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81
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Contrastive-Tension/BERT-Large-CT-STSb | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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} | 7 | null | ---
tags:
- adapter-transformers
- bart
datasets:
- glue
---
# Adapter `WillHeld/pfadapter-bart-base-tada-value-eraser` for facebook/bart-base
An [adapter](https://adapterhub.ml) for the `facebook/bart-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("facebook/bart-base")
adapter_name = model.load_adapter("WillHeld/pfadapter-bart-base-tada-value-eraser", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> |
Coolhand/Sentiment | []
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} | 0 | 2022-10-04T16:52:11Z | # Intro
Smartclide provides an environment to support the development of service-oriented software. This service classification aims to classify the same web services based on their functionality, which can be helpful in later stages such as service composition.
- [Requirements](#requirements)
- [Usage](#usage)
# Requirements
The list of the third-party library are listed on requirments.txt file; however, the two main used library and requirements are:
- Python 3.7+
- [HuggingFace](https://huggingface.co/)
- System Requirements: CPU: 2cv RAM: 8 GB
# Usage
The trained models have been packaged using the Python Setuptools library. This package is available in [this GitHub repository ](https://github.com/eclipse-opensmartclide/smartclide-smart-assistant/tree/main/smartclide-dle-models/serviceclassification) .
Moreover, the below class demonstrates using the published service classification model directly.
```python
#!/usr/bin/python3
# Eclipse Public License 2.0
import re
import os
import pandas as pd
import numpy as np
class PredictServiceClass:
TRAINED_MODEL="zakieh/serv_classification"
def __init__(self):
self.df = None
self.classifier_model=None
self.tokenizer_class = None
self.classifier_config=None
self.max_desc_len_public=150
def loadTrainedClassifier(self):
"""
Load trained web service classifier
:return: trained model obj
"""
import pickle
from transformers import BertForSequenceClassification, AdamW, BertConfig
from transformers import BertTokenizer, BertForMaskedLM,BertConfig
# try:
model_hub = self.TRAINED_MODEL
self.classifier_model = BertForSequenceClassification.from_pretrained(model_hub,force_download=True)
self.tokenizer_class = BertTokenizer.from_pretrained(model_hub,force_download=True)
self.classifier_config= BertConfig.from_pretrained(model_hub,force_download=True)
return (self.classifier_model)
def get_prediction(self, text):
"""
Predict service class based on user input text and DL trained model
:return: string param specifies service class
"""
import torch
k=2
# prepare our text into tokenized sequence
inputs = self.tokenizer_class(text, padding=True, truncation=True, max_length=100, return_tensors="pt")
# perform inference to our model
outputs = self.classifier_model(**inputs)
probs = outputs[0].softmax(1)
top_tensors = torch.topk(probs.flatten(), k).indices
# get id and lable from model config
id2label = self.classifier_config.id2label
top_cat_id = []
for i in range(0, k ):
top_cat_id.append(top_tensors[i].item())
top_cat_lable = []
for i in range(0, k ):
class_ = id2label[(top_cat_id[i])]
top_cat_lable.append(class_)
precentage=torch.topk(probs.flatten(), 1).values.item()*100
res=list(set([top_cat_lable[0],precentage]))
res2=list(set([top_cat_lable[1],precentage]))
return [res,res2]
def classify_service_data(self,df,clm_name):
if df is not None:
self.df = df
if clm_name in self.df:
self.df['class_precent'] = self.df[clm_name].astype(str).apply(self.get_prediction)
return self.df
```
Use class
```python
#Loading model recommended to execute on background
obj=PredictServiceClass()
obj.loadTrainedClassifier()
#use model
Service_description="The TransLoc OpenAPI is a public RESTful API which allows developers to access real-time vehicle tracking information and incorporate this data into their website or mobile application."
service_classes=obj.get_prediction(Service_description)
print(service_classes)
```
|
Corvus/DialoGPT-medium-CaptainPrice-Extended | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 7 | 2022-10-04T17:07:13Z | ---
license: openrail
---
pip install --upgrade diffusers transformers scipy
huggingface-cli login
import torch
from torch import autocast
from diffusers import StableDiffusionPipeline
model_id = "CompVis/stable-diffusion-v1-4"
device = "cuda"
pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=True)
pipe = pipe.to(device)
prompt = "a photo of an astronaut riding a horse on mars"
with autocast("cuda"):
image = pipe(prompt, guidance_scale=7.5).images[0]
image.save("astronaut_rides_horse.png")
import torch
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16", use_auth_token=True)
pipe = pipe.to(device)
prompt = "a photo of an astronaut riding a horse on mars"
with autocast("cuda"):
image = pipe(prompt, guidance_scale=7.5).images[0]
image.save("astronaut_rides_horse.png")
from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler
model_id = "CompVis/stable-diffusion-v1-4"
# Use the K-LMS scheduler here instead
scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)
pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, use_auth_token=True)
pipe = pipe.to("cuda")
prompt = "a photo of an astronaut riding a horse on mars"
with autocast("cuda"):
image = pipe(prompt, guidance_scale=7.5).images[0]
image.save("astronaut_rides_horse.png")
|
Corvus/DialoGPT-medium-CaptainPrice | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 7 | null | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- Colby/autotrain-data-ai-image-detector
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 7.940487247386902
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1519658722
- CO2 Emissions (in grams): 7.9405
## Validation Metrics
- Loss: 0.163
- Accuracy: 0.942
- Precision: 0.938
- Recall: 0.978
- AUC: 0.980
- F1: 0.958
# License Notice
This work is licensed under a [Creative Commons Attribution-NoDerivatives 4.0 International License](https://creativecommons.org/licenses/by-nd/4.0/).
You may distribute and make this model available to others as part of your own web page, app, or service so long as you provide attribution. However, use of this model within text-to-image systems to evade AI image detection would be considered a "derivative work" and as such prohibited by the license terms. |
CouchCat/ma_ner_v6_distil | [
"pytorch",
"distilbert",
"token-classification",
"en",
"transformers",
"ner",
"license:mit",
"autotrain_compatible"
]
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} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5829
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.9134 | 1.0 | 557 | 1.8595 |
| 1.545 | 2.0 | 1114 | 1.5882 |
| 1.1889 | 3.0 | 1671 | 1.5829 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
|
CoveJH/ConBot | []
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} | 0 | null | ---
license: creativeml-openrail-m
---
## Description
Elynia Diffusion is a latent text-to-image diffusion model based on the original CompVis Stable Diffusion v1.4 and then fine-tuned on the main character of 'Battle for Wesnoth' add-ons using Dreambooth. This model has been created to explore the possibilities and limitations of Dreambooth training and to study how it learns when low-resolution pixelart videogame sprites are added to the dataset in addition to realistic artwork.
## Model Description
The model originally used for fine-tuning is Stable Diffusion V1-4, which is a latent image diffusion model trained on LAION2B-en.
The current model has been fine-tuned with a learning rate of 5.0e-6 for 800 steps using Dreambooth on character portraits and pixel-art videogame sprites.
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies:
You can't use the model to deliberately produce nor share illegal or harmful outputs or content
The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
## Downstream Uses
This model can be used for entertainment purposes and as a generative art assistant.
## Acknowledgements
This project would not have been possible without the incredible work by the CompVis Researchers, Wesnoth devs, artists and user made content makers.
The dataset for training currently resides here https://drive.google.com/drive/folders/1gskg6q8s-VWLlav-eVkkAzFP6xiVjY8U?usp=sharing.
TODO: make a proper huggingface dataset for the rest of Wesnoth and its fandom content.
|
Coyotl/DialoGPT-test3-arthurmorgan | [
"conversational"
]
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: aa
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. -->
# aa
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 15.9757
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 14.5628 | 3.33 | 20 | 16.1808 | 1.0 |
| 14.5379 | 6.67 | 40 | 16.1005 | 1.0 |
| 14.3379 | 10.0 | 60 | 15.9757 | 1.0 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.10.0+cu102
- Datasets 1.4.1
- Tokenizers 0.12.1
|
Craak/GJ0001 | []
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} | 0 | null | ---
language: sv
license: mit
datasets:
- "Gabriel/citesum_swe"
tags:
- summarization
widget:
- text: 'Många samtidiga programmeringsmodeller möjliggör både transaktionsminne och meddelandepassage. För sådana modeller har forskare byggt upp allt effektivare implementeringar och fastställt rimliga korrekthetskriterier, samtidigt som det fortfarande är ett öppet problem att få det bästa av båda världarna. Vi presenterar en programmeringsmodell som är den första som har ogenomskinliga transaktioner, säkert asynkront meddelande som passerar, och ett effektivt genomförande. Våra semantik använder preliminärt meddelande passerar och håller reda på beroenden för att möjliggöra ångra meddelande passerar om en transaktion avbryter. Vi kan programmera kommunikation idiomer som barriär och mötesplats som inte dödläge när de används i ett atomblock. Våra experiment visar att vår modell tillför lite overhead till rena transaktioner, och att den är betydligt effektivare än Transaktionshändelser. Vi använder en ny definition av säkert meddelande som kan vara av oberoende intresse.'
inference:
parameters:
temperature: 0.7
min_length: 30
max_length: 120
model-index:
- name: Gabriel/bart-base-cnn-xsum-cite-swe
results:
- task:
type: summarization
name: summarization
dataset:
name: Gabriel/citesum_swe
type: Gabriel/citesum_swe
split: validation
metrics:
- name: Validation ROGUE-1.
type: rouge-1
value: 29.6279
verified: true
- name: Validation ROGUE-2
type: rouge-2
value: 11.5697
verified: true
- name: Validation ROGUE-L
type: rouge-l
value: 24.2429
verified: true
- name: Validation ROGUE-L-SUM
type: rouge-l-sum
value: 24.4557
verified: true
train-eval-index:
- config: Gabriel--citesum_swe
task: summarization
task_id: summarization
splits:
eval_split: test
col_mapping:
document: text
summary: target
co2_eq_emissions:
emissions: 0.0334
source: Google Colab
training_type: fine-tuning
geographical_location: Fredericia, Denmark
hardware_used: Tesla P100-PCIE-16GB
---
<!-- 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-cnn-xsum-cite-swe
This model is a fine-tuned version of [Gabriel/bart-base-cnn-xsum-swe](https://huggingface.co/Gabriel/bart-base-cnn-xsum-swe) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4203
- Rouge1: 29.6279
- Rouge2: 11.5697
- Rougel: 24.2429
- Rougelsum: 24.4557
- Gen Len: 19.9371
## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 2.4833 | 1.0 | 2558 | 2.4203 | 29.6279 | 11.5697 | 24.2429 | 24.4557 | 19.9371 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Crasher222/kaggle-comp-test | [
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"en",
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"transformers",
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} | 29 | null | ---
license: apache-2.0
tags:
- afro-digits-speech
datasets:
- crowd-speech-africa
metrics:
- accuracy
model-index:
- name: afrospeech-wav2vec-all-6
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: Afro Speech
type: chrisjay/crowd-speech-africa
args: no
metrics:
- name: Validation Accuracy
type: accuracy
value: 0.6205
---
# afrospeech-wav2vec-all-6
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the [crowd-speech-africa](https://huggingface.co/datasets/chrisjay/crowd-speech-africa), which was a crowd-sourced dataset collected using the [afro-speech Space](https://huggingface.co/spaces/chrisjay/afro-speech).
## Training and evaluation data
The model was trained on a mixed audio data from 6 African languages - Igbo (`ibo`), Yoruba (`yor`), Rundi (`run`), Oshiwambo (`kua`), Shona (`sna`) and Oromo (`gax`).
- Size of training set: 1977
- Size of validation set: 396
Below is a distribution of the dataset (training and valdation)
## Evaluation performance
It achieves the following results on the [validation set](VALID_all_interesred_6_audiodata.csv):
- F1: 0.5787048581502744
- Accuracy: 0.6205357142857143
The confusion matrix below helps to give a better look at the model's performance across the digits. Through it, we can see the precision and recall of the model as well as other important insights.
## Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 64
- eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- num_epochs: 150
## Training results
| Training Loss | Epoch | Validation Accuracy |
|:-------------:|:-----:|:--------:|
| 2.0466 | 1 | 0.1130 |
| 0.0468 | 50 | 0.6116 |
| 0.0292 | 100 | 0.5305 |
| 0.0155 | 150 | 0.5319 |
## Framework versions
- Transformers 4.21.3
- Pytorch 1.12.0
- Datasets 1.14.0
- Tokenizers 0.12.1 |
CrayonShinchan/fine_tune_try_1 | []
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} | 0 | null | ---
tags:
- conversational
---
#707 DialoGPT Model |
Crisblair/Wkwk | []
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} | 0 | null | ---
license: apache-2.0
tags:
- afro-digits-speech
datasets:
- crowd-speech-africa
metrics:
- accuracy
model-index:
- name: afrospeech-wav2vec-ibo
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: Afro Speech
type: chrisjay/crowd-speech-africa
args: no
metrics:
- name: Validation Accuracy
type: accuracy
value: 1.0
---
# afrospeech-wav2vec-ibo
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the [crowd-speech-africa](https://huggingface.co/datasets/chrisjay/crowd-speech-africa), which was a crowd-sourced dataset collected using the [afro-speech Space](https://huggingface.co/spaces/chrisjay/afro-speech).
## Training and evaluation data
The model was trained on a mixed audio data from Igbo (`ibo`).
- Size of training set: 109
- Size of validation set: 28
Below is a distribution of the dataset (training and valdation)
## Evaluation performance
It achieves the following results on the [validation set](VALID_igbo_ibo_audio_data.csv):
- F1: 1.0
- Accuracy: 1.0
The confusion matrix below helps to give a better look at the model's performance across the digits. Through it, we can see the precision and recall of the model as well as other important insights.
## Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 64
- eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- num_epochs: 150
## Training results
| Training Loss | Epoch | Validation Accuracy |
|:-------------:|:-----:|:--------:|
| 0.1415 | 1 | 1.0 |
| 0.0241 | 50 | 1.0 |
| 0.0019 | 100 | 0.929 |
| 0.0012 | 150 | 0.892 |
## Framework versions
- Transformers 4.21.3
- Pytorch 1.12.0
- Datasets 1.14.0
- Tokenizers 0.12.1 |
Crispy/dialopt-small-kratos | []
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} | 0 | 2022-10-04T19:26:36Z | ---
license: apache-2.0
tags:
- afro-digits-speech
datasets:
- crowd-speech-africa
metrics:
- accuracy
model-index:
- name: afrospeech-wav2vec-gax
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: Afro Speech
type: chrisjay/crowd-speech-africa
args: no
metrics:
- name: Validation Accuracy
type: accuracy
value: 1.0
---
# afrospeech-wav2vec-gax
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the [crowd-speech-africa](https://huggingface.co/datasets/chrisjay/crowd-speech-africa), which was a crowd-sourced dataset collected using the [afro-speech Space](https://huggingface.co/spaces/chrisjay/afro-speech).
## Training and evaluation data
The model was trained on a mixed audio data from Oromo (`gax`).
- Size of training set: 32
- Size of validation set: 8
Below is a distribution of the dataset (training and valdation)
## Evaluation performance
It achieves the following results on the [validation set](VALID_oromo_gax_audio_data.csv):
- F1: 1.0
- Accuracy: 1.0
The confusion matrix below helps to give a better look at the model's performance across the digits. Through it, we can see the precision and recall of the model as well as other important insights.
## Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 64
- eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- num_epochs: 150
## Training results
| Training Loss | Epoch | Validation Accuracy |
|:-------------:|:-----:|:--------:|
| 0.0699 | 1 | 1.0 |
| 0.0021 | 50 | 0.875 |
| 0.0026 | 100 | 0.875 |
| 0.0017 | 150 | 0.875 |
## Framework versions
- Transformers 4.21.3
- Pytorch 1.12.0
- Datasets 1.14.0
- Tokenizers 0.12.1 |
Crives/distilbert-base-uncased-finetuned-emotion | [
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"distilbert",
"text-classification",
"dataset:emotion",
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"generated_from_trainer",
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} | 31 | 2022-10-04T19:27:55Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: refinement-finetuned-mnli-kaggle-reversal
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-kaggle-reversal
This model is a fine-tuned version of [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0382
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.4093 | 1.0 | 12599 | 0.7861 |
| 0.5241 | 2.0 | 25198 | 0.9800 |
| 0.4969 | 3.0 | 37797 | 1.0316 |
| 0.4239 | 4.0 | 50396 | 1.0382 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.10.0
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Culmenus/XLMR-ENIS-finetuned-ner | [
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"xlm-roberta",
"token-classification",
"dataset:mim_gold_ner",
"transformers",
"generated_from_trainer",
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} | 6 | 2022-10-04T20:11:16Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: airlinesentiment
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. -->
# airlinesentiment
This model is a fine-tuned version of [PDatt/outcome](https://huggingface.co/PDatt/outcome) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2552
- Accuracy: 0.9587
- F1: 0.9586
- Precision: 0.9585
- Recall: 0.9587
## 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
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Culmenus/checkpoint-168500-finetuned-de-to-is_nr2 | []
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} | 0 | 2022-10-04T20:19:03Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: gpt2-large-lr-1e5-span-head-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-large-lr-1e5-span-head-finetuned-squad
This model is a fine-tuned version of [gpt2-large](https://huggingface.co/gpt2-large) 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 16
- total_eval_batch_size: 16
- 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
|
Culmenus/opus-mt-de-is-finetuned-de-to-is_35g65cc_1 | []
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} | 0 | 2022-10-04T20:39:57Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-16-finetuned-squad-seq2seq-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-seq2seq-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: 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: 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
|
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} | 0 | 2022-10-04T20:43:45Z | ---
datasets:
- tner/btc
metrics:
- f1
- precision
- recall
model-index:
- name: tner/deberta-v3-large-btc
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/btc
type: tner/btc
args: tner/btc
metrics:
- name: F1
type: f1
value: 0.8399238265934805
- name: Precision
type: precision
value: 0.8237749945067018
- name: Recall
type: recall
value: 0.8567184643510055
- name: F1 (macro)
type: f1_macro
value: 0.7921150390682584
- name: Precision (macro)
type: precision_macro
value: 0.7766126681668878
- name: Recall (macro)
type: recall_macro
value: 0.8103758198218992
- name: F1 (entity span)
type: f1_entity_span
value: 0.9134087599417496
- name: Precision (entity span)
type: precision_entity_span
value: 0.8958470665787739
- name: Recall (entity span)
type: recall_entity_span
value: 0.931672760511883
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/deberta-v3-large-btc
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the
[tner/btc](https://huggingface.co/datasets/tner/btc) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.8399238265934805
- Precision (micro): 0.8237749945067018
- Recall (micro): 0.8567184643510055
- F1 (macro): 0.7921150390682584
- Precision (macro): 0.7766126681668878
- Recall (macro): 0.8103758198218992
The per-entity breakdown of the F1 score on the test set are below:
- location: 0.7503949447077408
- organization: 0.7042372881355932
- person: 0.9217128843614413
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.8283024935970381, 0.8507400882379221]
- 95%: [0.8260021524132041, 0.8526162579659953]
- F1 (macro):
- 90%: [0.8283024935970381, 0.8507400882379221]
- 95%: [0.8260021524132041, 0.8526162579659953]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-v3-large-btc/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/deberta-v3-large-btc/raw/main/eval/metric_span.json).
### Usage
This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip
```shell
pip install tner
```
and activate model as below.
```python
from tner import TransformersNER
model = TransformersNER("tner/deberta-v3-large-btc")
model.predict(["Jacob Collier is a Grammy awarded English artist from London"])
```
It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.
### Training hyperparameters
The following hyperparameters were used during training:
- dataset: ['tner/btc']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: microsoft/deberta-v3-large
- crf: True
- max_length: 128
- epoch: 15
- batch_size: 16
- lr: 1e-05
- random_seed: 42
- gradient_accumulation_steps: 8
- weight_decay: None
- lr_warmup_step_ratio: 0.1
- max_grad_norm: None
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/deberta-v3-large-btc/raw/main/trainer_config.json).
### Reference
If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-camacho-collados-2021-ner,
title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
author = "Ushio, Asahi and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-demos.7",
doi = "10.18653/v1/2021.eacl-demos.7",
pages = "53--62",
abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.",
}
```
|
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} | 0 | 2022-10-04T20:48:23Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-multilingual-cased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-cased-finetuned-squad
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0105
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0094 | 1.0 | 5555 | 0.9460 |
| 0.7542 | 2.0 | 11110 | 0.9429 |
| 0.5506 | 3.0 | 16665 | 1.0105 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Culmenus/opus-mt-de-is-finetuned-de-to-is_ekkicc | []
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} | 0 | 2022-10-04T20:49:18Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-16-finetuned-squad-seq2seq-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-seq2seq-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: 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: 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
|
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} | 0 | 2022-10-04T20:51:11Z | ---
title: SurvPred
emoji: 😻
colorFrom: red
colorTo: indigo
sdk: gradio
sdk_version: 3.1.7
app_file: app.py
pinned: false
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
CuongLD/wav2vec2-large-xlsr-vietnamese | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"vi",
"dataset:common_voice, infore_25h",
"arxiv:2006.11477",
"arxiv:2006.13979",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | {
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"Wav2Vec2ForCTC"
],
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} | 8 | 2022-10-04T21:00:09Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: test1000
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. -->
# test1000
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3276
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 13.8034 | 3.22 | 100 | 8.1488 | 1.0 |
| 5.6013 | 6.44 | 200 | 3.6813 | 1.0 |
| 3.4696 | 9.67 | 300 | 3.3448 | 1.0 |
| 3.396 | 12.89 | 400 | 3.3276 | 1.0 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.10.0+cu102
- Datasets 1.4.1
- Tokenizers 0.12.1
|
CurtisASmith/GPT-JRT | []
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} | 0 | null | Access to model CorethR/Elian is restricted and you are not in the authorized list. Visit https://huggingface.co/CorethR/Elian to ask for access. |
CurtisBowser/DialoGPT-medium-sora-three | []
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} | 0 | 2022-10-04T21:27:34Z | ---
tags:
- autotrain
- tabular
- classification
- tabular-classification
datasets:
- omarques/autotrain-data-in-class-test-demo
co2_eq_emissions:
emissions: 3.2447037790637503
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1659958764
- CO2 Emissions (in grams): 3.2447
## Validation Metrics
- Loss: 0.044
- Accuracy: 0.991
- Precision: 1.000
- Recall: 0.977
- AUC: 0.999
- F1: 0.988
## Usage
```python
import json
import joblib
import pandas as pd
model = joblib.load('model.joblib')
config = json.load(open('config.json'))
features = config['features']
# data = pd.read_csv("data.csv")
data = data[features]
data.columns = ["feat_" + str(col) for col in data.columns]
predictions = model.predict(data) # or model.predict_proba(data)
``` |
CurtisBowser/DialoGPT-medium-sora-two | [
"pytorch",
"conversational"
]
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} | 0 | 2022-10-04T21:27:48Z | ---
tags:
- autotrain
- tabular
- classification
- tabular-classification
datasets:
- omarques/autotrain-data-in-class-test-demo
co2_eq_emissions:
emissions: 0.15031698776128047
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1659958767
- CO2 Emissions (in grams): 0.1503
## Validation Metrics
- Loss: 0.076
- Accuracy: 0.983
- Precision: 1.000
- Recall: 0.953
- AUC: 0.999
- F1: 0.976
## Usage
```python
import json
import joblib
import pandas as pd
model = joblib.load('model.joblib')
config = json.load(open('config.json'))
features = config['features']
# data = pd.read_csv("data.csv")
data = data[features]
data.columns = ["feat_" + str(col) for col in data.columns]
predictions = model.predict(data) # or model.predict_proba(data)
``` |
CurtisBowser/DialoGPT-small-sora | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
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} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-16-finetuned-squad-seq2seq-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-seq2seq-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: 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
|
CyberMuffin/DialoGPT-small-ChandlerBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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"GPT2LMHeadModel"
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} | 9 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: finetuning-sentiment-model-3000-samples
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-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:
- eval_loss: 0.6942
- eval_accuracy: 0.5
- eval_f1: 0.0
- eval_runtime: 272.0623
- eval_samples_per_second: 1.103
- eval_steps_per_second: 0.07
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Cyrell/Cyrell | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-32-finetuned-squad-seq2seq-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-seq2seq-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: 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: 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
|
D-Keqi/espnet_asr_train_asr_streaming_transformer_raw_en_bpe500_sp_valid.acc.ave | []
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} | 11 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-32-finetuned-squad-seq2seq-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-32-finetuned-squad-seq2seq-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: 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: 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
|
D3vil/DialoGPT-smaall-harrypotter | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-32-finetuned-squad-seq2seq-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-32-finetuned-squad-seq2seq-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: 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
|
D3vil/DialoGPT-smaall-harrypottery | []
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} | 0 | 2022-10-04T22:15:27Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-64-finetuned-squad-seq2seq-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-64-finetuned-squad-seq2seq-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: 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: 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
|
D4RL1NG/yes | []
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} | 0 | null | ---
license: mit
---
### snoot fang on Stable Diffusion via Dreambooth
This your the Stable Diffusion model fine-tuned the snoot fang concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **sks** snoot fang
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) |
DARKVIP3R/DialoGPT-medium-Anakin | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 13 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-64-finetuned-squad-seq2seq-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-64-finetuned-squad-seq2seq-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: 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: 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
|
DCU-NLP/bert-base-irish-cased-v1 | [
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"tf",
"bert",
"fill-mask",
"transformers",
"generated_from_keras_callback",
"autotrain_compatible"
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} | 1,244 | 2022-10-04T22:25:55Z | ---
license: apache-2.0
tags:
- afro-digits-speech
datasets:
- crowd-speech-africa
metrics:
- accuracy
model-index:
- name: afrospeech-wav2vec-kua
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: Afro Speech
type: chrisjay/crowd-speech-africa
args: no
metrics:
- name: Validation Accuracy
type: accuracy
value: 0.9921875
---
# afrospeech-wav2vec-kua
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the [crowd-speech-africa](https://huggingface.co/datasets/chrisjay/crowd-speech-africa), which was a crowd-sourced dataset collected using the [afro-speech Space](https://huggingface.co/spaces/chrisjay/afro-speech).
## Training and evaluation data
The model was trained on a mixed audio data from Oshiwambo (`kua`).
- Size of training set: 1376
- Size of validation set: 345
Below is a distribution of the dataset (training and valdation)
## Evaluation performance
It achieves the following results on the [validation set](VALID_oshiwambo_kua_audio_data.csv):
- F1: 0.9913480945477086
- Accuracy: 0.9921875
The confusion matrix below helps to give a better look at the model's performance across the digits. Through it, we can see the precision and recall of the model as well as other important insights.
## Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 64
- eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- num_epochs: 150
## Training results
| Training Loss | Epoch | Validation Accuracy |
|:-------------:|:-----:|:--------:|
| 0.0096 | 1 | 0.9843 |
| 0.2555 | 50 | 0.9843 |
| 0.00145 | 100 | 0.98177 |
| 0.00053 | 150 | 0.97770 |
## Framework versions
- Transformers 4.21.3
- Pytorch 1.12.0
- Datasets 1.14.0
- Tokenizers 0.12.1 |
DCU-NLP/electra-base-irish-cased-discriminator-v1 | [
"pytorch",
"electra",
"pretraining",
"ga",
"transformers",
"irish",
"license:apache-2.0"
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} | 4 | 2022-10-04T22:29:21Z | ---
license: apache-2.0
tags:
- afro-digits-speech
datasets:
- crowd-speech-africa
metrics:
- accuracy
model-index:
- name: afrospeech-wav2vec-run
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: Afro Speech
type: chrisjay/crowd-speech-africa
args: no
metrics:
- name: Validation Accuracy
type: accuracy
value: 0.8
---
# afrospeech-wav2vec-run
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the [crowd-speech-africa](https://huggingface.co/datasets/chrisjay/crowd-speech-africa), which was a crowd-sourced dataset collected using the [afro-speech Space](https://huggingface.co/spaces/chrisjay/afro-speech).
## Training and evaluation data
The model was trained on a mixed audio data from Rundi (`run`).
- Size of training set: 16
- Size of validation set: 5
Below is a distribution of the dataset (training and valdation)
## Evaluation performance
It achieves the following results on the [validation set](VALID_rundi_run_audio_data.csv):
- F1: 0.8
- Accuracy: 0.8
The confusion matrix below helps to give a better look at the model's performance across the digits. Through it, we can see the precision and recall of the model as well as other important insights.
## Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 64
- eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- num_epochs: 150
## Training results
| Training Loss | Epoch | Validation Accuracy |
|:-------------:|:-----:|:--------:|
|0.00183 | 1 | 0.6 |
|0.0003991 | 50 | 0.8 |
| 0.0002174 | 100 | 0.6 |
|0.0043911 | 150 | 0.4 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.0
- Datasets 1.14.0
- Tokenizers 0.12.1 |
DHBaek/gpt2-stackoverflow-question-contents-generator | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 14 | null | ---
license: apache-2.0
tags:
- afro-digits-speech
datasets:
- crowd-speech-africa
metrics:
- accuracy
model-index:
- name: afrospeech-wav2vec-sna
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: Afro Speech
type: chrisjay/crowd-speech-africa
args: no
metrics:
- name: Validation Accuracy
type: accuracy
value: 1.0
---
# afrospeech-wav2vec-sna
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the [crowd-speech-africa](https://huggingface.co/datasets/chrisjay/crowd-speech-africa), which was a crowd-sourced dataset collected using the [afro-speech Space](https://huggingface.co/spaces/chrisjay/afro-speech).
## Training and evaluation data
The model was trained on a mixed audio data from Shona (`sna`).
- Size of training set: 24
- Size of validation set: 6
Below is a distribution of the dataset (training and valdation)
## Evaluation performance
It achieves the following results on the [validation set](VALID_shona_sna_audio_data.csv):
- F1: 1.0
- Accuracy: 1.0
The confusion matrix below helps to give a better look at the model's performance across the digits. Through it, we can see the precision and recall of the model as well as other important insights.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 64
- eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- num_epochs: 150
### Training results
| Training Loss | Epoch | Validation Accuracy |
|:-------------:|:-----:|:--------:|
| 0.02387 | 1 | 1.0 |
| 0.0021066 | 50 | 1.0 |
| 0.001157 | 100 | 1.0 |
| 0.0009537 | 150 | 1.0 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.0
- Datasets 1.14.0
- Tokenizers 0.12.1 |
DHBaek/xlm-roberta-large-korquad-mask | [
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
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} | 9 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-64-finetuned-squad-seq2seq-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-seq2seq-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: 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
|
DJSammy/bert-base-danish-uncased_BotXO-ai | [
"pytorch",
"jax",
"da",
"dataset:common_crawl",
"dataset:wikipedia",
"transformers",
"bert",
"masked-lm",
"license:cc-by-4.0",
"fill-mask"
]
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} | 14 | 2022-10-04T22:35:51Z | ---
license: apache-2.0
tags:
- afro-digits-speech
datasets:
- crowd-speech-africa
metrics:
- accuracy
model-index:
- name: afrospeech-wav2vec-yor
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: Afro Speech
type: chrisjay/crowd-speech-africa
args: no
metrics:
- name: Validation Accuracy
type: accuracy
value: 0.83
---
# afrospeech-wav2vec-yor
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the [crowd-speech-africa](https://huggingface.co/datasets/chrisjay/crowd-speech-africa), which was a crowd-sourced dataset collected using the [afro-speech Space](https://huggingface.co/spaces/chrisjay/afro-speech).
## Training and evaluation data
The model was trained on a mixed audio data from Yoruba (`yor`).
- Size of training set: 22
- Size of validation set: 6
Below is a distribution of the dataset (training and valdation)
## Evaluation performace
It achieves the following results on the [validation set](VALID_yoruba_yor_audio_data.csv):
- F1: 0.83
- Accuracy: 0.83
The confusion matrix below helps to give a better look at the model's performance across the digits. Through it, we can see the precision and recall of the model as well as other important insights.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 64
- eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- num_epochs: 150
### Training results
| Training Loss | Epoch | Validation Accuracy |
|:-------------:|:-----:|:--------:|
|0.596 | 1 | 0.5 |
| 0.0220 | 50 | 0.5 |
|0.00305 | 100 | 0.667 |
|0.0993 | 150 | 0.667 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.0
- Datasets 1.14.0
- Tokenizers 0.12.1 |
DJSammy/bert-base-swedish-uncased_BotXO-ai | [
"pytorch",
"transformers"
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} | 1 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-128-finetuned-squad-seq2seq-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-128-finetuned-squad-seq2seq-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: 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: linear
- 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
|
DJStomp/TestingSalvoNET | [
"transformers"
]
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} | 1 | 2022-10-04T22:47:23Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rare-puppers
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
# rare-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### house cat
#### lion
#### tiger
 |
DKpro000/DialoGPT-small-harrypotter | []
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} | 0 | 2022-10-04T22:53:50Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-128-finetuned-squad-seq2seq-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-128-finetuned-squad-seq2seq-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: 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: linear
- 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
|
DLNLP/t5-small-finetuned-xsum | []
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} | 0 | 2022-10-04T23:03:19Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-128-finetuned-squad-seq2seq-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-128-finetuned-squad-seq2seq-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: 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
- 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
|
DSI/TweetBasedSA | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
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} | 29 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-256-finetuned-squad-seq2seq-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-256-finetuned-squad-seq2seq-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: 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: linear
- 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
|
DSI/ar_emotion_6 | [
"pytorch",
"bert",
"transformers"
]
| null | {
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} | 1 | 2022-10-04T23:14:11Z | ---
license: mit
---
### Bauti on Stable Diffusion via Dreambooth
#### model by Kuanchy
This your the Stable Diffusion model fine-tuned the Bauti concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **a sks person **
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:
|
DSI/personal_sentiment | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
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} | 25 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-256-finetuned-squad-seq2seq-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-seq2seq-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: 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: linear
- 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
|
DTAI-KULeuven/mbert-corona-tweets-belgium-topics | [
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"jax",
"bert",
"text-classification",
"multilingual",
"nl",
"fr",
"en",
"arxiv:2104.09947",
"transformers",
"Dutch",
"French",
"English",
"Tweets",
"Topic classification"
]
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} | 167 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-256-finetuned-squad-seq2seq-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-seq2seq-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: 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
- 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
|
DTAI-KULeuven/robbertje-1-gb-merged | [
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"fill-mask",
"nl",
"dataset:oscar",
"dataset:oscar (NL)",
"dataset:dbrd",
"dataset:lassy-ud",
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"dataset:conll2002",
"arxiv:2101.05716",
"transformers",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"license:mit",
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} | 1 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
model-index:
- name: distilbert-base-uncased-finetuned-clinc
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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
DTAI-KULeuven/robbertje-1-gb-shuffled | [
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"roberta",
"fill-mask",
"nl",
"dataset:oscar",
"dataset:oscar (NL)",
"dataset:dbrd",
"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2101.05716",
"transformers",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"license:mit",
"autotrain_compatible"
]
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],
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} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-512-finetuned-squad-seq2seq-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-seq2seq-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: 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: linear
- 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
|
alexandrainst/da-emotion-classification-base | [
"pytorch",
"tf",
"bert",
"text-classification",
"da",
"transformers",
"license:cc-by-sa-4.0"
]
| text-classification | {
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"BertForSequenceClassification"
],
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} | 837 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-512-finetuned-squad-seq2seq-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-seq2seq-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: 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: linear
- 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
|
alexandrainst/da-ner-base | [
"pytorch",
"tf",
"bert",
"token-classification",
"da",
"dataset:dane",
"transformers",
"license:cc-by-sa-4.0",
"autotrain_compatible"
]
| token-classification | {
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} | 78 | 2022-10-05T00:25:39Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-512-finetuned-squad-seq2seq-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-seq2seq-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: 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
- 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
|
alexandrainst/da-hatespeech-detection-small | [
"pytorch",
"electra",
"text-classification",
"da",
"transformers",
"license:cc-by-4.0"
]
| text-classification | {
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"ElectraForSequenceClassification"
],
"model_type": "electra",
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}
} | 1,506 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: twitter-emotion-classifier-BERT
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. -->
# twitter-emotion-classifier-BERT
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1487
- Train Sparse Categorical Accuracy: 0.9374
- Validation Loss: 0.1447
- Validation Sparse Categorical Accuracy: 0.9390
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
|:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
| 0.5268 | 0.8156 | 0.2002 | 0.9265 | 0 |
| 0.1487 | 0.9374 | 0.1447 | 0.9390 | 1 |
### Framework versions
- Transformers 4.22.2
- TensorFlow 2.8.2
- Datasets 2.5.2
- Tokenizers 0.12.1
|
DaisyMak/bert-finetuned-squad-accelerate-10epoch_transformerfrozen | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
} | 1,907 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-1024-finetuned-squad-seq2seq-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-seq2seq-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: 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: linear
- 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
|
DarkWolf/kn-electra-small | [
"pytorch",
"electra",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "electra",
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}
} | 4 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/irys_en/1664943465291/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/1506068019847393280/P45Eu82L_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">IRyS💎holoEN✨One Step at a Time & Gravity</div>
<div style="text-align: center; font-size: 14px;">@irys_en</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 IRyS💎holoEN✨One Step at a Time & Gravity.
| Data | IRyS💎holoEN✨One Step at a Time & Gravity |
| --- | --- |
| Tweets downloaded | 3248 |
| Retweets | 1427 |
| Short tweets | 290 |
| Tweets kept | 1531 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2hzb8c49/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 @irys_en's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/197zdo46) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/197zdo46/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/irys_en')
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)
|
DarkestSky/distilbert-base-uncased-finetuned-ner | []
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}
} | 0 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rare-puppers
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.44086021184921265
---
# rare-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### DISCRIMINATION
#### NIGGA
#### PROFANITY
#### RACISM
#### RACIST
 |
Darya/layoutlmv2-finetuned-funsd-test | []
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}
} | 0 | 2022-10-05T06:00:07Z | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ijelid-bert-base-multilingual
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. -->
# ijelid-bert-base-multilingual
This model is a fine-tuned version of [BERT multilingual base model (cased)](https://huggingface.co/bert-base-multilingual-cased) on the Indonesian-Javanese-English code-mixed Twitter dataset.
Label ID and its corresponding name:
| Label ID | Label Name |
|:---------------:|:------------------------------------------:
| LABEL_0 | English (EN) |
| LABEL_1 | Indonesian (ID) |
| LABEL_2 | Javanese (JV) |
| LABEL_3 | Mixed Indonesian-English (MIX-ID-EN) |
| LABEL_4 | Mixed Indonesian-Javanese (MIX-ID-JV) |
| LABEL_5 | Mixed Javanese-English (MIX-JV-EN) |
| LABEL_6 | Other (O) |
It achieves the following results on the evaluation set:
- Loss: 0.3553
- Precision: 0.9189
- Recall: 0.9188
- F1: 0.9187
- Accuracy: 0.9451
It achieves the following results on the test set:
- Overall Precision: 0.9249
- Overall Recall: 0.9251
- Overall F1: 0.925
- Overall Accuracy: 0.951
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 386 | 0.2340 | 0.8956 | 0.8507 | 0.8715 | 0.9239 |
| 0.3379 | 2.0 | 772 | 0.2101 | 0.9057 | 0.8904 | 0.8962 | 0.9342 |
| 0.1603 | 3.0 | 1158 | 0.2231 | 0.9252 | 0.8896 | 0.9065 | 0.9367 |
| 0.1079 | 4.0 | 1544 | 0.2013 | 0.9272 | 0.8902 | 0.9070 | 0.9420 |
| 0.1079 | 5.0 | 1930 | 0.2179 | 0.9031 | 0.9179 | 0.9103 | 0.9425 |
| 0.0701 | 6.0 | 2316 | 0.2330 | 0.9075 | 0.9165 | 0.9114 | 0.9435 |
| 0.051 | 7.0 | 2702 | 0.2433 | 0.9117 | 0.9190 | 0.9150 | 0.9432 |
| 0.0384 | 8.0 | 3088 | 0.2545 | 0.9001 | 0.9167 | 0.9078 | 0.9439 |
| 0.0384 | 9.0 | 3474 | 0.2629 | 0.9164 | 0.9159 | 0.9158 | 0.9444 |
| 0.0293 | 10.0 | 3860 | 0.2881 | 0.9263 | 0.9096 | 0.9178 | 0.9427 |
| 0.022 | 11.0 | 4246 | 0.2882 | 0.9167 | 0.9222 | 0.9191 | 0.9450 |
| 0.0171 | 12.0 | 4632 | 0.3028 | 0.9203 | 0.9152 | 0.9177 | 0.9447 |
| 0.0143 | 13.0 | 5018 | 0.3236 | 0.9155 | 0.9167 | 0.9158 | 0.9440 |
| 0.0143 | 14.0 | 5404 | 0.3301 | 0.9237 | 0.9163 | 0.9199 | 0.9444 |
| 0.0109 | 15.0 | 5790 | 0.3290 | 0.9187 | 0.9154 | 0.9169 | 0.9442 |
| 0.0092 | 16.0 | 6176 | 0.3308 | 0.9213 | 0.9178 | 0.9194 | 0.9448 |
| 0.0075 | 17.0 | 6562 | 0.3501 | 0.9273 | 0.9142 | 0.9206 | 0.9445 |
| 0.0075 | 18.0 | 6948 | 0.3520 | 0.9200 | 0.9184 | 0.9190 | 0.9447 |
| 0.0062 | 19.0 | 7334 | 0.3524 | 0.9238 | 0.9183 | 0.9210 | 0.9458 |
| 0.0051 | 20.0 | 7720 | 0.3553 | 0.9189 | 0.9188 | 0.9187 | 0.9451 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.7.1
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Davlan/bert-base-multilingual-cased-finetuned-luganda | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"BertForMaskedLM"
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} | 16 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/anandmahindra-elonmusk-sahilbloom/1664960172385/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/1572573363255525377/Xz3fufYY_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/1574971893765251073/GglyevNe_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/1462779172451983370/xAsgPikz_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">Elon Musk & anand mahindra & Sahil Bloom</div>
<div style="text-align: center; font-size: 14px;">@anandmahindra-elonmusk-sahilbloom</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Elon Musk & anand mahindra & Sahil Bloom.
| Data | Elon Musk | anand mahindra | Sahil Bloom |
| --- | --- | --- | --- |
| Tweets downloaded | 3200 | 3240 | 3250 |
| Retweets | 123 | 705 | 202 |
| Short tweets | 970 | 174 | 693 |
| Tweets kept | 2107 | 2361 | 2355 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1diitahr/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 @anandmahindra-elonmusk-sahilbloom's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1qdx5m74) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1qdx5m74/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/anandmahindra-elonmusk-sahilbloom')
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)
|
Declan/CNN_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 3 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# ronanki/MiniLM-L12-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('ronanki/MiniLM-L12-v2')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('ronanki/MiniLM-L12-v2')
model = AutoModel.from_pretrained('ronanki/MiniLM-L12-v2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=ronanki/MiniLM-L12-v2)
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 329 with parameters:
```
{'batch_size': 64}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 20,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 658,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Declan/ChicagoTribune_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: lewtun/distilgpt2-finetuned-shakespeare
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. -->
# lewtun/distilgpt2-finetuned-shakespeare
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.9411
- Validation Loss: 3.5767
- Epoch: 29
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 4.2112 | 3.8253 | 0 |
| 3.8997 | 3.6898 | 1 |
| 3.7783 | 3.6304 | 2 |
| 3.7046 | 3.5846 | 3 |
| 3.6477 | 3.5667 | 4 |
| 3.6001 | 3.5445 | 5 |
| 3.5563 | 3.5333 | 6 |
| 3.5198 | 3.5240 | 7 |
| 3.4842 | 3.5146 | 8 |
| 3.4505 | 3.5126 | 9 |
| 3.4184 | 3.5022 | 10 |
| 3.3912 | 3.5027 | 11 |
| 3.3613 | 3.5003 | 12 |
| 3.3337 | 3.4985 | 13 |
| 3.3045 | 3.5004 | 14 |
| 3.2772 | 3.5014 | 15 |
| 3.2527 | 3.5018 | 16 |
| 3.2274 | 3.5053 | 17 |
| 3.2011 | 3.5106 | 18 |
| 3.1754 | 3.5143 | 19 |
| 3.1512 | 3.5181 | 20 |
| 3.1259 | 3.5274 | 21 |
| 3.1003 | 3.5215 | 22 |
| 3.0809 | 3.5354 | 23 |
| 3.0568 | 3.5335 | 24 |
| 3.0306 | 3.5502 | 25 |
| 3.0080 | 3.5574 | 26 |
| 2.9857 | 3.5587 | 27 |
| 2.9654 | 3.5760 | 28 |
| 2.9411 | 3.5767 | 29 |
### Framework versions
- Transformers 4.22.2
- TensorFlow 2.10.0
- Datasets 2.5.2
- Tokenizers 0.11.6
|
DeividasM/wav2vec2-large-xlsr-53-lithuanian | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"lt",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
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}
}
} | 7 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="summary71/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Dkwkk/W | []
| null | {
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} | 0 | null | ---
tags:
- generated_from_trainer
model-index:
- name: kobert-finetuned-klue-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# kobert-finetuned-klue-v2
This model is a fine-tuned version of [monologg/kobert](https://huggingface.co/monologg/kobert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.3234
## 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
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 5.5898 | 1.08 | 500 | 5.2618 |
| 5.217 | 2.16 | 1000 | 5.1505 |
| 5.1044 | 3.24 | 1500 | 5.0895 |
| 5.0048 | 4.32 | 2000 | 5.0649 |
| 4.8292 | 5.4 | 2500 | 4.9589 |
| 4.5451 | 6.48 | 3000 | 4.8549 |
| 4.2284 | 7.56 | 3500 | 4.8801 |
| 3.9195 | 8.64 | 4000 | 4.8797 |
| 3.6506 | 9.72 | 4500 | 4.8009 |
| 3.4175 | 10.8 | 5000 | 4.8996 |
| 3.1964 | 11.88 | 5500 | 4.9734 |
| 3.0401 | 12.96 | 6000 | 4.9378 |
| 2.8965 | 14.04 | 6500 | 5.3631 |
| 2.7672 | 15.12 | 7000 | 5.3234 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-50 | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
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}
} | 28 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: guma/distilgpt2-finetuned-shakespeare
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. -->
# guma/distilgpt2-finetuned-shakespeare
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.1769
- Validation Loss: 3.5116
- Epoch: 19
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 4.2136 | 3.8303 | 0 |
| 3.8997 | 3.6993 | 1 |
| 3.7790 | 3.6344 | 2 |
| 3.7061 | 3.5923 | 3 |
| 3.6476 | 3.5653 | 4 |
| 3.6003 | 3.5513 | 5 |
| 3.5578 | 3.5360 | 6 |
| 3.5204 | 3.5277 | 7 |
| 3.4843 | 3.5171 | 8 |
| 3.4514 | 3.5117 | 9 |
| 3.4194 | 3.5048 | 10 |
| 3.3903 | 3.5040 | 11 |
| 3.3627 | 3.5006 | 12 |
| 3.3332 | 3.5006 | 13 |
| 3.3052 | 3.5019 | 14 |
| 3.2772 | 3.5051 | 15 |
| 3.2514 | 3.5043 | 16 |
| 3.2249 | 3.5026 | 17 |
| 3.2019 | 3.5129 | 18 |
| 3.1769 | 3.5116 | 19 |
### Framework versions
- Transformers 4.22.2
- TensorFlow 2.8.2
- Datasets 2.5.2
- Tokenizers 0.12.1
|
albert-base-v1 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
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} | 38,156 | 2022-10-06T10:07:39Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: mn367/mark-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. -->
# mn367/mark-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.0868
- Validation Loss: 2.7662
- 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': -523, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.0868 | 2.7662 | 0 |
### Framework versions
- Transformers 4.22.2
- TensorFlow 2.8.2
- Datasets 2.5.2
- Tokenizers 0.12.1
|
albert-base-v2 | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
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"AlbertForMaskedLM"
],
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} | 4,785,283 | 2022-10-06T10:08:28Z | ---
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
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5477951635989807
---
<!-- 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: 0.8133
- Matthews Correlation: 0.5478
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5259 | 1.0 | 535 | 0.5401 | 0.4009 |
| 0.3513 | 2.0 | 1070 | 0.5403 | 0.4876 |
| 0.2373 | 3.0 | 1605 | 0.5422 | 0.5384 |
| 0.1795 | 4.0 | 2140 | 0.7586 | 0.5309 |
| 0.1282 | 5.0 | 2675 | 0.8133 | 0.5478 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
albert-large-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
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} | 26,792 | 2022-10-06T10:11:18Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tsl_news
metrics:
- accuracy
- f1
model-index:
- name: news-tsl-train
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tsl_news
type: tsl_news
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 1.0
- name: F1
type: f1
value: 1.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# news-tsl-train
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tsl_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0125
- Accuracy: 1.0
- F1: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 10
### Training results
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
|
bert-base-chinese | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"zh",
"arxiv:1810.04805",
"transformers",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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} | 3,377,486 | 2022-10-06T11:05:23Z | ## BUDDI Table Factory: A toolbox for generating synthetic documents with annotated tables and cells
**About**
In table detection, we initialize the weights with a pre-trained CDeCNet model using COCO dataset. We re-train the model for five epochs using a stochastic gradient descent optimizer with a learning rate of 0.00125, the momentum of 0.9, and weight decay of 0.0001.
***Hardware Used***
We perform all the experiments on NVIDIA GeForce RTX 2080 Ti GPU with 12 GB GPU memory, Intel(R) Xeon(R) CPU E5-2640 v2 @ 2.00GHz, and 128 GB of RAM.
**Table Detection Model & Training Parameter**
***Optimizer***
| Parameter |Value |
|--|--|
| Type | SGD |
| Learning Rate |0.00125 |
| Momentum | 0.8 |
| Weight Decay |0.001 |
*** Learning Policy ***
| Parameter |Value |
|--|--|
| Policy | Step |
|Warmup | Linear |
| Warmup Iteration | 100 |
| Warmup Ratio |0.001 |
| Step | 4,16,32 |
***General Parameter***
| Parameter |Value |
|--|--|
| Epoch | 5 |
| Step Interval |50 |
***Model Paper Reference***
CDeC-Net: Composite Deformable Cascade Network for Table Detection in Document Images
https://arxiv.org/abs/2008.10831 |
bert-base-german-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
} | 175,983 | 2022-10-06T11:20:12Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-burak-new-300-v2-4
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. -->
# wav2vec2-burak-new-300-v2-4
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3402
- Wer: 0.2237
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 131
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 7.7711 | 2.45 | 500 | 3.1768 | 1.0 |
| 3.1194 | 4.9 | 1000 | 2.6401 | 1.0 |
| 1.4593 | 7.35 | 1500 | 0.5243 | 0.5960 |
| 0.7581 | 9.8 | 2000 | 0.3534 | 0.4432 |
| 0.5843 | 12.25 | 2500 | 0.3159 | 0.4157 |
| 0.4703 | 14.71 | 3000 | 0.3003 | 0.3668 |
| 0.4045 | 17.16 | 3500 | 0.2891 | 0.3414 |
| 0.3581 | 19.61 | 4000 | 0.2609 | 0.3207 |
| 0.3268 | 22.06 | 4500 | 0.2622 | 0.3207 |
| 0.3063 | 24.51 | 5000 | 0.2805 | 0.3193 |
| 0.2729 | 26.96 | 5500 | 0.2674 | 0.2884 |
| 0.249 | 29.41 | 6000 | 0.2740 | 0.2953 |
| 0.2275 | 31.86 | 6500 | 0.2729 | 0.2753 |
| 0.2295 | 34.31 | 7000 | 0.2801 | 0.2691 |
| 0.2105 | 36.76 | 7500 | 0.2992 | 0.2801 |
| 0.1905 | 39.22 | 8000 | 0.2967 | 0.2663 |
| 0.1884 | 41.67 | 8500 | 0.2911 | 0.2691 |
| 0.1773 | 44.12 | 9000 | 0.2966 | 0.2753 |
| 0.1672 | 46.57 | 9500 | 0.3051 | 0.2505 |
| 0.1632 | 49.02 | 10000 | 0.2872 | 0.2491 |
| 0.1553 | 51.47 | 10500 | 0.3121 | 0.2629 |
| 0.1619 | 53.92 | 11000 | 0.3044 | 0.2581 |
| 0.1444 | 56.37 | 11500 | 0.3135 | 0.2567 |
| 0.1451 | 58.82 | 12000 | 0.3033 | 0.2519 |
| 0.1386 | 61.27 | 12500 | 0.3079 | 0.2622 |
| 0.1261 | 63.73 | 13000 | 0.3037 | 0.2395 |
| 0.1287 | 66.18 | 13500 | 0.3221 | 0.2409 |
| 0.1236 | 68.63 | 14000 | 0.3179 | 0.2464 |
| 0.1215 | 71.08 | 14500 | 0.3521 | 0.2429 |
| 0.1208 | 73.53 | 15000 | 0.3481 | 0.2540 |
| 0.1128 | 75.98 | 15500 | 0.3288 | 0.2402 |
| 0.1108 | 78.43 | 16000 | 0.3238 | 0.2450 |
| 0.1074 | 80.88 | 16500 | 0.3178 | 0.2416 |
| 0.1086 | 83.33 | 17000 | 0.3461 | 0.2361 |
| 0.1059 | 85.78 | 17500 | 0.3342 | 0.2457 |
| 0.0981 | 88.24 | 18000 | 0.3382 | 0.2354 |
| 0.0995 | 90.69 | 18500 | 0.3466 | 0.2416 |
| 0.0995 | 93.14 | 19000 | 0.3326 | 0.2271 |
| 0.0929 | 95.59 | 19500 | 0.3526 | 0.2237 |
| 0.0944 | 98.04 | 20000 | 0.3516 | 0.2347 |
| 0.089 | 100.49 | 20500 | 0.3504 | 0.2271 |
| 0.0915 | 102.94 | 21000 | 0.3425 | 0.2285 |
| 0.0845 | 105.39 | 21500 | 0.3309 | 0.2306 |
| 0.0887 | 107.84 | 22000 | 0.3196 | 0.2264 |
| 0.0812 | 110.29 | 22500 | 0.3285 | 0.2264 |
| 0.0856 | 112.75 | 23000 | 0.3347 | 0.2251 |
| 0.0778 | 115.2 | 23500 | 0.3403 | 0.2271 |
| 0.0748 | 117.65 | 24000 | 0.3427 | 0.2278 |
| 0.0803 | 120.1 | 24500 | 0.3380 | 0.2223 |
| 0.0768 | 122.55 | 25000 | 0.3392 | 0.2189 |
| 0.0764 | 125.0 | 25500 | 0.3423 | 0.2278 |
| 0.0786 | 127.45 | 26000 | 0.3423 | 0.2230 |
| 0.0766 | 129.9 | 26500 | 0.3402 | 0.2237 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
|
bert-base-german-dbmdz-uncased | [
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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} | 68,305 | 2022-10-06T11:23:45Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- math_qa
metrics:
- rouge
model-index:
- name: t5-small-mathT5-finetune_qatoexp
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: math_qa
type: math_qa
config: default
split: train
args: default
metrics:
- name: Rouge1
type: rouge
value: 21.9174
---
<!-- 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-mathT5-finetune_qatoexp
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the math_qa dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8677
- Rouge1: 21.9174
- Rouge2: 8.4401
- Rougel: 19.1645
- Rougelsum: 19.8239
- Gen Len: 18.9765
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
We have trained T5-small on MathQA dataset for sequence to sequence generation of explanations from given math problem.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.4496 | 1.0 | 2984 | 2.2096 | 19.6477 | 6.508 | 16.9295 | 17.5212 | 18.9064 |
| 2.2893 | 2.0 | 5968 | 2.0837 | 20.4879 | 7.2528 | 17.7778 | 18.4085 | 18.968 |
| 2.1869 | 3.0 | 8952 | 2.0125 | 20.8462 | 7.6105 | 18.1516 | 18.8343 | 18.9837 |
| 2.1456 | 4.0 | 11936 | 1.9633 | 20.7623 | 7.7113 | 18.1274 | 18.783 | 18.9886 |
| 2.1171 | 5.0 | 14920 | 1.9321 | 21.0648 | 7.8897 | 18.4162 | 19.0551 | 18.9844 |
| 2.0854 | 6.0 | 17904 | 1.9061 | 21.4445 | 8.0883 | 18.8038 | 19.4176 | 18.9812 |
| 2.0592 | 7.0 | 20888 | 1.8902 | 21.5714 | 8.2751 | 18.8864 | 19.537 | 18.9772 |
| 2.0609 | 8.0 | 23872 | 1.8770 | 21.7737 | 8.3297 | 19.022 | 19.6897 | 18.9763 |
| 2.0285 | 9.0 | 26856 | 1.8701 | 21.964 | 8.4358 | 19.1701 | 19.845 | 18.9747 |
| 2.0165 | 10.0 | 29840 | 1.8677 | 21.9174 | 8.4401 | 19.1645 | 19.8239 | 18.9765 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
|
bert-large-uncased-whole-word-masking-finetuned-squad | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
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} | 480,510 | 2022-10-06T12:10:37Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: materials
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8928571343421936
---
# materials
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### brick
#### metal
#### paper
#### plastic
#### wood
 |
bert-large-uncased-whole-word-masking | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
} | 76,685 | 2022-10-06T12:12:18Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: medieval-style-crap
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.89552241563797
---
# medieval-style-crap
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### castle
#### ruins
#### tower
 |
AAli/distilbert-base-uncased-finetuned-ner | []
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} | 0 | 2022-10-06T22:08:16Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: amazon-review-sentiment-analysis2
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. -->
# amazon-review-sentiment-analysis2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0333
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.22.1
- Pytorch 1.11.0
- Datasets 2.4.0
- Tokenizers 0.12.1
|
AI4Sec/cyner-xlm-roberta-large | [
"xlm-roberta",
"token-classification",
"transformers",
"license:mit",
"autotrain_compatible"
]
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} | 4 | null | ---
license: mit
---
### alisa on Stable Diffusion
This is the `<alisa-selezneva>` 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`:
|
AVSilva/bertimbau-large-fine-tuned-sd | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:mit",
"autotrain_compatible"
]
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} | 10 | 2022-10-07T06:17:30Z | ---
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
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5439723028804963
---
<!-- 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: 0.8336
- Matthews Correlation: 0.5440
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5211 | 1.0 | 535 | 0.5317 | 0.4158 |
| 0.3505 | 2.0 | 1070 | 0.5131 | 0.4542 |
| 0.2383 | 3.0 | 1605 | 0.5371 | 0.5335 |
| 0.1772 | 4.0 | 2140 | 0.7725 | 0.5376 |
| 0.1241 | 5.0 | 2675 | 0.8336 | 0.5440 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.1+cpu
- Datasets 2.5.2
- Tokenizers 0.12.1
|
Aakansha/hateSpeechClassification | []
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} | 0 | 2022-10-07T07:01:58Z | ---
tags:
- pyannote
- pyannote-audio
- pyannote-audio-pipeline
- audio
- voice
- speech
- speaker
- speaker-diarization
- speaker-change-detection
- voice-activity-detection
- overlapped-speech-detection
datasets:
- ami
- dihard
- voxconverse
- aishell
- repere
- voxceleb
license: mit
---
# 🎹 Speaker diarization
Relies on pyannote.audio 2.0: see [installation instructions](https://github.com/pyannote/pyannote-audio/tree/develop#installation).
## TL;DR
```python
# load the pipeline from Hugginface Hub
from pyannote.audio import Pipeline
pipeline = Pipeline.from_pretrained("pyannote/[email protected]")
# apply the pipeline to an audio file
diarization = pipeline("audio.wav")
# dump the diarization output to disk using RTTM format
with open("audio.rttm", "w") as rttm:
diarization.write_rttm(rttm)
```
## Advanced usage
In case the number of speakers is known in advance, one can use the `num_speakers` option:
```python
diarization = pipeline("audio.wav", num_speakers=2)
```
One can also provide lower and/or upper bounds on the number of speakers using `min_speakers` and `max_speakers` options:
```python
diarization = pipeline("audio.wav", min_speakers=2, max_speakers=5)
```
If you feel adventurous, you can try and play with the various pipeline hyper-parameters.
For instance, one can use a more aggressive voice activity detection by increasing the value of `segmentation_onset` threshold:
```python
hparams = pipeline.parameters(instantiated=True)
hparams["segmentation_onset"] += 0.1
pipeline.instantiate(hparams)
```
## Benchmark
### Real-time factor
Real-time factor is around 5% using one Nvidia Tesla V100 SXM2 GPU (for the neural inference part) and one Intel Cascade Lake 6248 CPU (for the clustering part).
In other words, it takes approximately 3 minutes to process a one hour conversation.
### Accuracy
This pipeline is benchmarked on a growing collection of datasets.
Processing is fully automatic:
* no manual voice activity detection (as is sometimes the case in the literature)
* no manual number of speakers (though it is possible to provide it to the pipeline)
* no fine-tuning of the internal models nor tuning of the pipeline hyper-parameters to each dataset
... with the least forgiving diarization error rate (DER) setup (named *"Full"* in [this paper](https://doi.org/10.1016/j.csl.2021.101254)):
* no forgiveness collar
* evaluation of overlapped speech
| Benchmark | [DER%](. "Diarization error rate") | [FA%](. "False alarm rate") | [Miss%](. "Missed detection rate") | [Conf%](. "Speaker confusion rate") | Expected output | File-level evaluation |
| ---------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------- | --------------------------- | ---------------------------------- | ----------------------------------- | ------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------ |
| [AISHELL-4](http://www.openslr.org/111/) | 14.61 | 3.31 | 4.35 | 6.95 | [RTTM](reproducible_research/AISHELL.SpeakerDiarization.Full.test.rttm) | [eval](reproducible_research/AISHELL.SpeakerDiarization.Full.test.eval) |
| [AMI *Mix-Headset*](https://groups.inf.ed.ac.uk/ami/corpus/) [*only_words*](https://github.com/BUTSpeechFIT/AMI-diarization-setup) | 18.21 | 3.28 | 11.07 | 3.87 | [RTTM](reproducible_research/2022.07/AMI.SpeakerDiarization.only_words.test.rttm) | [eval](reproducible_research/2022.07/AMI.SpeakerDiarization.only_words.test.eval) |
| [AMI *Array1-01*](https://groups.inf.ed.ac.uk/ami/corpus/) [*only_words*](https://github.com/BUTSpeechFIT/AMI-diarization-setup) | 29.00 | 2.71 | 21.61 | 4.68 | [RTTM](reproducible_research/2022.07/AMI-SDM.SpeakerDiarization.only_words.test.rttm) | [eval](reproducible_research/2022.07/AMI-SDM.SpeakerDiarization.only_words.test.eval) |
| [CALLHOME](https://catalog.ldc.upenn.edu/LDC2001S97) [*Part2*](https://github.com/BUTSpeechFIT/CALLHOME_sublists/issues/1) | 30.24 | 3.71 | 16.86 | 9.66 | [RTTM](reproducible_research/2022.07/CALLHOME.SpeakerDiarization.CALLHOME.test.rttm) | [eval](reproducible_research/2022.07/CALLHOME.SpeakerDiarization.CALLHOME.test.eval) |
| [DIHARD 3 *Full*](https://arxiv.org/abs/2012.01477) | 20.99 | 4.25 | 10.74 | 6.00 | [RTTM](reproducible_research/2022.07/DIHARD.SpeakerDiarization.Full.test.rttm) | [eval](reproducible_research/2022.07/DIHARD.SpeakerDiarization.Full.test.eval) |
| [REPERE *Phase 2*](https://islrn.org/resources/360-758-359-485-0/) | 12.62 | 1.55 | 3.30 | 7.76 | [RTTM](reproducible_research/2022.07/REPERE.SpeakerDiarization.Full.test.rttm) | [eval](reproducible_research/2022.07/REPERE.SpeakerDiarization.Full.test.eval) |
| [VoxConverse *v0.0.2*](https://github.com/joonson/voxconverse) | 12.76 | 3.45 | 3.85 | 5.46 | [RTTM](reproducible_research/2022.07/VoxConverse.SpeakerDiarization.VoxConverse.test.rttm) | [eval](reproducible_research/2022.07/VoxConverse.SpeakerDiarization.VoxConverse.test.eval) |
## Support
For commercial enquiries and scientific consulting, please contact [me](mailto:[email protected]).
For [technical questions](https://github.com/pyannote/pyannote-audio/discussions) and [bug reports](https://github.com/pyannote/pyannote-audio/issues), please check [pyannote.audio](https://github.com/pyannote/pyannote-audio) Github repository.
## Citations
```bibtex
@inproceedings{Bredin2021,
Title = {{End-to-end speaker segmentation for overlap-aware resegmentation}},
Author = {{Bredin}, Herv{\'e} and {Laurent}, Antoine},
Booktitle = {Proc. Interspeech 2021},
Address = {Brno, Czech Republic},
Month = {August},
Year = {2021},
}
```
```bibtex
@inproceedings{Bredin2020,
Title = {{pyannote.audio: neural building blocks for speaker diarization}},
Author = {{Bredin}, Herv{\'e} and {Yin}, Ruiqing and {Coria}, Juan Manuel and {Gelly}, Gregory and {Korshunov}, Pavel and {Lavechin}, Marvin and {Fustes}, Diego and {Titeux}, Hadrien and {Bouaziz}, Wassim and {Gill}, Marie-Philippe},
Booktitle = {ICASSP 2020, IEEE International Conference on Acoustics, Speech, and Signal Processing},
Address = {Barcelona, Spain},
Month = {May},
Year = {2020},
}
```
|
Adnan/UrduNewsHeadlines | []
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} | 0 | null | ---
language:
- "code"
thumbnail: "https://to-be-updated"
tags:
- code generation
- code translation
- bug fixing
license: "mit"
datasets:
- CodeSearchNet
- CodeXGLUE
metrics:
- EM
- CodeBLEU
---
Pretrained model for NatGen: Generative Pre-training by “Naturalizing” Source Code [[`paper`]](https://dl.acm.org/doi/abs/10.1145/3540250.3549162),[[`code`]](https://github.com/saikat107/NatGen),[[`slide`]](https://docs.google.com/presentation/d/1T6kjiohAAR1YvcNvTASR94HptA3xHGCl/edit?usp=sharing&ouid=111755026725574085503&rtpof=true&sd=true).
To load the model,
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("saikatc/NatGen")
model = AutoModelForSeq2SeqLM.from_pretrained("saikatc/NatGen")
```
For citation,
```
@inproceedings{chakraborty2022natgen,
author = {Chakraborty, Saikat and Ahmed, Toufique and Ding, Yangruibo and Devanbu, Premkumar T. and Ray, Baishakhi},
title = {NatGen: Generative Pre-Training by “Naturalizing” Source Code},
year = {2022},
isbn = {9781450394130},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3540250.3549162},
doi = {10.1145/3540250.3549162},
booktitle = {Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering},
pages = {18–30},
numpages = {13},
keywords = {Neural Network, Semantic Preserving Transformation, Source Code Transformer, Source Code Pre-training},
location = {Singapore, Singapore},
series = {ESEC/FSE 2022}
}
```
|
Akuva2001/SocialGraph | [
"has_space"
]
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} | 0 | null | ---
license: apache-2.0
---
# Text-Summarizer
## About
An Abstractive text summarizer trained using lstm based sequence to sequence model with attention mechanisim. The attention model is used for generating each word of the summary conditioned on the input sentence.
Used CNN_DailyMail dataset.
### Training Model Overview
loss graph
encoder-decoder overview
#### Conclusion
🫶 The machine learning model to convert a text document to abstract is done successfully.
🫶 Created a Flask app using an api call from this repository & deployed the app in heroku app.
##### Deployment:
🫶 https://text-summariser-v1.herokuapp.com/ |
Al/mymodel | []
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} | 0 | null | ---
language:
- en
tags:
- stable-diffusion
- text-to-image
inference: false
---
[hakurei/waifu-diffusion-v1-3](https://huggingface.co/hakurei/waifu-diffusion-v1-3) fine-tuned on 800 samples from [https://www.kaggle.com/datasets/stevenevan99/face-of-pixiv-top-daily-illustration-2020](https://www.kaggle.com/datasets/stevenevan99/face-of-pixiv-top-daily-illustration-2020) at 384x384 resolution (because some of the images here are very low-res and I don't want artifacts)
examples of "highres, sketch, rkgk, pfp"
"1girl bangs blush crop top earrings grey eyes hair ornament hairclip indoors jewelry mole mole under eye necklace highres, sketch, rkgk"
 |
Alaeddin/convbert-base-turkish-ner-cased | [
"pytorch",
"convbert",
"token-classification",
"transformers",
"autotrain_compatible"
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} | 9 | null | Access to model Sania67/Fine_tunning_on_Urdu_V3 is restricted and you are not in the authorized list. Visit https://huggingface.co/Sania67/Fine_tunning_on_Urdu_V3 to ask for access. |
AlanDev/test | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9807407407407407
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0613
- Accuracy: 0.9807
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2578 | 1.0 | 190 | 0.1447 | 0.9530 |
| 0.1733 | 2.0 | 380 | 0.0787 | 0.9733 |
| 0.1139 | 3.0 | 570 | 0.0613 | 0.9807 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
|
Aleksandar/electra-srb-ner-setimes-lr | []
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} | 0 | null | ---
tags:
- generated_from_trainer
model-index:
- name: bert-base-low_resource-wellness
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-low_resource-wellness
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0853
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8441 | 1.0 | 285 | 1.5346 |
| 1.5211 | 2.0 | 570 | 1.4483 |
| 1.3907 | 3.0 | 855 | 1.3713 |
| 1.3227 | 4.0 | 1140 | 1.2589 |
| 1.2745 | 5.0 | 1425 | 1.2187 |
| 1.2411 | 6.0 | 1710 | 1.2395 |
| 1.1769 | 7.0 | 1995 | 1.1925 |
| 1.1377 | 8.0 | 2280 | 1.1454 |
| 1.1443 | 9.0 | 2565 | 1.1225 |
| 1.11 | 10.0 | 2850 | 1.0843 |
### Framework versions
- Transformers 4.23.0
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.1
|
AlekseyKorshuk/comedy-scripts | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 20 | null | ---
tags:
- generated_from_keras_callback
model-index:
- name: t5-base-medium-title-generation
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. -->
# t5-base-medium-title-generation
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.22.2
- TensorFlow 2.8.2
- Datasets 2.5.2
- Tokenizers 0.12.1
|
AlekseyKulnevich/Pegasus-Summarization | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
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} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned-wikitext2
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-wikitext2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2342
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.5534 | 1.0 | 3350 | 1.4516 |
| 1.4138 | 2.0 | 6700 | nan |
| 1.3001 | 3.0 | 10050 | nan |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Alexander-Learn/bert-finetuned-ner | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
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} | 8 | null | ---
license: openrail
---
# kun_uz_classification_model
|
AlgoveraAI/dcgan | [
"pytorch",
"transformers"
]
| null | {
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} | 12 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: distilbart-cnn-12-6-finetuned-1.2.0
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. -->
# distilbart-cnn-12-6-finetuned-1.2.0
This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2276
- Rouge1: 38.3653
- Rouge2: 18.587
- Rougel: 32.3348
- Rougelsum: 32.4829
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 2.6933 | 1.0 | 98 | 2.2276 | 38.3653 | 18.587 | 32.3348 | 32.4829 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
|
AliReza/distilbert-emotion | []
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} | 0 | null | ---
license: creativeml-openrail-m
---
just my dump and stash, nothin to see here. |
aisoftware/Loquela | [
"onnx"
]
| null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-finetuned-squad-infilling-lr-3e-5-decay-001
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-infilling-lr-3e-5-decay-001
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: 12
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 24
- total_eval_batch_size: 16
- 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
|
AmirHussein/test | []
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} | 0 | null | ---
tags:
- vision
- image-segmentation
datasets:
- segments/sidewalk-semantic
finetuned_from:
- nvidia/mit-b5
widget:
- src: https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/admin-tobias/439f6843-80c5-47ce-9b17-0b2a1d54dbeb.jpg
example_title: Brugge
---
# SegFormer (b5-sized) model fine-tuned on sidewalk-semantic dataset.
SegFormer model fine-tuned on SegmentsAI [`sidewalk-semantic`](https://huggingface.co/datasets/segments/sidewalk-semantic). It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](https://github.com/NVlabs/SegFormer).
## Model description
SegFormer consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great results on semantic segmentation benchmarks such as ADE20K and Cityscapes. The hierarchical Transformer is first pre-trained on ImageNet-1k, after which a decode head is added and fine-tuned altogether on a downstream dataset.
## Code and Notebook
Here is how to use this model to classify an image of the sidewalk dataset:
```python
from transformers import SegformerFeatureExtractor, SegformerForImageClassification
from PIL import Image
import requests
url = "https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/admin-tobias/439f6843-80c5-47ce-9b17-0b2a1d54dbeb.jpg"
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = SegformerFeatureExtractor.from_pretrained("zoheb/mit-b5-finetuned-sidewalk-semantic")
model = SegformerForImageClassification.from_pretrained("zoheb/mit-b5-finetuned-sidewalk-semantic")
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 35 Sidewalk classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
You can go through its detailed notebook [here](https://github.com/ZohebAbai/Deep-Learning-Projects/blob/master/09_HF_Image_Segmentation_using_Transformers.ipynb).
For more code examples, refer to the [documentation](https://huggingface.co/transformers/model_doc/segformer.html#).
## License
The license for this model can be found [here](https://github.com/NVlabs/SegFormer/blob/master/LICENSE).
## BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2105-15203,
author = {Enze Xie and
Wenhai Wang and
Zhiding Yu and
Anima Anandkumar and
Jose M. Alvarez and
Ping Luo},
title = {SegFormer: Simple and Efficient Design for Semantic Segmentation with
Transformers},
journal = {CoRR},
volume = {abs/2105.15203},
year = {2021},
url = {https://arxiv.org/abs/2105.15203},
eprinttype = {arXiv},
eprint = {2105.15203},
timestamp = {Wed, 02 Jun 2021 11:46:42 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2105-15203.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
Amro-Kamal/gpt | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-large-finetuned-squad-infilling-lr-5e-6-decay-01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-finetuned-squad-infilling-lr-5e-6-decay-01
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) 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: 5e-06
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 24
- total_eval_batch_size: 16
- 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
|
Amrrs/south-indian-foods | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"huggingpics",
"model-index",
"autotrain_compatible"
]
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} | 21 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-model2-0810
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-model2-0810
This model is a fine-tuned version of [theojolliffe/bart-model2-1409](https://huggingface.co/theojolliffe/bart-model2-1409) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2627
- Rouge1: 58.8322
- Rouge2: 56.2696
- Rougel: 58.8934
- Rougelsum: 58.3106
- Gen Len: 19.2222
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 169 | 0.3839 | 53.2948 | 45.0992 | 52.1785 | 53.8143 | 18.0 |
| No log | 2.0 | 338 | 0.3099 | 55.227 | 49.17 | 55.1602 | 55.6483 | 17.8889 |
| 0.3831 | 3.0 | 507 | 0.2566 | 56.6535 | 52.9359 | 56.1953 | 56.0607 | 18.8889 |
| 0.3831 | 4.0 | 676 | 0.2627 | 58.8322 | 56.2696 | 58.8934 | 58.3106 | 19.2222 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
|
Amrrs/wav2vec2-large-xlsr-53-tamil | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"ta",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index",
"has_space"
]
| automatic-speech-recognition | {
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} | 31 | null | ---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: pegasus-model3-0810
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-model3-0810
This model is a fine-tuned version of [theojolliffe/pegasus-model-3-x25](https://huggingface.co/theojolliffe/pegasus-model-3-x25) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0128
- Rouge1: 74.5218
- Rouge2: 73.9903
- Rougel: 74.4946
- Rougelsum: 74.7509
- Gen Len: 122.9655
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 0.3663 | 1.0 | 547 | 0.0960 | 70.3803 | 65.6296 | 64.7957 | 69.7556 | 122.7241 |
| 0.127 | 2.0 | 1094 | 0.0276 | 74.3121 | 73.3963 | 74.026 | 74.502 | 122.9655 |
| 0.0706 | 3.0 | 1641 | 0.0157 | 74.4541 | 73.5817 | 74.1314 | 74.5238 | 122.9655 |
| 0.046 | 4.0 | 2188 | 0.0128 | 74.5218 | 73.9903 | 74.4946 | 74.7509 | 122.9655 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
|
Ana1315/A | []
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} | 0 | null | ---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.76 +/- 0.43
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="joelearn22/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Andranik/TestQA2 | [
"pytorch",
"electra",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| question-answering | {
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"ElectraForQuestionAnswering"
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} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-finetuned-squad-infilling-lr-5e-6-decay-01
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-infilling-lr-5e-6-decay-01
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: 5e-06
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 24
- total_eval_batch_size: 16
- 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
|
Andranik/TestQaV1 | [
"pytorch",
"rust",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
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"RobertaForQuestionAnswering"
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} | 4 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 5683 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 5683,
"warmup_steps": 569,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Andrija/RobertaFastBPE | []
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} | 0 | 2022-10-08T17:20:00Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-large-few-shot-k-16-finetuned-squad-infilling-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-large-few-shot-k-16-finetuned-squad-infilling-seed-0
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) 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: 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
|
Andrija/SRoBERTa-base-NER | [
"pytorch",
"roberta",
"token-classification",
"hr",
"sr",
"multilingual",
"dataset:hr500k",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
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}
} | 12 | null | Model Trained Using distilbert
Problem type: Multi-class Classification |
Andrija/SRoBERTa-base | [
"pytorch",
"roberta",
"fill-mask",
"hr",
"sr",
"multilingual",
"dataset:oscar",
"dataset:leipzig",
"transformers",
"masked-lm",
"license:apache-2.0",
"autotrain_compatible"
]
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} | 80 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/elymitra_/1665251561234/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/1570597171904356354/qYC_zXje_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">the ely and its own 🌇🌇</div>
<div style="text-align: center; font-size: 14px;">@elymitra_</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from the ely and its own 🌇🌇.
| Data | the ely and its own 🌇🌇 |
| --- | --- |
| Tweets downloaded | 2421 |
| Retweets | 201 |
| Short tweets | 206 |
| Tweets kept | 2014 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/lfxdp292/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 @elymitra_'s tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3stifz52) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3stifz52/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/elymitra_')
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)
|
Ann2020/rubert-base-cased-sentence-finetuned-ner | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-base-few-shot-k-32-finetuned-squad-infilling-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-32-finetuned-squad-infilling-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: 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: 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
|
AnonymousSub/SR_rule_based_hier_quadruplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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} | 1 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-base-few-shot-k-1024-finetuned-squad-infilling-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-1024-finetuned-squad-infilling-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: 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: linear
- 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
|
AnonymousSub/SR_rule_based_hier_triplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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} | 1 | null | ---
license: mit
---
# Description
Trainer: Hank
Clownpiece from Touhou
# Dataset
>Training: 8 images
>Regularization: 2 images
# Info
>WD1-3 E8 Clownpiece fairy_girl_blonde_hair_red_eyes 3k
>Model Used: Waifu Diffusion 1.3 Epoch 8
>Steps: 3000
>Keyword: clownpiece (Use this in the prompt)
>Class Phrase: fairy_girl_blonde_hair_red_eyes |
AnonymousSub/SR_rule_based_roberta_bert_triplet_epochs_1_shard_1 | [
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"roberta",
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"transformers"
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} | 2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-base-finetuned-squad-infilling-lr-3e-5
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-finetuned-squad-infilling-lr-3e-5
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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 24
- total_eval_batch_size: 16
- 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
|
AnonymousSub/SR_rule_based_roberta_only_classfn_epochs_1_shard_1 | [
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} | 2 | null | Access to model chelmyers/sd is restricted and you are not in the authorized list. Visit https://huggingface.co/chelmyers/sd to ask for access. |
AnonymousSub/SR_rule_based_roberta_twostagetriplet_hier_epochs_1_shard_1 | [
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"roberta",
"feature-extraction",
"transformers"
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} | 4 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-base-few-shot-k-1024-finetuned-squad-infilling-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-1024-finetuned-squad-infilling-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
- 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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