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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
mbshr/urt5-base | 1396b3e791a3a2249b7f7670c3b711a8888ee970 | 2022-06-26T17:13:24.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | mbshr | null | mbshr/urt5-base | 6 | null | transformers | 15,800 | Entry not found |
Moo/kobart-counsel-sum | d3faf0ada35dd49d4adf9489805425ab0079ef29 | 2022-06-27T02:13:57.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| text2text-generation | false | Moo | null | Moo/kobart-counsel-sum | 6 | null | transformers | 15,801 | ---
license: apache-2.0
---
|
Parsa/Drug_Induced_Liver_Injury_classification | 50993e7c3647ac12e62646ffb6aab303432a07e2 | 2022-06-27T03:47:17.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | false | Parsa | null | Parsa/Drug_Induced_Liver_Injury_classification | 6 | null | transformers | 15,802 | Entry not found |
ajders/distilled_wav2vec2_xls_r_300m | bf3547d7501359440fc05dbff328b0a45510fc14 | 2022-07-04T13:18:25.000Z | [
"pytorch",
"wav2vec2",
"pretraining",
"transformers"
]
| null | false | ajders | null | ajders/distilled_wav2vec2_xls_r_300m | 6 | null | transformers | 15,803 | Entry not found |
sanskar/JewelleryReviews | bf1b40bca7815ca8419db7cd3d08293f51d2fca0 | 2022-06-27T13:42:08.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | false | sanskar | null | sanskar/JewelleryReviews | 6 | null | transformers | 15,804 | Entry not found |
annahaz/distilbert-base-multilingual-cased-finetuned-misogyny-multilingual | 2c7c7a0bbce67be716df042710b36c90209bef37 | 2022-06-28T22:01:28.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | annahaz | null | annahaz/distilbert-base-multilingual-cased-finetuned-misogyny-multilingual | 6 | null | transformers | 15,805 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-base-multilingual-cased-finetuned-misogyny-multilingual
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-multilingual-cased-finetuned-misogyny-multilingual
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9917
- Accuracy: 0.8808
- F1: 0.7543
- Precision: 0.7669
- Recall: 0.7421
- Mae: 0.1192
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
| 0.3366 | 1.0 | 1407 | 0.3297 | 0.8630 | 0.6862 | 0.7886 | 0.6073 | 0.1370 |
| 0.2371 | 2.0 | 2814 | 0.3423 | 0.8802 | 0.7468 | 0.7802 | 0.7161 | 0.1198 |
| 0.1714 | 3.0 | 4221 | 0.4373 | 0.8749 | 0.7351 | 0.7693 | 0.7039 | 0.1251 |
| 0.1161 | 4.0 | 5628 | 0.5584 | 0.8699 | 0.7525 | 0.7089 | 0.8019 | 0.1301 |
| 0.0646 | 5.0 | 7035 | 0.7005 | 0.8788 | 0.7357 | 0.7961 | 0.6837 | 0.1212 |
| 0.0539 | 6.0 | 8442 | 0.7866 | 0.8710 | 0.7465 | 0.7243 | 0.7702 | 0.1290 |
| 0.0336 | 7.0 | 9849 | 0.8967 | 0.8783 | 0.7396 | 0.7828 | 0.7010 | 0.1217 |
| 0.0202 | 8.0 | 11256 | 0.9053 | 0.8810 | 0.7472 | 0.7845 | 0.7133 | 0.1190 |
| 0.018 | 9.0 | 12663 | 0.9785 | 0.8792 | 0.7478 | 0.7706 | 0.7262 | 0.1208 |
| 0.0069 | 10.0 | 14070 | 0.9917 | 0.8808 | 0.7543 | 0.7669 | 0.7421 | 0.1192 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.9.0+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Jeevesh8/goog_bert_ft_cola-0 | 95fef8e89dce02354c26255948bb9a9a80a22f41 | 2022-06-29T17:31:49.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-0 | 6 | null | transformers | 15,806 | Entry not found |
domenicrosati/deberta-xsmall-dapt-scientific-papers-pubmed | da3960403aecaab056295a8555245afade351ffd | 2022-06-29T21:30:24.000Z | [
"pytorch",
"tensorboard",
"deberta-v2",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | false | domenicrosati | null | domenicrosati/deberta-xsmall-dapt-scientific-papers-pubmed | 6 | null | transformers | 15,807 | Entry not found |
ps29/distilbert-base-uncased-finetuned-emotion | 7dac28b0a21bc74a409727ba58a6e0c0aa1b1a1f | 2022-06-30T05:45:12.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | ps29 | null | ps29/distilbert-base-uncased-finetuned-emotion | 6 | null | transformers | 15,808 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.925
- name: F1
type: f1
value: 0.9249836806712254
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2130
- Accuracy: 0.925
- F1: 0.9250
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8035 | 1.0 | 250 | 0.3075 | 0.908 | 0.9063 |
| 0.2445 | 2.0 | 500 | 0.2130 | 0.925 | 0.9250 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
abhishek/autotrain-imdbtestmodel-9215210 | 93e9d340ee9f63cff89d5470b3a451df115822dc | 2022-06-30T13:36:05.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:abhishek/autotrain-data-imdbtestmodel",
"transformers",
"autotrain",
"co2_eq_emissions"
]
| text-classification | false | abhishek | null | abhishek/autotrain-imdbtestmodel-9215210 | 6 | null | transformers | 15,809 | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- abhishek/autotrain-data-imdbtestmodel
co2_eq_emissions: 0.2757084122251468
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 9215210
- CO2 Emissions (in grams): 0.2757084122251468
## Validation Metrics
- Loss: 0.1699502319097519
- Accuracy: 0.9372
- Precision: 0.9277551659361303
- Recall: 0.94824
- AUC: 0.9837227744
- F1: 0.9378857414147808
## 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/abhishek/autotrain-imdbtestmodel-9215210
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("abhishek/autotrain-imdbtestmodel-9215210", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("abhishek/autotrain-imdbtestmodel-9215210", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
Gunulhona/tbSTmodel_v1 | f53232ac2f626fa72ed654fc5c3fc91e39a89419 | 2022-07-02T15:05:19.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | Gunulhona | null | Gunulhona/tbSTmodel_v1 | 6 | null | transformers | 15,810 | Entry not found |
Gunulhona/tbscmodel_v1 | ebfb9c96759ae4f2898b77ff9ee3423f4680554c | 2022-06-30T15:53:21.000Z | [
"pytorch",
"bart",
"text-classification",
"transformers"
]
| text-classification | false | Gunulhona | null | Gunulhona/tbscmodel_v1 | 6 | null | transformers | 15,811 | Entry not found |
Evelyn18/distilbert-base-uncased-becas-3 | 687997b2539b030e878690556b985449a7b5c46a | 2022-07-02T03:06:25.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"dataset:becasv2",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| question-answering | false | Evelyn18 | null | Evelyn18/distilbert-base-uncased-becas-3 | 6 | null | transformers | 15,812 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: distilbert-base-uncased-becas-3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-becas-3
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 5.9817
## 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: 20
- eval_batch_size: 20
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 4 | 4.7485 |
| No log | 2.0 | 8 | 4.9898 |
| No log | 3.0 | 12 | 4.5283 |
| No log | 4.0 | 16 | 5.2474 |
| No log | 5.0 | 20 | 5.7884 |
| No log | 6.0 | 24 | 5.7276 |
| No log | 7.0 | 28 | 6.1736 |
| No log | 8.0 | 32 | 6.2020 |
| No log | 9.0 | 36 | 5.9669 |
| No log | 10.0 | 40 | 5.9817 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Evelyn18/distilbert-base-uncased-becas-6 | 81d89c4ed20842b7ed409cb5f160638bd0cff66a | 2022-07-02T03:41:02.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"dataset:becasv2",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| question-answering | false | Evelyn18 | null | Evelyn18/distilbert-base-uncased-becas-6 | 6 | null | transformers | 15,813 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: distilbert-base-uncased-becas-6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-becas-6
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 4.4429
## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 4 | 5.7244 |
| No log | 2.0 | 8 | 5.3950 |
| No log | 3.0 | 12 | 5.1709 |
| No log | 4.0 | 16 | 4.9720 |
| No log | 5.0 | 20 | 4.7402 |
| No log | 6.0 | 24 | 4.5832 |
| No log | 7.0 | 28 | 4.5499 |
| No log | 8.0 | 32 | 4.5004 |
| No log | 9.0 | 36 | 4.4665 |
| No log | 10.0 | 40 | 4.4429 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Smith123/tiny-bert-sst2-distilled_L4_H_512_New | 18eabaa91500e12edf22ec7f46e61d2d96930260 | 2022-07-01T03:54:39.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Smith123 | null | Smith123/tiny-bert-sst2-distilled_L4_H_512_New | 6 | null | transformers | 15,814 | Entry not found |
tmoodley/rare-bottle | 144fa0573afe00fbd4778cdd8abbc49e3cc161b2 | 2022-07-02T13:21:56.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"huggingpics",
"model-index"
]
| image-classification | false | tmoodley | null | tmoodley/rare-bottle | 6 | 1 | transformers | 15,815 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rare-bottle
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.6770833134651184
---
# rare-bottle
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Don Julio
#### Jack Daniels
#### Southern Comfort
#### bacardi
#### johnny walker
 |
BigSalmon/InformalToFormalLincoln54 | 45a830fbc2bdaa5d17084ee8f618ec107360e941 | 2022-07-04T01:20:37.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | false | BigSalmon | null | BigSalmon/InformalToFormalLincoln54 | 6 | null | transformers | 15,816 | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln54")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln54")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- nebraska
- unicamerical legislature
- different from federal house and senate
text: featuring a unicameral legislature, nebraska's political system stands in stark contrast to the federal model, comprised of a house and senate.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
```
```
original: had trouble deciding.
translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation.
***
original:
```
```
input: not loyal
1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ).
***
input:
``` |
abdulmatinomotoso/testing_news | 0870ccfb5237b4343ffc53a924eb2dc166b5751d | 2022-07-03T20:26:41.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | false | abdulmatinomotoso | null | abdulmatinomotoso/testing_news | 6 | null | transformers | 15,817 | Entry not found |
emekaboris/code_t5_small_git_diff | 4a29f92f1d14a52598b19727b4940c79c0a99c86 | 2022-07-04T10:55:38.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | emekaboris | null | emekaboris/code_t5_small_git_diff | 6 | null | transformers | 15,818 | Entry not found |
AnonymousSub/fpdm_bert_pert_sent_0.01_squad2.0 | 43d3523e6c9ae35ea9b2b1489d9da2be6fbe33e5 | 2022-07-06T00:12:22.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | false | AnonymousSub | null | AnonymousSub/fpdm_bert_pert_sent_0.01_squad2.0 | 6 | null | transformers | 15,819 | Entry not found |
Shunichiro/distilbert-base-uncased-finetuned-squad | fc91b5645074ccbfb7602414f62862106a1935e1 | 2022-07-22T05:11:33.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| question-answering | false | Shunichiro | null | Shunichiro/distilbert-base-uncased-finetuned-squad | 6 | null | transformers | 15,820 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.0244
## 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: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 30 | 3.5643 |
| No log | 2.0 | 60 | 2.4546 |
| No log | 3.0 | 90 | 2.3018 |
| No log | 4.0 | 120 | 2.4636 |
| No log | 5.0 | 150 | 2.4736 |
| No log | 6.0 | 180 | 2.5580 |
| No log | 7.0 | 210 | 2.6686 |
| No log | 8.0 | 240 | 2.7249 |
| No log | 9.0 | 270 | 3.2596 |
| No log | 10.0 | 300 | 3.5904 |
| No log | 11.0 | 330 | 3.6709 |
| No log | 12.0 | 360 | 3.6431 |
| No log | 13.0 | 390 | 3.6343 |
| No log | 14.0 | 420 | 3.8316 |
| No log | 15.0 | 450 | 3.6363 |
| No log | 16.0 | 480 | 3.8468 |
| 0.8931 | 17.0 | 510 | 3.7114 |
| 0.8931 | 18.0 | 540 | 3.8719 |
| 0.8931 | 19.0 | 570 | 4.0872 |
| 0.8931 | 20.0 | 600 | 4.2989 |
| 0.8931 | 21.0 | 630 | 4.5494 |
| 0.8931 | 22.0 | 660 | 4.2565 |
| 0.8931 | 23.0 | 690 | 4.3009 |
| 0.8931 | 24.0 | 720 | 4.1816 |
| 0.8931 | 25.0 | 750 | 4.2583 |
| 0.8931 | 26.0 | 780 | 4.2276 |
| 0.8931 | 27.0 | 810 | 4.3481 |
| 0.8931 | 28.0 | 840 | 4.4369 |
| 0.8931 | 29.0 | 870 | 4.4891 |
| 0.8931 | 30.0 | 900 | 4.5521 |
| 0.8931 | 31.0 | 930 | 4.5201 |
| 0.8931 | 32.0 | 960 | 4.6323 |
| 0.8931 | 33.0 | 990 | 4.4766 |
| 0.0297 | 34.0 | 1020 | 4.7612 |
| 0.0297 | 35.0 | 1050 | 4.9057 |
| 0.0297 | 36.0 | 1080 | 4.7580 |
| 0.0297 | 37.0 | 1110 | 4.6351 |
| 0.0297 | 38.0 | 1140 | 4.6495 |
| 0.0297 | 39.0 | 1170 | 4.5980 |
| 0.0297 | 40.0 | 1200 | 4.6370 |
| 0.0297 | 41.0 | 1230 | 4.6523 |
| 0.0297 | 42.0 | 1260 | 4.5802 |
| 0.0297 | 43.0 | 1290 | 4.6304 |
| 0.0297 | 44.0 | 1320 | 4.7111 |
| 0.0297 | 45.0 | 1350 | 4.7219 |
| 0.0297 | 46.0 | 1380 | 4.7323 |
| 0.0297 | 47.0 | 1410 | 4.9115 |
| 0.0297 | 48.0 | 1440 | 4.7873 |
| 0.0297 | 49.0 | 1470 | 4.9340 |
| 0.0023 | 50.0 | 1500 | 5.0638 |
| 0.0023 | 51.0 | 1530 | 5.0750 |
| 0.0023 | 52.0 | 1560 | 4.9338 |
| 0.0023 | 53.0 | 1590 | 4.9197 |
| 0.0023 | 54.0 | 1620 | 4.9282 |
| 0.0023 | 55.0 | 1650 | 5.0038 |
| 0.0023 | 56.0 | 1680 | 4.9848 |
| 0.0023 | 57.0 | 1710 | 4.9932 |
| 0.0023 | 58.0 | 1740 | 5.0134 |
| 0.0023 | 59.0 | 1770 | 5.0303 |
| 0.0023 | 60.0 | 1800 | 5.0244 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Tokenizers 0.12.1
|
vinayak361/token_fine_tunned_flipkart | 2520e1d74b4fb54f8f050245745c94fb2717371f | 2022-07-06T09:32:50.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| token-classification | false | vinayak361 | null | vinayak361/token_fine_tunned_flipkart | 6 | null | transformers | 15,821 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: token_fine_tunned_flipkart
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. -->
# token_fine_tunned_flipkart
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0992
- Precision: 0.9526
- Recall: 0.9669
- F1: 0.9597
- Accuracy: 0.9730
## 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 135 | 0.5967 | 0.7227 | 0.7830 | 0.7516 | 0.7932 |
| No log | 2.0 | 270 | 0.3673 | 0.8105 | 0.8623 | 0.8356 | 0.8747 |
| No log | 3.0 | 405 | 0.2679 | 0.8676 | 0.8854 | 0.8764 | 0.9094 |
| 0.6219 | 4.0 | 540 | 0.1972 | 0.8955 | 0.9217 | 0.9084 | 0.9355 |
| 0.6219 | 5.0 | 675 | 0.1500 | 0.9229 | 0.9374 | 0.9301 | 0.9525 |
| 0.6219 | 6.0 | 810 | 0.1240 | 0.9341 | 0.9509 | 0.9424 | 0.9609 |
| 0.6219 | 7.0 | 945 | 0.1041 | 0.9516 | 0.9650 | 0.9582 | 0.9720 |
| 0.2085 | 8.0 | 1080 | 0.0992 | 0.9526 | 0.9669 | 0.9597 | 0.9730 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1
|
ahadda5/bart_wikikp_ftuned_cve50k | f8bc8ec5d3358d1c621d4d4a089bfe61f9eac0db | 2022-07-06T15:18:10.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | ahadda5 | null | ahadda5/bart_wikikp_ftuned_cve50k | 6 | null | transformers | 15,822 | Bart wikikp , masked on cve50k |
Aktsvigun/bart-base_aeslc_6585777 | b84a4291d721565c03af9428104891e0b0daa9d8 | 2022-07-07T15:20:09.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | Aktsvigun | null | Aktsvigun/bart-base_aeslc_6585777 | 6 | null | transformers | 15,823 | Entry not found |
Aktsvigun/bart-base_aeslc_5893459 | 2ccb70abdd449959d158ce48d6ccf6b9fb92fffb | 2022-07-07T15:27:44.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | Aktsvigun | null | Aktsvigun/bart-base_aeslc_5893459 | 6 | null | transformers | 15,824 | Entry not found |
Aktsvigun/bart-base_aeslc_7629317 | 1826189334fd3e7b2efe3755f9618ed493966ec5 | 2022-07-07T15:38:13.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | Aktsvigun | null | Aktsvigun/bart-base_aeslc_7629317 | 6 | null | transformers | 15,825 | Entry not found |
Aktsvigun/bart-base_aeslc_6880281 | 45c4bb46b8d114abcb78451f6dc00b546799de32 | 2022-07-07T15:25:32.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | Aktsvigun | null | Aktsvigun/bart-base_aeslc_6880281 | 6 | null | transformers | 15,826 | Entry not found |
IIC/mt5-large-lfqa-es | fb10f72a2f9c98fd0867a4554e8a7d2fb3f3b844 | 2022-07-07T11:21:53.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | IIC | null | IIC/mt5-large-lfqa-es | 6 | null | transformers | 15,827 | Entry not found |
PronayGhosh18/dummy-model_101_pronay_ghosh | bfba57b8b04ec5d6ce624e9f4c4d721faaa90de0 | 2022-07-08T07:22:55.000Z | [
"pytorch",
"camembert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | false | PronayGhosh18 | null | PronayGhosh18/dummy-model_101_pronay_ghosh | 6 | null | transformers | 15,828 | Entry not found |
ghadeermobasher/Original-scibert_scivocab_cased-BioRED-Chem-512-5-30 | 26d490d421340cb0ce7dada784a860f605b01788 | 2022-07-11T09:11:50.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/Original-scibert_scivocab_cased-BioRED-Chem-512-5-30 | 6 | null | transformers | 15,829 | |
ghadeermobasher/Modified-scibert_scivocab_cased-BioRED-Chem-512-5-30 | 77bee4f31a93e509c896819f28eaf2dddada6225 | 2022-07-08T18:00:50.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/Modified-scibert_scivocab_cased-BioRED-Chem-512-5-30 | 6 | null | transformers | 15,830 | Entry not found |
nvidia/stt_es_conformer_ctc_large | 8853e9e151d598ca2724e524db07a722af517c5d | 2022-07-13T16:40:22.000Z | [
"nemo",
"es",
"dataset:Fisher",
"dataset:VoxPopuli",
"dataset:facebook/multilingual_librispeech",
"dataset:mozilla-foundation/common_voice_7_0",
"arxiv:2005.08100",
"automatic-speech-recognition",
"speech",
"audio",
"CTC",
"Conformer",
"Transformer",
"pytorch",
"NeMo",
"hf-asr-leaderboard",
"Riva",
"license:cc-by-4.0",
"model-index"
]
| automatic-speech-recognition | false | nvidia | null | nvidia/stt_es_conformer_ctc_large | 6 | 1 | nemo | 15,831 | ---
language:
- es
library_name: nemo
datasets:
- Fisher
- VoxPopuli
- facebook/multilingual_librispeech
- mozilla-foundation/common_voice_7_0
thumbnail: null
tags:
- automatic-speech-recognition
- speech
- audio
- CTC
- Conformer
- Transformer
- pytorch
- NeMo
- hf-asr-leaderboard
- Riva
license: cc-by-4.0
model-index:
- name: stt_es_conformer_ctc_large
results:
- task:
type: Automatic Speech Recognition
name: speech-recognition
dataset:
name: common-voice-7-0-6
type: mozilla-foundation/common_voice_7_0
config: es
split: dev
args:
language: es
metrics:
- name: Dev WER
type: wer
value: 5.0
- task:
type: Automatic Speech Recognition
name: speech-recognition
dataset:
name: common-voice-7-0-6
type: mozilla-foundation/common_voice_7_0
config: es
split: test
args:
language: es
metrics:
- name: Test WER
type: wer
value: 5.5
- task:
type: Automatic Speech Recognition
name: automatic-speech-recognition
dataset:
name: Multilingual LibriSpeech
type: facebook/multilingual_librispeech
config: spanish
split: dev
args:
language: es
metrics:
- name: Dev WER
type: wer
value: 3.6
- task:
type: Automatic Speech Recognition
name: automatic-speech-recognition
dataset:
name: Multilingual LibriSpeech
type: facebook/multilingual_librispeech
config: spanish
split: test
args:
language: es
metrics:
- name: Test WER
type: wer
value: 3.6
---
# NVIDIA Conformer-CTC Large (es)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-architecture)
| [](#datasets)
| [](#deployment-with-nvidia-riva) |
This model transcribes speech in lowercase Spanish alphabet including spaces, and was trained on a composite dataset comprising of 1340 hours of Spanish speech. It is a non-autoregressive "large" variant of Conformer, with around 120 million parameters.
See the [model architecture](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#conformer-ctc) for complete architecture details.
It is also compatible with NVIDIA Riva for [production-grade server deployments](#deployment-with-nvidia-riva).
## Usage
The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest PyTorch version.
```
pip install nemo_toolkit['all']
```
### Automatically instantiate the model
```python
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained("nvidia/stt_es_conformer_ctc_large")
```
### Transcribing using Python
First, let's get a sample
```
wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav
```
Then simply do:
```
asr_model.transcribe(['2086-149220-0033.wav'])
```
### Transcribing many audio files
```shell
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py
pretrained_name="nvidia/stt_es_conformer_ctc_large"
audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
```
### Input
This model accepts 16000 kHz Mono-channel Audio (wav files) as input.
### Output
This model provides transcribed speech as a string for a given audio sample.
## Model Architecture
Conformer-CTC model is a non-autoregressive variant of Conformer model [1] for Automatic Speech Recognition which uses CTC loss/decoding instead of Transducer. You may find more info on the detail of this model here: [Conformer-CTC Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#conformer-ctc).
## Training
The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/conformer/conformer_ctc_bpe.yaml).
The tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).
The checkpoint of the language model used as the neural rescorer can be found [here](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_es_conformer_ctc_large/files). You may find more info on how to train and use language models for ASR models here: [ASR Language Modeling](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/asr_language_modeling.html)
### Datasets
All the models in this collection are trained on a composite dataset (NeMo ASRSET) comprising of 1340 hours of Spanish speech:
- Mozilla Common Voice 7.0 (Spanish) - 289 hours after data cleaning
- Multilingual LibriSpeech (Spanish) - 801 hours after data cleaning
- Voxpopuli transcribed subset (Spanish) - 110 hours after data cleaning
- Fisher dataset (Spanish) - 140 hours after data cleaning
## Performance
The list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.
| Version | Tokenizer | Vocabulary Size | MCV 7.0 Dev | MCV 7.0 Test | MLS Dev | MLS Test | Voxpopuli Dev | Voxpopuli Test | Fisher Dev | Fisher Test| Train Dataset |
|---------|-----------------------|-----------------|-------------|--------------|---------|----------|---------------|----------------|------------|-------------|-----------------|
| 1.8.0 | SentencePiece Unigram | 1024 | 6.3 | 6.9 | 4.3 | 4.2 | 6.1 | 7.5 | 18.3 | 18.5 | NeMo ASRSET 2.0 |
While deploying with [NVIDIA Riva](https://developer.nvidia.com/riva), you can combine this model with external language models to further improve WER. The WER(%) of the latest model with different language modeling techniques are reported in the following table.
| Language Modeling | Training Dataset | MCV 7.0 Dev | MCV 7.0 Test | MLS Dev | MLS Test | Voxpopuli Dev | Voxpopuli Test | Fisher Dev | Fisher Test| Comment |
|-------------------|------------------------------------------------------------------------------|-------------|--------------|---------|----------|---------------|----------------|----------------|----------------|--------------------------------------------------------|
| N-gram LM | Spanish News Crawl corpus (50M sentences) + NeMo ASRSET training transcripts | 5.0 | 5.5 | 3.6 | 3.6 | 5.5 | 6.7 | 17.4 | 17.5 | N=4, beam_width=128, n_gram_alpha=0.8, n_gram_beta=1.5 |
## Limitations
Since this model was trained on publicly available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.
## Deployment with NVIDIA Riva
For the best real-time accuracy, latency, and throughput, deploy the model with [NVIDIA Riva](https://developer.nvidia.com/riva), an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, at the edge, and embedded.
Additionally, Riva provides:
* World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours
* Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization
* Streaming speech recognition, Kubernetes compatible scaling, and Enterprise-grade support
Check out [Riva live demo](https://developer.nvidia.com/riva#demos).
## References
- [1] [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100)
- [2] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)
- [3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo) |
yam1ke/distilbert-base-uncased-finetuned-ner | 9c1be937a4b7a3c2e0dad2b0f4f048a2ed3e9ce4 | 2022-07-10T00:33:07.000Z | [
"pytorch",
"distilbert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| token-classification | false | yam1ke | null | yam1ke/distilbert-base-uncased-finetuned-ner | 6 | null | transformers | 15,832 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9285476533895485
- name: Recall
type: recall
value: 0.9362344781295447
- name: F1
type: f1
value: 0.9323752228163993
- name: Accuracy
type: accuracy
value: 0.9838753236850049
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0607
- Precision: 0.9285
- Recall: 0.9362
- F1: 0.9324
- Accuracy: 0.9839
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2452 | 1.0 | 878 | 0.0709 | 0.9184 | 0.9206 | 0.9195 | 0.9803 |
| 0.0501 | 2.0 | 1756 | 0.0621 | 0.9212 | 0.9328 | 0.9270 | 0.9830 |
| 0.0299 | 3.0 | 2634 | 0.0607 | 0.9285 | 0.9362 | 0.9324 | 0.9839 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
huangjia/pegasus-samsum | c078b0916f6dfa6a0c634a178f062195e0889978 | 2022-07-10T10:06:39.000Z | [
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"dataset:samsum",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
]
| text2text-generation | false | huangjia | null | huangjia/pegasus-samsum | 6 | null | transformers | 15,833 | ---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.2
- Datasets 1.18.4
- Tokenizers 0.10.3
|
ghadeermobasher/Modified-BiomedNLP-PubMedBERT-base-uncased-abstract-BioRED-Chem-512-5-10 | 7e03d90f518c76451116e806f07f46349701854c | 2022-07-11T10:22:38.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/Modified-BiomedNLP-PubMedBERT-base-uncased-abstract-BioRED-Chem-512-5-10 | 6 | null | transformers | 15,834 | Entry not found |
ghadeermobasher/Original-BiomedNLP-PubMedBERT-base-uncased-abstract-BioRED-Chem-512-5-10 | 79a5aeb6d16466b38ec991852bf841970cfbb828 | 2022-07-11T10:23:55.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/Original-BiomedNLP-PubMedBERT-base-uncased-abstract-BioRED-Chem-512-5-10 | 6 | null | transformers | 15,835 | Entry not found |
paola-md/recipe-roberta-upper-tIs | d849044c59e30e19878e743a5ee3e4f7592fc414 | 2022-07-12T00:11:13.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
]
| fill-mask | false | paola-md | null | paola-md/recipe-roberta-upper-tIs | 6 | null | transformers | 15,836 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: recipe-roberta-upper-tIs
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. -->
# recipe-roberta-upper-tIs
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7904
## 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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2671 | 1.0 | 1281 | 1.0554 |
| 1.0995 | 2.0 | 2562 | 0.9832 |
| 1.0339 | 3.0 | 3843 | 0.9389 |
| 0.9925 | 4.0 | 5124 | 0.9095 |
| 0.964 | 5.0 | 6405 | 0.8914 |
| 0.9426 | 6.0 | 7686 | 0.8708 |
| 0.9227 | 7.0 | 8967 | 0.8590 |
| 0.9082 | 8.0 | 10248 | 0.8448 |
| 0.8963 | 9.0 | 11529 | 0.8361 |
| 0.8847 | 10.0 | 12810 | 0.8249 |
| 0.8756 | 11.0 | 14091 | 0.8204 |
| 0.8672 | 12.0 | 15372 | 0.8105 |
| 0.8612 | 13.0 | 16653 | 0.8106 |
| 0.8561 | 14.0 | 17934 | 0.8041 |
| 0.8485 | 15.0 | 19215 | 0.7979 |
| 0.8452 | 16.0 | 20496 | 0.7910 |
| 0.8403 | 17.0 | 21777 | 0.7991 |
| 0.8389 | 18.0 | 23058 | 0.7928 |
| 0.8371 | 19.0 | 24339 | 0.7926 |
| 0.8341 | 20.0 | 25620 | 0.7904 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Evelyn18/legalectra-small-spanish-becasv3-6 | 526339a729283b20b3f844ba6b75b5d59d779594 | 2022-07-12T05:05:14.000Z | [
"pytorch",
"tensorboard",
"electra",
"question-answering",
"dataset:becasv2",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
]
| question-answering | false | Evelyn18 | null | Evelyn18/legalectra-small-spanish-becasv3-6 | 6 | null | transformers | 15,837 | ---
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: legalectra-small-spanish-becasv3-6
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. -->
# legalectra-small-spanish-becasv3-6
This model is a fine-tuned version of [mrm8488/legalectra-small-spanish](https://huggingface.co/mrm8488/legalectra-small-spanish) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8441
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 5 | 5.6469 |
| No log | 2.0 | 10 | 5.5104 |
| No log | 3.0 | 15 | 5.4071 |
| No log | 4.0 | 20 | 5.3313 |
| No log | 5.0 | 25 | 5.2629 |
| No log | 6.0 | 30 | 5.1972 |
| No log | 7.0 | 35 | 5.1336 |
| No log | 8.0 | 40 | 5.0667 |
| No log | 9.0 | 45 | 5.0030 |
| No log | 10.0 | 50 | 4.9302 |
| No log | 11.0 | 55 | 4.8646 |
| No log | 12.0 | 60 | 4.7963 |
| No log | 13.0 | 65 | 4.7328 |
| No log | 14.0 | 70 | 4.6735 |
| No log | 15.0 | 75 | 4.6258 |
| No log | 16.0 | 80 | 4.5869 |
| No log | 17.0 | 85 | 4.5528 |
| No log | 18.0 | 90 | 4.5177 |
| No log | 19.0 | 95 | 4.4916 |
| No log | 20.0 | 100 | 4.4685 |
| No log | 21.0 | 105 | 4.4371 |
| No log | 22.0 | 110 | 4.4271 |
| No log | 23.0 | 115 | 4.3905 |
| No log | 24.0 | 120 | 4.3931 |
| No log | 25.0 | 125 | 4.3902 |
| No log | 26.0 | 130 | 4.3772 |
| No log | 27.0 | 135 | 4.3981 |
| No log | 28.0 | 140 | 4.4463 |
| No log | 29.0 | 145 | 4.4501 |
| No log | 30.0 | 150 | 4.4654 |
| No log | 31.0 | 155 | 4.4069 |
| No log | 32.0 | 160 | 4.4108 |
| No log | 33.0 | 165 | 4.4394 |
| No log | 34.0 | 170 | 4.4320 |
| No log | 35.0 | 175 | 4.3541 |
| No log | 36.0 | 180 | 4.4534 |
| No log | 37.0 | 185 | 4.2616 |
| No log | 38.0 | 190 | 4.2474 |
| No log | 39.0 | 195 | 4.4358 |
| No log | 40.0 | 200 | 4.3060 |
| No log | 41.0 | 205 | 4.1866 |
| No log | 42.0 | 210 | 4.2735 |
| No log | 43.0 | 215 | 4.2739 |
| No log | 44.0 | 220 | 4.1812 |
| No log | 45.0 | 225 | 4.2484 |
| No log | 46.0 | 230 | 4.3706 |
| No log | 47.0 | 235 | 4.3487 |
| No log | 48.0 | 240 | 4.2805 |
| No log | 49.0 | 245 | 4.3180 |
| No log | 50.0 | 250 | 4.3574 |
| No log | 51.0 | 255 | 4.2823 |
| No log | 52.0 | 260 | 4.0643 |
| No log | 53.0 | 265 | 4.0729 |
| No log | 54.0 | 270 | 4.2368 |
| No log | 55.0 | 275 | 4.2845 |
| No log | 56.0 | 280 | 4.1009 |
| No log | 57.0 | 285 | 4.0629 |
| No log | 58.0 | 290 | 4.1250 |
| No log | 59.0 | 295 | 4.2048 |
| No log | 60.0 | 300 | 4.2412 |
| No log | 61.0 | 305 | 4.1653 |
| No log | 62.0 | 310 | 4.1433 |
| No log | 63.0 | 315 | 4.1309 |
| No log | 64.0 | 320 | 4.1381 |
| No log | 65.0 | 325 | 4.2162 |
| No log | 66.0 | 330 | 4.1858 |
| No log | 67.0 | 335 | 4.1342 |
| No log | 68.0 | 340 | 4.1247 |
| No log | 69.0 | 345 | 4.1701 |
| No log | 70.0 | 350 | 4.1915 |
| No log | 71.0 | 355 | 4.1356 |
| No log | 72.0 | 360 | 4.1766 |
| No log | 73.0 | 365 | 4.1296 |
| No log | 74.0 | 370 | 4.0594 |
| No log | 75.0 | 375 | 4.0601 |
| No log | 76.0 | 380 | 4.0328 |
| No log | 77.0 | 385 | 3.9978 |
| No log | 78.0 | 390 | 4.0070 |
| No log | 79.0 | 395 | 4.0519 |
| No log | 80.0 | 400 | 4.1000 |
| No log | 81.0 | 405 | 3.9550 |
| No log | 82.0 | 410 | 3.9159 |
| No log | 83.0 | 415 | 3.9494 |
| No log | 84.0 | 420 | 4.0546 |
| No log | 85.0 | 425 | 4.2223 |
| No log | 86.0 | 430 | 4.2665 |
| No log | 87.0 | 435 | 3.8892 |
| No log | 88.0 | 440 | 3.7763 |
| No log | 89.0 | 445 | 3.8576 |
| No log | 90.0 | 450 | 4.0089 |
| No log | 91.0 | 455 | 4.1495 |
| No log | 92.0 | 460 | 4.1545 |
| No log | 93.0 | 465 | 4.0164 |
| No log | 94.0 | 470 | 3.9175 |
| No log | 95.0 | 475 | 3.9308 |
| No log | 96.0 | 480 | 3.9658 |
| No log | 97.0 | 485 | 3.9856 |
| No log | 98.0 | 490 | 3.9691 |
| No log | 99.0 | 495 | 3.9082 |
| 3.2873 | 100.0 | 500 | 3.8736 |
| 3.2873 | 101.0 | 505 | 3.8963 |
| 3.2873 | 102.0 | 510 | 3.9391 |
| 3.2873 | 103.0 | 515 | 3.9408 |
| 3.2873 | 104.0 | 520 | 3.9075 |
| 3.2873 | 105.0 | 525 | 3.8258 |
| 3.2873 | 106.0 | 530 | 3.7917 |
| 3.2873 | 107.0 | 535 | 3.7981 |
| 3.2873 | 108.0 | 540 | 3.8272 |
| 3.2873 | 109.0 | 545 | 3.8655 |
| 3.2873 | 110.0 | 550 | 3.8234 |
| 3.2873 | 111.0 | 555 | 3.7126 |
| 3.2873 | 112.0 | 560 | 3.6981 |
| 3.2873 | 113.0 | 565 | 3.7327 |
| 3.2873 | 114.0 | 570 | 3.8470 |
| 3.2873 | 115.0 | 575 | 4.0036 |
| 3.2873 | 116.0 | 580 | 4.0412 |
| 3.2873 | 117.0 | 585 | 4.0487 |
| 3.2873 | 118.0 | 590 | 4.0524 |
| 3.2873 | 119.0 | 595 | 4.0375 |
| 3.2873 | 120.0 | 600 | 3.9971 |
| 3.2873 | 121.0 | 605 | 3.8959 |
| 3.2873 | 122.0 | 610 | 3.8834 |
| 3.2873 | 123.0 | 615 | 3.9279 |
| 3.2873 | 124.0 | 620 | 3.9374 |
| 3.2873 | 125.0 | 625 | 3.9515 |
| 3.2873 | 126.0 | 630 | 3.9625 |
| 3.2873 | 127.0 | 635 | 3.9635 |
| 3.2873 | 128.0 | 640 | 3.9596 |
| 3.2873 | 129.0 | 645 | 3.8871 |
| 3.2873 | 130.0 | 650 | 3.8307 |
| 3.2873 | 131.0 | 655 | 3.8318 |
| 3.2873 | 132.0 | 660 | 3.8403 |
| 3.2873 | 133.0 | 665 | 3.8560 |
| 3.2873 | 134.0 | 670 | 3.8650 |
| 3.2873 | 135.0 | 675 | 3.8734 |
| 3.2873 | 136.0 | 680 | 3.8756 |
| 3.2873 | 137.0 | 685 | 3.8613 |
| 3.2873 | 138.0 | 690 | 3.8447 |
| 3.2873 | 139.0 | 695 | 3.8362 |
| 3.2873 | 140.0 | 700 | 3.8328 |
| 3.2873 | 141.0 | 705 | 3.8350 |
| 3.2873 | 142.0 | 710 | 3.8377 |
| 3.2873 | 143.0 | 715 | 3.8399 |
| 3.2873 | 144.0 | 720 | 3.8414 |
| 3.2873 | 145.0 | 725 | 3.8422 |
| 3.2873 | 146.0 | 730 | 3.8435 |
| 3.2873 | 147.0 | 735 | 3.8437 |
| 3.2873 | 148.0 | 740 | 3.8437 |
| 3.2873 | 149.0 | 745 | 3.8440 |
| 3.2873 | 150.0 | 750 | 3.8441 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ghadeermobasher/Original-BiomedNLP-PubMedBERT-base-uncased-abstract-BioRED-Dis-512-5-30 | 0543e98e4da4893e1e216f0a7a04d830d757f758 | 2022-07-12T11:33:09.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/Original-BiomedNLP-PubMedBERT-base-uncased-abstract-BioRED-Dis-512-5-30 | 6 | null | transformers | 15,838 | Entry not found |
ghadeermobasher/Original-biobert-v1.1-BioRED_Dis-320-8-10 | 8059f6895398c98bb586c5e2997cfcb9faa98a29 | 2022-07-12T14:37:31.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/Original-biobert-v1.1-BioRED_Dis-320-8-10 | 6 | null | transformers | 15,839 | Entry not found |
ghadeermobasher/Modified-biobert-v1.1-BioRED-Dis-320-8-10 | 8582e854020f91cb9922963dd255fcec34182e0b | 2022-07-13T12:57:26.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/Modified-biobert-v1.1-BioRED-Dis-320-8-10 | 6 | null | transformers | 15,840 | |
ghadeermobasher/Original-scibert_scivocab_cased-BioRED_Dis-320-8-10 | cfff8e379cd2412e15a3267f878c51ae223d2213 | 2022-07-12T14:41:34.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/Original-scibert_scivocab_cased-BioRED_Dis-320-8-10 | 6 | null | transformers | 15,841 | Entry not found |
ghadeermobasher/Modified-scibert_scivocab_cased-BioRED-Dis-320-8-10 | 3d716b334d2b09f6d996eaa70ed481fe04c97a81 | 2022-07-12T14:42:36.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/Modified-scibert_scivocab_cased-BioRED-Dis-320-8-10 | 6 | null | transformers | 15,842 | Entry not found |
andreaschandra/xlm-roberta-base-finetuned-panx-en | 6d4f27a5ad8b1597cd443502445d0b7374ccebb3 | 2022-07-12T15:39:20.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
]
| token-classification | false | andreaschandra | null | andreaschandra/xlm-roberta-base-finetuned-panx-en | 6 | null | transformers | 15,843 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.6774373259052925
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3932
- F1: 0.6774
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0236 | 1.0 | 50 | 0.5462 | 0.5109 |
| 0.5047 | 2.0 | 100 | 0.4387 | 0.6370 |
| 0.3716 | 3.0 | 150 | 0.3932 | 0.6774 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
zluvolyote/s288cExpressionPrediction_k6 | 8d3d6fc7417ce119c2b24b76b81da48f084508b0 | 2022-07-12T16:54:43.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | zluvolyote | null | zluvolyote/s288cExpressionPrediction_k6 | 6 | null | transformers | 15,844 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: s288cExpressionPrediction_k6
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. -->
# s288cExpressionPrediction_k6
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4418
- Accuracy: 0.8067
- F1: 0.7882
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 58 | 0.5315 | 0.7278 | 0.7572 |
| No log | 2.0 | 116 | 0.4604 | 0.7853 | 0.7841 |
| No log | 3.0 | 174 | 0.4418 | 0.8067 | 0.7882 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Team-PIXEL/pixel-base-finetuned-pos-ud-chinese-gsd | d64d39625423a61eddf730c45b4fe7c734b7063c | 2022-07-13T00:25:47.000Z | [
"pytorch",
"pixel",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | Team-PIXEL | null | Team-PIXEL/pixel-base-finetuned-pos-ud-chinese-gsd | 6 | null | transformers | 15,845 | Entry not found |
Team-PIXEL/pixel-base-finetuned-parsing-ud-chinese-gsd | 5a37a7cd43599c02e2241085e324f575b49144ef | 2022-07-13T01:54:48.000Z | [
"pytorch",
"pixel",
"transformers"
]
| null | false | Team-PIXEL | null | Team-PIXEL/pixel-base-finetuned-parsing-ud-chinese-gsd | 6 | null | transformers | 15,846 | Entry not found |
NimaBoscarino/STPushToHub-test2 | 30b65d59877dd9c81c416df4187012e766e8485a | 2022-07-13T05:57:37.000Z | [
"pytorch",
"distilbert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
]
| sentence-similarity | false | NimaBoscarino | null | NimaBoscarino/STPushToHub-test2 | 6 | null | sentence-transformers | 15,847 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# NimaBoscarino/STPushToHub-test2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('NimaBoscarino/STPushToHub-test2')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('NimaBoscarino/STPushToHub-test2')
model = AutoModel.from_pretrained('NimaBoscarino/STPushToHub-test2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=NimaBoscarino/STPushToHub-test2)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 360 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 4,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 144,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Team-PIXEL/pixel-base-finetuned-parsing-ud-coptic-scriptorium | 42cdd6f05fedbfb571c6d3c8555be9b04b0f0ddc | 2022-07-13T14:36:01.000Z | [
"pytorch",
"pixel",
"transformers"
]
| null | false | Team-PIXEL | null | Team-PIXEL/pixel-base-finetuned-parsing-ud-coptic-scriptorium | 6 | null | transformers | 15,848 | Entry not found |
Hamzaaa/wav2vec2-base-finetuned-greek | 98d486d2a5a9fa8f23a927981d808ac276d5ce6e | 2022-07-13T17:43:56.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"transformers"
]
| audio-classification | false | Hamzaaa | null | Hamzaaa/wav2vec2-base-finetuned-greek | 6 | null | transformers | 15,849 | Entry not found |
Hamzaaa/wav2vec2-base-finetuned-Tess-excluded | 6cccf2ffbdb719557fefe1e50bc186c1eda1c461 | 2022-07-13T20:26:32.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"transformers"
]
| audio-classification | false | Hamzaaa | null | Hamzaaa/wav2vec2-base-finetuned-Tess-excluded | 6 | null | transformers | 15,850 | Entry not found |
leokai/distilbert-base-uncased-finetuned-wikiandmark | 4293fabc9a2b3763fb77ae7dbbd492ad8c4258bf | 2022-07-14T09:51:53.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | leokai | null | leokai/distilbert-base-uncased-finetuned-wikiandmark | 6 | null | transformers | 15,851 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-wikiandmark
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-wikiandmark
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0329
- Accuracy: 0.9962
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0058 | 1.0 | 1490 | 0.0261 | 0.9954 |
| 0.0058 | 2.0 | 2980 | 0.0335 | 0.9945 |
| 0.0024 | 3.0 | 4470 | 0.0309 | 0.9961 |
| 0.0007 | 4.0 | 5960 | 0.0323 | 0.9961 |
| 0.0009 | 5.0 | 7450 | 0.0329 | 0.9962 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
liyijing024/swin-base-patch4-window7-224-in22k-Chinese-finetuned | d852360a6933f5d5e8eaba7655923007077ae434 | 2022-07-14T18:04:48.000Z | [
"pytorch",
"tensorboard",
"swin",
"image-classification",
"dataset:imagefolder",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| image-classification | false | liyijing024 | null | liyijing024/swin-base-patch4-window7-224-in22k-Chinese-finetuned | 6 | null | transformers | 15,852 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-base-patch4-window7-224-in22k-Chinese-finetuned
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-base-patch4-window7-224-in22k-Chinese-finetuned
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0121 | 0.99 | 140 | 0.0001 | 1.0 |
| 0.0103 | 1.99 | 280 | 0.0001 | 1.0 |
| 0.0049 | 2.99 | 420 | 0.0000 | 1.0 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.8.0+cu111
- Datasets 2.3.3.dev0
- Tokenizers 0.12.1
|
jinwooChoi/KDW_SA_base_32_5e4 | c44e34aa58e15368791c015d705f091da39717ed | 2022-07-15T08:18:12.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
]
| text-classification | false | jinwooChoi | null | jinwooChoi/KDW_SA_base_32_5e4 | 6 | null | transformers | 15,853 | Entry not found |
huggingtweets/thes_standsfor | 56746a5eb7e2f7ff3cd50bbc6e5e0165e9fab6c8 | 2022-07-16T02:33:44.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
]
| text-generation | false | huggingtweets | null | huggingtweets/thes_standsfor | 6 | null | transformers | 15,854 | ---
language: en
thumbnail: http://www.huggingtweets.com/thes_standsfor/1657938820053/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/1544350525558525952/duMyGvoZ_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">DIDN’T DISAPPOINT A PICTURE?</div>
<div style="text-align: center; font-size: 14px;">@thes_standsfor</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 DIDN’T DISAPPOINT A PICTURE?.
| Data | DIDN’T DISAPPOINT A PICTURE? |
| --- | --- |
| Tweets downloaded | 3234 |
| Retweets | 1939 |
| Short tweets | 314 |
| Tweets kept | 981 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/p6qccgkf/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 @thes_standsfor's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1zso9llp) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1zso9llp/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/thes_standsfor')
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)
|
CaoHaiNam/vietnamese-address-embedding | 9f7316158cf8ffc08886fd7a52533864c206a680 | 2022-07-15T13:15:49.000Z | [
"pytorch",
"bert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
]
| sentence-similarity | false | CaoHaiNam | null | CaoHaiNam/vietnamese-address-embedding | 6 | null | sentence-transformers | 15,855 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# CaoHaiNam/vietnamese-address-embedding
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('CaoHaiNam/vietnamese-address-embedding')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('CaoHaiNam/vietnamese-address-embedding')
model = AutoModel.from_pretrained('CaoHaiNam/vietnamese-address-embedding')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=CaoHaiNam/vietnamese-address-embedding)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 8626 with parameters:
```
{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 64, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
nloc2578/3 | e461de2acca8e77b49d3e4810e21ef5f360065b3 | 2022-07-16T05:36:32.000Z | [
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
]
| text2text-generation | false | nloc2578 | null | nloc2578/3 | 6 | null | transformers | 15,856 | ---
tags:
- generated_from_trainer
model-index:
- name: '3'
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 3
This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## 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: 0.0015
- 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
- lr_scheduler_warmup_steps: 150
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.9957 | 0.3 | 1000 | 1.8064 |
| 1.9022 | 0.6 | 2000 | 1.7976 |
| 1.937 | 0.9 | 3000 | 1.7962 |
| 1.7922 | 1.2 | 4000 | 1.7951 |
| 1.6093 | 1.5 | 5000 | 1.7943 |
| 1.6786 | 1.8 | 6000 | 1.7938 |
| 1.6979 | 2.1 | 7000 | nan |
| 0.0 | 2.4 | 8000 | nan |
| 0.0 | 2.7 | 9000 | nan |
| 0.0 | 2.99 | 10000 | nan |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Tokenizers 0.12.1
|
mipatov/t5_test_no_spaces | 85a59f1ec9263f045cd7dc98369d198ec493c331 | 2022-07-16T08:33:48.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | mipatov | null | mipatov/t5_test_no_spaces | 6 | null | transformers | 15,857 | Entry not found |
abdulmatinomotoso/testing_headline_generator_2 | 55fe2426dbe3fe2f452bd55d505522c722141e0c | 2022-07-17T11:34:28.000Z | [
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
]
| text2text-generation | false | abdulmatinomotoso | null | abdulmatinomotoso/testing_headline_generator_2 | 6 | null | transformers | 15,858 | ---
tags:
- generated_from_trainer
model-index:
- name: testing_headline_generator_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# testing_headline_generator_2
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pegasus-multi_news) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 7.5747
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 7.7589 | 0.73 | 100 | 7.5747 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
jinwooChoi/hjw_base | afdde3e8e884afe3179eed944ae0664918c85bbb | 2022-07-18T02:04:05.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
]
| text-classification | false | jinwooChoi | null | jinwooChoi/hjw_base | 6 | null | transformers | 15,859 | Entry not found |
jinwooChoi/KDW_SA_mix_64_1e4 | 9337a65ddba4c16232242d675f5e8e7435c1b244 | 2022-07-18T02:19:08.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
]
| text-classification | false | jinwooChoi | null | jinwooChoi/KDW_SA_mix_64_1e4 | 6 | null | transformers | 15,860 | Entry not found |
jinwooChoi/KDW_SA_mix_48_1e5 | bcb275d87a1b22b3fa9606c1af149ba0449f1ff6 | 2022-07-18T02:59:41.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
]
| text-classification | false | jinwooChoi | null | jinwooChoi/KDW_SA_mix_48_1e5 | 6 | null | transformers | 15,861 | Entry not found |
jinwooChoi/KDW_SA_small_mix_16_1e5 | ad5d8f5733cfc89c808522378b6fd5a9b541857f | 2022-07-18T08:09:23.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
]
| text-classification | false | jinwooChoi | null | jinwooChoi/KDW_SA_small_mix_16_1e5 | 6 | null | transformers | 15,862 | Entry not found |
jordyvl/bert-base-portuguese-cased_harem-selective-CRF-first-ner | 4286fb94aa8b683d2e0ba03175dacb6a55f24f9a | 2022-07-19T09:06:13.000Z | [
"pytorch",
"tensorboard",
"bert",
"dataset:harem",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
]
| null | false | jordyvl | null | jordyvl/bert-base-portuguese-cased_harem-selective-CRF-first-ner | 6 | null | transformers | 15,863 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- harem
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-portuguese-cased_harem-selective-CRF-first-ner
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-portuguese-cased_harem-selective-CRF-first-ner
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the harem dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2045
- Precision: 0.5352
- Recall: 0.4351
- F1: 0.48
- Accuracy: 0.9484
## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.102 | 1.0 | 2517 | 0.2498 | 0.4367 | 0.3817 | 0.4073 | 0.9332 |
| 0.0614 | 2.0 | 5034 | 0.1842 | 0.4756 | 0.4084 | 0.4394 | 0.9408 |
| 0.0455 | 3.0 | 7551 | 0.2045 | 0.5352 | 0.4351 | 0.48 | 0.9484 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1
|
jinwooChoi/SKKU_AP_SA_KES | 736fb2cf150f0191bcb89ec74f0532d1fe09499c | 2022-07-19T02:35:53.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
]
| text-classification | false | jinwooChoi | null | jinwooChoi/SKKU_AP_SA_KES | 6 | null | transformers | 15,864 | Entry not found |
abdulmatinomotoso/testing_headline_generator_3 | 0ad74ab69fa6cef5a177d48735d7590400e710b8 | 2022-07-19T09:38:13.000Z | [
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
]
| text2text-generation | false | abdulmatinomotoso | null | abdulmatinomotoso/testing_headline_generator_3 | 6 | null | transformers | 15,865 | ---
tags:
- generated_from_trainer
model-index:
- name: testing_headline_generator_3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# testing_headline_generator_3
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pegasus-multi_news) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.2949
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 7.781 | 0.4 | 100 | 7.5730 |
| 5.7967 | 0.8 | 200 | 5.2949 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
google/ddpm-ema-church-256 | b653e4de0ed723fd28006939480042a42edd28e2 | 2022-07-21T15:00:20.000Z | [
"diffusers",
"arxiv:2006.11239",
"pytorch",
"unconditional-image-generation",
"license:apache-2.0"
]
| unconditional-image-generation | false | google | null | google/ddpm-ema-church-256 | 6 | null | diffusers | 15,866 | ---
license: apache-2.0
tags:
- pytorch
- diffusers
- unconditional-image-generation
---
# Denoising Diffusion Probabilistic Models (DDPM)
**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel
**Abstract**:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.*
## Inference
**DDPM** models can use *discrete noise schedulers* such as:
- [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py)
- [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py)
- [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py)
for inference. Note that while the *ddpm* scheduler yields the highest quality, it also takes the longest.
For a good trade-off between quality and inference speed you might want to consider the *ddim* or *pndm* schedulers instead.
See the following code:
```python
# !pip install diffusers
from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline
model_id = "google/ddpm-ema-church-256"
# load model and scheduler
ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference
# run pipeline in inference (sample random noise and denoise)
image = ddpm()["sample"]
# save image
image[0].save("ddpm_generated_image.png")
```
For more in-detail information, please have a look at the [official inference example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb)
## Training
If you want to train your own model, please have a look at the [official training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
## Samples
1. 
2. 
3. 
4.  |
jinwooChoi/KDW_SA_base_mix_64_1e4 | 18d16ccf7c3189d1c997773a63012219563343e0 | 2022-07-20T05:21:33.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
]
| text-classification | false | jinwooChoi | null | jinwooChoi/KDW_SA_base_mix_64_1e4 | 6 | null | transformers | 15,867 | Entry not found |
nloc2578/3.5 | 4496abc8d4e88a7983da55dccc1088091669d924 | 2022-07-20T11:32:24.000Z | [
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
]
| text2text-generation | false | nloc2578 | null | nloc2578/3.5 | 6 | null | transformers | 15,868 | ---
tags:
- generated_from_trainer
model-index:
- name: '3.5'
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 3.5
This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4461
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 150
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.854 | 0.11 | 1000 | 1.6732 |
| 1.736 | 0.22 | 2000 | 1.5991 |
| 1.6452 | 0.33 | 3000 | 1.5589 |
| 1.6176 | 0.45 | 4000 | 1.5310 |
| 1.6151 | 0.56 | 5000 | 1.5173 |
| 1.5707 | 0.67 | 6000 | 1.4982 |
| 1.5557 | 0.78 | 7000 | 1.4946 |
| 1.5307 | 0.89 | 8000 | 1.4748 |
| 1.5393 | 1.0 | 9000 | 1.4635 |
| 1.3077 | 1.11 | 10000 | 1.4662 |
| 1.3419 | 1.22 | 11000 | 1.4705 |
| 1.3245 | 1.34 | 12000 | 1.4653 |
| 1.3584 | 1.45 | 13000 | 1.4448 |
| 1.3403 | 1.56 | 14000 | 1.4452 |
| 1.2745 | 1.67 | 15000 | 1.4353 |
| 1.2979 | 1.78 | 16000 | 1.4333 |
| 1.3084 | 1.89 | 17000 | 1.4284 |
| 1.3009 | 2.0 | 18000 | 1.4286 |
| 1.1523 | 2.11 | 19000 | 1.4609 |
| 1.1352 | 2.23 | 20000 | 1.4565 |
| 1.1484 | 2.34 | 21000 | 1.4588 |
| 1.1482 | 2.45 | 22000 | 1.4548 |
| 1.1355 | 2.56 | 23000 | 1.4535 |
| 1.1429 | 2.67 | 24000 | 1.4485 |
| 1.1328 | 2.78 | 25000 | 1.4499 |
| 1.1487 | 2.89 | 26000 | 1.4461 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Tokenizers 0.12.1
|
juliensimon/distilbert-amazon-shoe-reviews-tensorboard | 7951d664f44c7131ca71cad1852a560065269cb5 | 2022-07-20T09:22:34.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | juliensimon | null | juliensimon/distilbert-amazon-shoe-reviews-tensorboard | 6 | null | transformers | 15,869 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-amazon-shoe-reviews-tensorboard
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-amazon-shoe-reviews-tensorboard
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9534
- Accuracy: 0.5779
- F1: [0.63189419 0.46645049 0.50381304 0.55843496 0.73060507]
- Precision: [0.62953754 0.47008547 0.48669202 0.58801498 0.71780957]
- Recall: [0.63426854 0.46287129 0.52218256 0.53168844 0.74386503]
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------------------------------------------------------:|:--------------------------------------------------------:|:--------------------------------------------------------:|
| 0.8776 | 1.0 | 2813 | 0.9534 | 0.5779 | [0.63189419 0.46645049 0.50381304 0.55843496 0.73060507] | [0.62953754 0.47008547 0.48669202 0.58801498 0.71780957] | [0.63426854 0.46287129 0.52218256 0.53168844 0.74386503] |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
koanlp/bart-large-cnn-finetuned-wiki | 3a9e5ff5f5f0a0ba125cb22751ea84eb5602cc1d | 2022-07-21T04:03:42.000Z | [
"pytorch",
"bart",
"text2text-generation",
"dataset:wiki_lingua",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
]
| text2text-generation | false | koanlp | null | koanlp/bart-large-cnn-finetuned-wiki | 6 | null | transformers | 15,870 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- wiki_lingua
model-index:
- name: bart-large-cnn-finetuned-wiki
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-finetuned-wiki
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the wiki_lingua dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 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: 1
- label_smoothing_factor: 0.1
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
okho0653/Bio_ClinicalBERT-zero-shot-finetuned-50cad | 834dbf7bdce8dcf465078473881a0d4c34475417 | 2022-07-22T05:42:33.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
]
| text-classification | false | okho0653 | null | okho0653/Bio_ClinicalBERT-zero-shot-finetuned-50cad | 6 | null | transformers | 15,871 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: Bio_ClinicalBERT-zero-shot-finetuned-50cad
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. -->
# Bio_ClinicalBERT-zero-shot-finetuned-50cad
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1475
- Accuracy: 0.5
- F1: 0.6667
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
okho0653/Bio_ClinicalBERT-zero-shot-finetuned-50noncad | 58d720434eab2fb250d896582032e3c15dc17cd3 | 2022-07-22T05:55:47.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
]
| text-classification | false | okho0653 | null | okho0653/Bio_ClinicalBERT-zero-shot-finetuned-50noncad | 6 | null | transformers | 15,872 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: Bio_ClinicalBERT-zero-shot-finetuned-50noncad
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. -->
# Bio_ClinicalBERT-zero-shot-finetuned-50noncad
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8046
- Accuracy: 0.5
- F1: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jinwooChoi/SKKU_SA_HJW_0722_0 | e7b62d24b85eea167907d12ea7f675bd5233511a | 2022-07-22T07:46:17.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
]
| text-classification | false | jinwooChoi | null | jinwooChoi/SKKU_SA_HJW_0722_0 | 6 | null | transformers | 15,873 | Entry not found |
ronanki/all-mpnet-base-v2-2022-07-18_15-29-33 | cd428edb20616d43b0beabb7c2742154bcf565f8 | 2022-07-22T11:11:58.000Z | [
"pytorch",
"mpnet",
"feature-extraction",
"sentence-transformers",
"sentence-similarity"
]
| sentence-similarity | false | ronanki | null | ronanki/all-mpnet-base-v2-2022-07-18_15-29-33 | 6 | null | sentence-transformers | 15,874 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# ronanki/all-mpnet-base-v2-2022-07-18_15-29-33
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('ronanki/all-mpnet-base-v2-2022-07-18_15-29-33')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=ronanki/all-mpnet-base-v2-2022-07-18_15-29-33)
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 22 with parameters:
```
{'batch_size': 64}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 22,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
shiulian/t5-end2end-questions-generation | 1f6a6a25327cd8eaa73b95563be49d24f8e6e065 | 2022-07-23T14:19:26.000Z | [
"pytorch",
"t5",
"text2text-generation",
"dataset:squad_modified_for_t5_qg",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| text2text-generation | false | shiulian | null | shiulian/t5-end2end-questions-generation | 6 | null | transformers | 15,875 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_modified_for_t5_qg
model-index:
- name: t5-end2end-questions-generation
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-end2end-questions-generation
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the squad_modified_for_t5_qg dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5679
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5866 | 0.34 | 100 | 1.9116 |
| 1.9674 | 0.68 | 200 | 1.7280 |
| 1.8487 | 1.02 | 300 | 1.6650 |
| 1.7429 | 1.36 | 400 | 1.6400 |
| 1.7148 | 1.69 | 500 | 1.6214 |
| 1.695 | 2.03 | 600 | 1.6076 |
| 1.6321 | 2.37 | 700 | 1.5979 |
| 1.6276 | 2.71 | 800 | 1.5910 |
| 1.6171 | 3.05 | 900 | 1.5875 |
| 1.5712 | 3.39 | 1000 | 1.5898 |
| 1.5702 | 3.73 | 1100 | 1.5749 |
| 1.5594 | 4.07 | 1200 | 1.5798 |
| 1.5352 | 4.41 | 1300 | 1.5733 |
| 1.5228 | 4.75 | 1400 | 1.5733 |
| 1.524 | 5.08 | 1500 | 1.5727 |
| 1.4954 | 5.42 | 1600 | 1.5699 |
| 1.4866 | 5.76 | 1700 | 1.5696 |
| 1.5089 | 6.1 | 1800 | 1.5696 |
| 1.4771 | 6.44 | 1900 | 1.5736 |
| 1.4772 | 6.78 | 2000 | 1.5679 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Siyong/MT_LM | 16f547917ca7534ef8b3d37a968c5294822219e5 | 2022-07-23T17:03:19.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | false | Siyong | null | Siyong/MT_LM | 6 | null | transformers | 15,876 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec-base-Millad_TIMIT
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. -->
# wav2vec-base-Millad_TIMIT
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3772
- Wer: 0.6859
- Cer: 0.3217
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5000
- num_epochs: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| No log | 2.36 | 2000 | 2.6233 | 1.0130 | 0.6241 |
| No log | 4.73 | 4000 | 2.2206 | 0.9535 | 0.5032 |
| No log | 7.09 | 6000 | 2.3036 | 0.9368 | 0.5063 |
| 1.235 | 9.46 | 8000 | 1.9932 | 0.9275 | 0.5032 |
| 1.235 | 11.82 | 10000 | 2.0207 | 0.8922 | 0.4498 |
| 1.235 | 14.18 | 12000 | 1.6171 | 0.7993 | 0.3976 |
| 1.235 | 16.55 | 14000 | 1.6729 | 0.8309 | 0.4209 |
| 0.2779 | 18.91 | 16000 | 1.7043 | 0.8141 | 0.4340 |
| 0.2779 | 21.28 | 18000 | 1.7426 | 0.7658 | 0.3960 |
| 0.2779 | 23.64 | 20000 | 1.5230 | 0.7361 | 0.3830 |
| 0.2779 | 26.0 | 22000 | 1.4286 | 0.7658 | 0.3794 |
| 0.1929 | 28.37 | 24000 | 1.4450 | 0.7379 | 0.3644 |
| 0.1929 | 30.73 | 26000 | 1.5922 | 0.7491 | 0.3826 |
| 0.1929 | 33.1 | 28000 | 1.4443 | 0.7454 | 0.3617 |
| 0.1929 | 35.46 | 30000 | 1.5450 | 0.7268 | 0.3621 |
| 0.1394 | 37.83 | 32000 | 1.9268 | 0.7491 | 0.3763 |
| 0.1394 | 40.19 | 34000 | 1.7094 | 0.7342 | 0.3783 |
| 0.1394 | 42.55 | 36000 | 1.4024 | 0.7082 | 0.3494 |
| 0.1394 | 44.92 | 38000 | 1.4467 | 0.6840 | 0.3395 |
| 0.104 | 47.28 | 40000 | 1.4145 | 0.6933 | 0.3407 |
| 0.104 | 49.65 | 42000 | 1.3901 | 0.6970 | 0.3403 |
| 0.104 | 52.01 | 44000 | 1.3589 | 0.6636 | 0.3348 |
| 0.104 | 54.37 | 46000 | 1.3716 | 0.6952 | 0.3340 |
| 0.0781 | 56.74 | 48000 | 1.4025 | 0.6896 | 0.3312 |
| 0.0781 | 59.1 | 50000 | 1.3772 | 0.6859 | 0.3217 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.12.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
jcashmoney123/autotrain-amz-1171143428 | d15e662ff875dbba21317d86c0b2dab3ded04491 | 2022-07-23T18:31:20.000Z | [
"pytorch",
"bart",
"text2text-generation",
"unk",
"dataset:jcashmoney123/autotrain-data-amz",
"transformers",
"autotrain",
"co2_eq_emissions",
"autotrain_compatible"
]
| text2text-generation | false | jcashmoney123 | null | jcashmoney123/autotrain-amz-1171143428 | 6 | null | transformers | 15,877 | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- jcashmoney123/autotrain-data-amz
co2_eq_emissions: 5.4331208624177245
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1171143428
- CO2 Emissions (in grams): 5.4331208624177245
## Validation Metrics
- Loss: 2.5859596729278564
- Rouge1: 19.3601
- Rouge2: 4.6055
- RougeL: 17.4309
- RougeLsum: 17.4621
- Gen Len: 15.2938
## 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/jcashmoney123/autotrain-amz-1171143428
``` |
erikanesse/great-books-bot-2 | 693cd4ff288f079c43dc6b51a9df3388a0fa44fd | 2022-07-30T00:59:39.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"model-index"
]
| text-generation | false | erikanesse | null | erikanesse/great-books-bot-2 | 6 | null | transformers | 15,878 | ---
tags:
- generated_from_trainer
model-index:
- name: great-books-bot-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# great-books-bot-2
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 5.6204
- eval_runtime: 12.3909
- eval_samples_per_second: 0.484
- eval_steps_per_second: 0.081
- epoch: 0.06
- step: 20
## 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: 3
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 100
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Migga/ViT-BERT-Chess-V2 | 3192e80982517c93bbbae18f04aa1a455651b3b3 | 2022-07-25T07:28:02.000Z | [
"pytorch",
"vision-encoder-decoder",
"transformers",
"generated_from_trainer",
"model-index"
]
| null | false | Migga | null | Migga/ViT-BERT-Chess-V2 | 6 | null | transformers | 15,879 | ---
tags:
- generated_from_trainer
model-index:
- name: ViT-BERT-Chess-V2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ViT-BERT-Chess-V2
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7128
## 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: 10
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.0385 | 1.0 | 2770 | 3.9132 |
| 3.7453 | 2.0 | 5540 | 3.7552 |
| 3.6513 | 3.0 | 8310 | 3.7128 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
bongsoo/mdistilbertV1 | 0a6df2085032a9b6d658e7c55a798fe5b5558495 | 2022-07-26T06:15:55.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"en",
"ko",
"autotrain_compatible"
]
| fill-mask | false | bongsoo | null | bongsoo/mdistilbertV1 | 6 | null | transformers | 15,880 | ---
pipeline_tag: fill-mask
tags:
- fill-mask
- transformers
- en
- ko
---
distil-base-multilingual-cased 에 kowiki20220620 정제된 말뭉치로 한국어 vocab 추가하여 한국어 추가학습 시킨 모델
|
MikkelGroenning/distilbert-base-uncased-finetuned-emotion | 59458c7ae43df5fcecae095966a9f0ba3deddb4f | 2022-07-25T07:55:37.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | false | MikkelGroenning | null | MikkelGroenning/distilbert-base-uncased-finetuned-emotion | 6 | null | transformers | 15,881 | Entry not found |
philschmid/distilbert-imdb-habana-remote-runner | 8ad757ff4a389318d04cfa5a27ac9cafe6cba9cd | 2022-07-25T08:22:00.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | false | philschmid | null | philschmid/distilbert-imdb-habana-remote-runner | 6 | null | transformers | 15,882 | Entry not found |
singhajeet13/autotrain-summarization-test-1177043812 | d0f23884a4f24e8e3b9406c82a1468cda9adedca | 2022-07-26T02:15:55.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:singhajeet13/autotrain-data-summarization-test",
"transformers",
"autotrain",
"co2_eq_emissions",
"autotrain_compatible"
]
| text2text-generation | false | singhajeet13 | null | singhajeet13/autotrain-summarization-test-1177043812 | 6 | null | transformers | 15,883 | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- singhajeet13/autotrain-data-summarization-test
co2_eq_emissions: 1166.308824861558
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1177043812
- CO2 Emissions (in grams): 1166.308824861558
## Validation Metrics
- Loss: 1.6226013898849487
- Rouge1: 39.5734
- Rouge2: 18.9817
- RougeL: 33.257
- RougeLsum: 33.2571
- Gen Len: 19.84
## 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/singhajeet13/autotrain-summarization-test-1177043812
``` |
huggingtweets/csjonas1mical-gunkbrain1-moeterpussy | 90a40887db9b6cf73bf47ed05c6469e3fc12cd14 | 2022-07-26T04:21:26.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
]
| text-generation | false | huggingtweets | null | huggingtweets/csjonas1mical-gunkbrain1-moeterpussy | 6 | null | transformers | 15,884 | ---
language: en
thumbnail: http://www.huggingtweets.com/csjonas1mical-gunkbrain1-moeterpussy/1658809281049/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/1525202290088595457/GfbtEnPO_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1525207243133689857/h9zu4iMK_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1525206197619642370/HPsBR4xY_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">freddy macintosh & Moe Ner & tobash tendril</div>
<div style="text-align: center; font-size: 14px;">@csjonas1mical-gunkbrain1-moeterpussy</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 freddy macintosh & Moe Ner & tobash tendril.
| Data | freddy macintosh | Moe Ner | tobash tendril |
| --- | --- | --- | --- |
| Tweets downloaded | 126 | 266 | 165 |
| Retweets | 9 | 25 | 12 |
| Short tweets | 15 | 23 | 19 |
| Tweets kept | 102 | 218 | 134 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2eeslx7w/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 @csjonas1mical-gunkbrain1-moeterpussy's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/184slvzr) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/184slvzr/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/csjonas1mical-gunkbrain1-moeterpussy')
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)
|
PGT/graphnystromformer-artificial-balanced-max500-210000-0 | f41baa129366a4a0600eed7d9aa4561ea5957588 | 2022-07-25T20:10:00.000Z | [
"pytorch",
"graph_nystromformer",
"text-classification",
"transformers"
]
| text-classification | false | PGT | null | PGT/graphnystromformer-artificial-balanced-max500-210000-0 | 6 | null | transformers | 15,885 | Entry not found |
mshoaibsarwar/finetuning-sentiment-model-samples | b3d39392ba9b540355900b4c65416b7839947706 | 2022-07-25T21:54:57.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:imdb",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | mshoaibsarwar | null | mshoaibsarwar/finetuning-sentiment-model-samples | 6 | null | transformers | 15,886 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: finetuning-sentiment-model-samples
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb 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: 2
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BirdL/SimulacraPromptGPT | 32206c09253390442d472498d9ae18eb1b753d7f | 2022-07-26T00:33:19.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"license:apache-2.0"
]
| text-generation | false | BirdL | null | BirdL/SimulacraPromptGPT | 6 | null | transformers | 15,887 | ---
license: apache-2.0
---
|
ultra-coder54732/roberta-base-twitter-prop-16-train-set | f035bd9dc3048f70a6731b4052c88d590865e214 | 2022-07-26T02:01:21.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"transformers"
]
| text-classification | false | ultra-coder54732 | null | ultra-coder54732/roberta-base-twitter-prop-16-train-set | 6 | null | transformers | 15,888 | Entry not found |
bongsoo/mdistilbertV1.1 | 5129e83cc11189921475cbd6c6104ae72006df85 | 2022-07-26T06:16:33.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"en",
"ko",
"autotrain_compatible"
]
| fill-mask | false | bongsoo | null | bongsoo/mdistilbertV1.1 | 6 | null | transformers | 15,889 | ---
pipeline_tag: fill-mask
tags:
- fill-mask
- transformers
- en
- ko
---
distil-base-multilingual-cased 에 kowiki20220620 정제된 말뭉치로 한국어 vocab 추가하여 한국어 추가학습 시킨 모델 |
WENGSYX/Dagnosis_Chinese_BERT | 33ade7cec05955caeb4a0ab7d7fc57906958d467 | 2022-07-26T09:40:08.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"license:mit",
"autotrain_compatible"
]
| fill-mask | false | WENGSYX | null | WENGSYX/Dagnosis_Chinese_BERT | 6 | null | transformers | 15,890 | ---
license: mit
---
|
ejin/bert-base-cased-finetuned-ner | 4eb06cfdf2a9dfa715e1585de5721bd47942c0fb | 2022-07-27T21:16:41.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| token-classification | false | ejin | null | ejin/bert-base-cased-finetuned-ner | 6 | null | transformers | 15,891 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-cased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: train
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.8940432730834298
- name: Recall
type: recall
value: 0.9008612955320294
- name: F1
type: f1
value: 0.8974393350315055
- name: Accuracy
type: accuracy
value: 0.9749955848590098
---
<!-- 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-cased-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0919
- Precision: 0.8940
- Recall: 0.9009
- F1: 0.8974
- Accuracy: 0.9750
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1147 | 1.0 | 1756 | 0.0919 | 0.8940 | 0.9009 | 0.8974 | 0.9750 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
huggingtweets/khorax | c52afc72a9bfe692293da8861bddaaedd13e997e | 2022-07-26T21:15:41.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
]
| text-generation | false | huggingtweets | null | huggingtweets/khorax | 6 | null | transformers | 15,892 | ---
language: en
thumbnail: http://www.huggingtweets.com/khorax/1658870136126/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/1544440184653156353/O0KtLUg__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">Khorax "Kho" Lugnut</div>
<div style="text-align: center; font-size: 14px;">@khorax</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 Khorax "Kho" Lugnut.
| Data | Khorax "Kho" Lugnut |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 352 |
| Short tweets | 363 |
| Tweets kept | 2532 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/32yjy9s3/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 @khorax's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ws4j0jn) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ws4j0jn/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/khorax')
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)
|
ultra-coder54732/robertabaseproper-prop-16-train-set | 7e23b9e390c5f08de09db7ca125d4b040e847677 | 2022-07-27T00:19:39.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
]
| text-classification | false | ultra-coder54732 | null | ultra-coder54732/robertabaseproper-prop-16-train-set | 6 | null | transformers | 15,893 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: robertabaseproper-prop-16-train-set
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. -->
# robertabaseproper-prop-16-train-set
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) 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: 3.0
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
dminiotas05/distilbert-base-uncased-finetuned-ft780_class | 9d9ac4906b577cba872d1d28b8c8bb561cf06cdf | 2022-07-27T12:16:52.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | dminiotas05 | null | dminiotas05/distilbert-base-uncased-finetuned-ft780_class | 6 | null | transformers | 15,894 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-ft780_class
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ft780_class
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9843
- Accuracy: 0.2047
- F1: 0.1823
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 2.1065 | 1.0 | 188 | 2.0425 | 0.1747 | 0.1248 |
| 1.9642 | 2.0 | 376 | 1.9959 | 0.1987 | 0.1701 |
| 1.9019 | 3.0 | 564 | 1.9843 | 0.2047 | 0.1823 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
wgarstka/knotted_proteins_demo_model | ea2c6d330e1e18ed9bdaf3ef77147644524562a7 | 2022-07-28T09:06:52.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | wgarstka | null | wgarstka/knotted_proteins_demo_model | 6 | null | transformers | 15,895 | Entry not found |
zhenglianchi/NER-model | fdcbea9c4dd540f0b6f9020f3e8b1acf9b252859 | 2022-07-28T09:20:45.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | zhenglianchi | null | zhenglianchi/NER-model | 6 | null | transformers | 15,896 | Entry not found |
okite97/roberta-base-news3 | 94d4924d3093200a5187f239a5893f7a99832d44 | 2022-07-28T15:40:06.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
]
| text-classification | false | okite97 | null | okite97/roberta-base-news3 | 6 | null | transformers | 15,897 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: roberta-base-news3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-news3
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: 0.3468
- Accuracy: 0.8986
- F1: 0.9002
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.3102 | 1.0 | 61 | 0.3032 | 0.8971 | 0.8977 |
| 0.1949 | 2.0 | 122 | 0.3036 | 0.8986 | 0.8976 |
| 0.1322 | 3.0 | 183 | 0.3106 | 0.9029 | 0.9024 |
| 0.0988 | 4.0 | 244 | 0.3468 | 0.8986 | 0.9002 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
yanaiela/roberta-base-epoch_83 | dd858e6b17f0729249a30394d24341fa7b93ec5e | 2022-07-29T23:10:09.000Z | [
"pytorch",
"roberta",
"fill-mask",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:2207.14251",
"transformers",
"roberta-base",
"roberta-base-epoch_83",
"license:mit",
"autotrain_compatible"
]
| fill-mask | false | yanaiela | null | yanaiela/roberta-base-epoch_83 | 6 | null | transformers | 15,898 | ---
language: en
tags:
- roberta-base
- roberta-base-epoch_83
license: mit
datasets:
- wikipedia
- bookcorpus
---
# RoBERTa, Intermediate Checkpoint - Epoch 83
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the training)
to provide the ability to study the training dynamics of such models, and other possible use-cases.
These models were trained in part of a work that studies how simple statistics from data,
such as co-occurrences affects model predictions, which are described in the paper
[Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions](https://arxiv.org/abs/2207.14251).
This is RoBERTa-base epoch_83.
## Model Description
This model was captured during a reproduction of
[RoBERTa-base](https://huggingface.co/roberta-base), for English: it
is a Transformers model pretrained on a large corpus of English data, using the
Masked Language Modelling (MLM).
The intended uses, limitations, training data and training procedure for the fully trained model are similar
to [RoBERTa-base](https://huggingface.co/roberta-base). Two major
differences with the original model:
* We trained our model for 100K steps, instead of 500K
* We only use Wikipedia and the Book Corpus, as corpora which are publicly available.
### How to use
Using code from
[RoBERTa-base](https://huggingface.co/roberta-base), here is an example based on
PyTorch:
```
from transformers import pipeline
model = pipeline("fill-mask", model='yanaiela/roberta-base-epoch_83', device=-1, top_k=10)
model("Hello, I'm the <mask> RoBERTa-base language model")
```
## Citation info
```bibtex
@article{2207.14251,
Author = {Yanai Elazar and Nora Kassner and Shauli Ravfogel and Amir Feder and Abhilasha Ravichander and Marius Mosbach and Yonatan Belinkov and Hinrich Schütze and Yoav Goldberg},
Title = {Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions},
Year = {2022},
Eprint = {arXiv:2207.14251},
}
```
|
platzi/platzi-vit-base-beans-omar-espejel | bda4572bdc7ff2e83bf63afa5b992940f37aab44 | 2022-07-28T18:59:01.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"dataset:beans",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| image-classification | false | platzi | null | platzi/platzi-vit-base-beans-omar-espejel | 6 | null | transformers | 15,899 | ---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
widget:
- src: https://huggingface.co/platzi/platzi-vit-base-beans/resolve/main/healthy.jpeg
example_title: Healthy
- src: https://huggingface.co/platzi/platzi-vit-base-beans/resolve/main/bean_rust.jpeg
example_title: Bean Rust
model-index:
- name: platzi-vit-base-beans
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9924812030075187
---
<!-- 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. -->
# platzi-vit-base-beans
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0336
- Accuracy: 0.9925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1381 | 3.85 | 500 | 0.0336 | 0.9925 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
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