modelId stringlengths 6 107 | label list | readme stringlengths 0 56.2k | readme_len int64 0 56.2k |
|---|---|---|---|
Jeevesh8/std_pnt_04_feather_berts-55 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/std_pnt_04_feather_berts-57 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/std_pnt_04_feather_berts-72 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/std_pnt_04_feather_berts-50 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
course5i/SEAD-L-6_H-256_A-8-wnli | [
"0",
"1"
] | ---
language:
- en
license: apache-2.0
tags:
- SEAD
datasets:
- glue
- wnli
---
## Paper
## [SEAD: SIMPLE ENSEMBLE AND KNOWLEDGE DISTILLATION FRAMEWORK FOR NATURAL LANGUAGE UNDERSTANDING](https://www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63)
Aurthors: *Moyan Mei*, *Rohit Sroch*
## Abstract
With the widespread use of pre-trained language models (PLM), there has been increased research on how to make them applicable, especially in limited-resource or low latency high throughput scenarios. One of the dominant approaches is knowledge distillation (KD), where a smaller model is trained by receiving guidance from a large PLM. While there are many successful designs for learning knowledge from teachers, it remains unclear how students can learn better. Inspired by real university teaching processes, in this work we further explore knowledge distillation and propose a very simple yet effective framework, SEAD, to further improve task-specific generalization by utilizing multiple teachers. Our experiments show that SEAD leads to better performance compared to other popular KD methods [[1](https://arxiv.org/abs/1910.01108)] [[2](https://arxiv.org/abs/1909.10351)] [[3](https://arxiv.org/abs/2002.10957)] and achieves comparable or superior performance to its teacher model such as BERT [[4](https://arxiv.org/abs/1810.04805)] on total 13 tasks for the GLUE [[5](https://arxiv.org/abs/1804.07461)] and SuperGLUE [[6](https://arxiv.org/abs/1905.00537)] benchmarks.
*Moyan Mei and Rohit Sroch. 2022. [SEAD: Simple ensemble and knowledge distillation framework for natural language understanding](https://www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63).
Lattice, THE MACHINE LEARNING JOURNAL by Association of Data Scientists, 3(1).*
## SEAD-L-6_H-256_A-8-wnli
This is a student model distilled from [**BERT base**](https://huggingface.co/bert-base-uncased) as teacher by using SEAD framework on **wnli** task. For weights initialization, we used [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased)
## All SEAD Checkpoints
Other Community Checkpoints: [here](https://huggingface.co/models?search=SEAD)
## Intended uses & limitations
More information needed
### Training hyperparameters
Please take a look at the `training_args.bin` file
```python
$ import torch
$ hyperparameters = torch.load(os.path.join('training_args.bin'))
```
### Evaluation results
| eval_accuracy | eval_runtime | eval_samples_per_second | eval_steps_per_second | eval_loss | eval_samples |
|:-------------:|:------------:|:-----------------------:|:---------------------:|:---------:|:------------:|
| 0.5634 | 1.2474 | 56.919 | 2.405 | 0.6859 | 71 |
### Framework versions
- Transformers >=4.8.0
- Pytorch >=1.6.0
- TensorFlow >=2.5.0
- Flax >=0.3.5
- Datasets >=1.10.2
- Tokenizers >=0.11.6
If you use these models, please cite the following paper:
```
@article{article,
author={Mei, Moyan and Sroch, Rohit},
title={SEAD: Simple Ensemble and Knowledge Distillation Framework for Natural Language Understanding},
volume={3},
number={1},
journal={Lattice, The Machine Learning Journal by Association of Data Scientists},
day={26},
year={2022},
month={Feb},
url = {www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63}
}
```
| 3,672 |
Happyb/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | Entry not found | 15 |
Alireza1044/mobilebert_stsb | [
"LABEL_0"
] | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- spearmanr
model-index:
- name: stsb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE STSB
type: glue
args: stsb
metrics:
- name: Spearmanr
type: spearmanr
value: 0.8735136732190296
---
<!-- 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. -->
# stsb
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5348
- Pearson: 0.8773
- Spearmanr: 0.8735
- Combined Score: 0.8754
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
| 1,442 |
Suryabhan/tiny-bert-sst2-distilled | [
"negative",
"positive"
] | Entry not found | 15 |
Willy/bert-base-spanish-wwm-cased-finetuned-NLP-IE-2 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-spanish-wwm-cased-finetuned-NLP-IE-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. -->
# bert-base-spanish-wwm-cased-finetuned-NLP-IE-2
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5279
- Accuracy: 0.7836
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6008 | 1.0 | 9 | 0.5243 | 0.7836 |
| 0.6014 | 2.0 | 18 | 0.5279 | 0.7836 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
| 1,468 |
Sayan01/tiny-bert-sst2-distilled | [
"negative",
"positive"
] | Entry not found | 15 |
Sayan01/tiny-bert-mrpc-distilled | [
"equivalent",
"not_equivalent"
] | Entry not found | 15 |
Sayan01/tiny-bert-rte-distilled | [
"entailment",
"not_entailment"
] | Entry not found | 15 |
Sayan01/tiny-bert-wnli-distilled | [
"entailment",
"not_entailment"
] | Entry not found | 15 |
deepesh0x/autotrain-finetunedmodelbert-1034335535 | [
"negative",
"positive"
] | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- deepesh0x/autotrain-data-finetunedmodelbert
co2_eq_emissions: 7.1805069109958835
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1034335535
- CO2 Emissions (in grams): 7.1805069109958835
## Validation Metrics
- Loss: 0.05866553634405136
- Accuracy: 0.9793615441722346
- Precision: 0.9811170212765957
- Recall: 0.9819004524886877
- AUC: 0.9976735725727466
- F1: 0.9815085805507516
## 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/deepesh0x/autotrain-finetunedmodelbert-1034335535
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("deepesh0x/autotrain-finetunedmodelbert-1034335535", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("deepesh0x/autotrain-finetunedmodelbert-1034335535", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,222 |
domenicrosati/deberta-v3-xsmall-finetuned-DAGPap22 | null | ---
license: mit
tags:
- text-classification
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: deberta-v3-xsmall-finetuned-DAGPap22
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. -->
# deberta-v3-xsmall-finetuned-DAGPap22
This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0798
- Accuracy: 0.9907
- F1: 0.9934
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4.5e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 402 | 0.1626 | 0.9477 | 0.9616 |
| 0.4003 | 2.0 | 804 | 0.0586 | 0.9794 | 0.9853 |
| 0.1075 | 3.0 | 1206 | 0.0342 | 0.9907 | 0.9933 |
| 0.0581 | 4.0 | 1608 | 0.1140 | 0.9776 | 0.9838 |
| 0.0245 | 5.0 | 2010 | 0.1409 | 0.9776 | 0.9842 |
| 0.0245 | 6.0 | 2412 | 0.0732 | 0.9832 | 0.9881 |
| 0.0167 | 7.0 | 2814 | 0.1996 | 0.9682 | 0.9778 |
| 0.0139 | 8.0 | 3216 | 0.1219 | 0.9850 | 0.9894 |
| 0.006 | 9.0 | 3618 | 0.0670 | 0.9907 | 0.9934 |
| 0.0067 | 10.0 | 4020 | 0.1036 | 0.9869 | 0.9907 |
| 0.0067 | 11.0 | 4422 | 0.1220 | 0.9776 | 0.9838 |
| 0.0041 | 12.0 | 4824 | 0.1768 | 0.9776 | 0.9839 |
| 0.0007 | 13.0 | 5226 | 0.0943 | 0.9888 | 0.9920 |
| 0.0 | 14.0 | 5628 | 0.0959 | 0.9907 | 0.9934 |
| 0.0054 | 15.0 | 6030 | 0.0915 | 0.9888 | 0.9921 |
| 0.0054 | 16.0 | 6432 | 0.1618 | 0.9794 | 0.9855 |
| 0.0019 | 17.0 | 6834 | 0.0794 | 0.9907 | 0.9934 |
| 0.0 | 18.0 | 7236 | 0.0799 | 0.9907 | 0.9934 |
| 0.0 | 19.0 | 7638 | 0.0797 | 0.9907 | 0.9934 |
| 0.0 | 20.0 | 8040 | 0.0798 | 0.9907 | 0.9934 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
| 2,865 |
sports-ru/antihate | null | Entry not found | 15 |
MichalRoztocki/finetuning-sentiment-model-3000-samples | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8766666666666667
- name: F1
type: f1
value: 0.877887788778878
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3085
- Accuracy: 0.8767
- F1: 0.8779
## 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
| 1,520 |
pardeep/distilbert-base-uncased-finetuned-emotion-ch02 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion-ch02
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.934
- name: F1
type: f1
value: 0.9341801255709286
---
<!-- 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-ch02
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: 0.1703
- Accuracy: 0.934
- F1: 0.9342
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.2923 | 1.0 | 250 | 0.2001 | 0.9275 | 0.9263 |
| 0.1485 | 2.0 | 500 | 0.1703 | 0.934 | 0.9342 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,808 |
jinwooChoi/SKKU_AP_SA_KBT1 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
epiphacc/csabstract-classification | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | Entry not found | 15 |
jinwooChoi/SKKU_SA_KES | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
AndyChiang/my-test-model | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | ---
tags:
- generated_from_keras_callback
model-index:
- name: my-test-model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# my-test-model
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
| 860 |
Krs/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
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: 0.921
- name: F1
type: f1
value: 0.9213674244320441
---
<!-- 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: 0.2197
- Accuracy: 0.921
- F1: 0.9214
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8255 | 1.0 | 250 | 0.3172 | 0.9055 | 0.9039 |
| 0.2506 | 2.0 | 500 | 0.2197 | 0.921 | 0.9214 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2
- Datasets 2.3.2
- Tokenizers 0.12.1
| 1,798 |
FinanceInc/auditor_sentiment_finetuned | [
"Negative",
"Neutral",
"Positive"
] | ---
language: en
tags:
- autotrain
- DEV
widget:
- text: "Operating profit jumped to EUR 47 million from EUR 6.6 million"
datasets:
- rajistics/autotrain-data-auditor-sentiment
- FinanceInc/auditor_sentiment
co2_eq_emissions: 3.165771608457648
model-index:
- name: auditor_sentiment_finetuned
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: FinanceInc/auditor_sentiment
type: glue
split: validation
metrics:
- name: Accuracy
type: accuracy
value: 0.862
verified: true
- name: F1
type: f1
value: 0.845
verified: true
- name: Recall
type: recall
value: 0.846
verified: true
- name: Precision
type: precision
value: 0.844
verified: true
- task:
type: text-classification
name: Text Classification
dataset:
name: FinanceInc/auditor_sentiment_2021
type: glue
split: validation
metrics:
- name: Accuracy
type: accuracy
value: 0.848937
verified: true
- name: F1
type: f1
value: 0.848282
verified: true
- name: Recall
type: recall
value: 0.808937
verified: true
- name: Precision
type: precision
value: 0.818542
verified: true
---
# Auditor Review Sentiment Model
This model has been finetuned from the proprietary version of [FinBERT](https://huggingface.co/FinanceInc/finbert-pretrain) trained internally using demo.org proprietary dataset of auditor evaluation of sentiment.
FinBERT is a BERT model pre-trained on a large corpora of financial texts. The purpose is to enhance financial NLP research and practice in the financial domain, hoping that financial practitioners and researchers can benefit from this model without the necessity of the significant computational resources required to train the model.
# Training Data
This model was fine-tuned using [Autotrain](https://ui.autotrain.huggingface.co/11671/metrics) from the demo-org/auditor_review review dataset.
# Model Status
This model is currently being evaluated in development until the end of the quarter. Based on the results, it may be elevated to production.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: [1167143226](https://huggingface.co/rajistics/autotrain-auditor-sentiment-1167143226)
- CO2 Emissions (in grams): 3.165771608457648
## Validation Metrics
- Loss: 0.3418470025062561
- Accuracy: 0.8617131062951496
- Macro F1: 0.8448284352912685
- Micro F1: 0.8617131062951496
- Weighted F1: 0.8612696670395574
- Macro Precision: 0.8440532616584138
- Micro Precision: 0.8617131062951496
- Weighted Precision: 0.8612762332366959
- Macro Recall: 0.8461980005490884
- Micro Recall: 0.8617131062951496
- Weighted Recall: 0.8617131062951496
## 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/rajistics/autotrain-auditor-sentiment-1167143226
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("rajistics/autotrain-auditor-sentiment-1167143226", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("rajistics/autotrain-auditor-sentiment-1167143226", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 3,878 |
123abhiALFLKFO/distilbert-base-uncased-finetuned-cola | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model_index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metric:
name: Matthews Correlation
type: matthews_correlation
value: 0.5331291095663535
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8628
- Matthews Correlation: 0.5331
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5253 | 1.0 | 535 | 0.5214 | 0.3943 |
| 0.3459 | 2.0 | 1070 | 0.5551 | 0.4693 |
| 0.2326 | 3.0 | 1605 | 0.6371 | 0.5059 |
| 0.1718 | 4.0 | 2140 | 0.7851 | 0.5111 |
| 0.1262 | 5.0 | 2675 | 0.8628 | 0.5331 |
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
| 1,997 |
AIDA-UPM/bertweet-base-multi-mami | [
"misogynous",
"objectification",
"shaming",
"stereotype",
"violence"
] | ---
pipeline_tag: text-classification
tags:
- text-classification
- misogyny
language: en
license: apache-2.0
widget:
- text: "Women wear yoga pants because men don't stare at their personality"
example_title: "Misogyny detection"
---
# bertweet-base-multi-mami
This is a Bertweet model: It maps sentences & paragraphs to a 768 dimensional dense vector space and classifies them into 5 multi labels.
# Multilabels
label2id={
"misogynous": 0,
"shaming": 1,
"stereotype": 2,
"objectification": 3,
"violence": 4,
},
| 565 |
Alireza1044/albert-base-v2-mrpc | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model_index:
- name: mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metric:
name: F1
type: f1
value: 0.901060070671378
---
<!-- 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. -->
# mrpc
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4171
- Accuracy: 0.8627
- F1: 0.9011
- Combined Score: 0.8819
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.0
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
| 1,401 |
Andranik/TestPytorchClassification | [
"about",
"iot",
"ml",
"mobile",
"qa"
] | Entry not found | 15 |
AnjanBiswas/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | Entry not found | 15 |
AnonymousSub/dummy_2 | null | Entry not found | 15 |
AnonymousSub/rule_based_roberta_hier_triplet_epochs_1_shard_1_wikiqa | null | Entry not found | 15 |
AnonymousSub/rule_based_roberta_only_classfn_twostage_epochs_1_shard_1_wikiqa | null | Entry not found | 15 |
Anthos23/my-awesome-model | [
"negative",
"neutral",
"positive"
] | Entry not found | 15 |
Azaghast/DistilBERT-SCP-Class-Classification | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | Entry not found | 15 |
CLTL/icf-levels-etn | [
"LABEL_0"
] | ---
language: nl
license: mit
pipeline_tag: text-classification
inference: false
---
# Regression Model for Eating Functioning Levels (ICF d550)
## Description
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing eating functions. The model is based on a pre-trained Dutch medical language model ([link to be added]()): a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. To detect sentences about eating functions in clinical text in Dutch, use the [icf-domains](https://huggingface.co/CLTL/icf-domains) classification model.
## Functioning levels
Level | Meaning
---|---
4 | Can eat independently (in culturally acceptable ways), good intake, eats according to her/his needs.
3 | Can eat independently but with adjustments, and/or somewhat reduced intake (>75% of her/his needs), and/or good intake can be achieved with proper advice.
2 | Reduced intake, and/or stimulus / feeding modules / nutrition drinks are needed (but not tube feeding / TPN).
1 | Intake is severely reduced (<50% of her/his needs), and/or tube feeding / TPN is needed.
0 | Cannot eat, and/or fully dependent on tube feeding / TPN.
The predictions generated by the model might sometimes be outside of the scale (e.g. 4.2); this is normal in a regression model.
## Intended uses and limitations
- The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records).
- The model was fine-tuned with the [Simple Transformers](https://simpletransformers.ai/) library. This library is based on Transformers but the model cannot be used directly with Transformers `pipeline` and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled.
## How to use
To generate predictions with the model, use the [Simple Transformers](https://simpletransformers.ai/) library:
```
from simpletransformers.classification import ClassificationModel
model = ClassificationModel(
'roberta',
'CLTL/icf-levels-etn',
use_cuda=False,
)
example = 'Sondevoeding is geïndiceerd'
_, raw_outputs = model.predict([example])
predictions = np.squeeze(raw_outputs)
```
The prediction on the example is:
```
0.89
```
The raw outputs look like this:
```
[[0.8872931]]
```
## Training data
- The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released.
- The annotation guidelines used for the project can be found [here](https://github.com/cltl/a-proof-zonmw/tree/main/resources/annotation_guidelines).
## Training procedure
The default training parameters of Simple Transformers were used, including:
- Optimizer: AdamW
- Learning rate: 4e-5
- Num train epochs: 1
- Train batch size: 8
## Evaluation results
The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals).
| | Sentence-level | Note-level
|---|---|---
mean absolute error | 0.59 | 0.50
mean squared error | 0.65 | 0.47
root mean squared error | 0.81 | 0.68
## Authors and references
### Authors
Jenia Kim, Piek Vossen
### References
TBD
| 3,371 |
CLTL/icf-levels-ins | [
"LABEL_0"
] | ---
language: nl
license: mit
pipeline_tag: text-classification
inference: false
---
# Regression Model for Exercise Tolerance Functioning Levels (ICF b455)
## Description
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing exercise tolerance functions. The model is based on a pre-trained Dutch medical language model ([link to be added]()): a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. To detect sentences about exercise tolerance functions in clinical text in Dutch, use the [icf-domains](https://huggingface.co/CLTL/icf-domains) classification model.
## Functioning levels
Level | Meaning
---|---
5 | MET>6. Can tolerate jogging, hard exercises, running, climbing stairs fast, sports.
4 | 4≤MET≤6. Can tolerate walking / cycling at a brisk pace, considerable effort (e.g. cycling from 16 km/h), heavy housework.
3 | 3≤MET<4. Can tolerate walking / cycling at a normal pace, gardening, exercises without equipment.
2 | 2≤MET<3. Can tolerate walking at a slow to moderate pace, grocery shopping, light housework.
1 | 1≤MET<2. Can tolerate sitting activities.
0 | 0≤MET<1. Can physically tolerate only recumbent activities.
The predictions generated by the model might sometimes be outside of the scale (e.g. 5.2); this is normal in a regression model.
## Intended uses and limitations
- The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records).
- The model was fine-tuned with the [Simple Transformers](https://simpletransformers.ai/) library. This library is based on Transformers but the model cannot be used directly with Transformers `pipeline` and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled.
## How to use
To generate predictions with the model, use the [Simple Transformers](https://simpletransformers.ai/) library:
```
from simpletransformers.classification import ClassificationModel
model = ClassificationModel(
'roberta',
'CLTL/icf-levels-ins',
use_cuda=False,
)
example = 'kan nog goed traplopen, maar flink ingeleverd aan conditie na Corona'
_, raw_outputs = model.predict([example])
predictions = np.squeeze(raw_outputs)
```
The prediction on the example is:
```
3.13
```
The raw outputs look like this:
```
[[3.1300993]]
```
## Training data
- The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released.
- The annotation guidelines used for the project can be found [here](https://github.com/cltl/a-proof-zonmw/tree/main/resources/annotation_guidelines).
## Training procedure
The default training parameters of Simple Transformers were used, including:
- Optimizer: AdamW
- Learning rate: 4e-5
- Num train epochs: 1
- Train batch size: 8
## Evaluation results
The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals).
| | Sentence-level | Note-level
|---|---|---
mean absolute error | 0.69 | 0.61
mean squared error | 0.80 | 0.64
root mean squared error | 0.89 | 0.80
## Authors and references
### Authors
Jenia Kim, Piek Vossen
### References
TBD
| 3,466 |
CenIA/albert-xxlarge-spanish-finetuned-pawsx | null | Entry not found | 15 |
CouchCat/ma_mlc_v7_distil | [
"delivery",
"return",
"product",
"monetary"
] | ---
language: en
license: mit
tags:
- multi-label
widget:
- text: "I would like to return these pants and shoes"
---
### Description
A Multi-label text classification model trained on a customer feedback data using DistilBert.
Possible labels are:
- Delivery (delivery status, time of arrival, etc.)
- Return (return confirmation, return label requests, etc.)
- Product (quality, complaint, etc.)
- Monetary (pending transactions, refund, etc.)
### Usage
```
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("CouchCat/ma_mlc_v7_distil")
model = AutoModelForSequenceClassification.from_pretrained("CouchCat/ma_mlc_v7_distil")
``` | 709 |
EhsanAghazadeh/bert-based-uncased-sst2-e4 | [
"negative",
"positive"
] | Entry not found | 15 |
EthanChen0418/six-classed-domain-cls | [
"contradiction",
"entailment",
"neutral"
] | Entry not found | 15 |
Fan-s/reddit-tc-bert | [
"matched",
"unmatched"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-uncased-base
---
<!-- 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-uncased-base
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an Reddit-dialogue dataset.
This model can be used for Text Classification: Given two sentences, see if they are related.
It achieves the following results on the evaluation set:
- Loss: 0.2297
- Accuracy: 0.9267
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 320
- eval_batch_size: 80
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.0
- Tokenizers 0.11.0
## Usage (HuggingFace Transformers)
You can use the model like this:
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
# label_list
label_list = ['matched', 'unmatched']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained("Fan-s/reddit-tc-bert", use_fast=True)
model = AutoModelForSequenceClassification.from_pretrained("Fan-s/reddit-tc-bert")
# Set the input
post = "don't make gravy with asbestos."
response = "i'd expect someone with a culinary background to know that. since we're talking about school dinner ladies, they need to learn this pronto."
# Predict whether the two sentences are matched
def predict(post, response, max_seq_length=128):
with torch.no_grad():
args = (post, response)
input = tokenizer(*args, padding="max_length", max_length=max_seq_length, truncation=True, return_tensors="pt")
output = model(**input)
logits = output.logits
item = torch.argmax(logits, dim=1)
predict_label = label_list[item]
return predict_label, logits
predict_label, logits = predict(post, response)
# Matched
print("predict_label:", predict_label)
``` | 2,258 |
GeniusVoice/bot-selector | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Hax/filipino-text-version1 | null | Entry not found | 15 |
JonatanGk/roberta-base-ca-finetuned-tecla | [
"Societat",
"Política",
"Música",
"Educació",
"Empresa",
"Cultura",
"Unió_Europea",
"Govern",
"Infraestructures",
"Treball",
"Mobilitat",
"Cinema",
"Partits",
"Teatre",
"Turisme",
"Equipaments_i_patrimoni",
"Lletres",
"Meteorologia",
"Comerç",
"Govern_espanyol",
"Món",
"Fes... | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tecla
metrics:
- accuracy
model-index:
- name: roberta-base-ca-finetuned-mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tecla
type: tecla
args: tecla
metrics:
- name: Accuracy
type: accuracy
value: 0.7361816335412737
---
<!-- 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-ca-finetuned-mnli
This model is a fine-tuned version of [BSC-TeMU/roberta-base-ca](https://huggingface.co/BSC-TeMU/roberta-base-ca) on the tecla dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9354
- Accuracy: 0.7362
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8465 | 1.0 | 6888 | 0.8222 | 0.6990 |
| 0.6966 | 2.0 | 13776 | 0.7872 | 0.7157 |
| 0.5643 | 3.0 | 20664 | 0.8060 | 0.7268 |
| 0.4435 | 4.0 | 27552 | 0.8470 | 0.7333 |
| 0.3206 | 5.0 | 34440 | 0.9354 | 0.7362 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3
| 1,865 |
Katsiaryna/stsb-TinyBERT-L-4-finetuned_auc_151221-5-001 | [
"LABEL_0"
] | Entry not found | 15 |
Katsiaryna/stsb-TinyBERT-L-4-finetuned_auc_40000-top3 | [
"LABEL_0"
] | Entry not found | 15 |
Kayvane/distilbert-undersampled-noweights | [
"Actor",
"AmusementParkAttraction",
"Animal",
"Artist",
"Athlete",
"BodyOfWater",
"Boxer",
"BritishRoyalty",
"Broadcaster",
"Building",
"Cartoon",
"CelestialBody",
"Cleric",
"ClericalAdministrativeRegion",
"Coach",
"Comic",
"ComicsCharacter",
"Company",
"Database",
"Educational... | ---
tags:
- generated_from_trainer
model-index:
- name: distilbert-undersampled-noweights
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-undersampled-noweights
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
| 1,026 |
M-FAC/bert-mini-finetuned-stsb | [
"LABEL_0"
] | # BERT-mini model finetuned with M-FAC
This model is finetuned on STS-B dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf).
## Finetuning setup
For fair comparison against default Adam baseline, we finetune the model in the same framework as described here [https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) and just swap Adam optimizer with M-FAC.
Hyperparameters used by M-FAC optimizer:
```bash
learning rate = 1e-4
number of gradients = 512
dampening = 1e-6
```
## Results
We share the best model out of 5 runs with the following score on STS-B validation set:
```bash
pearson = 85.03
spearman = 85.06
```
Mean and standard deviation for 5 runs on STS-B validation set:
| | Pearson | Spearman |
|:----:|:-----------:|:----------:|
| Adam | 82.09 ± 0.54 | 82.64 ± 0.71 |
| M-FAC | 84.66 ± 0.30 | 84.65 ± 0.30 |
Results can be reproduced by adding M-FAC optimizer code in [https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) and running the following bash script:
```bash
CUDA_VISIBLE_DEVICES=0 python run_glue.py \
--seed 7 \
--model_name_or_path prajjwal1/bert-mini \
--task_name stsb \
--do_train \
--do_eval \
--max_seq_length 128 \
--per_device_train_batch_size 32 \
--learning_rate 1e-4 \
--num_train_epochs 5 \
--output_dir out_dir/ \
--optim MFAC \
--optim_args '{"lr": 1e-4, "num_grads": 512, "damp": 1e-6}'
```
We believe these results could be improved with modest tuning of hyperparameters: `per_device_train_batch_size`, `learning_rate`, `num_train_epochs`, `num_grads` and `damp`. For the sake of fair comparison and a robust default setup we use the same hyperparameters across all models (`bert-tiny`, `bert-mini`) and all datasets (SQuAD version 2 and GLUE).
Our code for M-FAC can be found here: [https://github.com/IST-DASLab/M-FAC](https://github.com/IST-DASLab/M-FAC).
A step-by-step tutorial on how to integrate and use M-FAC with any repository can be found here: [https://github.com/IST-DASLab/M-FAC/tree/master/tutorials](https://github.com/IST-DASLab/M-FAC/tree/master/tutorials).
## BibTeX entry and citation info
```bibtex
@article{frantar2021m,
title={M-FAC: Efficient Matrix-Free Approximations of Second-Order Information},
author={Frantar, Elias and Kurtic, Eldar and Alistarh, Dan},
journal={Advances in Neural Information Processing Systems},
volume={35},
year={2021}
}
```
| 2,797 |
MadhurJindalWorkMail/autonlp-Gibb-Detect-515314387 | [
"clean",
"mild gibberish",
"noise",
"word salad"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- MadhurJindalWorkMail/autonlp-data-Gibb-Detect
co2_eq_emissions: 70.95647633212745
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 515314387
- CO2 Emissions (in grams): 70.95647633212745
## Validation Metrics
- Loss: 0.08077705651521683
- Accuracy: 0.9760103738923709
- Macro F1: 0.9728412857204902
- Micro F1: 0.9760103738923709
- Weighted F1: 0.9759907151741426
- Macro Precision: 0.9736622407675567
- Micro Precision: 0.9760103738923709
- Weighted Precision: 0.97673611876005
- Macro Recall: 0.9728978421381711
- Micro Recall: 0.9760103738923709
- Weighted Recall: 0.9760103738923709
## 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 AutoNLP"}' https://api-inference.huggingface.co/models/MadhurJindalWorkMail/autonlp-Gibb-Detect-515314387
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("MadhurJindalWorkMail/autonlp-Gibb-Detect-515314387", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("MadhurJindalWorkMail/autonlp-Gibb-Detect-515314387", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 1,427 |
Maelstrom77/rtevib | [
"LABEL_0",
"LABEL_1"
] | Entry not found | 15 |
Maha/hin-trac1_fin | null | Entry not found | 15 |
Majed/internet2 | [
"LABEL_0"
] | Entry not found | 15 |
MelissaTESSA/distilbert-base-uncased-finetuned-cola | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5206791471093309
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6324
- Matthews Correlation: 0.5207
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5245 | 1.0 | 535 | 0.5155 | 0.4181 |
| 0.3446 | 2.0 | 1070 | 0.5623 | 0.4777 |
| 0.2331 | 3.0 | 1605 | 0.6324 | 0.5207 |
| 0.1678 | 4.0 | 2140 | 0.7706 | 0.5106 |
| 0.1255 | 5.0 | 2675 | 0.8852 | 0.4998 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
| 2,000 |
MiBo/SABERT | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | Entry not found | 15 |
MickyMike/0-GPT2SP-aptanastudio | [
"LABEL_0"
] | Entry not found | 15 |
MickyMike/0-GPT2SP-bamboo | [
"LABEL_0"
] | Entry not found | 15 |
MickyMike/0-GPT2SP-datamanagement | [
"LABEL_0"
] | Entry not found | 15 |
MickyMike/0-GPT2SP-moodle | [
"LABEL_0"
] | Entry not found | 15 |
MickyMike/0-GPT2SP-springxd | [
"LABEL_0"
] | Entry not found | 15 |
MickyMike/000-GPT2SP-talendesb-mesos | [
"LABEL_0"
] | Entry not found | 15 |
MickyMike/1-GPT2SP-datamanagement | [
"LABEL_0"
] | Entry not found | 15 |
MickyMike/6-GPT2SP-jirasoftware | [
"LABEL_0"
] | Entry not found | 15 |
MickyMike/7-GPT2SP-talenddataquality | [
"LABEL_0"
] | Entry not found | 15 |
MickyMike/777-GPT2SP-mule-titanium | [
"LABEL_0"
] | Entry not found | 15 |
Monsia/autonlp-tweets-classification-23044997 | [
"Harmful_Traditional_practice",
"Physical_violence",
"economic_violence",
"emotional_violence",
"sexual_violence"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Monsia/autonlp-data-tweets-classification
co2_eq_emissions: 4.819872182577655
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 23044997
- CO2 Emissions (in grams): 4.819872182577655
## Validation Metrics
- Loss: 0.001594889909029007
- Accuracy: 0.9997478885667465
- Macro F1: 0.9991190902836993
- Micro F1: 0.9997478885667465
- Weighted F1: 0.9997476735518704
- Macro Precision: 0.9998014460161265
- Micro Precision: 0.9997478885667465
- Weighted Precision: 0.9997479944069787
- Macro Recall: 0.9984426545713851
- Micro Recall: 0.9997478885667465
- Weighted Recall: 0.9997478885667465
## 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 AutoNLP"}' https://api-inference.huggingface.co/models/Monsia/autonlp-tweets-classification-23044997
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Monsia/autonlp-tweets-classification-23044997", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Monsia/autonlp-tweets-classification-23044997", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 1,410 |
NaliniK/distilbert-base-uncased-finetuned-cola | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5494735380761103
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8239
- Matthews Correlation: 0.5495
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5235 | 1.0 | 535 | 0.5402 | 0.4156 |
| 0.3484 | 2.0 | 1070 | 0.5272 | 0.5233 |
| 0.2381 | 3.0 | 1605 | 0.6665 | 0.5050 |
| 0.1746 | 4.0 | 2140 | 0.7512 | 0.5429 |
| 0.1308 | 5.0 | 2675 | 0.8239 | 0.5495 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 2,000 |
NikolajW/BaselineThesis | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_11",
"LABEL_12",
"LABEL_13",
"LABEL_14",
"LABEL_15",
"LABEL_16",
"LABEL_17",
"LABEL_18",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8",
"LABEL_9"
] | Entry not found | 15 |
Pratibha/xlm-roberta-base-finetuned-marc-en | [
"good",
"great",
"ok",
"poor",
"terrible"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc-en
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. -->
# xlm-roberta-base-finetuned-marc-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9575
- Mae: 0.5488
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1253 | 1.0 | 235 | 0.9960 | 0.5366 |
| 0.9708 | 2.0 | 470 | 0.9575 | 0.5488 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
| 1,429 |
Prompsit/paraphrase-bert-pt | [
"Not Paraphrase",
"Paraphrase"
] | ---
pipeline_tag: text-classification
inference: false
language: pt
tags:
- transformers
---
# Prompsit/paraphrase-bert-pt
This model allows to evaluate paraphrases for a given phrase.
We have fine-tuned this model from pretrained "neuralmind/bert-base-portuguese-cased".
Model built under a TSI-100905-2019-4 project, co-financed by Ministry of Economic Affairs and Digital Transformation from the Government of Spain.
# How to use it
The model answer the following question: Is "phrase B" a paraphrase of "phrase A".
Please note that we're considering phrases instead of sentences. Therefore, we must take into account that the model doesn't expect to find punctuation marks or long pieces of text.
Resulting probabilities correspond to classes:
* 0: Not a paraphrase
* 1: It's a paraphrase
So, considering the phrase "logo após o homicídio" and a candidate paraphrase like "pouco depois do assassinato", you can use the model like this:
```
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Prompsit/paraphrase-bert-pt")
model = AutoModelForSequenceClassification.from_pretrained("Prompsit/paraphrase-bert-pt")
input = tokenizer('logo após o homicídio','pouco depois do assassinato',return_tensors='pt')
logits = model(**input).logits
soft = torch.nn.Softmax(dim=1)
print(soft(logits))
```
Code output is:
```
tensor([[0.2137, 0.7863]], grad_fn=<SoftmaxBackward>)
```
As the probability of 1 (=It's a paraphrase) is 0.7863 and the probability of 0 (=It is not a paraphrase) is 0.2137, we can conclude, for our previous example, that "pouco depois do assassinato" is a paraphrase of "logo após o homicidio".
# Evaluation results
We have used as test dataset 16500 pairs of phrases human tagged.
Metrics obtained are:
```
metrics={
'test_loss': 0.6074697375297546,
'test_accuracy': 0.7809,
'test_precision': 0.7157638466220329,
'test_recall': 0.40551724137931033,
'test_f1': 0.5177195685670262,
'test_matthews_correlation': 0.41603913834665324,
'test_runtime': 16.4585,
'test_samples_per_second': 607.587,
'test_steps_per_second': 19.017
}
``` | 2,177 |
Prompsit/paraphrase-roberta-es | [
"Not Paraphrase",
"Paraphrase"
] | ---
pipeline_tag: text-classification
inference: false
language: es
tags:
- transformers
---
# Prompsit/paraphrase-roberta-es
This model allows to evaluate paraphrases for a given phrase.
We have fine-tuned this model from pretrained "PlanTL-GOB-ES/roberta-base-bne".
Model built under a TSI-100905-2019-4 project, co-financed by Ministry of Economic Affairs and Digital Transformation from the Government of Spain.
# How to use it
The model answer the following question: Is "phrase B" a paraphrase of "phrase A".
Please note that we're considering phrases instead of sentences. Therefore, we must take into account that the model doesn't expect to find punctuation marks or long pieces of text.
Resulting probabilities correspond to classes:
* 0: Not a paraphrase
* 1: It's a paraphrase
So, considering the phrase "se buscarán acuerdos" and a candidate paraphrase like "se deberá obtener el acuerdo", you can use the model like this:
```
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Prompsit/paraphrase-roberta-es")
model = AutoModelForSequenceClassification.from_pretrained("Prompsit/paraphrase-roberta-es")
input = tokenizer('se buscarán acuerdos','se deberá obtener el acuerdo',return_tensors='pt')
logits = model(**input).logits
soft = torch.nn.Softmax(dim=1)
print(soft(logits))
```
Code output is:
```
tensor([[0.2266, 0.7734]], grad_fn=<SoftmaxBackward>)
```
As the probability of 1 (=It's a paraphrase) is 0.77 and the probability of 0 (=It is not a paraphrase) is 0.22, we can conclude, for our previous example, that "se deberá obtener el acuerdo" is a paraphrase of "se buscarán acuerdos".
# Evaluation results
We have used as test dataset 16500 pairs of phrases human tagged.
Metrics obtained are:
```
metrics={
'test_loss': 0.4869941473007202,
'test_accuracy': 0.8003636363636364,
'test_precision': 0.6692456479690522,
'test_recall': 0.5896889646357052,
'test_f1': 0.6269535673839184,
'test_matthews_correlation': 0.49324489316659575,
'test_runtime': 27.1537,
'test_samples_per_second': 607.652,
'test_steps_per_second': 19.003
}
``` | 2,188 |
Radella/quora_helpful_answers_classifier | [
"helpful",
"unhelpful"
] | Entry not found | 15 |
Riad/finetuned-bert-mrpc | [
"equivalent",
"not equivalent"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: finetuned-bert-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8676470588235294
- name: F1
type: f1
value: 0.9084745762711864
---
<!-- 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. -->
# finetuned-bert-mrpc
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4382
- Accuracy: 0.8676
- F1: 0.9085
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5454 | 1.0 | 230 | 0.4396 | 0.8309 | 0.8871 |
| 0.3387 | 2.0 | 460 | 0.3783 | 0.8529 | 0.8976 |
| 0.1956 | 3.0 | 690 | 0.4382 | 0.8676 | 0.9085 |
### Framework versions
- Transformers 4.10.0
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
| 1,816 |
Ruizhou/bert-base-uncased-finetuned-cola | null | Entry not found | 15 |
SetFit/deberta-v3-large__sst2__train-16-2 | [
"negative",
"positive"
] | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large__sst2__train-16-2
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. -->
# deberta-v3-large__sst2__train-16-2
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6959
- Accuracy: 0.5008
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7079 | 1.0 | 7 | 0.7361 | 0.2857 |
| 0.6815 | 2.0 | 14 | 0.7659 | 0.2857 |
| 0.6938 | 3.0 | 21 | 0.7944 | 0.2857 |
| 0.4584 | 4.0 | 28 | 1.2441 | 0.2857 |
| 0.4949 | 5.0 | 35 | 1.2285 | 0.5714 |
| 0.0574 | 6.0 | 42 | 1.7796 | 0.5714 |
| 0.0156 | 7.0 | 49 | 2.6027 | 0.5714 |
| 0.0051 | 8.0 | 56 | 2.8717 | 0.5714 |
| 0.0017 | 9.0 | 63 | 2.8491 | 0.5714 |
| 0.0023 | 10.0 | 70 | 1.7149 | 0.7143 |
| 0.001 | 11.0 | 77 | 1.1101 | 0.7143 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
| 2,030 |
SetFit/deberta-v3-large__sst2__train-8-7 | [
"negative",
"positive"
] | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large__sst2__train-8-7
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. -->
# deberta-v3-large__sst2__train-8-7
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7037
- Accuracy: 0.5008
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6864 | 1.0 | 3 | 0.7800 | 0.25 |
| 0.6483 | 2.0 | 6 | 0.8067 | 0.25 |
| 0.6028 | 3.0 | 9 | 0.8500 | 0.25 |
| 0.4086 | 4.0 | 12 | 1.0661 | 0.25 |
| 0.2923 | 5.0 | 15 | 1.2302 | 0.25 |
| 0.2059 | 6.0 | 18 | 1.0312 | 0.5 |
| 0.1238 | 7.0 | 21 | 1.1271 | 0.5 |
| 0.0711 | 8.0 | 24 | 1.3100 | 0.5 |
| 0.0453 | 9.0 | 27 | 1.4208 | 0.5 |
| 0.0198 | 10.0 | 30 | 1.5988 | 0.5 |
| 0.0135 | 11.0 | 33 | 1.9174 | 0.5 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
| 2,028 |
SetFit/distilbert-base-uncased__hate_speech_offensive__train-16-0 | [
"hate speech",
"neither",
"offensive language"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__hate_speech_offensive__train-16-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__hate_speech_offensive__train-16-0
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2707
- Accuracy: 0.517
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0943 | 1.0 | 10 | 1.1095 | 0.3 |
| 1.0602 | 2.0 | 20 | 1.1086 | 0.4 |
| 1.0159 | 3.0 | 30 | 1.1165 | 0.4 |
| 0.9027 | 4.0 | 40 | 1.1377 | 0.4 |
| 0.8364 | 5.0 | 50 | 1.0126 | 0.5 |
| 0.6653 | 6.0 | 60 | 0.9298 | 0.5 |
| 0.535 | 7.0 | 70 | 0.9555 | 0.5 |
| 0.3713 | 8.0 | 80 | 0.8543 | 0.4 |
| 0.1633 | 9.0 | 90 | 0.9876 | 0.4 |
| 0.1069 | 10.0 | 100 | 0.8383 | 0.6 |
| 0.0591 | 11.0 | 110 | 0.8056 | 0.6 |
| 0.0344 | 12.0 | 120 | 0.8915 | 0.6 |
| 0.0265 | 13.0 | 130 | 0.8722 | 0.6 |
| 0.0196 | 14.0 | 140 | 1.0064 | 0.6 |
| 0.0158 | 15.0 | 150 | 1.0479 | 0.6 |
| 0.0128 | 16.0 | 160 | 1.0723 | 0.6 |
| 0.0121 | 17.0 | 170 | 1.0758 | 0.6 |
| 0.0093 | 18.0 | 180 | 1.1236 | 0.6 |
| 0.0085 | 19.0 | 190 | 1.1480 | 0.6 |
| 0.0084 | 20.0 | 200 | 1.1651 | 0.6 |
| 0.0077 | 21.0 | 210 | 1.1832 | 0.6 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
| 2,698 |
SetFit/distilbert-base-uncased__hate_speech_offensive__train-32-3 | [
"hate speech",
"neither",
"offensive language"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__hate_speech_offensive__train-32-3
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__hate_speech_offensive__train-32-3
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: 0.8286
- Accuracy: 0.661
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1041 | 1.0 | 19 | 1.0658 | 0.5 |
| 1.009 | 2.0 | 38 | 0.9892 | 0.7 |
| 0.7925 | 3.0 | 57 | 0.8516 | 0.7 |
| 0.5279 | 4.0 | 76 | 0.7877 | 0.65 |
| 0.2932 | 5.0 | 95 | 0.7592 | 0.65 |
| 0.1166 | 6.0 | 114 | 0.9437 | 0.65 |
| 0.044 | 7.0 | 133 | 1.0315 | 0.75 |
| 0.0197 | 8.0 | 152 | 1.3513 | 0.55 |
| 0.0126 | 9.0 | 171 | 1.1702 | 0.7 |
| 0.0083 | 10.0 | 190 | 1.2272 | 0.7 |
| 0.0068 | 11.0 | 209 | 1.2889 | 0.7 |
| 0.0059 | 12.0 | 228 | 1.3073 | 0.7 |
| 0.0052 | 13.0 | 247 | 1.3595 | 0.7 |
| 0.0041 | 14.0 | 266 | 1.4443 | 0.7 |
| 0.0038 | 15.0 | 285 | 1.4709 | 0.7 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
| 2,326 |
SetFit/distilbert-base-uncased__sst2__train-16-6 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-16-6
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__sst2__train-16-6
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: 0.8356
- Accuracy: 0.6480
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6978 | 1.0 | 7 | 0.6807 | 0.4286 |
| 0.6482 | 2.0 | 14 | 0.6775 | 0.4286 |
| 0.6051 | 3.0 | 21 | 0.6623 | 0.5714 |
| 0.486 | 4.0 | 28 | 0.6710 | 0.5714 |
| 0.4612 | 5.0 | 35 | 0.5325 | 0.7143 |
| 0.2233 | 6.0 | 42 | 0.4992 | 0.7143 |
| 0.1328 | 7.0 | 49 | 0.4753 | 0.7143 |
| 0.0905 | 8.0 | 56 | 0.2416 | 1.0 |
| 0.0413 | 9.0 | 63 | 0.2079 | 1.0 |
| 0.0356 | 10.0 | 70 | 0.2234 | 0.8571 |
| 0.0217 | 11.0 | 77 | 0.2639 | 0.8571 |
| 0.0121 | 12.0 | 84 | 0.2977 | 0.8571 |
| 0.0105 | 13.0 | 91 | 0.3468 | 0.8571 |
| 0.0085 | 14.0 | 98 | 0.3912 | 0.8571 |
| 0.0077 | 15.0 | 105 | 0.4000 | 0.8571 |
| 0.0071 | 16.0 | 112 | 0.4015 | 0.8571 |
| 0.0078 | 17.0 | 119 | 0.3865 | 0.8571 |
| 0.0059 | 18.0 | 126 | 0.3603 | 0.8571 |
| 0.0051 | 19.0 | 133 | 0.3231 | 0.8571 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
| 2,541 |
SetFit/distilbert-base-uncased__sst2__train-32-0 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-32-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__sst2__train-32-0
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: 0.8558
- Accuracy: 0.7183
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7088 | 1.0 | 13 | 0.6819 | 0.6154 |
| 0.635 | 2.0 | 26 | 0.6318 | 0.7692 |
| 0.547 | 3.0 | 39 | 0.5356 | 0.7692 |
| 0.3497 | 4.0 | 52 | 0.4456 | 0.6923 |
| 0.1979 | 5.0 | 65 | 0.3993 | 0.7692 |
| 0.098 | 6.0 | 78 | 0.3613 | 0.7692 |
| 0.0268 | 7.0 | 91 | 0.3561 | 0.9231 |
| 0.0137 | 8.0 | 104 | 0.3755 | 0.9231 |
| 0.0083 | 9.0 | 117 | 0.4194 | 0.7692 |
| 0.0065 | 10.0 | 130 | 0.4446 | 0.7692 |
| 0.005 | 11.0 | 143 | 0.4527 | 0.7692 |
| 0.0038 | 12.0 | 156 | 0.4645 | 0.7692 |
| 0.0033 | 13.0 | 169 | 0.4735 | 0.7692 |
| 0.0033 | 14.0 | 182 | 0.4874 | 0.7692 |
| 0.0029 | 15.0 | 195 | 0.5041 | 0.7692 |
| 0.0025 | 16.0 | 208 | 0.5148 | 0.7692 |
| 0.0024 | 17.0 | 221 | 0.5228 | 0.7692 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
| 2,417 |
SetFit/distilbert-base-uncased__sst2__train-32-4 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-32-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__sst2__train-32-4
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: 0.5001
- Accuracy: 0.7650
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7175 | 1.0 | 13 | 0.6822 | 0.5385 |
| 0.6559 | 2.0 | 26 | 0.6533 | 0.6154 |
| 0.6052 | 3.0 | 39 | 0.5762 | 0.7692 |
| 0.4587 | 4.0 | 52 | 0.4477 | 0.8462 |
| 0.2459 | 5.0 | 65 | 0.4288 | 0.7692 |
| 0.1001 | 6.0 | 78 | 0.5219 | 0.7692 |
| 0.0308 | 7.0 | 91 | 0.8540 | 0.7692 |
| 0.014 | 8.0 | 104 | 0.7789 | 0.7692 |
| 0.0083 | 9.0 | 117 | 0.7996 | 0.7692 |
| 0.0064 | 10.0 | 130 | 0.8342 | 0.7692 |
| 0.0049 | 11.0 | 143 | 0.8612 | 0.7692 |
| 0.0036 | 12.0 | 156 | 0.8834 | 0.7692 |
| 0.0032 | 13.0 | 169 | 0.9067 | 0.7692 |
| 0.003 | 14.0 | 182 | 0.9332 | 0.7692 |
| 0.0028 | 15.0 | 195 | 0.9511 | 0.7692 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
| 2,293 |
SetFit/distilbert-base-uncased__sst2__train-32-9 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-32-9
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__sst2__train-32-9
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: 0.5625
- Accuracy: 0.7353
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7057 | 1.0 | 13 | 0.6805 | 0.5385 |
| 0.6642 | 2.0 | 26 | 0.6526 | 0.7692 |
| 0.5869 | 3.0 | 39 | 0.5773 | 0.8462 |
| 0.4085 | 4.0 | 52 | 0.4959 | 0.8462 |
| 0.2181 | 5.0 | 65 | 0.4902 | 0.6923 |
| 0.069 | 6.0 | 78 | 0.5065 | 0.8462 |
| 0.0522 | 7.0 | 91 | 0.6082 | 0.7692 |
| 0.0135 | 8.0 | 104 | 0.6924 | 0.7692 |
| 0.0084 | 9.0 | 117 | 0.5921 | 0.7692 |
| 0.0061 | 10.0 | 130 | 0.6477 | 0.7692 |
| 0.0047 | 11.0 | 143 | 0.6648 | 0.7692 |
| 0.0035 | 12.0 | 156 | 0.6640 | 0.7692 |
| 0.0031 | 13.0 | 169 | 0.6615 | 0.7692 |
| 0.0029 | 14.0 | 182 | 0.6605 | 0.7692 |
| 0.0026 | 15.0 | 195 | 0.6538 | 0.8462 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
| 2,293 |
SetFit/distilbert-base-uncased__subj__train-8-7 | [
"objective",
"subjective"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-7
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__subj__train-8-7
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: 0.2766
- Accuracy: 0.8845
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7044 | 1.0 | 3 | 0.6909 | 0.5 |
| 0.6678 | 2.0 | 6 | 0.6901 | 0.5 |
| 0.6336 | 3.0 | 9 | 0.6807 | 0.5 |
| 0.5926 | 4.0 | 12 | 0.6726 | 0.5 |
| 0.5221 | 5.0 | 15 | 0.6648 | 0.5 |
| 0.4573 | 6.0 | 18 | 0.6470 | 0.5 |
| 0.4177 | 7.0 | 21 | 0.6251 | 0.5 |
| 0.3252 | 8.0 | 24 | 0.5994 | 0.5 |
| 0.2831 | 9.0 | 27 | 0.5529 | 0.5 |
| 0.213 | 10.0 | 30 | 0.5078 | 0.75 |
| 0.1808 | 11.0 | 33 | 0.4521 | 1.0 |
| 0.1355 | 12.0 | 36 | 0.3996 | 1.0 |
| 0.1027 | 13.0 | 39 | 0.3557 | 1.0 |
| 0.0862 | 14.0 | 42 | 0.3121 | 1.0 |
| 0.0682 | 15.0 | 45 | 0.2828 | 1.0 |
| 0.0517 | 16.0 | 48 | 0.2603 | 1.0 |
| 0.0466 | 17.0 | 51 | 0.2412 | 1.0 |
| 0.038 | 18.0 | 54 | 0.2241 | 1.0 |
| 0.0276 | 19.0 | 57 | 0.2096 | 1.0 |
| 0.0246 | 20.0 | 60 | 0.1969 | 1.0 |
| 0.0249 | 21.0 | 63 | 0.1859 | 1.0 |
| 0.0201 | 22.0 | 66 | 0.1770 | 1.0 |
| 0.018 | 23.0 | 69 | 0.1703 | 1.0 |
| 0.0164 | 24.0 | 72 | 0.1670 | 1.0 |
| 0.0172 | 25.0 | 75 | 0.1639 | 1.0 |
| 0.0135 | 26.0 | 78 | 0.1604 | 1.0 |
| 0.014 | 27.0 | 81 | 0.1585 | 1.0 |
| 0.0108 | 28.0 | 84 | 0.1569 | 1.0 |
| 0.0116 | 29.0 | 87 | 0.1549 | 1.0 |
| 0.0111 | 30.0 | 90 | 0.1532 | 1.0 |
| 0.0113 | 31.0 | 93 | 0.1513 | 1.0 |
| 0.0104 | 32.0 | 96 | 0.1503 | 1.0 |
| 0.01 | 33.0 | 99 | 0.1490 | 1.0 |
| 0.0079 | 34.0 | 102 | 0.1479 | 1.0 |
| 0.0097 | 35.0 | 105 | 0.1466 | 1.0 |
| 0.0112 | 36.0 | 108 | 0.1458 | 1.0 |
| 0.0091 | 37.0 | 111 | 0.1457 | 1.0 |
| 0.0098 | 38.0 | 114 | 0.1454 | 1.0 |
| 0.0076 | 39.0 | 117 | 0.1451 | 1.0 |
| 0.0085 | 40.0 | 120 | 0.1448 | 1.0 |
| 0.0079 | 41.0 | 123 | 0.1445 | 1.0 |
| 0.0096 | 42.0 | 126 | 0.1440 | 1.0 |
| 0.0081 | 43.0 | 129 | 0.1430 | 1.0 |
| 0.0083 | 44.0 | 132 | 0.1424 | 1.0 |
| 0.0088 | 45.0 | 135 | 0.1418 | 1.0 |
| 0.0077 | 46.0 | 138 | 0.1414 | 1.0 |
| 0.0073 | 47.0 | 141 | 0.1413 | 1.0 |
| 0.0084 | 48.0 | 144 | 0.1412 | 1.0 |
| 0.0072 | 49.0 | 147 | 0.1411 | 1.0 |
| 0.0077 | 50.0 | 150 | 0.1411 | 1.0 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
| 4,461 |
SetFit/distilbert-base-uncased__subj__train-8-9 | [
"objective",
"subjective"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-9
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__subj__train-8-9
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: 0.4865
- Accuracy: 0.778
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7024 | 1.0 | 3 | 0.6843 | 0.75 |
| 0.67 | 2.0 | 6 | 0.6807 | 0.5 |
| 0.6371 | 3.0 | 9 | 0.6677 | 0.5 |
| 0.585 | 4.0 | 12 | 0.6649 | 0.5 |
| 0.5122 | 5.0 | 15 | 0.6707 | 0.5 |
| 0.4379 | 6.0 | 18 | 0.6660 | 0.5 |
| 0.4035 | 7.0 | 21 | 0.6666 | 0.5 |
| 0.323 | 8.0 | 24 | 0.6672 | 0.5 |
| 0.2841 | 9.0 | 27 | 0.6534 | 0.5 |
| 0.21 | 10.0 | 30 | 0.6456 | 0.5 |
| 0.1735 | 11.0 | 33 | 0.6325 | 0.5 |
| 0.133 | 12.0 | 36 | 0.6214 | 0.5 |
| 0.0986 | 13.0 | 39 | 0.6351 | 0.5 |
| 0.081 | 14.0 | 42 | 0.6495 | 0.5 |
| 0.0638 | 15.0 | 45 | 0.6671 | 0.5 |
| 0.0449 | 16.0 | 48 | 0.7156 | 0.5 |
| 0.0399 | 17.0 | 51 | 0.7608 | 0.5 |
| 0.0314 | 18.0 | 54 | 0.7796 | 0.5 |
| 0.0243 | 19.0 | 57 | 0.7789 | 0.5 |
| 0.0227 | 20.0 | 60 | 0.7684 | 0.5 |
| 0.0221 | 21.0 | 63 | 0.7628 | 0.5 |
| 0.0192 | 22.0 | 66 | 0.7728 | 0.5 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
| 2,724 |
TehranNLP-org/electra-base-avg-mnli-2e-5 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
TehranNLP-org/roberta-base-mnli-2e-5-42 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
TehranNLP-org/xlnet-base-cased-avg-cola-2e-5-42 | null | Entry not found | 15 |
Tejas3/Xlnet_base_80 | [
"NEGATIVE",
"NEUTRAL",
"POSITIVE"
] | Entry not found | 15 |
TransQuest/monotransquest-da-ne_en-wiki | [
"LABEL_0"
] | ---
language: ne-en
tags:
- Quality Estimation
- monotransquest
- DA
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commercial translation workflows as they have numerous potential uses. They can be employed to select the best translation when several translation engines are available or can inform the end user about the reliability of automatically translated content. In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human. The quality estimation can be done at different levels: document level, sentence level and word level.
With TransQuest, we have opensourced our research in translation quality estimation which also won the sentence-level direct assessment quality estimation shared task in [WMT 2020](http://www.statmt.org/wmt20/quality-estimation-task.html). TransQuest outperforms current open-source quality estimation frameworks such as [OpenKiwi](https://github.com/Unbabel/OpenKiwi) and [DeepQuest](https://github.com/sheffieldnlp/deepQuest).
## Features
- Sentence-level translation quality estimation on both aspects: predicting post editing efforts and direct assessment.
- Word-level translation quality estimation capable of predicting quality of source words, target words and target gaps.
- Outperform current state-of-the-art quality estimation methods like DeepQuest and OpenKiwi in all the languages experimented.
- Pre-trained quality estimation models for fifteen language pairs are available in [HuggingFace.](https://huggingface.co/TransQuest)
## Installation
### From pip
```bash
pip install transquest
```
### From Source
```bash
git clone https://github.com/TharinduDR/TransQuest.git
cd TransQuest
pip install -r requirements.txt
```
## Using Pre-trained Models
```python
import torch
from transquest.algo.sentence_level.monotransquest.run_model import MonoTransQuestModel
model = MonoTransQuestModel("xlmroberta", "TransQuest/monotransquest-da-ne_en-wiki", num_labels=1, use_cuda=torch.cuda.is_available())
predictions, raw_outputs = model.predict([["Reducerea acestor conflicte este importantă pentru conservare.", "Reducing these conflicts is not important for preservation."]])
print(predictions)
```
## Documentation
For more details follow the documentation.
1. **[Installation](https://tharindudr.github.io/TransQuest/install/)** - Install TransQuest locally using pip.
2. **Architectures** - Checkout the architectures implemented in TransQuest
1. [Sentence-level Architectures](https://tharindudr.github.io/TransQuest/architectures/sentence_level_architectures/) - We have released two architectures; MonoTransQuest and SiameseTransQuest to perform sentence level quality estimation.
2. [Word-level Architecture](https://tharindudr.github.io/TransQuest/architectures/word_level_architecture/) - We have released MicroTransQuest to perform word level quality estimation.
3. **Examples** - We have provided several examples on how to use TransQuest in recent WMT quality estimation shared tasks.
1. [Sentence-level Examples](https://tharindudr.github.io/TransQuest/examples/sentence_level_examples/)
2. [Word-level Examples](https://tharindudr.github.io/TransQuest/examples/word_level_examples/)
4. **Pre-trained Models** - We have provided pretrained quality estimation models for fifteen language pairs covering both sentence-level and word-level
1. [Sentence-level Models](https://tharindudr.github.io/TransQuest/models/sentence_level_pretrained/)
2. [Word-level Models](https://tharindudr.github.io/TransQuest/models/word_level_pretrained/)
5. **[Contact](https://tharindudr.github.io/TransQuest/contact/)** - Contact us for any issues with TransQuest
## Citations
If you are using the word-level architecture, please consider citing this paper which is accepted to [ACL 2021](https://2021.aclweb.org/).
```bash
@InProceedings{ranasinghe2021,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {An Exploratory Analysis of Multilingual Word Level Quality Estimation with Cross-Lingual Transformers},
booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics},
year = {2021}
}
```
If you are using the sentence-level architectures, please consider citing these papers which were presented in [COLING 2020](https://coling2020.org/) and in [WMT 2020](http://www.statmt.org/wmt20/) at EMNLP 2020.
```bash
@InProceedings{transquest:2020a,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest: Translation Quality Estimation with Cross-lingual Transformers},
booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
year = {2020}
}
```
```bash
@InProceedings{transquest:2020b,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest at WMT2020: Sentence-Level Direct Assessment},
booktitle = {Proceedings of the Fifth Conference on Machine Translation},
year = {2020}
}
```
| 5,401 |
XSY/roberta-scarcasm-discriminator | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-scarcasm-discriminator
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-scarcasm-discriminator
roberta-base
label0: unsarcasitic
label1: sarcastic
The fine tune method in my github https://github.com/yangyangxusheng/Fine-tune-use-transformers
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: 0.1844
- Accuracy: 0.9698
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.144 | 1.0 | 2179 | 0.2522 | 0.9215 |
| 0.116 | 2.0 | 4358 | 0.2105 | 0.9530 |
| 0.0689 | 3.0 | 6537 | 0.2015 | 0.9610 |
| 0.028 | 4.0 | 8716 | 0.1844 | 0.9698 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
| 1,707 |
aXhyra/demo_hate_1234567 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: demo_hate_1234567
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: hate
metrics:
- name: F1
type: f1
value: 0.7772939485986298
---
<!-- 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. -->
# demo_hate_1234567
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8697
- F1: 0.7773
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.320702985778492e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 282 | 0.4850 | 0.7645 |
| 0.3877 | 2.0 | 564 | 0.5160 | 0.7856 |
| 0.3877 | 3.0 | 846 | 0.6927 | 0.7802 |
| 0.1343 | 4.0 | 1128 | 0.8697 | 0.7773 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,760 |
aXhyra/demo_irony_1234567 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: demo_irony_1234567
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: irony
metrics:
- name: F1
type: f1
value: 0.685764300192161
---
<!-- 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. -->
# demo_irony_1234567
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2905
- F1: 0.6858
## 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: 2.7735294032820418e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 358 | 0.5872 | 0.6786 |
| 0.5869 | 2.0 | 716 | 0.6884 | 0.6952 |
| 0.3417 | 3.0 | 1074 | 0.9824 | 0.6995 |
| 0.3417 | 4.0 | 1432 | 1.2905 | 0.6858 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,761 |
aXhyra/demo_sentiment_1234567 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: demo_sentiment_1234567
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: sentiment
metrics:
- name: F1
type: f1
value: 0.7113620044371958
---
<!-- 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. -->
# demo_sentiment_1234567
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6332
- F1: 0.7114
## 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: 8.62486660723695e-06
- train_batch_size: 64
- eval_batch_size: 64
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7592 | 1.0 | 713 | 0.6509 | 0.6834 |
| 0.6389 | 2.0 | 1426 | 0.6318 | 0.7011 |
| 0.5647 | 3.0 | 2139 | 0.6320 | 0.7041 |
| 0.5391 | 4.0 | 2852 | 0.6332 | 0.7114 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,774 |
aXhyra/irony_trained_31415 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: irony_trained_31415
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: irony
metrics:
- name: F1
type: f1
value: 0.6690050628690761
---
<!-- 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. -->
# irony_trained_31415
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6608
- F1: 0.6690
## 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: 2.6774391860025942e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 31415
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6547 | 1.0 | 716 | 0.6173 | 0.6508 |
| 0.57 | 2.0 | 1432 | 0.8629 | 0.6577 |
| 0.2955 | 3.0 | 2148 | 1.4836 | 0.6722 |
| 0.1903 | 4.0 | 2864 | 1.6608 | 0.6690 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,768 |
aXhyra/presentation_irony_1234567 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: presentation_irony_1234567
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: irony
metrics:
- name: F1
type: f1
value: 0.674604535422547
---
<!-- 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. -->
# presentation_irony_1234567
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9493
- F1: 0.6746
## 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: 5.1637764704815665e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 1234567
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5514 | 1.0 | 90 | 0.5917 | 0.6767 |
| 0.6107 | 2.0 | 180 | 0.6123 | 0.6730 |
| 0.1327 | 3.0 | 270 | 0.7463 | 0.6970 |
| 0.1068 | 4.0 | 360 | 0.9493 | 0.6746 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,785 |
aXhyra/presentation_irony_31415 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: presentation_irony_31415
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: irony
metrics:
- name: F1
type: f1
value: 0.6753923142373446
---
<!-- 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. -->
# presentation_irony_31415
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9694
- F1: 0.6754
## 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: 5.1637764704815665e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 31415
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6601 | 1.0 | 90 | 0.6298 | 0.6230 |
| 0.4887 | 2.0 | 180 | 0.6039 | 0.6816 |
| 0.2543 | 3.0 | 270 | 0.7362 | 0.6803 |
| 0.1472 | 4.0 | 360 | 0.9694 | 0.6754 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,780 |
aXhyra/presentation_irony_42 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: presentation_irony_42
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: irony
metrics:
- name: F1
type: f1
value: 0.6745358521762839
---
<!-- 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. -->
# presentation_irony_42
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9344
- F1: 0.6745
## 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: 5.1637764704815665e-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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6675 | 1.0 | 90 | 0.5988 | 0.6684 |
| 0.5872 | 2.0 | 180 | 0.6039 | 0.6742 |
| 0.3953 | 3.0 | 270 | 0.8549 | 0.6557 |
| 0.0355 | 4.0 | 360 | 0.9344 | 0.6745 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,771 |
aXhyra/sentiment_trained | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: sentiment_trained
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: sentiment
metrics:
- name: F1
type: f1
value: 0.7253452834090693
---
<!-- 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. -->
# sentiment_trained
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2671
- F1: 0.7253
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.2140338797769864e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.6647 | 1.0 | 11404 | 0.6424 | 0.7189 |
| 0.6018 | 2.0 | 22808 | 0.7947 | 0.7170 |
| 0.5004 | 3.0 | 34212 | 1.0811 | 0.7200 |
| 0.3761 | 4.0 | 45616 | 1.2671 | 0.7253 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,770 |
aXhyra/sentiment_trained_31415 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: sentiment_trained_31415
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: sentiment
metrics:
- name: F1
type: f1
value: 0.7188262432133108
---
<!-- 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. -->
# sentiment_trained_31415
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2481
- F1: 0.7188
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.2140338797769864e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 31415
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.651 | 1.0 | 11404 | 0.6669 | 0.7141 |
| 0.6066 | 2.0 | 22808 | 0.8160 | 0.7198 |
| 0.503 | 3.0 | 34212 | 1.0659 | 0.7182 |
| 0.386 | 4.0 | 45616 | 1.2481 | 0.7188 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,786 |
abhishek/autonlp-imdb_eval-71421 | [
"0",
"1"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- abhishek/autonlp-data-imdb_eval
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 71421
## Validation Metrics
- Loss: 0.4114699363708496
- Accuracy: 0.8248248248248248
- Precision: 0.8305439330543933
- Recall: 0.8085539714867617
- AUC: 0.9088033420466026
- F1: 0.8194014447884417
## 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 AutoNLP"}' https://api-inference.huggingface.co/models/abhishek/autonlp-imdb_eval-71421
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-imdb_eval-71421", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-imdb_eval-71421", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 1,059 |
adamlin/ml999_explosion_proof_electrical_equipment | [
"0",
"1"
] | Entry not found | 15 |
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