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CarlCochet/trajectory-transformer-hopper-expert-v2 | 35ece73107915392c3429f9ab72e3a1c576a330f | 2022-05-12T17:02:57.000Z | [
"pytorch",
"trajectory_transformer",
"feature-extraction",
"transformers",
"license:mit"
] | feature-extraction | false | CarlCochet | null | CarlCochet/trajectory-transformer-hopper-expert-v2 | 2 | null | transformers | 25,800 | ---
license: mit
---
|
CarlCochet/trajectory-transformer-hopper-medium-v2 | 4ffd408a079417fab9f1a93aa8d6d974834d7686 | 2022-05-12T17:05:33.000Z | [
"pytorch",
"trajectory_transformer",
"feature-extraction",
"transformers",
"license:mit"
] | feature-extraction | false | CarlCochet | null | CarlCochet/trajectory-transformer-hopper-medium-v2 | 2 | null | transformers | 25,801 | ---
license: mit
---
|
PSW/low_resource_percent1_randomswap_seed42 | 431a3d094f4daf1388f89a372f51680eabf223d7 | 2022-05-05T09:23:29.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent1_randomswap_seed42 | 2 | null | transformers | 25,802 | Entry not found |
CarlCochet/trajectory-transformer-walker2d-expert-v2 | 436cb9384717f392425c5d3b62253b3b951cabd6 | 2022-05-12T17:06:16.000Z | [
"pytorch",
"trajectory_transformer",
"feature-extraction",
"transformers",
"license:mit"
] | feature-extraction | false | CarlCochet | null | CarlCochet/trajectory-transformer-walker2d-expert-v2 | 2 | null | transformers | 25,803 | ---
license: mit
---
|
CarlCochet/trajectory-transformer-walker2d-medium-expert-v2 | 6a5ed504de727b79677a8cf076ce0968f4072159 | 2022-05-12T17:06:58.000Z | [
"pytorch",
"trajectory_transformer",
"feature-extraction",
"transformers",
"license:mit"
] | feature-extraction | false | CarlCochet | null | CarlCochet/trajectory-transformer-walker2d-medium-expert-v2 | 2 | null | transformers | 25,804 | ---
license: mit
---
|
PSW/low_resource_percent1_seed27 | e9c311ba2d76aee3fd0c47f21563ff317699feea | 2022-05-05T09:35:53.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent1_seed27 | 2 | null | transformers | 25,805 | Entry not found |
Gootter/autotrain-Bart_683-825526269 | 50b6c3ad3efac7980f60ec0e32ae88be9fbd61f9 | 2022-05-05T10:03:01.000Z | [
"pytorch",
"bart",
"text2text-generation",
"unk",
"dataset:Gootter/autotrain-data-Bart_683",
"transformers",
"autotrain",
"co2_eq_emissions",
"autotrain_compatible"
] | text2text-generation | false | Gootter | null | Gootter/autotrain-Bart_683-825526269 | 2 | null | transformers | 25,806 | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Gootter/autotrain-data-Bart_683
co2_eq_emissions: 28.12268287254098
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 825526269
- CO2 Emissions (in grams): 28.12268287254098
## Validation Metrics
- Loss: 2.836289644241333
- Rouge1: 31.9867
- Rouge2: 10.3239
- RougeL: 21.0603
- RougeLsum: 30.0862
- Gen Len: 142.0
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/Gootter/autotrain-Bart_683-825526269
``` |
aware-ai/wav2vec2-base-5gram-german | 56ab2c73bd4c718e5b1fab45ea833e699e694694 | 2022-05-19T17:34:14.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | aware-ai | null | aware-ai/wav2vec2-base-5gram-german | 2 | null | transformers | 25,807 | ---
language: de
datasets:
- common_voice
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- speech
license: apache-2.0
model-index:
- name: wav2vec2-base-5gram-german with LM by Florian Zimmermeister @A\\Ware
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice de
type: common_voice
args: de
metrics:
- name: Test WER
type: wer
value: 8.9
---
## Evaluation
The model can be evaluated as follows on the German test data of Common Voice.
```python
import torch
from transformers import AutoModelForCTC, AutoProcessor
from unidecode import unidecode
import re
from datasets import load_dataset, load_metric
import datasets
counter = 0
wer_counter = 0
cer_counter = 0
device = "cuda" if torch.cuda.is_available() else "cpu"
special_chars = [["Ä"," AE "], ["Ö"," OE "], ["Ü"," UE "], ["ä"," ae "], ["ö"," oe "], ["ü"," ue "]]
def clean_text(sentence):
for special in special_chars:
sentence = sentence.replace(special[0], special[1])
sentence = unidecode(sentence)
for special in special_chars:
sentence = sentence.replace(special[1], special[0])
sentence = re.sub("[^a-zA-Z0-9öäüÖÄÜ ,.!?]", " ", sentence)
return sentence
def main(model_id):
print("load model")
model = AutoModelForCTC.from_pretrained(model_id).to(device)
print("load processor")
processor = AutoProcessor.from_pretrained(processor_id)
print("load metrics")
wer = load_metric("wer")
cer = load_metric("cer")
ds = load_dataset("mozilla-foundation/common_voice_9_0","de")
ds = ds["test"]
ds = ds.cast_column(
"audio", datasets.features.Audio(sampling_rate=16_000)
)
def calculate_metrics(batch):
global counter, wer_counter, cer_counter
resampled_audio = batch["audio"]["array"]
input_values = processor(resampled_audio, return_tensors="pt", sampling_rate=16_000).input_values
with torch.no_grad():
logits = model(input_values.to(device)).logits.cpu().numpy()[0]
decoded = processor.decode(logits)
pred = decoded.text.lower()
ref = clean_text(batch["sentence"]).lower()
wer_result = wer.compute(predictions=[pred], references=[ref])
cer_result = cer.compute(predictions=[pred], references=[ref])
counter += 1
wer_counter += wer_result
cer_counter += cer_result
if counter % 100 == True:
print(f"WER: {(wer_counter/counter)*100} | CER: {(cer_counter/counter)*100}")
return batch
ds.map(calculate_metrics, remove_columns=ds.column_names)
print(f"WER: {(wer_counter/counter)*100} | CER: {(cer_counter/counter)*100}")
model_id = "flozi00/wav2vec2-base-5gram-german"
main(model_id)
``` |
arxyzan/data2vec-wav2vec2-base | bc1754585c2df95f9bde411bbcb4c4fbc3235278 | 2022-05-16T09:00:23.000Z | [
"pytorch",
"wav2vec2",
"feature-extraction",
"arxiv:2202.03555",
"transformers"
] | feature-extraction | false | arxyzan | null | arxyzan/data2vec-wav2vec2-base | 2 | null | transformers | 25,808 | A Wav2Vec2 model trained using Data2Vec based on the paper [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://arxiv.org/abs/2202.03555).<br>
This model is provided here for [this repo](https://github.com/AryanShekarlaban/data2vec-pytorch) but was NOT trained using that codebase but instead, copied from `facebook/data2vec-wav2vec2-base` for convenience and reproducibility.
### BibTeX entry and citation info
```bibtex
@misc{https://doi.org/10.48550/arxiv.2202.03555,
doi = {10.48550/ARXIV.2202.03555},
url = {https://arxiv.org/abs/2202.03555},
author = {Baevski, Alexei and Hsu, Wei-Ning and Xu, Qiantong and Babu, Arun and Gu, Jiatao and Auli, Michael},
keywords = {Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
``` |
masakhane/byt5_lug_en_news | 0dd3e81e7ddb313114c7d2f04a7d05bd0de71fc2 | 2022-05-05T13:50:20.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/byt5_lug_en_news | 2 | null | transformers | 25,809 | ---
license: afl-3.0
---
|
masakhane/m2m100_418M_en_lug_news | b3b50f40e12ee0362b04915a3b40ec8bd3b0fa9a | 2022-05-05T14:13:52.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/m2m100_418M_en_lug_news | 2 | null | transformers | 25,810 | ---
license: afl-3.0
---
|
masakhane/m2m100_418M_lug_en_news | 537af4b31fa621722ac45be2f9068162a61cb813 | 2022-05-05T14:14:02.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/m2m100_418M_lug_en_news | 2 | null | transformers | 25,811 | ---
license: afl-3.0
---
|
masakhane/m2m100_418M_lug_en_rel_news | daaa16d7aa54b803822ccf3d45619a87939d7a3e | 2022-05-05T14:13:57.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/m2m100_418M_lug_en_rel_news | 2 | null | transformers | 25,812 | ---
license: afl-3.0
---
|
masakhane/m2m100_418M_en_lug_rel_ft | 3233a3d42db2e6078cdd1865d78719813b2b44c4 | 2022-05-05T14:22:56.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/m2m100_418M_en_lug_rel_ft | 2 | null | transformers | 25,813 | ---
license: afl-3.0
---
|
PSW/low_resource_percent10_minsimdel_seed42 | 4022e4954012a8e547111b9b7aae3f2d4788ef71 | 2022-05-05T11:59:29.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent10_minsimdel_seed42 | 2 | null | transformers | 25,814 | Entry not found |
PSW/low_resource_percent10_randomdel_seed1 | 37bef189b4ca530691a1218d27f1d5dbafb0413f | 2022-05-05T12:14:27.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent10_randomdel_seed1 | 2 | null | transformers | 25,815 | Entry not found |
PSW/low_resource_percent10_randomdel_seed27 | 75da7272bbfec741a784f1ef489fa7561ebccb5c | 2022-05-05T12:29:39.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent10_randomdel_seed27 | 2 | null | transformers | 25,816 | Entry not found |
PSW/low_resource_percent10_randomdel_seed42 | 4165c95944938a9aff409b87db75e03b6cdc14af | 2022-05-05T12:44:30.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent10_randomdel_seed42 | 2 | null | transformers | 25,817 | Entry not found |
alexjercan/codet5-base-buggy-code-repair | 1b83d825aa1365a8b71fdc859d79e030190f65c0 | 2022-05-06T14:06:24.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | alexjercan | null | alexjercan/codet5-base-buggy-code-repair | 2 | null | transformers | 25,818 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: codet5-base-buggy-code-repair
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. -->
# codet5-base-buggy-code-repair
This model is a fine-tuned version of [Salesforce/codet5-base](https://huggingface.co/Salesforce/codet5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8033
- Accuracy: 0.2516
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.11.6
|
PSW/low_resource_percent10_randomins_seed27 | c30c4e05ac90f7de039490821b5693ffb7466118 | 2022-05-05T13:13:33.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent10_randomins_seed27 | 2 | null | transformers | 25,819 | Entry not found |
PSW/low_resource_percent10_randomins_seed42 | 22d83840baaba3d9886d428d6055ee4455fb92d5 | 2022-05-05T13:26:35.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent10_randomins_seed42 | 2 | null | transformers | 25,820 | Entry not found |
PSW/low_resource_percent10_randomswap_seed1 | ff5e6d596d853d0ed6fb42cc7ff9cf0e2b74350b | 2022-05-05T13:41:11.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent10_randomswap_seed1 | 2 | null | transformers | 25,821 | Entry not found |
PSW/low_resource_percent10_randomswap_seed42 | 11deb2bc7481994aa862df6ce742b8b06aee9e46 | 2022-05-05T14:10:26.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent10_randomswap_seed42 | 2 | null | transformers | 25,822 | Entry not found |
PSW/low_resource_percent10_seed27 | fab1e808cdd617eaf3081670b903963725d34720 | 2022-05-05T14:29:31.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent10_seed27 | 2 | null | transformers | 25,823 | Entry not found |
sniffle/distilbert-rater | 399901388e1142923ef6a46b65f6b55a4930a166 | 2022-05-05T14:51:39.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | sniffle | null | sniffle/distilbert-rater | 2 | null | transformers | 25,824 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-rater
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-rater
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.11.6
|
PSW/low_resource_percent10_seed42 | e3c4fb72a6d296e656cbe3fc52db5a89ae0efae0 | 2022-05-05T14:41:51.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent10_seed42 | 2 | null | transformers | 25,825 | Entry not found |
thetatez/distilbert-rater | 4c5a11ec93f79a4f64edf9c58aab9bf92b134bfa | 2022-05-05T15:14:45.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | thetatez | null | thetatez/distilbert-rater | 2 | null | transformers | 25,826 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-rater
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-rater
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.9.1
- Tokenizers 0.12.1
|
PSW/low_resource_percent1_seed1 | 3493272b41ce2e9a292ec20ccb7614d9536d036b | 2022-05-05T14:54:39.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent1_seed1 | 2 | null | transformers | 25,827 | Entry not found |
shoubhik/electra_abbv_20k_data_multiclass | 17b36ed3f0bca79703088726acef6a4b44e7e6ae | 2022-05-05T15:37:34.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
] | text-classification | false | shoubhik | null | shoubhik/electra_abbv_20k_data_multiclass | 2 | null | transformers | 25,828 | Entry not found |
PSW/low_resource_percent20_maxsimins_seed42 | ac92deb8dcc1025febc2a99d20bea5a2b26244fb | 2022-05-05T15:53:57.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent20_maxsimins_seed42 | 2 | null | transformers | 25,829 | Entry not found |
PSW/low_resource_percent20_minsimdel_seed42 | 871dba76c4b6e07bf088257a054b622deb961245 | 2022-05-05T17:26:51.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent20_minsimdel_seed42 | 2 | null | transformers | 25,830 | Entry not found |
dyyyyyyyy/MVR_panx_XLM-RoBERTa-base | c01adabb249d2d8d109c758c8e4ba31baf81b1d8 | 2022-05-06T05:19:25.000Z | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | dyyyyyyyy | null | dyyyyyyyy/MVR_panx_XLM-RoBERTa-base | 2 | null | transformers | 25,831 | Entry not found |
dyyyyyyyy/MVR_squad_BERT-base-multilingual-cased | 7e4e38747f1f67865687bfd4b201f97db0d89e71 | 2022-05-06T06:40:41.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | dyyyyyyyy | null | dyyyyyyyy/MVR_squad_BERT-base-multilingual-cased | 2 | null | transformers | 25,832 | Entry not found |
PSW/low_resource_percent20_randomswap_seed1 | f4e57c395279d5d69808bb528488ee35493f4c5f | 2022-05-05T19:15:57.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent20_randomswap_seed1 | 2 | null | transformers | 25,833 | Entry not found |
nguyenmanhbao/my-finetuned-bert | 278a05cfd29cbcb7c413e439d94b3b17b42d4f61 | 2022-05-05T19:27:33.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | nguyenmanhbao | null | nguyenmanhbao/my-finetuned-bert | 2 | null | transformers | 25,834 | Entry not found |
abhilashawasthi/bert-base-uncased-reviews-128 | 976472fe62721322050f437a2ab1821d7d7ff962 | 2022-05-05T23:42:52.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | fill-mask | false | abhilashawasthi | null | abhilashawasthi/bert-base-uncased-reviews-128 | 2 | null | transformers | 25,835 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-reviews-128
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-reviews-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 48
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1
|
lilitket/20220505-222633 | ec7e6ef4986e2eb59ddaf1f82bb5b8c52fd96242 | 2022-05-06T03:05:49.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | lilitket | null | lilitket/20220505-222633 | 2 | null | transformers | 25,836 | Entry not found |
asahi417/tner-roberta-base-tweet-2020 | 0b3173bfe3cc71b34afada62ada729a3ad64a3d2 | 2022-05-06T11:07:17.000Z | [
"pytorch",
"roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | asahi417 | null | asahi417/tner-roberta-base-tweet-2020 | 2 | null | transformers | 25,837 | Entry not found |
daihaha/albert-base-v2-finetuned-swag | c82e56725e3095bec4bf876d3ee0c5b1d425d034 | 2022-05-06T10:09:14.000Z | [
"pytorch",
"tensorboard",
"albert",
"multiple-choice",
"transformers"
] | multiple-choice | false | daihaha | null | daihaha/albert-base-v2-finetuned-swag | 2 | null | transformers | 25,838 | Entry not found |
scasutt/wav2vec2-large-xlsr-53_final_train1 | 23cd7de2fa28e4e29f5a5603bb8bdc614e5a5415 | 2022-05-06T21:56:27.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | scasutt | null | scasutt/wav2vec2-large-xlsr-53_final_train1 | 2 | null | transformers | 25,839 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xlsr-53_final_train1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53_final_train1
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: 0.6432
- Wer: 0.6298
## 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: 1000
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.6199 | 0.25 | 250 | 3.6163 | 1.0 |
| 3.0927 | 0.5 | 500 | 3.5932 | 1.0 |
| 3.0837 | 0.76 | 750 | 3.2418 | 1.0 |
| 2.2385 | 1.01 | 1000 | 1.2621 | 0.9855 |
| 1.743 | 1.26 | 1250 | 1.0830 | 0.9442 |
| 1.6661 | 1.51 | 1500 | 0.7926 | 0.8051 |
| 1.5661 | 1.77 | 1750 | 0.6432 | 0.6298 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1
|
crabz/exp1 | 1505c44c938a9b23491a0f406b2f9a91dd00029d | 2022-05-06T09:53:13.000Z | [
"pytorch",
"roberta",
"fill-mask",
"sk",
"dataset:c4-sk",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | crabz | null | crabz/exp1 | 2 | null | transformers | 25,840 | ---
language: sk
license: mit
tags:
- fill-mask
- roberta
datasets:
- c4-sk
inference: false
---
|
crabz/exp3 | 444690d1cb0c1cf233e09c7d8843a6ae92cb8993 | 2022-05-06T09:59:23.000Z | [
"pytorch",
"roberta",
"transformers"
] | null | false | crabz | null | crabz/exp3 | 2 | null | transformers | 25,841 | Entry not found |
crabz/exp5 | 6baa3dc7980aaf62e1abfa000dfe075ef0c0b884 | 2022-05-06T10:05:49.000Z | [
"pytorch",
"roberta",
"transformers"
] | null | false | crabz | null | crabz/exp5 | 2 | null | transformers | 25,842 | Entry not found |
samuel30810/Black_box_3 | 5b7791eea9839587e2f955694b7809a2f8c22a36 | 2022-05-06T11:23:24.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | samuel30810 | null | samuel30810/Black_box_3 | 2 | null | transformers | 25,843 | ---
license: apache-2.0
---
|
h4d35/Wav2Vec2-hi | f3217f5f83b690bf13e254e2121be0a5f736f71c | 2022-05-06T13:59:21.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | h4d35 | null | h4d35/Wav2Vec2-hi | 2 | null | transformers | 25,844 | Entry not found |
mp6kv/IQA_classification | 185819a6eea84f9af378e2c9fb507312fd3643be | 2022-05-06T17:43:28.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | mp6kv | null | mp6kv/IQA_classification | 2 | null | transformers | 25,845 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: IQA_classification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# IQA_classification
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0718
- Accuracy: 0.4862
- Precision: 0.3398
- Recall: 0.4862
- F1: 0.3270
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.3973 | 1.0 | 28 | 1.1588 | 0.4771 | 0.2276 | 0.4771 | 0.3082 |
| 1.1575 | 2.0 | 56 | 1.0718 | 0.4862 | 0.3398 | 0.4862 | 0.3270 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
nepp1d0/prot_bert_classification_finetuned | d2e6cd3f2adcbfd371ba63a947bb73e4b0b6916c | 2022-05-09T20:15:49.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"model-index"
] | text-classification | false | nepp1d0 | null | nepp1d0/prot_bert_classification_finetuned | 2 | null | transformers | 25,846 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: prot_bert_classification_finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# prot_bert_classification_finetuned
This model is a fine-tuned version of [nepp1d0/prot_bert-finetuned-smiles-bindingDB](https://huggingface.co/nepp1d0/prot_bert-finetuned-smiles-bindingDB) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5675
- Accuracy: 0.7299
- F1: 0.7377
- Precision: 0.6995
- Recall: 0.7803
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 3
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4221 | 1.0 | 3332 | 0.6152 | 0.6615 | 0.6711 | 0.6367 | 0.7093 |
| 0.4133 | 2.0 | 6664 | 0.5840 | 0.6845 | 0.6718 | 0.6805 | 0.6634 |
| 0.4293 | 3.0 | 9996 | 0.5727 | 0.7116 | 0.7094 | 0.6961 | 0.7232 |
| 0.3098 | 4.0 | 13328 | 0.5636 | 0.7163 | 0.7220 | 0.6904 | 0.7566 |
| 0.3881 | 5.0 | 16660 | 0.5629 | 0.7265 | 0.7377 | 0.6918 | 0.7900 |
| 0.4943 | 6.0 | 19992 | 0.5675 | 0.7299 | 0.7377 | 0.6995 | 0.7803 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
davidlekve/distilroberta-base-finetuned-kendrick-lamar | 7cb10f047aef15587c74067b83d0db69b7f0af79 | 2022-05-06T19:25:14.000Z | [
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | fill-mask | false | davidlekve | null | davidlekve/distilroberta-base-finetuned-kendrick-lamar | 2 | null | transformers | 25,847 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-finetuned-kendrick-lamar
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-finetuned-kendrick-lamar
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0142
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 111 | 3.0981 |
| No log | 2.0 | 222 | 3.0078 |
| No log | 3.0 | 333 | 3.0142 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cpu
- Datasets 2.1.0
- Tokenizers 0.12.1
|
davidsantiago1011/gpt2-small-spanish | 88b59ef0daf628a97554fdbbeaf078fc8db98287 | 2022-05-06T20:26:34.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"model-index"
] | text-generation | false | davidsantiago1011 | null | davidsantiago1011/gpt2-small-spanish | 2 | null | transformers | 25,848 | ---
tags:
- generated_from_trainer
model-index:
- name: gpt2-small-spanish
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-small-spanish
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5051
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5681 | 1.0 | 110 | 2.8562 |
| 2.7732 | 2.0 | 220 | 2.5769 |
| 3.0083 | 3.0 | 330 | 2.5051 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.4
- Tokenizers 0.12.1
|
davidlekve/distilroberta-base-finetuned-the-beatles | 86f23945a18ff9b46d890ed30739b960e6e8c68a | 2022-05-06T19:49:40.000Z | [
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | fill-mask | false | davidlekve | null | davidlekve/distilroberta-base-finetuned-the-beatles | 2 | null | transformers | 25,849 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-finetuned-the-beatles
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-finetuned-the-beatles
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5186
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 84 | 2.6517 |
| No log | 2.0 | 168 | 2.6433 |
| No log | 3.0 | 252 | 2.5186 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cpu
- Datasets 2.1.0
- Tokenizers 0.12.1
|
vuiseng9/bert-l-squadv1.1-sl256 | 2b92921786a072af90f2aa3f40f43ff3861b983a | 2022-05-07T03:41:17.000Z | [
"pytorch",
"onnx",
"bert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | vuiseng9 | null | vuiseng9/bert-l-squadv1.1-sl256 | 2 | null | transformers | 25,850 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: run01-bert-l-uwwm-squadv1.1-sl256-ds128-e2-tbs16
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. -->
# run01-bert-l-uwwm-squadv1.1-sl256-ds128-e2-tbs16
This model is a fine-tuned version of [bert-large-uncased-whole-word-masking](https://huggingface.co/bert-large-uncased-whole-word-masking) on the squad dataset. ONNX and OpenVINO IR are enclosed here.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
```bash
NEPOCH=2
TBS=16
EBS=64
SL=256
DS=128
cmd="
python run_qa.py \
--model_name_or_path ${BASEM} \
--dataset_name squad \
--do_eval \
--do_train \
--evaluation_strategy steps \
--eval_steps 500 \
--learning_rate 3e-5 \
--fp16 \
--num_train_epochs $NEPOCH \
--per_device_eval_batch_size $EBS \
--per_device_train_batch_size $TBS \
--max_seq_length $SL \
--doc_stride $DS \
--save_steps 1000 \
--logging_steps 1 \
--overwrite_output_dir \
--run_name $RUNID \
--output_dir $OUTDIR
"
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
- mixed_precision_training: Native AMP
### Training results
Best checkpoint was at step 11500 but it was not saved. This is final checkpoint (12K+).
```
eval_exact_match = 86.9347
eval_f1 = 93.1359
eval_samples = 12097
```
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Khalsuu/english-filipino-wav2vec2-l-xls-r-test-06 | 99004ec4bdb4172960cdd304a59e9665943a0186 | 2022-05-07T11:12:33.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:filipino_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | Khalsuu | null | Khalsuu/english-filipino-wav2vec2-l-xls-r-test-06 | 2 | null | transformers | 25,851 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- filipino_voice
model-index:
- name: english-filipino-wav2vec2-l-xls-r-test-06
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# english-filipino-wav2vec2-l-xls-r-test-06
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the filipino_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5806
- Wer: 0.6568
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.0031 | 2.09 | 400 | 1.2366 | 0.8780 |
| 0.9084 | 4.19 | 800 | 1.0653 | 0.8081 |
| 0.6484 | 6.28 | 1200 | 1.1648 | 0.8258 |
| 0.5335 | 8.38 | 1600 | 1.0903 | 0.7542 |
| 0.4359 | 10.47 | 2000 | 0.9466 | 0.7058 |
| 0.3629 | 12.57 | 2400 | 0.9266 | 0.7048 |
| 0.3057 | 14.66 | 2800 | 1.0879 | 0.7018 |
| 0.2477 | 16.75 | 3200 | 1.1113 | 0.7022 |
| 0.208 | 18.85 | 3600 | 1.1345 | 0.6742 |
| 0.1781 | 20.94 | 4000 | 1.3117 | 0.6974 |
| 0.1465 | 23.04 | 4400 | 1.3248 | 0.6916 |
| 0.1288 | 25.13 | 4800 | 1.4306 | 0.6523 |
| 0.1108 | 27.23 | 5200 | 1.5155 | 0.6685 |
| 0.099 | 29.32 | 5600 | 1.5806 | 0.6568 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
crystina-z/mdpr-question-msmarco | 81be0f2586d633d3b5da54935b93739ed45fec0f | 2022-05-07T07:49:33.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | crystina-z | null | crystina-z/mdpr-question-msmarco | 2 | null | transformers | 25,852 | Entry not found |
lilitket/20220507-092401 | b0008962e3fc0dcd9ccab738be904b831eea851a | 2022-05-07T11:23:55.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | lilitket | null | lilitket/20220507-092401 | 2 | null | transformers | 25,853 | Entry not found |
huggingtweets/doodles | cf81177cc7a4e51bc62b6deb4cf996d1691105fb | 2022-05-07T11:26:42.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/doodles | 2 | null | transformers | 25,854 | ---
language: en
thumbnail: http://www.huggingtweets.com/doodles/1651922797827/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/1484416288097116160/xLR2e4eu_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">doodles</div>
<div style="text-align: center; font-size: 14px;">@doodles</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 doodles.
| Data | doodles |
| --- | --- |
| Tweets downloaded | 1876 |
| Retweets | 401 |
| Short tweets | 916 |
| Tweets kept | 559 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1jpd1iuz/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 @doodles's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/11wbfkyl) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/11wbfkyl/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/doodles')
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)
|
KoichiYasuoka/roberta-small-coptic | b6bf5c6726077cdd242c3633e5f7df5a6d918e92 | 2022-05-08T05:05:10.000Z | [
"pytorch",
"roberta",
"fill-mask",
"cop",
"transformers",
"coptic",
"masked-lm",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | fill-mask | false | KoichiYasuoka | null | KoichiYasuoka/roberta-small-coptic | 2 | null | transformers | 25,855 | ---
language:
- "cop"
tags:
- "coptic"
- "masked-lm"
license: "cc-by-sa-4.0"
pipeline_tag: "fill-mask"
mask_token: "[MASK]"
---
# roberta-small-coptic
## Model Description
This is a RoBERTa model pre-trained on Coptic Scriptorium Corpora. You can fine-tune `roberta-small-coptic` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-small-coptic-upos), dependency-parsing, and so on.
## How to Use
```py
from transformers import AutoTokenizer,AutoModelForMaskedLM
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-small-coptic")
model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-small-coptic")
```
|
prashanth/mbart-large-cc25-finetuned-en-to-hi | 1ed323d07d9a897fcc3fdcd7a6f53f0d41ceffb6 | 2022-05-08T12:38:32.000Z | [
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"dataset:hindi_english_machine_translation",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | prashanth | null | prashanth/mbart-large-cc25-finetuned-en-to-hi | 2 | null | transformers | 25,856 | ---
tags:
- generated_from_trainer
datasets:
- hindi_english_machine_translation
model-index:
- name: mbart-large-cc25-finetuned-en-to-hi
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mbart-large-cc25-finetuned-en-to-hi
This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the hindi_english_machine_translation 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: 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 4.18.0
- Pytorch 1.11.0+cu102
- Datasets 1.18.0
- Tokenizers 0.12.1
|
camiloa2m/gpt2-spanish-finetuned-gpt2-spanish | 133a0ec9e818f1e91753e444f22716bf5776b933 | 2022-05-07T15:45:03.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-generation | false | camiloa2m | null | camiloa2m/gpt2-spanish-finetuned-gpt2-spanish | 2 | null | transformers | 25,857 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-spanish-finetuned-gpt2-spanish
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-spanish-finetuned-gpt2-spanish
This model is a fine-tuned version of [DeepESP/gpt2-spanish](https://huggingface.co/DeepESP/gpt2-spanish) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9709
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 263 | 2.0389 |
| 2.1522 | 2.0 | 526 | 1.9829 |
| 2.1522 | 3.0 | 789 | 1.9709 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.4
- Tokenizers 0.12.1
|
lucifermorninstar011/autotrain-lucifer_multi_auto_all-837626708 | 331c2d7934b85da3e14a1daa5af267e0f0f5c8cf | 2022-05-08T01:56:10.000Z | [
"pytorch",
"distilbert",
"text-classification",
"en",
"dataset:lucifermorninstar011/autotrain-data-lucifer_multi_auto_all",
"transformers",
"autotrain",
"co2_eq_emissions"
] | text-classification | false | lucifermorninstar011 | null | lucifermorninstar011/autotrain-lucifer_multi_auto_all-837626708 | 2 | null | transformers | 25,858 | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- lucifermorninstar011/autotrain-data-lucifer_multi_auto_all
co2_eq_emissions: 675.6911996033854
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 837626708
- CO2 Emissions (in grams): 675.6911996033854
## Validation Metrics
- Loss: 0.008128546178340912
- Accuracy: 0.9977804696723191
- Macro F1: 0.9942781700973885
- Micro F1: 0.9977804696723191
- Weighted F1: 0.9977851755386459
- Macro Precision: 0.9923939243012706
- Micro Precision: 0.9977804696723191
- Weighted Precision: 0.9977957481683986
- Macro Recall: 0.9961924323977192
- Micro Recall: 0.9977804696723191
- Weighted Recall: 0.9977804696723191
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/lucifermorninstar011/autotrain-lucifer_multi_auto_all-837626708
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("lucifermorninstar011/autotrain-lucifer_multi_auto_all-837626708", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("lucifermorninstar011/autotrain-lucifer_multi_auto_all-837626708", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
lucifermorninstar011/autotrain-lucifer_multi_auto_all-837626712 | 5adc0ae9b2e21b6822e4285e81d4dd19c3532149 | 2022-05-08T02:44:35.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:lucifermorninstar011/autotrain-data-lucifer_multi_auto_all",
"transformers",
"autotrain",
"co2_eq_emissions"
] | text-classification | false | lucifermorninstar011 | null | lucifermorninstar011/autotrain-lucifer_multi_auto_all-837626712 | 2 | null | transformers | 25,859 | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- lucifermorninstar011/autotrain-data-lucifer_multi_auto_all
co2_eq_emissions: 772.9316141161539
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 837626712
- CO2 Emissions (in grams): 772.9316141161539
## Validation Metrics
- Loss: 0.006297225132584572
- Accuracy: 0.998357670693734
- Macro F1: 0.9947282131241516
- Micro F1: 0.998357670693734
- Weighted F1: 0.9983564218124292
- Macro Precision: 0.9937572688417448
- Micro Precision: 0.998357670693734
- Weighted Precision: 0.9983587534033106
- Macro Recall: 0.9957326552198976
- Micro Recall: 0.998357670693734
- Weighted Recall: 0.998357670693734
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/lucifermorninstar011/autotrain-lucifer_multi_auto_all-837626712
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("lucifermorninstar011/autotrain-lucifer_multi_auto_all-837626712", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("lucifermorninstar011/autotrain-lucifer_multi_auto_all-837626712", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
theojolliffe/distill-pegasus-cnn-arxiv-pubmed | f32b2e98d68b35d674acf7f7820e49cf608b2ac5 | 2022-05-07T22:40:32.000Z | [
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"dataset:scientific_papers",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | theojolliffe | null | theojolliffe/distill-pegasus-cnn-arxiv-pubmed | 2 | null | transformers | 25,860 | ---
tags:
- generated_from_trainer
datasets:
- scientific_papers
metrics:
- rouge
model-index:
- name: distill-pegasus-cnn-16-4-finetuned-arxiv-pubmed
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: scientific_papers
type: scientific_papers
args: pubmed
metrics:
- name: Rouge1
type: rouge
value: 31.5968
---
<!-- 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. -->
# distill-pegasus-cnn-16-4-finetuned-arxiv-pubmed
This model is a fine-tuned version of [theojolliffe/distill-pegasus-cnn-16-4-finetuned-arxiv](https://huggingface.co/theojolliffe/distill-pegasus-cnn-16-4-finetuned-arxiv) on the scientific_papers dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0433
- Rouge1: 31.5968
- Rouge2: 12.5841
- Rougel: 21.0778
- Rougelsum: 28.3167
- Gen Len: 118.9566
## 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 3.5173 | 1.0 | 3748 | 3.0433 | 31.5968 | 12.5841 | 21.0778 | 28.3167 | 118.9566 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
bkh6722/bach-arb | a404b97ac5dcdb6f13180e93b41900c6c4d1439f | 2022-05-15T02:34:26.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | bkh6722 | null | bkh6722/bach-arb | 2 | null | transformers | 25,861 |
<!-- 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. -->
# bach-arb
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-german](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-german) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9404
- Wer: 0.6130
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 115
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 27.8653 | 7.14 | 100 | 3.1369 | 1.0 |
| 2.5975 | 14.28 | 200 | 2.1223 | 0.9976 |
| 1.2001 | 21.41 | 300 | 1.7455 | 0.8774 |
| 0.5938 | 28.55 | 400 | 1.8534 | 0.7981 |
| 0.4001 | 35.69 | 500 | 2.3318 | 0.7740 |
| 0.2895 | 42.83 | 600 | 2.2214 | 0.7163 |
| 0.1853 | 49.97 | 700 | 2.4841 | 0.7043 |
| 0.1318 | 57.14 | 800 | 2.9749 | 0.7139 |
| 0.1067 | 64.28 | 900 | 2.4759 | 0.7115 |
| 0.0635 | 71.41 | 1000 | 2.6708 | 0.6635 |
| 0.0515 | 78.55 | 1100 | 3.0593 | 0.6923 |
| 0.0455 | 85.69 | 1200 | 2.9637 | 0.6587 |
| 0.0329 | 92.83 | 1300 | 2.9837 | 0.6346 |
| 0.0232 | 99.97 | 1400 | 2.9361 | 0.6178 |
| 0.021 | 107.14 | 1500 | 2.9221 | 0.6010 |
| 0.0193 | 114.28 | 1600 | 2.9404 | 0.6130 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
BigSalmon/InformalToFormalLincoln43 | 84729e977fd35f1da6535e50c79c7bc186f25230 | 2022-05-07T22:51:01.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | BigSalmon | null | BigSalmon/InformalToFormalLincoln43 | 2 | null | transformers | 25,862 | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln43")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln43")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
```
```
original: had trouble deciding.
translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation.
***
original:
``` |
theojolliffe/bart-cnn-pubmed-arxiv-pubmed | f41958beb871e6f3f89492fe8d35ddf8300e3d67 | 2022-05-08T04:30:20.000Z | [
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"dataset:scientific_papers",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | theojolliffe | null | theojolliffe/bart-cnn-pubmed-arxiv-pubmed | 2 | null | transformers | 25,863 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- scientific_papers
metrics:
- rouge
model-index:
- name: bart-cnn-pubmed-arxiv-pubmed
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: scientific_papers
type: scientific_papers
args: pubmed
metrics:
- name: Rouge1
type: rouge
value: 37.3328
---
<!-- 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-cnn-pubmed-arxiv-pubmed
This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv) on the scientific_papers dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9245
- Rouge1: 37.3328
- Rouge2: 15.5894
- Rougel: 23.0297
- Rougelsum: 33.952
- Gen Len: 136.3568
## 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 2.0272 | 1.0 | 29981 | 1.9245 | 37.3328 | 15.5894 | 23.0297 | 33.952 | 136.3568 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Khalsuu/english-filipino-wav2vec2-l-xls-r-test-09 | bf299183fc64473d8b887fd98b7a8162c167ebe7 | 2022-05-08T04:30:40.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:filipino_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | Khalsuu | null | Khalsuu/english-filipino-wav2vec2-l-xls-r-test-09 | 2 | null | transformers | 25,864 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- filipino_voice
model-index:
- name: english-filipino-wav2vec2-l-xls-r-test-09
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# english-filipino-wav2vec2-l-xls-r-test-09
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the filipino_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0054
- Wer: 0.5750
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.001 | 2.09 | 400 | 1.3499 | 0.9595 |
| 0.8606 | 4.19 | 800 | 0.8159 | 0.6942 |
| 0.5819 | 6.28 | 1200 | 0.7372 | 0.6700 |
| 0.4751 | 8.38 | 1600 | 0.7310 | 0.6405 |
| 0.3777 | 10.47 | 2000 | 0.7841 | 0.6414 |
| 0.2918 | 12.57 | 2400 | 0.7898 | 0.5951 |
| 0.2209 | 14.66 | 2800 | 0.8558 | 0.5751 |
| 0.1671 | 16.75 | 3200 | 0.9881 | 0.5979 |
| 0.129 | 18.85 | 3600 | 1.0054 | 0.5750 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Jiexing/sparc_add_coref_t5_3b-2432 | 88c2bdc5f045f5c0068a3887b7f653474b6d0dba | 2022-05-08T04:51:29.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Jiexing | null | Jiexing/sparc_add_coref_t5_3b-2432 | 2 | null | transformers | 25,865 | Entry not found |
KoichiYasuoka/roberta-base-coptic | 36e60145b360bd013e94915df25c7b06b50e7423 | 2022-05-08T05:16:14.000Z | [
"pytorch",
"roberta",
"fill-mask",
"cop",
"transformers",
"coptic",
"masked-lm",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | fill-mask | false | KoichiYasuoka | null | KoichiYasuoka/roberta-base-coptic | 2 | null | transformers | 25,866 | ---
language:
- "cop"
tags:
- "coptic"
- "masked-lm"
license: "cc-by-sa-4.0"
pipeline_tag: "fill-mask"
mask_token: "[MASK]"
---
# roberta-base-coptic
## Model Description
This is a RoBERTa model pre-trained on Coptic Scriptorium Corpora. You can fine-tune `roberta-base-coptic` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-base-coptic-upos), dependency-parsing, and so on.
## How to Use
```py
from transformers import AutoTokenizer,AutoModelForMaskedLM
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-base-coptic")
model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-base-coptic")
```
|
Henrywang/dummy-model | 58a4f28d4bcfbe2db38f3b254f8cdc7206fc0996 | 2022-05-08T08:41:54.000Z | [
"pytorch",
"camembert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | Henrywang | null | Henrywang/dummy-model | 2 | 0 | transformers | 25,867 | Entry not found |
pier297/autotrain-chemprot-re-838426740 | fb09b7130ff43ec43d7c670f21389bf509bd9f16 | 2022-05-08T09:31:00.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:pier297/autotrain-data-chemprot-re",
"transformers",
"autotrain",
"co2_eq_emissions"
] | text-classification | false | pier297 | null | pier297/autotrain-chemprot-re-838426740 | 2 | 1 | transformers | 25,868 | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- pier297/autotrain-data-chemprot-re
co2_eq_emissions: 0.0911766483095575
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 838426740
- CO2 Emissions (in grams): 0.0911766483095575
## Validation Metrics
- Loss: 0.3866589665412903
- Accuracy: 0.9137332672285573
- Macro F1: 0.6518117007658014
- Micro F1: 0.9137332672285573
- Weighted F1: 0.9110993117549759
- Macro Precision: 0.649358664024301
- Micro Precision: 0.9137332672285573
- Weighted Precision: 0.9091854625539633
- Macro Recall: 0.6551854233645032
- Micro Recall: 0.9137332672285573
- Weighted Recall: 0.9137332672285573
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/pier297/autotrain-chemprot-re-838426740
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("pier297/autotrain-chemprot-re-838426740", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("pier297/autotrain-chemprot-re-838426740", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
theojolliffe/distill-pegasus-cnn-arxiv-pubmed-v3-e16 | 0248f1aed405def2e7ed0c3c8dc46816cd18c8f8 | 2022-05-08T14:17:05.000Z | [
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | theojolliffe | null | theojolliffe/distill-pegasus-cnn-arxiv-pubmed-v3-e16 | 2 | null | transformers | 25,869 | ---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: distill-pegasus-cnn-arxiv-pubmed-v3-e16
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. -->
# distill-pegasus-cnn-arxiv-pubmed-v3-e16
This model is a fine-tuned version of [theojolliffe/distill-pegasus-cnn-arxiv-pubmed](https://huggingface.co/theojolliffe/distill-pegasus-cnn-arxiv-pubmed) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4922
- Rouge1: 53.3238
- Rouge2: 36.6165
- Rougel: 38.9255
- Rougelsum: 50.4853
- Gen Len: 125.7407
## 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: 16
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 2.7655 | 1.0 | 795 | 2.1110 | 49.0541 | 29.7039 | 33.8403 | 44.2825 | 126.1296 |
| 2.2882 | 2.0 | 1590 | 1.9469 | 48.4651 | 30.1425 | 33.9702 | 44.3518 | 125.7778 |
| 2.1958 | 3.0 | 2385 | 1.8079 | 49.2302 | 31.0952 | 34.4448 | 45.5764 | 125.7778 |
| 2.0221 | 4.0 | 3180 | 1.7501 | 48.1928 | 29.9098 | 33.0587 | 44.6023 | 125.3148 |
| 1.9078 | 5.0 | 3975 | 1.6677 | 49.697 | 31.671 | 34.3162 | 46.5108 | 125.5185 |
| 1.8624 | 6.0 | 4770 | 1.6393 | 49.6517 | 31.7371 | 35.2019 | 46.2846 | 125.6852 |
| 1.7853 | 7.0 | 5565 | 1.6038 | 50.3151 | 33.0952 | 36.0028 | 47.3894 | 125.6852 |
| 1.7513 | 8.0 | 6360 | 1.5717 | 50.299 | 33.038 | 35.6841 | 47.4086 | 124.5556 |
| 1.7026 | 9.0 | 7155 | 1.5570 | 51.6216 | 34.7609 | 37.5598 | 48.5247 | 124.7037 |
| 1.6999 | 10.0 | 7950 | 1.5365 | 51.0888 | 34.2642 | 37.0603 | 48.5712 | 125.3519 |
| 1.6832 | 11.0 | 8745 | 1.5249 | 51.3422 | 34.2941 | 37.7111 | 48.556 | 124.9259 |
| 1.6093 | 12.0 | 9540 | 1.5092 | 51.4622 | 34.6397 | 38.1768 | 48.6346 | 124.8889 |
| 1.6049 | 13.0 | 10335 | 1.5002 | 52.2463 | 35.4629 | 38.2049 | 49.4066 | 124.7963 |
| 1.5904 | 14.0 | 11130 | 1.4957 | 51.6498 | 34.9739 | 38.4215 | 48.9704 | 125.0185 |
| 1.5963 | 15.0 | 11925 | 1.4920 | 52.769 | 35.9563 | 38.4861 | 49.9185 | 125.6481 |
| 1.5742 | 16.0 | 12720 | 1.4922 | 53.3238 | 36.6165 | 38.9255 | 50.4853 | 125.7407 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Siyam/Dansk-wav2vec2-stt | 7210bd5219b2e70827ec36963094c77fcd109042 | 2022-05-08T20:58:42.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | Siyam | null | Siyam/Dansk-wav2vec2-stt | 2 | null | transformers | 25,870 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: Dansk-wav2vec2-stt
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. -->
# Dansk-wav2vec2-stt
This model is a fine-tuned version of [Siyam/Dansk-wav2vec21](https://huggingface.co/Siyam/Dansk-wav2vec21) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7500
- Wer: 0.3929
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0298 | 4.26 | 400 | 0.8420 | 0.4579 |
| 0.0479 | 8.51 | 800 | 0.8713 | 0.4461 |
| 0.0387 | 12.77 | 1200 | 0.8307 | 0.4404 |
| 0.0336 | 17.02 | 1600 | 0.8322 | 0.4144 |
| 0.0322 | 21.28 | 2000 | 0.7493 | 0.4081 |
| 0.0288 | 25.53 | 2400 | 0.7361 | 0.3951 |
| 0.0264 | 29.79 | 2800 | 0.7500 | 0.3929 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 2.1.0
- Tokenizers 0.10.3
|
lilitket/20220509-013433 | 7b41d6ee183070a8ef5833a423e5d5aba7e79f3b | 2022-05-08T23:20:24.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | lilitket | null | lilitket/20220509-013433 | 2 | null | transformers | 25,871 | Entry not found |
kaakekhan/tiny-bert-sst2-distilled | 2ddb3e97bf10c7eae895c9ab79f6b88650861c23 | 2022-05-09T00:39:14.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | kaakekhan | null | kaakekhan/tiny-bert-sst2-distilled | 2 | null | transformers | 25,872 | Entry not found |
BigSalmon/InformalToFormalLincoln44 | 32c25a13ba4d1fd93b38918d00c11383c025acb9 | 2022-05-09T01:38:00.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | BigSalmon | null | BigSalmon/InformalToFormalLincoln44 | 2 | null | transformers | 25,873 | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln44")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln44")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
```
```
original: had trouble deciding.
translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation.
***
original:
``` |
JGraves/distilbert-base-uncased-finetuned-ner | f2a97250d28cfb07b7d5bfeac3a3a4f8cc0c697f | 2022-05-13T03:46:10.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | JGraves | null | JGraves/distilbert-base-uncased-finetuned-ner | 2 | null | transformers | 25,874 | Entry not found |
Diegomejia/ucb-bert-finetunned | adfb29c39c3f20e5df2276efc1957dfbeb7b0732 | 2022-05-11T06:31:03.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | Diegomejia | null | Diegomejia/ucb-bert-finetunned | 2 | null | transformers | 25,875 | Entry not found |
Xikun/greaselm-obqa | c1025505c85bcc185fee5623c217fba1fe8b894c | 2022-05-09T05:15:00.000Z | [
"pytorch",
"greaselm",
"transformers"
] | null | false | Xikun | null | Xikun/greaselm-obqa | 2 | null | transformers | 25,876 | Entry not found |
anuragshas/wav2vec2-xls-r-300m-hi-cv9-with-lm | 3a2d8e2bd3a69db69db2f4900b36580bac8e9eb3 | 2022-05-25T14:56:19.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hi",
"dataset:mozilla-foundation/common_voice_9_0",
"transformers",
"mozilla-foundation/common_voice_9_0",
"generated_from_trainer",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | anuragshas | null | anuragshas/wav2vec2-xls-r-300m-hi-cv9-with-lm | 2 | null | transformers | 25,877 | ---
language:
- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_9_0
- generated_from_trainer
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_9_0
metrics:
- wer
model-index:
- name: XLS-R-300M - Hindi
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: mozilla-foundation/common_voice_9_0
name: Common Voice 9
args: hi
metrics:
- type: wer
value: 21.145
name: Test WER
- name: Test CER
type: cer
value: 7.709
---
<!-- 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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_9_0 - HI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5164
- Wer: 0.3349
- Cer: 0.1082
## 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: 7.5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- 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
- training_steps: 9815
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| 3.9471 | 8.16 | 400 | 3.7109 | 1.0 | 1.0 |
| 3.274 | 16.32 | 800 | 3.1582 | 0.9917 | 0.9573 |
| 1.5889 | 24.48 | 1200 | 0.7763 | 0.6030 | 0.1990 |
| 1.3647 | 32.65 | 1600 | 0.6051 | 0.5135 | 0.1687 |
| 1.2532 | 40.81 | 2000 | 0.5423 | 0.4712 | 0.1539 |
| 1.1905 | 48.97 | 2400 | 0.5180 | 0.4532 | 0.1490 |
| 1.1193 | 57.14 | 2800 | 0.4906 | 0.4248 | 0.1393 |
| 1.0584 | 65.3 | 3200 | 0.4854 | 0.4069 | 0.1332 |
| 1.0095 | 73.46 | 3600 | 0.4780 | 0.3926 | 0.1287 |
| 0.9759 | 81.63 | 4000 | 0.4666 | 0.3925 | 0.1269 |
| 0.9593 | 89.79 | 4400 | 0.4808 | 0.3830 | 0.1247 |
| 0.909 | 97.95 | 4800 | 0.4798 | 0.3765 | 0.1212 |
| 0.8788 | 106.12 | 5200 | 0.4906 | 0.3608 | 0.1162 |
| 0.8471 | 114.28 | 5600 | 0.4759 | 0.3604 | 0.1166 |
| 0.8116 | 122.44 | 6000 | 0.5080 | 0.3627 | 0.1176 |
| 0.7881 | 130.61 | 6400 | 0.4868 | 0.3489 | 0.1135 |
| 0.766 | 138.77 | 6800 | 0.4955 | 0.3492 | 0.1136 |
| 0.7333 | 146.93 | 7200 | 0.5019 | 0.3461 | 0.1125 |
| 0.709 | 155.1 | 7600 | 0.5084 | 0.3468 | 0.1117 |
| 0.6911 | 163.26 | 8000 | 0.5144 | 0.3412 | 0.1106 |
| 0.6683 | 171.42 | 8400 | 0.5219 | 0.3409 | 0.1117 |
| 0.659 | 179.59 | 8800 | 0.5230 | 0.3376 | 0.1096 |
| 0.6475 | 187.75 | 9200 | 0.5229 | 0.3398 | 0.1097 |
| 0.6419 | 195.91 | 9600 | 0.5200 | 0.3337 | 0.1084 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.1.1.dev0
- Tokenizers 0.12.1
|
huggingtweets/computerforever | dfbdbe2da7f21be2606d15bffe48b5c3f91aa9e3 | 2022-05-09T05:19:58.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/computerforever | 2 | null | transformers | 25,878 | ---
language: en
thumbnail: http://www.huggingtweets.com/computerforever/1652073594573/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/1518444670266839045/38xr9OAd_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">computer sweetie</div>
<div style="text-align: center; font-size: 14px;">@computerforever</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 computer sweetie.
| Data | computer sweetie |
| --- | --- |
| Tweets downloaded | 2170 |
| Retweets | 48 |
| Short tweets | 313 |
| Tweets kept | 1809 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/9j3sj0ot/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 @computerforever's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2iw1hcff) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2iw1hcff/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/computerforever')
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)
|
PrajwalS/wav2vec2_med_custom_train_large | a1551564b94ee28f5a6254e5e10b8c82bdd844e1 | 2022-05-09T09:15:28.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | PrajwalS | null | PrajwalS/wav2vec2_med_custom_train_large | 2 | null | transformers | 25,879 | Entry not found |
ChrisRhw/DialoGPT-medium-Chizuru | 4871e2a65b1d704be141029c1b864675855059ab | 2022-05-09T06:01:42.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | ChrisRhw | null | ChrisRhw/DialoGPT-medium-Chizuru | 2 | null | transformers | 25,880 | ---
tags:
- conversational
--- |
fujiki/t5-base-en2ja | f55aa99ce2d1c7a6ebaf11ad0c918e73a3ed8826 | 2022-05-11T19:43:53.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | fujiki | null | fujiki/t5-base-en2ja | 2 | null | transformers | 25,881 | # Tokenizer
- the tokenizer is imported from [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese)
# License
[CC-BY SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/deed.ja) |
Jiexing/cosql_add_coref_t5_3b-1280 | a02f8d814a31b8549f1eef4cd7ee877cb62aeeaa | 2022-05-09T08:52:01.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Jiexing | null | Jiexing/cosql_add_coref_t5_3b-1280 | 2 | null | transformers | 25,882 | Entry not found |
ders/wav2vec2-large-xlsr-53-demo-laptop-hp-omen-15-dc1xxx-gpu | 37859949c47c93f1e7416b90a64df16e011fcd4d | 2022-05-14T17:41:01.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | ders | null | ders/wav2vec2-large-xlsr-53-demo-laptop-hp-omen-15-dc1xxx-gpu | 2 | null | transformers | 25,883 | Entry not found |
theojolliffe/bart-cnn-pubmed-arxiv-pubmed-v3-e10 | b367aeeb3b4ed83829d4e9ea88636d9467cd8ba2 | 2022-05-09T12:37:02.000Z | [
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | theojolliffe | null | theojolliffe/bart-cnn-pubmed-arxiv-pubmed-v3-e10 | 2 | null | transformers | 25,884 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-cnn-pubmed-arxiv-pubmed-v3-e10
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-cnn-pubmed-arxiv-pubmed-v3-e10
This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv-pubmed) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8410
- Rouge1: 56.5123
- Rouge2: 41.1641
- Rougel: 43.4495
- Rougelsum: 54.544
- Gen Len: 141.6667
## 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 1.254 | 1.0 | 795 | 0.9244 | 52.4478 | 32.5958 | 34.8756 | 49.8059 | 142.0 |
| 0.6985 | 2.0 | 1590 | 0.8156 | 52.4786 | 33.2296 | 35.5063 | 49.737 | 141.7963 |
| 0.5252 | 3.0 | 2385 | 0.7821 | 52.0494 | 32.953 | 36.5502 | 49.7292 | 142.0 |
| 0.3389 | 4.0 | 3180 | 0.7422 | 53.5408 | 36.2206 | 39.8389 | 51.6693 | 142.0 |
| 0.26 | 5.0 | 3975 | 0.7670 | 54.4279 | 36.5972 | 40.255 | 52.0877 | 142.0 |
| 0.1678 | 6.0 | 4770 | 0.8106 | 54.6811 | 37.8329 | 40.8512 | 52.3482 | 141.963 |
| 0.1243 | 7.0 | 5565 | 0.7926 | 54.5081 | 37.9596 | 41.912 | 52.5097 | 142.0 |
| 0.0967 | 8.0 | 6360 | 0.8079 | 56.0795 | 40.0954 | 43.7055 | 54.2041 | 142.0 |
| 0.0709 | 9.0 | 7155 | 0.8390 | 55.5257 | 38.5546 | 42.1562 | 53.5524 | 141.963 |
| 0.0691 | 10.0 | 7950 | 0.8410 | 56.5123 | 41.1641 | 43.4495 | 54.544 | 141.6667 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
guhuawuli/distilbert-base-uncased-finetuned-cola | cb06312f2fba67ba42f3299f68411c46ee01b786 | 2022-05-09T13:05:15.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | guhuawuli | null | guhuawuli/distilbert-base-uncased-finetuned-cola | 2 | null | transformers | 25,885 | Entry not found |
husnu/wav2vec2-large-xls-r-300m-turkish-colab_common_voice-8_4 | 77a95ee910d37c9187202ad86079a23a7b7de35e | 2022-05-10T04:41:58.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | husnu | null | husnu/wav2vec2-large-xls-r-300m-turkish-colab_common_voice-8_4 | 2 | null | transformers | 25,886 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-colab_common_voice-8_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-large-xls-r-300m-turkish-colab_common_voice-8_4
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3201
- Wer: 0.3295
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 11
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.9268 | 0.51 | 400 | 1.3204 | 0.9175 |
| 0.7491 | 1.02 | 800 | 0.5880 | 0.6388 |
| 0.4911 | 1.53 | 1200 | 0.4680 | 0.5613 |
| 0.4265 | 2.04 | 1600 | 0.4213 | 0.5059 |
| 0.3473 | 2.55 | 2000 | 0.4199 | 0.4955 |
| 0.3291 | 3.07 | 2400 | 0.4323 | 0.5061 |
| 0.2819 | 3.58 | 2800 | 0.4026 | 0.4490 |
| 0.2628 | 4.09 | 3200 | 0.3831 | 0.4446 |
| 0.2371 | 4.6 | 3600 | 0.3622 | 0.4234 |
| 0.2274 | 5.11 | 4000 | 0.3473 | 0.4012 |
| 0.2051 | 5.62 | 4400 | 0.3471 | 0.3998 |
| 0.1985 | 6.13 | 4800 | 0.3759 | 0.4088 |
| 0.1767 | 6.64 | 5200 | 0.3620 | 0.4012 |
| 0.1707 | 7.15 | 5600 | 0.3415 | 0.3700 |
| 0.1559 | 7.66 | 6000 | 0.3317 | 0.3661 |
| 0.147 | 8.17 | 6400 | 0.3265 | 0.3618 |
| 0.1339 | 8.68 | 6800 | 0.3293 | 0.3586 |
| 0.126 | 9.2 | 7200 | 0.3386 | 0.3458 |
| 0.1149 | 9.71 | 7600 | 0.3305 | 0.3397 |
| 0.1051 | 10.22 | 8000 | 0.3235 | 0.3354 |
| 0.1005 | 10.73 | 8400 | 0.3201 | 0.3295 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 2.1.0
- Tokenizers 0.10.3
|
veronica320/SPTE_roberta-large-mnli_200 | 7315c8b18bdd2d5e4caf1f1d3544249f4f81f44e | 2022-05-09T21:31:09.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | veronica320 | null | veronica320/SPTE_roberta-large-mnli_200 | 2 | null | transformers | 25,887 | Entry not found |
veronica320/MPTE_MPE_roberta_200 | d5623f589c7220c368572ea6e1e1b95761c01ca1 | 2022-05-09T21:31:45.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | veronica320 | null | veronica320/MPTE_MPE_roberta_200 | 2 | null | transformers | 25,888 | Entry not found |
Kailash/wav2vec2-large-xls-r-300m-turkish-colab | f10e153fd8dff00c7a39b4add0fc0979aa79280a | 2022-05-10T09:17:23.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | Kailash | null | Kailash/wav2vec2-large-xls-r-300m-turkish-colab | 2 | null | transformers | 25,889 | Entry not found |
masakhane/mt5_en_yor_news | a6d7931a46d612e3950f7842bf6fd49eed26b11e | 2022-05-10T12:59:17.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/mt5_en_yor_news | 2 | null | transformers | 25,890 | ---
license: afl-3.0
---
|
ceggian/bert_post_trained_reddit_batch512 | 0d791e93d813b15a324cc85ab68486548bc0d0d6 | 2022-05-10T13:53:42.000Z | [
"pytorch",
"bert",
"pretraining",
"transformers"
] | null | false | ceggian | null | ceggian/bert_post_trained_reddit_batch512 | 2 | null | transformers | 25,891 | Entry not found |
moshew/MiniLM-L3-clinc-distilled | 82e5220010c5fedccc92d695d84553035fa1e414 | 2022-05-10T16:53:49.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | moshew | null | moshew/MiniLM-L3-clinc-distilled | 2 | null | transformers | 25,892 | Entry not found |
ziedhajyahia/autotrain-ok-848227025 | b485844ca37dfb60b69a87b3c85fbf21f7ac351f | 2022-05-10T15:21:50.000Z | [
"pytorch",
"camembert",
"text-classification",
"fr",
"dataset:ziedhajyahia/autotrain-data-ok",
"transformers",
"autotrain",
"co2_eq_emissions"
] | text-classification | false | ziedhajyahia | null | ziedhajyahia/autotrain-ok-848227025 | 2 | null | transformers | 25,893 | ---
tags: autotrain
language: fr
widget:
- text: "I love AutoTrain 🤗"
datasets:
- ziedhajyahia/autotrain-data-ok
co2_eq_emissions: 5.096755166899446
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 848227025
- CO2 Emissions (in grams): 5.096755166899446
## Validation Metrics
- Loss: 2.1917402744293213
- Accuracy: 0.44666666666666666
- Macro F1: 0.20291677804725128
- Micro F1: 0.44666666666666666
- Weighted F1: 0.37709801275435956
- Macro Precision: 0.19919016697588127
- Micro Precision: 0.44666666666666666
- Weighted Precision: 0.3478004329004329
- Macro Recall: 0.23167713239141807
- Micro Recall: 0.44666666666666666
- Weighted Recall: 0.44666666666666666
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/ziedhajyahia/autotrain-ok-848227025
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("ziedhajyahia/autotrain-ok-848227025", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("ziedhajyahia/autotrain-ok-848227025", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
IljaSamoilov/MBART-estonian-subtitles | 5af8140ea5fec5faa71ec783191fbc2ab0324d9c | 2022-05-11T08:12:33.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"et",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | IljaSamoilov | null | IljaSamoilov/MBART-estonian-subtitles | 2 | null | transformers | 25,894 | ---
language:
- et
widget:
- text: "te olete ka noh, noh, päris korralikult ka Rahvusringhäälingu teatud mõttes sellisesse keerulisse olukorda pannud,"
- text: "Et, et, et miks mitte olla siis tasakaalus, ma noh, hüpoteetiliselt viskan selle palli üles,"
---
Model usage:
```
tokenizer = MBart50Tokenizer.from_pretrained("IljaSamoilov/MBART-estonian-subtitles", src_lang="et_EE", tgt_lang="et_EE")
model = MBartForConditionalGeneration.from_pretrained("IljaSamoilov/MBART-estonian-subtitles")
``` |
nihaldsouza1/covid-hatespeech-detection | 9d994ae7126dd55ec149821fcbc655399bac37cb | 2022-05-10T18:40:34.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | nihaldsouza1 | null | nihaldsouza1/covid-hatespeech-detection | 2 | null | transformers | 25,895 | Since the start of the COVID-19 pandemic, there has been a widespread increase in the amount of hate-speech being propagated online against the Asian community. This project builds upon and explores the work of He et al. Their COVID-HATE dataset contains 206 million tweets focused around anti-Asian hate speech. Using tweet data from before the COVID-19 pandemic, as well as the COVID-HATE dataset from He et al, we performed transfer learning. We tested several different models, including BERT, RoBERTa, LSTM, and BERT-CNN.
Some of these models hindered the performance of He et al’s model, while others improved it.
|
moshew/Mini-bert-distilled | 819afe22dddded5b58ee8c434a4fe57781e2c299 | 2022-05-10T19:42:57.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | moshew | null | moshew/Mini-bert-distilled | 2 | null | transformers | 25,896 | Entry not found |
enoriega/kw_pubmed_1000_0.00006 | 3887554baa4fe038dba9c3109b809e65f403011c | 2022-05-10T20:48:49.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | enoriega | null | enoriega/kw_pubmed_1000_0.00006 | 2 | null | transformers | 25,897 | Entry not found |
huggingtweets/cdrsuperheroga1 | cd7b6ad8d9d9d43f9d00e5f88f6ec2dd60240138 | 2022-05-11T01:15:45.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/cdrsuperheroga1 | 2 | null | transformers | 25,898 | ---
language: en
thumbnail: http://www.huggingtweets.com/cdrsuperheroga1/1652231741388/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/1244518578537160704/ZWf8X6PO_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">cdrsuperhero_gaming</div>
<div style="text-align: center; font-size: 14px;">@cdrsuperheroga1</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 cdrsuperhero_gaming.
| Data | cdrsuperhero_gaming |
| --- | --- |
| Tweets downloaded | 2739 |
| Retweets | 296 |
| Short tweets | 858 |
| Tweets kept | 1585 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3sjvj649/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 @cdrsuperheroga1's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/227tkbwp) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/227tkbwp/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/cdrsuperheroga1')
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)
|
dreamerdeo/da-xlarge | 7efb1992327bd16249f0b7679b53cb6edaa4bc50 | 2022-05-11T03:05:51.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | dreamerdeo | null | dreamerdeo/da-xlarge | 2 | null | transformers | 25,899 | Entry not found |
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