model-description) model.
# Example Outputs
Here are several Example outputs
 |
DeepPavlov/bert-base-bg-cs-pl-ru-cased | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"bg",
"cs",
"pl",
"ru",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
"translation_en_to_fr": {
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},
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}
}
} | 1,614 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-finetuned-cassava
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8705607476635514
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-in21k-finetuned-cassava
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3742
- Accuracy: 0.8706
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5628 | 1.0 | 150 | 0.5357 | 0.8308 |
| 0.4398 | 2.0 | 300 | 0.4311 | 0.8598 |
| 0.4022 | 3.0 | 450 | 0.3958 | 0.8668 |
| 0.3855 | 4.0 | 600 | 0.4030 | 0.8598 |
| 0.3659 | 5.0 | 750 | 0.4125 | 0.8617 |
| 0.3393 | 6.0 | 900 | 0.3840 | 0.8673 |
| 0.3022 | 7.0 | 1050 | 0.3775 | 0.8673 |
| 0.2941 | 8.0 | 1200 | 0.3742 | 0.8706 |
| 0.2903 | 9.0 | 1350 | 0.3809 | 0.8696 |
| 0.2584 | 10.0 | 1500 | 0.3756 | 0.8696 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Devmapall/paraphrase-quora | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 3 | null | ---
datasets:
- cardiffnlp/tweet_topic_multi
metrics:
- f1
- accuracy
model-index:
- name: cardiffnlp/roberta-base-tweet-topic-multi-2020
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: cardiffnlp/tweet_topic_multi
type: cardiffnlp/tweet_topic_multi
args: cardiffnlp/tweet_topic_multi
split: test_2021
metrics:
- name: F1
type: f1
value: 0.7252289758534556
- name: F1 (macro)
type: f1_macro
value: 0.5612608131902519
- name: Accuracy
type: accuracy
value: 0.4991066110780226
pipeline_tag: text-classification
widget:
- text: "I'm sure the {@Tampa Bay Lightning@} would’ve rather faced the Flyers but man does their experience versus the Blue Jackets this year and last help them a lot versus this Islanders team. Another meat grinder upcoming for the good guys"
example_title: "Example 1"
- text: "Love to take night time bike rides at the jersey shore. Seaside Heights boardwalk. Beautiful weather. Wishing everyone a safe Labor Day weekend in the US."
example_title: "Example 2"
---
# cardiffnlp/roberta-base-tweet-topic-multi-2020
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the [tweet_topic_multi](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi). This model is fine-tuned on `train_2020` split and validated on `test_2021` split of tweet_topic.
Fine-tuning script can be found [here](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi/blob/main/lm_finetuning.py). It achieves the following results on the test_2021 set:
- F1 (micro): 0.7252289758534556
- F1 (macro): 0.5612608131902519
- Accuracy: 0.4991066110780226
### Usage
```python
import math
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
def sigmoid(x):
return 1 / (1 + math.exp(-x))
tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/roberta-base-tweet-topic-multi-2020")
model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/roberta-base-tweet-topic-multi-2020", problem_type="multi_label_classification")
model.eval()
class_mapping = model.config.id2label
with torch.no_grad():
text = #NewVideo Cray Dollas- Water- Ft. Charlie Rose- (Official Music Video)- {{URL}} via {@YouTube@} #watchandlearn {{USERNAME}}
tokens = tokenizer(text, return_tensors='pt')
output = model(**tokens)
flags = [sigmoid(s) > 0.5 for s in output[0][0].detach().tolist()]
topic = [class_mapping[n] for n, i in enumerate(flags) if i]
print(topic)
```
### Reference
```
@inproceedings{dimosthenis-etal-2022-twitter,
title = "{T}witter {T}opic {C}lassification",
author = "Antypas, Dimosthenis and
Ushio, Asahi and
Camacho-Collados, Jose and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics"
}
```
|
Devrim/prism-default | [
"license:mit"
] | null | {
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}
}
} | 0 | null | ---
datasets:
- cardiffnlp/tweet_topic_multi
metrics:
- f1
- accuracy
model-index:
- name: cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-multi-2020
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: cardiffnlp/tweet_topic_multi
type: cardiffnlp/tweet_topic_multi
args: cardiffnlp/tweet_topic_multi
split: test_2021
metrics:
- name: F1
type: f1
value: 0.7367104440275171
- name: F1 (macro)
type: f1_macro
value: 0.5656244617373364
- name: Accuracy
type: accuracy
value: 0.5134008338296605
pipeline_tag: text-classification
widget:
- text: "I'm sure the {@Tampa Bay Lightning@} would’ve rather faced the Flyers but man does their experience versus the Blue Jackets this year and last help them a lot versus this Islanders team. Another meat grinder upcoming for the good guys"
example_title: "Example 1"
- text: "Love to take night time bike rides at the jersey shore. Seaside Heights boardwalk. Beautiful weather. Wishing everyone a safe Labor Day weekend in the US."
example_title: "Example 2"
---
# cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-multi-2020
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2019-90m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m) on the [tweet_topic_multi](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi). This model is fine-tuned on `train_2020` split and validated on `test_2021` split of tweet_topic.
Fine-tuning script can be found [here](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi/blob/main/lm_finetuning.py). It achieves the following results on the test_2021 set:
- F1 (micro): 0.7367104440275171
- F1 (macro): 0.5656244617373364
- Accuracy: 0.5134008338296605
### Usage
```python
import math
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
def sigmoid(x):
return 1 / (1 + math.exp(-x))
tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-multi-2020")
model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-multi-2020", problem_type="multi_label_classification")
model.eval()
class_mapping = model.config.id2label
with torch.no_grad():
text = #NewVideo Cray Dollas- Water- Ft. Charlie Rose- (Official Music Video)- {{URL}} via {@YouTube@} #watchandlearn {{USERNAME}}
tokens = tokenizer(text, return_tensors='pt')
output = model(**tokens)
flags = [sigmoid(s) > 0.5 for s in output[0][0].detach().tolist()]
topic = [class_mapping[n] for n, i in enumerate(flags) if i]
print(topic)
```
### Reference
```
@inproceedings{dimosthenis-etal-2022-twitter,
title = "{T}witter {T}opic {C}lassification",
author = "Antypas, Dimosthenis and
Ushio, Asahi and
Camacho-Collados, Jose and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics"
}
```
|
Dimedrolza/DialoGPT-small-cyberpunk | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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}
}
} | 9 | null | ---
license: mit
---
### Takuji Kawano on Stable Diffusion
This is the `<takuji-kawano>` concept taught to Stable Diffusion via Textual Inversion.
I made an attempt at recreating the artstyle of Tekken 4's concept art. The results are mixed....
Here is the new concept you will be able to use as a `style`:
|
Doohae/q_encoder | [
"pytorch"
] | null | {
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}
} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.87
- name: F1
type: f1
value: 0.8695652173913044
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2983
- Accuracy: 0.87
- F1: 0.8696
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
DoyyingFace/bert-asian-hate-tweets-asonam-unclean | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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"BertForSequenceClassification"
],
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}
} | 30 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 15.90 +/- 13.19
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
albert-large-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
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},
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}
} | 687 | 2022-09-28T04:43:57Z | ---
language: en
thumbnail: http://www.huggingtweets.com/adarsh_nft-digitalartchick-themooncarl/1664340295585/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/1574947317207437312/A2mx8saC_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/1569378335507025920/88PvblRw_400x400.png')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1552672021959565312/DtMqt8Jp_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">0xadarsh.x & artchick.eth 🔥👠 & The Moon | Carl</div>
<div style="text-align: center; font-size: 14px;">@adarsh_nft-digitalartchick-themooncarl</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 0xadarsh.x & artchick.eth 🔥👠 & The Moon | Carl.
| Data | 0xadarsh.x | artchick.eth 🔥👠 | The Moon | Carl |
| --- | --- | --- | --- |
| Tweets downloaded | 1143 | 3242 | 3249 |
| Retweets | 298 | 502 | 649 |
| Short tweets | 344 | 259 | 506 |
| Tweets kept | 501 | 2481 | 2094 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/vjdvq6wv/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 @adarsh_nft-digitalartchick-themooncarl's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1zec5w5o) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1zec5w5o/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/adarsh_nft-digitalartchick-themooncarl')
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)
|
albert-xlarge-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
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},
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}
} | 341 | 2022-09-28T05:01:33Z | ---
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: 0.918
- name: F1
type: f1
value: 0.9179456491632857
---
<!-- 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: 0.2263
- Accuracy: 0.918
- F1: 0.9179
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8566 | 1.0 | 250 | 0.3283 | 0.903 | 0.9002 |
| 0.2607 | 2.0 | 500 | 0.2263 | 0.918 | 0.9179 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
distilbert-base-cased | [
"pytorch",
"tf",
"onnx",
"distilbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1910.01108",
"transformers",
"license:apache-2.0",
"has_space"
] | null | {
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}
} | 574,859 | 2022-09-28T06:53:24Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: mbart_finetuned_dialect_translation_4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mbart_finetuned_dialect_translation_4
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0133
- Bleu: 99.3104
- Gen Len: 13.927
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 0.0974 | 1.0 | 1250 | 0.0568 | 97.9989 | 13.9344 |
| 0.0393 | 2.0 | 2500 | 0.0253 | 98.7299 | 13.9272 |
| 0.0245 | 3.0 | 3750 | 0.0133 | 99.3104 | 13.927 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
distilbert-base-german-cased | [
"pytorch",
"safetensors",
"distilbert",
"fill-mask",
"de",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
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}
}
} | 43,667 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetune_dysarthria_F02
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. -->
# finetune_dysarthria_F02
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 154.3471
- Wer: 1.0
- Accuracy: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 10
- 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: 772
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---:|:--------:|
| 625.3283 | 3.75 | 1931 | 155.3374 | 1.0 | 0.0 |
| 358.2989 | 7.5 | 3862 | 146.5504 | 1.0 | 0.0 |
| 277.2634 | 11.25 | 5793 | 149.5156 | 1.0 | 0.0 |
| 141.6718 | 15.0 | 7724 | 154.3471 | 1.0 | 0.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
distilgpt2 | [
"pytorch",
"tf",
"jax",
"tflite",
"rust",
"coreml",
"safetensors",
"gpt2",
"text-generation",
"en",
"dataset:openwebtext",
"arxiv:1910.01108",
"arxiv:2201.08542",
"arxiv:2203.12574",
"arxiv:1910.09700",
"arxiv:1503.02531",
"transformers",
"exbert",
"license:apache-2.0",
"model-index",
"co2_eq_emissions",
"has_space"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
}
} | 1,611,668 | 2022-09-28T07:11:51Z | ---
license: mit
---
### s1m-naoto-ohshima on Stable Diffusion
This is the `<s1m-naoto-ohshima>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
AbhinavSaiTheGreat/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 10 | 2022-09-28T21:26:20Z | ---
license: apache-2.0
tags:
- generated_from_trainer
- distilgpt2
- email generation
- email
datasets:
- aeslc
- postbot/multi-emails-100k
widget:
- text: "Good Morning Professor Beans,
Hope you are doing well. I just wanted to reach out and ask if differential calculus will be on the exam"
example_title: "email to prof"
- text: "Hey <NAME>,\n\nThank you for signing up for my weekly newsletter. Before we get started, you'll have to confirm your email address."
example_title: "newsletter"
- text: "Hi <NAME>,\n\nI hope this email finds you well. I wanted to reach out and ask about office hours"
example_title: "office hours"
- text: "Greetings <NAME>,\n\nI hope you had a splendid evening at the Company sausage eating festival. I am reaching out because"
example_title: "festival"
- text: "Good Morning Harold,\n\nI was wondering when the next"
example_title: "event"
- text: "URGENT - I need the TPS reports"
example_title: "URGENT"
- text: "Hi Archibald,\n\nI hope this email finds you extremely well."
example_title: "emails that find you"
- text: "Hello there.\n\nI just wanted to reach out and check in to"
example_title: "checking in"
- text: "Hello <NAME>,\n\nI hope this email finds you well. I wanted to reach out and see if you've enjoyed your time with us"
example_title: "work well"
- text: "Hi <NAME>,\n\nI hope this email finds you well. I wanted to reach out and see if we could catch up"
example_title: "catch up"
- text: "I'm <NAME> and I just moved into the area and wanted to reach out and get some details on where I could get groceries and"
example_title: "grocery"
parameters:
min_length: 4
max_length: 128
length_penalty: 0.8
no_repeat_ngram_size: 2
do_sample: False
num_beams: 8
early_stopping: True
repetition_penalty: 5.5
---
# distilgpt2-emailgen: V2
[](https://colab.research.google.com/gist/pszemraj/d1c2d88b6120cca4ca7df078ea1d1e50/scratchpad.ipynb)
Why write the rest of your email when you can generate it?
```python
from transformers import pipeline
model_tag = "postbot/distilgpt2-emailgen-V2"
generator = pipeline(
'text-generation',
model=model_tag,
)
prompt = """
Hello,
Following up on the bubblegum shipment."""
result = generator(
prompt,
max_length=64,
do_sample=False,
early_stopping=True,
) # generate
print(result[0]['generated_text'])
```
## Model description
This model is a fine-tuned version of `distilgpt2` on the postbot/multi-emails-100k dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9126
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters (run 1/2)
TODO
### Training hyperparameters (run 2/2)
The following hyperparameters were used during training:
- learning_rate: 0.0006
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.9045 | 1.0 | 789 | 2.0006 |
| 1.8115 | 2.0 | 1578 | 1.9557 |
| 1.8501 | 3.0 | 2367 | 1.9110 |
| 1.7376 | 4.0 | 3156 | 1.9126 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.10.0+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
AdapterHub/bert-base-uncased-pf-conll2003 | [
"bert",
"en",
"dataset:conll2003",
"arxiv:2104.08247",
"adapter-transformers",
"token-classification",
"adapterhub:ner/conll2003"
] | token-classification | {
"architectures": null,
"model_type": "bert",
"task_specific_params": {
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},
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},
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},
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}
} | 3 | 2022-09-29T00:31:02Z | ---
license: mit
---
### partis on Stable Diffusion via Dreambooth
#### model by audiophobe
This your the Stable Diffusion model fine-tuned the partis concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **a photo of sks partis**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
Here are the images used for training this concept:
|
AdapterHub/bert-base-uncased-pf-wikihop | [
"bert",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"question-answering",
"adapterhub:qa/wikihop"
] | question-answering | {
"architectures": null,
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
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},
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} | 4 | null | https://colab.research.google.com/drive/1AD96dq3y0s2MSzWKgCpI9-oHMpzsbyR2?authuser=1 |
AdapterHub/roberta-base-pf-imdb | [
"roberta",
"en",
"dataset:imdb",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:sentiment/imdb"
] | text-classification | {
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} | 0 | null | ---
language:
- en
tags:
- text generation
- pytorch
- causal-lm
license: apache-2.0
---
# ReGPT-125M-200G
This model was trained on GPT-Neo-125M with [Mengzi Retrieval LM](https://github.com/Langboat/mengzi-retrieval-lm).
For more details, please refer to this [document](https://github.com/Langboat/mengzi-retrieval-lm/blob/main/README.md).
# How to use
You have to use a forked transformers: https://github.com/Langboat/transformers
```python
from transformers import Re_gptForCausalLM
model = Re_gptForCausalLM.from_pretrained('Langboat/ReGPT-125M-200G')
```
|
AdapterHub/roberta-base-pf-snli | [
"roberta",
"en",
"dataset:snli",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification"
] | text-classification | {
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} | 2 | null | ---
license: apache-2.0
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: kyoto_marian_mod_2_2_0
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. -->
# kyoto_marian_mod_2_2_0
This model is a fine-tuned version of [Hoax0930/kyoto_marian_mod_2_2](https://huggingface.co/Hoax0930/kyoto_marian_mod_2_2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8963
- Bleu: 20.5661
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Advertisement/FischlUWU | [] | null | {
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} | 0 | 2022-09-29T11:55:34Z | ---
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: sanitaer_nli
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. -->
# sanitaer_nli
This model is a fine-tuned version of [svalabs/gbert-large-zeroshot-nli](https://huggingface.co/svalabs/gbert-large-zeroshot-nli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3856
- F1: 0.9219
## 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: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 0.93 | 10 | 0.2410 | 0.9219 |
| No log | 1.93 | 20 | 0.5240 | 0.9149 |
| No log | 2.93 | 30 | 0.4756 | 0.9219 |
| No log | 3.93 | 40 | 0.3856 | 0.9219 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Akash7897/distilbert-base-uncased-finetuned-sst2 | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
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}
} | 31 | null | ---
language: ar
license: mit
widget:
- text: "يا سامح الله عيون المها فهن عون الدهر في حربه"
- text: "عجبت لعاصريها كيف ماتوا وقد صنعوا لنا ماء الحياة"
- text: "له بسطتا مجد فكف مفيدة وأخرى بأطراف القناة شقورها"
- text: "لا يستوي عند الكواعب لابس ثوب الشباب ولا الكبير الأنزع"
- text: "يرى بآرائه في اليوم أمر غد كأنما الغيب بالنجوى يناجيه"
- text: "أنخت ببابك العالي ركابي تقبلني وزودني رضاكا"
- text: "أوفى فأعشاك الصباح بضوئه وجرى فغرقك الفرات الزائد"
--- |
Akashpb13/Galician_xlsr | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"gl",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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}
} | 7 | 2022-09-29T15:14:14Z | ---
license: mit
---
### Road to Ruin on Stable Diffusion via Dreambooth
#### model by nlatina
This your the Stable Diffusion model fine-tuned the Road to Ruin concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **starry night. sks themed level design. tiki ruins, stone statues, night sky and black silhouettes **
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
Here are the images used for training this concept:
|
Akashpb13/Kabyle_xlsr | [
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"kab",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"sw",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
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}
} | 3 | 2022-09-29T15:15:15Z | ---
license: mit
---
### Transparent_90s_console on Stable Diffusion via Dreambooth
#### model by EnzymeZoo
This your the Stable Diffusion model fine-tuned the Transparent_90s_console concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **a photo of sks handheld gaming console**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
Here are the images used for training this concept:
|
Akashpb13/xlsr_hungarian_new | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hu",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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"max_length": null
},
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},
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},
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},
"translation_en_to_fr": {
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},
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}
}
} | 7 | 2022-09-29T15:23:46Z | ---
license: apache-2.0
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: kyoto_marian_mod_5_1
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. -->
# kyoto_marian_mod_5_1
This model is a fine-tuned version of [Hoax0930/kyoto_marian_mod_5_0](https://huggingface.co/Hoax0930/kyoto_marian_mod_5_0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7105
- Bleu: 20.5324
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Akashpb13/xlsr_maltese_wav2vec2 | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"mt",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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"max_length": null
},
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
} | 8 | 2022-09-29T15:32:43Z | ---
license: openrail
---
The model generates cooking directions by the list of ingredients. |
Akira-Yana/distilbert-base-uncased-finetuned-cola | [] | null | {
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} | 0 | 2022-09-29T15:35:33Z | ---
tags:
- generated_from_trainer
- deep-reinforcement-learning
- reinforcement-learning
- decision-transformer
- gym-continous-control
pipeline_tag: reinforcement-learning
datasets:
- decision_transformer_gym_replay
---
<!-- 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. -->
# Decision Transformer model trained on expert trajectories sampled from the Gym Walker2d environment
This is a trained [Decision Transformer](https://arxiv.org/abs/2106.01345) model trained from scratch on expert trajectories sampled from the Gym Walker2d environment based on the modified version of the example [training script](https://github.com/huggingface/blog/blob/main/notebooks/101_train-decision-transformers.ipynb) provided by HuggingFace
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- 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_ratio: 0.1
- num_epochs: 120
### Training results
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Akjder/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
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}
} | 8 | 2022-09-29T15:39:35Z | ---
license: mit
---
### soraumineko on Stable Diffusion via Dreambooth
#### model by karaage0703
This your the Stable Diffusion model fine-tuned the soraumineko concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **a photo of sks soraumineko**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Here are the images used for training this concept:
|
AkshayDev/BERT_Fine_Tuning | [] | null | {
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}
} | 0 | null | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1572592902672470016/kAEvgyZL_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/1487225505573183490/b3iFm538_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">apandah & big poo</div>
<div style="text-align: center; font-size: 14px;">@apandahvevo-apandeez</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 apandah & big poo.
| Data | apandah | big poo |
| --- | --- | --- |
| Tweets downloaded | 3229 | 657 |
| Retweets | 53 | 22 |
| Short tweets | 1470 | 341 |
| Tweets kept | 1706 | 294 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/36gnlq3h/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 @apandahvevo-apandeez's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3gv7a5fr) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3gv7a5fr/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/apandahvevo-apandeez')
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)
|
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} | 0 | 2022-09-29T16:32:15Z | ---
tags:
- generated_from_trainer
datasets:
- Graphcore/wikipedia-bert-128
- Graphcore/wikipedia-bert-512
model-index:
- name: groupbert-base-uncased
results: []
license: apache-2.0
language:
- en
---
<!-- 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. -->
# Graphcore/groupbert-base-uncased
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at [hf.co/hardware/graphcore](https://huggingface.co/hardware/graphcore).
Through HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.
## Model description
GroupBERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is designed by Graphcore to pretrain bidirectional representations from unlabelled texts. GroupBERT uses grouped convolutions and matmuls in the encoder, which allows to parallelize computation and achieve higher parameter efficiency. More details are described in the [GroupBERT paper](https://arxiv.org/pdf/2106.05822.pdf).
It was trained with two objectives in pretraining : Masked language modelling (MLM) and Next sentence prediction(NSP). First, MLM is different from traditional LM which sees the words one after another while BERT allows the model to learn a bidirectional representation. In addition to MLM, NSP is used for jointly pertaining text-pair representations. Similarly to BERT it enables easy and fast fine-tuning for different downstream tasks such as Sequence Classification, Named Entity Recognition, Question Answering, Multiple Choice and MaskedLM.
It reduces the need of many engineering efforts for building task specific architectures through pre-trained representation. And achieves state-of-the-art performance on a large suite of sentence-level and token-level tasks.
## Intended uses & limitations
This model is a pre-trained GroupBERT-Base trained in two phases on the [Graphcore/wikipedia-bert-128](https://huggingface.co/datasets/Graphcore/wikipedia-bert-128) and [Graphcore/wikipedia-bert-512](https://huggingface.co/datasets/Graphcore/wikipedia-bert-512) datasets.
It was trained on a Graphcore IPU-POD16 using [`optimum-graphcore`](https://github.com/huggingface/optimum-graphcore).
Graphcore and Hugging Face are working together to make training of Transformer models on IPUs fast and easy. Learn more about how to take advantage of the power of Graphcore IPUs to train Transformers models at [hf.co/hardware/graphcore](https://huggingface.co/hardware/graphcore).
## Training and evaluation data
Trained on wikipedia datasets:
- [Graphcore/wikipedia-bert-128](https://huggingface.co/datasets/Graphcore/wikipedia-bert-128)
- [Graphcore/wikipedia-bert-512](https://huggingface.co/datasets/Graphcore/wikipedia-bert-512)
## Fine-tuning with these weights
These weights can be used in either `transformers` or [`optimum-graphcore`](https://github.com/huggingface/optimum-graphcore).
For example, to fine-tune the SQuAD v1 with `optimum-graphcore` you can do:
```
python examples/question-answering/run_qa.py \
--model_name_or_path Graphcore/groupbert-base-uncased \
--ipu_config_name Graphcore/groupbert-base-uncased \
--dataset_name squad \
--version_2_with_negative False \
--do_train \
--do_eval \
--pad_on_batch_axis \
--num_train_epochs 1 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 16 \
--gradient_accumulation_steps 10 \
--pod_type pod16 \
--learning_rate 4e-4 \
--max_seq_length 384 \
--doc_stride 128 \
--seed 42 \
--lr_scheduler_type linear \
--lamb \
--loss_scaling 64 \
--weight_decay 0.01 \
--warmup_ratio 0.1 \
--logging_steps 5 \
--save_steps -1 \
--dataloader_num_workers 64 \
--output_dir output/squad_groupbert_base
```
## Training procedure
Trained MLM and NSP pre-training scheme from [Large Batch Optimization for Deep Learning: Training BERT in 76 minutes](https://arxiv.org/abs/1904.00962).
Trained on a Graphcore IPU-POD16 using [`optimum-graphcore`](https://github.com/huggingface/optimum-graphcore).
It was trained with the IPUConfig [Graphcore/bert-base-ipu](https://huggingface.co/Graphcore/bert-base-ipu/).
Command lines:
Phase 1:
```
python examples/language-modeling/run_pretraining.py \
--model_type groupbert \
--tokenizer_name bert-base-uncased \
--ipu_config_name Graphcore/bert-base-ipu \
--dataset_name Graphcore/wikipedia-bert-128 \
--do_train \
--logging_steps 5 \
--max_seq_length 128 \
--max_steps 10500 \
--is_already_preprocessed \
--dataloader_num_workers 64 \
--dataloader_mode async_rebatched \
--lamb \
--per_device_train_batch_size 8 \
--gradient_accumulation_steps 2000 \
--pod_type pod16 \
--learning_rate 0.012 \
--loss_scaling 16384 \
--weight_decay 0.01 \
--warmup_ratio 0.15 \
--groupbert_schedule \
--config_overrides "hidden_dropout_prob=0.0,attention_probs_dropout_prob=0.0" \
--ipu_config_overrides device_iterations="1,matmul_proportion=0.22,layers_per_ipu=[1 3 4 4]" \
--output_dir output-pretrain-groupbert-base-phase1
```
Phase 2:
```
python examples/language-modeling/run_pretraining.py \
--model_type groupbert \
--tokenizer_name bert-base-uncased \
--ipu_config_name Graphcore/bert-base-ipu \
--dataset_name Graphcore/wikipedia-bert-512 \
--model_name_or_path ./output-pretrain-bert-base-phase1 \
--do_train \
--logging_steps 5 \
--max_seq_length 512 \
--max_steps 2038 \
--is_already_preprocessed \
--dataloader_num_workers 128 \
--dataloader_mode async_rebatched \
--lamb \
--per_device_train_batch_size 2 \
--gradient_accumulation_steps 2048 \
--pod_type pod16 \
--learning_rate 0.01 \
--loss_scaling 128 \
--weight_decay 0.01 \
--warmup_ratio 0.15 \
--groupbert_schedule \
--config_overrides "hidden_dropout_prob=0.0,attention_probs_dropout_prob=0.0" \
--ipu_config_overrides "device_iterations=1,embedding_serialization_factor=2,matmul_proportion=0.22,layers_per_ipu=[1 3 4 4]" \
--output_dir output-pretrain-groupbert-base-phase2
```
### Training hyperparameters
The following hyperparameters were used during phase 1 training:
- learning_rate: 0.012
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 200
- total_train_batch_size: 64000
- total_eval_batch_size: 20
- optimizer: LAMB
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.15
- training_steps: 10500
- training precision: Mixed Precision
The following hyperparameters were used during phase 2 training:
- learning_rate: 0.01
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 2048
- total_train_batch_size: 16384
- total_eval_batch_size: 20
- optimizer: LAMB
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.15
- training_steps: 2038
- training precision: Mixed Precision
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.0+cpu
- Datasets 2.6.1
- Tokenizers 0.12.1 |
Alaeddin/convbert-base-turkish-ner-cased | [
"pytorch",
"convbert",
"token-classification",
"transformers",
"autotrain_compatible"
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} | 9 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: refinement-finetuned-mnli-2
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. -->
# refinement-finetuned-mnli-2
This model is a fine-tuned version of [mfreihaut/refinement-finetuned-mnli-1](https://huggingface.co/mfreihaut/refinement-finetuned-mnli-1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0242
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| No log | 1.0 | 303 | 0.3730 |
| 1.1146 | 2.0 | 606 | 0.9860 |
| 1.1146 | 3.0 | 909 | 0.7304 |
| 1.0018 | 4.0 | 1212 | 0.6386 |
| 1.0045 | 5.0 | 1515 | 0.4228 |
| 1.0045 | 6.0 | 1818 | 0.6769 |
| 0.9618 | 7.0 | 2121 | 0.3008 |
| 0.9618 | 8.0 | 2424 | 0.4496 |
| 0.964 | 9.0 | 2727 | 0.1826 |
| 0.9586 | 10.0 | 3030 | 0.0367 |
| 0.9586 | 11.0 | 3333 | 0.1811 |
| 1.0467 | 12.0 | 3636 | 0.1352 |
| 1.0467 | 13.0 | 3939 | 0.0612 |
| 1.0047 | 14.0 | 4242 | 0.1702 |
| 1.0012 | 15.0 | 4545 | 0.0622 |
| 1.0012 | 16.0 | 4848 | 0.7077 |
| 1.0514 | 17.0 | 5151 | 0.2146 |
| 1.0514 | 18.0 | 5454 | 0.5531 |
| 0.9389 | 19.0 | 5757 | 1.2304 |
| 0.9229 | 20.0 | 6060 | 0.6252 |
| 0.9229 | 21.0 | 6363 | 0.6844 |
| 0.9334 | 22.0 | 6666 | 0.5663 |
| 0.9334 | 23.0 | 6969 | 0.9912 |
| 0.9312 | 24.0 | 7272 | 0.3112 |
| 0.8971 | 25.0 | 7575 | 0.4511 |
| 0.8971 | 26.0 | 7878 | 0.3860 |
| 0.9022 | 27.0 | 8181 | 0.5904 |
| 0.9022 | 28.0 | 8484 | 0.4710 |
| 0.7568 | 29.0 | 8787 | 0.8233 |
| 0.6753 | 30.0 | 9090 | 0.6951 |
| 0.6753 | 31.0 | 9393 | 0.6363 |
| 0.5802 | 32.0 | 9696 | 0.8018 |
| 0.5802 | 33.0 | 9999 | 0.9381 |
| 0.5323 | 34.0 | 10302 | 0.9941 |
| 0.5218 | 35.0 | 10605 | 0.9418 |
| 0.5218 | 36.0 | 10908 | 0.9236 |
| 0.4558 | 37.0 | 11211 | 0.4542 |
| 0.4247 | 38.0 | 11514 | 0.9279 |
| 0.4247 | 39.0 | 11817 | 0.9567 |
| 0.43 | 40.0 | 12120 | 0.8077 |
| 0.43 | 41.0 | 12423 | 0.9595 |
| 0.352 | 42.0 | 12726 | 0.9189 |
| 0.3393 | 43.0 | 13029 | 0.8762 |
| 0.3393 | 44.0 | 13332 | 1.0505 |
| 0.316 | 45.0 | 13635 | 0.9273 |
| 0.316 | 46.0 | 13938 | 1.0716 |
| 0.2983 | 47.0 | 14241 | 1.0084 |
| 0.2503 | 48.0 | 14544 | 1.1027 |
| 0.2503 | 49.0 | 14847 | 1.0478 |
| 0.2462 | 50.0 | 15150 | 1.0242 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.10.0
- Datasets 2.5.1
- Tokenizers 0.12.1
|
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"bert",
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} | 15 | 2022-09-29T17:01:29Z | ---
datasets:
- cardiffnlp/tweet_topic_multi
metrics:
- f1
- accuracy
model-index:
- name: cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-multi-all
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: cardiffnlp/tweet_topic_multi
type: cardiffnlp/tweet_topic_multi
args: cardiffnlp/tweet_topic_multi
split: test_2021
metrics:
- name: F1
type: f1
value: 0.7647668393782383
- name: F1 (macro)
type: f1_macro
value: 0.6187022581213811
- name: Accuracy
type: accuracy
value: 0.5485407980941036
pipeline_tag: text-classification
widget:
- text: "I'm sure the {@Tampa Bay Lightning@} would’ve rather faced the Flyers but man does their experience versus the Blue Jackets this year and last help them a lot versus this Islanders team. Another meat grinder upcoming for the good guys"
example_title: "Example 1"
- text: "Love to take night time bike rides at the jersey shore. Seaside Heights boardwalk. Beautiful weather. Wishing everyone a safe Labor Day weekend in the US."
example_title: "Example 2"
---
# cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-multi-all
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2021](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021) on the [tweet_topic_multi](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi). This model is fine-tuned on `train_all` split and validated on `test_2021` split of tweet_topic.
Fine-tuning script can be found [here](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi/blob/main/lm_finetuning.py). It achieves the following results on the test_2021 set:
- F1 (micro): 0.7647668393782383
- F1 (macro): 0.6187022581213811
- Accuracy: 0.5485407980941036
### Usage
```python
import math
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
def sigmoid(x):
return 1 / (1 + math.exp(-x))
tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-multi-all")
model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-multi-all", problem_type="multi_label_classification")
model.eval()
class_mapping = model.config.id2label
with torch.no_grad():
text = #NewVideo Cray Dollas- Water- Ft. Charlie Rose- (Official Music Video)- {{URL}} via {@YouTube@} #watchandlearn {{USERNAME}}
tokens = tokenizer(text, return_tensors='pt')
output = model(**tokens)
flags = [sigmoid(s) > 0.5 for s in output[0][0].detach().tolist()]
topic = [class_mapping[n] for n, i in enumerate(flags) if i]
print(topic)
```
### Reference
```
@inproceedings{dimosthenis-etal-2022-twitter,
title = "{T}witter {T}opic {C}lassification",
author = "Antypas, Dimosthenis and
Ushio, Asahi and
Camacho-Collados, Jose and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics"
}
```
|
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} | 0 | 2022-09-29T17:37:15Z | ---
license: mit
---
### taras on Stable Diffusion via Dreambooth
#### model by kirilpok
Stable Diffusion model fine-tuned with the Taras Shevchenko (Ukrainian poet, writer, artist, public and political figure, as well as folklorist and ethnographer.) concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **photo of sks taras**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Here are the images used for training this concept:
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-finetuned-squad-seq2seq
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-finetuned-squad-seq2seq
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 8
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
AlexN/xls-r-300m-pt | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"robust-speech-event",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
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} | 15 | null | ---
tags:
- adapter-transformers
- bert
datasets:
- glue
---
# Adapter `WillHeld/pfadapter-bert-base-uncased-tada-value-small` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [glue](https://huggingface.co/datasets/glue/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("WillHeld/pfadapter-bert-base-uncased-tada-value-small", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> |
AlexaRyck/KEITH | [] | null | {
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} | 0 | null | ---
tags:
- adapter-transformers
- roberta
datasets:
- glue
---
# Adapter `WillHeld/pfadapter-roberta-base-tada-value-small` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [glue](https://huggingface.co/datasets/glue/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("WillHeld/pfadapter-roberta-base-tada-value-small", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> |
Alexander-Learn/bert-finetuned-ner-accelerate | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
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} | 4 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-16-finetuned-squad-seed-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-few-shot-k-16-finetuned-squad-seed-0
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 8
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-16-finetuned-squad-seed-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-few-shot-k-16-finetuned-squad-seed-2
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 8
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
Alexander-Learn/bert-finetuned-squad | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
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} | 7 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpole_2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
AlgoveraAI/dcgan | [
"pytorch",
"transformers"
] | null | {
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} | 12 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
model-index:
- name: output
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
type: amazon_reviews_multi
config: en
split: train
args: en
metrics:
- name: Accuracy
type: accuracy
value: 0.6182
---
<!-- 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. -->
# output
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9114
- Accuracy: 0.6182
## 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
- lr_scheduler_warmup_steps: 200
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9326 | 0.2 | 5000 | 0.9682 | 0.592 |
| 0.9318 | 0.4 | 10000 | 0.9387 | 0.5974 |
| 0.9053 | 0.6 | 15000 | 0.9169 | 0.6018 |
| 0.8942 | 0.8 | 20000 | 0.9233 | 0.6026 |
| 0.8825 | 1.0 | 25000 | 0.9090 | 0.612 |
| 0.8161 | 1.2 | 30000 | 0.9296 | 0.608 |
| 0.7988 | 1.4 | 35000 | 0.9221 | 0.6126 |
| 0.8326 | 1.6 | 40000 | 0.9112 | 0.6162 |
| 0.8185 | 1.8 | 45000 | 0.9093 | 0.6168 |
| 0.8131 | 2.0 | 50000 | 0.9114 | 0.6182 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
AliReza/distilbert-emotion | [] | null | {
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} | 0 | null | ---
license: mit
---
### srujan on Stable Diffusion via Dreambooth
#### model by Royal-lobster
This your the Stable Diffusion model fine-tuned the srujan concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **a photo of srujan**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Here are the images used for training this concept:
|
Alireza1044/albert-base-v2-qnli | [
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | {
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}
} | 41 | null | ---
license: mit
---
### Altyn-Helmet on Stable Diffusion
This is the `<Altyn>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
Alireza1044/albert-base-v2-rte | [
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
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} | 30 | null | ---
tags:
- bert
- adapter-transformers
datasets:
- glue
---
# Adapter `WillHeld/pfadapter-bert-base-uncased-tada-value-5k` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [glue](https://huggingface.co/datasets/glue/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("WillHeld/pfadapter-bert-base-uncased-tada-value-5k", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> |
Alireza1044/albert-base-v2-sst2 | [
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | {
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} | 52 | null | ---
language: en
license: mit
tags:
- spacy
- token-classification
---
English pipeline optimized for CPU. Components: ner.
|
Allybaby21/Allysai | [] | null | {
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} | 0 | 2022-09-29T22:33:20Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: fix_punct_cased_t5_small
results: []
datasets:
- https://huggingface.co/datasets/nbroad/fix_punctuation
widget:
- text: This is, a sentence. with odd punctuation to show off what, the model. can do
- text: What, should the proper. punctuation. in. this sentence be?
- text: Where are. we? What, is, the meaning, of this?
---
# fix_punct_cased_t5_small
This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the [NPR utterances dataset](https://www.kaggle.com/datasets/shuyangli94/interview-npr-media-dialog-transcripts?select=utterances.csv).
## Dataset
The model was trained on 80k rows from the above dataset consisting of NPR radio transcripts. Commans, periods, and semicolons were removed from the text and then random commas, periods, and semicolons were added. The model was trained to place those three punctuation marks in the correct location. The casing of the texts was not modified during training.
It achieves the following results on the evaluation set:
- Loss: 0.2744
- Rouge1: 93.3712
- Rouge2: 91.0027
- Rougel: 93.3618
- Rougelsum: 93.3479
- Gen Len: 46.0728
## Model description
The purpose of this model is to correct the punctuation in a sentence. For example, the phrase "This is, a sentence. with odd punctuation to show off what, the model. can do" gets changed to "This is a sentence with odd punctuation to show off what the model can do."
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 128
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.2254 | 1.0 | 600 | 0.3501 | 63.2952 | 59.8766 | 63.137 | 63.2022 | 16.2637 |
| 0.7345 | 2.0 | 1200 | 0.2815 | 64.896 | 61.6256 | 64.8677 | 64.8728 | 16.3625 |
| 0.6536 | 3.0 | 1800 | 0.2744 | 64.8724 | 61.6282 | 64.8483 | 64.8502 | 16.3906 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.11.0a0+17540c5
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Alstractor/distilbert-base-uncased-finetuned-cola | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | {
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} | 40 | 2022-09-29T23:08:39Z | ---
tags:
- generated_from_trainer
model-index:
- name: nlge24mixed
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. -->
# nlge24mixed
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 30
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Alvenir/wav2vec2-base-da | [
"pytorch",
"wav2vec2",
"pretraining",
"da",
"transformers",
"speech",
"license:apache-2.0"
] | null | {
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} | 62 | 2022-09-29T23:19:00Z | ---
license: mit
---
### ricky fort on Stable Diffusion via Dreambooth
#### model by machinelearnear
This your the Stable Diffusion model fine-tuned the ricky fort concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **a photo of sks ricky fort**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Here are the images used for training this concept:
|
AmanPriyanshu/DistilBert-Sentiment-Analysis | [
"tf",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 7 | 2022-09-30T00:29:29Z | ---
language: en
thumbnail: http://www.huggingtweets.com/pukicho/1664497866027/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/866045441942487041/xRAnnstd_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">Pukicho</div>
<div style="text-align: center; font-size: 14px;">@pukicho</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 Pukicho.
| Data | Pukicho |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 60 |
| Short tweets | 301 |
| Tweets kept | 2886 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1tuqgf1r/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 @pukicho's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3f17ip6z) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3f17ip6z/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/pukicho')
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)
|
AmazonScience/qanlu | [
"pytorch",
"roberta",
"question-answering",
"en",
"dataset:atis",
"transformers",
"license:cc-by-4.0",
"autotrain_compatible",
"has_space"
] | question-answering | {
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} | 494 | null | ---
license: mit
---
### Hensley art style on Stable Diffusion via Dreambooth
#### model by Pinguin
This your the Stable Diffusion model fine-tuned the Hensley art style concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **a painting in style of sks **
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Here are the images used for training this concept:
|
AmitT/test | [] | null | {
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} | 0 | null | Access to model sd-dreambooth-library/luisangel is restricted and you are not in the authorized list. Visit https://huggingface.co/sd-dreambooth-library/luisangel to ask for access. |
AndyJ/prompt_finetune | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 8 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: roberta-large-finetuned-code-mixed-DS
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-large-finetuned-code-mixed-DS
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1340
- Accuracy: 0.7203
- Precision: 0.6584
- Recall: 0.6548
- F1: 0.6558
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.9729 | 1.0 | 248 | 0.7491 | 0.6922 | 0.6434 | 0.6625 | 0.6358 |
| 0.7474 | 1.99 | 496 | 0.6947 | 0.7183 | 0.6712 | 0.6915 | 0.6760 |
| 0.5938 | 2.99 | 744 | 0.7370 | 0.7123 | 0.6624 | 0.6839 | 0.6642 |
| 0.4264 | 3.98 | 992 | 0.8820 | 0.7123 | 0.6540 | 0.6636 | 0.6492 |
| 0.2806 | 4.98 | 1240 | 1.2022 | 0.7404 | 0.6807 | 0.6694 | 0.6742 |
| 0.2239 | 5.98 | 1488 | 1.3933 | 0.7223 | 0.6593 | 0.6587 | 0.6568 |
| 0.1585 | 6.97 | 1736 | 1.8543 | 0.7304 | 0.6730 | 0.6763 | 0.6737 |
| 0.1302 | 7.97 | 1984 | 2.0783 | 0.7143 | 0.6495 | 0.6520 | 0.6504 |
| 0.1008 | 8.96 | 2232 | 2.3523 | 0.7183 | 0.6588 | 0.6561 | 0.6552 |
| 0.0793 | 9.96 | 2480 | 2.5260 | 0.7163 | 0.6516 | 0.6566 | 0.6538 |
| 0.0498 | 10.96 | 2728 | 2.6074 | 0.7425 | 0.6902 | 0.6817 | 0.6830 |
| 0.0484 | 11.95 | 2976 | 2.6758 | 0.7284 | 0.6687 | 0.6734 | 0.6709 |
| 0.0409 | 12.95 | 3224 | 2.8658 | 0.7425 | 0.6817 | 0.6756 | 0.6781 |
| 0.0239 | 13.94 | 3472 | 2.9484 | 0.7465 | 0.6980 | 0.6818 | 0.6870 |
| 0.025 | 14.94 | 3720 | 3.0827 | 0.7304 | 0.6778 | 0.6577 | 0.6641 |
| 0.0286 | 15.94 | 3968 | 3.0011 | 0.7183 | 0.6509 | 0.6475 | 0.6491 |
| 0.0264 | 16.93 | 4216 | 3.1581 | 0.7264 | 0.6645 | 0.6563 | 0.6595 |
| 0.009 | 17.93 | 4464 | 3.1200 | 0.7223 | 0.6589 | 0.6561 | 0.6569 |
| 0.012 | 18.92 | 4712 | 3.1364 | 0.7203 | 0.6573 | 0.6503 | 0.6525 |
| 0.017 | 19.92 | 4960 | 3.1340 | 0.7203 | 0.6584 | 0.6548 | 0.6558 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
|
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} | 0 | null | every single one I put every single one through vit-l on my laptop because I didn't want to spend time figuring out how to optimize for a particular token because I already had tried for almost an hour
They are padded properly with the first token, then the body (the token we're encoding) then the last, then 0s filling out the 77 context length
|
AnonymousSub/AR_rule_based_roberta_twostagetriplet_epochs_1_shard_1 | [
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} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-16-finetuned-squad-seed-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-few-shot-k-16-finetuned-squad-seed-4
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 8
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
AnonymousSub/AR_rule_based_roberta_twostagetriplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
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} | 2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-32-finetuned-squad-seed-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-few-shot-k-32-finetuned-squad-seed-0
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 8
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
AnonymousSub/EManuals_BERT_copy_wikiqa | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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} | 29 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-64-finetuned-squad-seed-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-few-shot-k-64-finetuned-squad-seed-4
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 8
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
AnonymousSub/SR_bert-base-uncased | [
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} | 3 | null | ---
license: cc0-1.0
widget:
- src: https://huggingface.co/carbon225/vit-base-patch16-224-hentai/resolve/main/samples/1.jpeg
- src: https://huggingface.co/carbon225/vit-base-patch16-224-hentai/resolve/main/samples/2.jpeg
---
# ViT for NSFW classification
## Model info
This is Google's [vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k)
finetuned for flagging images according to [vndb.org](https://vndb.org/d19) with 3 classes:
- safe
- suggestive
- explicit
## Training data
The model was trained on the vndb.org [database dump](https://vndb.org/d14)
using full size screenshots (`sf` in the database dump).
Because the dataset contains questionable images, I will not publish it.
## Intended use
The model can be used for flagging anime-style images for sexual content.
It can also be finetuned on other tasks related to anime images.
|
AnonymousSub/SR_consert | [
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} | 2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-256-finetuned-squad-seed-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-few-shot-k-256-finetuned-squad-seed-2
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 8
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
AnonymousSub/SR_declutr | [
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} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-256-finetuned-squad-seed-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-few-shot-k-256-finetuned-squad-seed-4
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 8
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
AnonymousSub/SR_rule_based_bert_quadruplet_epochs_1_shard_1 | [
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"bert",
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} | 1 | null | ---
tags:
- autotrain
- translation
language:
- de
- en
datasets:
- Tritkoman/autotrain-data-hhbgvffddf
co2_eq_emissions:
emissions: 0.262567988153626
---
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 1616457033
- CO2 Emissions (in grams): 0.2626
## Validation Metrics
- Loss: 1.995
- SacreBLEU: 16.326
- Gen len: 10.246 |
AnonymousSub/SR_rule_based_bert_triplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
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} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-512-finetuned-squad-seed-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-few-shot-k-512-finetuned-squad-seed-0
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 8
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
AnonymousSub/SR_rule_based_hier_triplet_epochs_1_shard_1 | [
"pytorch",
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} | 1 | 2022-09-30T12:26:24Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-512-finetuned-squad-seed-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-few-shot-k-512-finetuned-squad-seed-2
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 8
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
AnonymousSub/SR_rule_based_only_classfn_epochs_1_shard_1 | [
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} | 6 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 123.84 +/- 87.24
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
...
```
|
AnonymousSub/SR_rule_based_only_classfn_twostage_epochs_1_shard_1 | [
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} | 2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-512-finetuned-squad-seed-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-few-shot-k-512-finetuned-squad-seed-4
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 8
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
AnonymousSub/SR_rule_based_roberta_bert_quadruplet_epochs_1_shard_1 | [
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} | 2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-1024-finetuned-squad-seed-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-few-shot-k-1024-finetuned-squad-seed-0
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 8
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
AnonymousSub/SR_rule_based_roberta_bert_quadruplet_epochs_1_shard_10 | [
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} | 2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-1024-finetuned-squad-seed-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-few-shot-k-1024-finetuned-squad-seed-2
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 8
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
AnonymousSub/SR_rule_based_roberta_bert_triplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
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} | 2 | null | ---
tags:
- generated_from_trainer
model-index:
- name: italian2ep
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. -->
# italian2ep
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 30
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
AnonymousSub/SR_rule_based_roberta_hier_quadruplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
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"RobertaModel"
],
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} | 2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-disaster-tweet
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-disaster-tweet
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: 0.4052
- Accuracy: 0.8207
- F1: 0.8203
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5056 | 1.0 | 96 | 0.4139 | 0.8188 | 0.8179 |
| 0.3991 | 2.0 | 192 | 0.4052 | 0.8207 | 0.8203 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
AnonymousSub/SR_rule_based_roberta_hier_quadruplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
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} | 5 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: roberta-large-finetuned-combined-DS
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-large-finetuned-combined-DS
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2062
- Accuracy: 0.7001
- Precision: 0.6703
- Recall: 0.6700
- F1: 0.6701
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.8804 | 1.0 | 711 | 0.8517 | 0.6573 | 0.6786 | 0.6253 | 0.6231 |
| 0.6949 | 2.0 | 1422 | 0.7444 | 0.6833 | 0.6609 | 0.6647 | 0.6604 |
| 0.5674 | 3.0 | 2133 | 0.8379 | 0.6798 | 0.6571 | 0.6659 | 0.6575 |
| 0.433 | 3.99 | 2844 | 0.8703 | 0.7079 | 0.6947 | 0.6801 | 0.6809 |
| 0.3314 | 4.99 | 3555 | 1.1792 | 0.6861 | 0.6672 | 0.6558 | 0.6569 |
| 0.2519 | 5.99 | 4266 | 1.5574 | 0.6966 | 0.6761 | 0.6639 | 0.6662 |
| 0.2083 | 6.99 | 4977 | 1.8781 | 0.6952 | 0.6681 | 0.6592 | 0.6619 |
| 0.1773 | 7.99 | 5688 | 1.8687 | 0.6959 | 0.6677 | 0.6748 | 0.6675 |
| 0.1536 | 8.99 | 6399 | 2.2483 | 0.7037 | 0.6788 | 0.6674 | 0.6694 |
| 0.1305 | 9.99 | 7110 | 2.4602 | 0.6875 | 0.6597 | 0.6681 | 0.6612 |
| 0.0982 | 10.98 | 7821 | 2.5573 | 0.6994 | 0.6705 | 0.6728 | 0.6709 |
| 0.0858 | 11.98 | 8532 | 2.8048 | 0.6994 | 0.6765 | 0.6730 | 0.6737 |
| 0.0734 | 12.98 | 9243 | 3.0408 | 0.6945 | 0.6640 | 0.6628 | 0.6626 |
| 0.0625 | 13.98 | 9954 | 3.0047 | 0.7037 | 0.6784 | 0.6757 | 0.6764 |
| 0.0434 | 14.98 | 10665 | 3.0789 | 0.6987 | 0.6737 | 0.6669 | 0.6691 |
| 0.0432 | 15.98 | 11376 | 2.9647 | 0.6945 | 0.6649 | 0.6684 | 0.6663 |
| 0.0326 | 16.98 | 12087 | 3.3076 | 0.6931 | 0.6630 | 0.6563 | 0.6583 |
| 0.032 | 17.97 | 12798 | 3.1890 | 0.7022 | 0.6737 | 0.6702 | 0.6717 |
| 0.0275 | 18.97 | 13509 | 3.1798 | 0.7029 | 0.6738 | 0.6750 | 0.6744 |
| 0.0251 | 19.97 | 14220 | 3.2062 | 0.7001 | 0.6703 | 0.6700 | 0.6701 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
|
AnonymousSub/SR_rule_based_roberta_hier_triplet_epochs_1_shard_1_wikiqa_copy | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
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"RobertaModel"
],
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} | 2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-1024-finetuned-squad-seed-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-few-shot-k-1024-finetuned-squad-seed-4
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 8
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
AnonymousSub/SR_rule_based_roberta_only_classfn_twostage_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
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"RobertaModel"
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} | 4 | null | ---
tags:
- autotrain
- translation
language:
- de
- en
datasets:
- Tritkoman/autotrain-data-gurbswab2
co2_eq_emissions:
emissions: 0.2690788847590159
---
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 1619457138
- CO2 Emissions (in grams): 0.2691
## Validation Metrics
- Loss: 1.983
- SacreBLEU: 15.543
- Gen len: 15.608 |
AnonymousSub/SR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
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}
} | 4 | null | # GPT neo tuned for QA about Python abstract
## About
The model is the GPT_neo with 1,2B parameters. Was fine-tuned at 10% sample from SO dataset for QA task.
Base Promt: "Question: ...\nAnswer:" |
AnonymousSub/SR_rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
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} | 4 | null | ---
tags:
- conversational
---
# Mental Health Chatbot |
AnonymousSub/SR_rule_based_roberta_twostagetriplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
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"RobertaModel"
],
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} | 4 | null | ---
language:
- en
- is
- multilingual
tags:
- translation
inference:
parameters:
src_lang: en_XX
tgt_lang: is_IS
decoder_start_token_id: 2
max_length: 512
widget:
- text: I once owned a horse. It was black and white.
---
# mBART based translation model
This model was trained to translate multiple sentences at once, compared to one sentence at a time.
It will occasionally combine sentences or add an extra sentence.
This is the same model as are provided on CLARIN: https://repository.clarin.is/repository/xmlui/handle/20.500.12537/278
You can use the following example to get started:
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
import torch
device = torch.cuda.current_device() if torch.cuda.is_available() else -1
tokenizer = AutoTokenizer.from_pretrained("mideind/nmt-doc-en-is-2022-10",src_lang="en_XX",tgt_lang="is_IS")
model = AutoModelForSeq2SeqLM.from_pretrained("mideind/nmt-doc-en-is-2022-10")
translate = pipeline("translation_XX_to_YY",model=model,tokenizer=tokenizer,device=device,src_lang="en_XX",tgt_lang="is_IS")
target_seq = translate("I am using a translation model to translate text from English to Icelandic.",src_lang="en_XX",tgt_lang="is_IS",max_length=128)
print(target_seq[0]['translation_text'].strip('YY '))
|
AnonymousSub/SR_rule_based_twostagequadruplet_hier_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
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"BertModel"
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} | 2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: train
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5340667882909217
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8124
- Matthews Correlation: 0.5341
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5227 | 1.0 | 535 | 0.5222 | 0.4210 |
| 0.3467 | 2.0 | 1070 | 0.5046 | 0.4855 |
| 0.2335 | 3.0 | 1605 | 0.5637 | 0.5173 |
| 0.1813 | 4.0 | 2140 | 0.7634 | 0.5200 |
| 0.1334 | 5.0 | 2675 | 0.8124 | 0.5341 |
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.12.1+cu102
- Datasets 2.5.1
- Tokenizers 0.13.0
|
AnonymousSub/SR_rule_based_twostagetriplet_hier_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
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} | 2 | null | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1564252670956691456/kfLvrvas_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>
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<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">c</div>
<div style="text-align: center; font-size: 14px;">@0100sick</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 c.
| Data | c |
| --- | --- |
| Tweets downloaded | 1114 |
| Retweets | 300 |
| Short tweets | 78 |
| Tweets kept | 736 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/11ntjr17/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 @0100sick's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3mb5t9js) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3mb5t9js/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/0100sick')
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)
|
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