modelId stringlengths 6 107 | label list | readme stringlengths 0 56.2k | readme_len int64 0 56.2k |
|---|---|---|---|
CleveGreen/FieldClassifier | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_11",
"LABEL_12",
"LABEL_13",
"LABEL_14",
"LABEL_15",
"LABEL_16",
"LABEL_17",
"LABEL_18",
"LABEL_19",
"LABEL_2",
"LABEL_20",
"LABEL_21",
"LABEL_22",
"LABEL_23",
"LABEL_24",
"LABEL_25",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"... | Entry not found | 15 |
Crasher222/kaggle-comp-test | [
"0",
"1",
"2",
"3",
"4"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Crasher222/autonlp-data-kaggle-test
co2_eq_emissions: 60.744727079482495
---
# Model Finetuned from BERT-base for
- Problem type: Multi-class Classification
- Model ID: 25805800
## Validation Metrics
- Loss: 0.4422711133956909
- Accuracy: 0.8615328555811976
- Macro F1: 0.8642434650461513
- Micro F1: 0.8615328555811976
- Weighted F1: 0.8617743626671308
- Macro Precision: 0.8649112225076049
- Micro Precision: 0.8615328555811976
- Weighted Precision: 0.8625407179375096
- Macro Recall: 0.8640777539828228
- Micro Recall: 0.8615328555811976
- Weighted Recall: 0.8615328555811976
## Usage
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Crasher222/kaggle-comp-test")
tokenizer = AutoTokenizer.from_pretrained("Crasher222/kaggle-comp-test")
inputs = tokenizer("I am in love with you", return_tensors="pt")
outputs = model(**inputs)
``` | 1,013 |
DaNLP/da-xlmr-ned | [
"mentioned",
"not mentioned"
] |
---
language:
- da
tags:
- ned
- xlm-roberta
- pytorch
- transformers
license: cc-by-sa-4.0
datasets:
- DaNED
- DaWikiNED
metrics:
- f1
---
# XLM-Roberta fine-tuned for Named Entity Disambiguation
Given a sentence and a knowledge graph context, the model detects whether a specific entity (represented by the knowledge graph context) is mentioned in the sentence (binary classification).
The base language model used is the [xlm-roberta-base](https://huggingface.co/xlm-roberta-base).
Here is how to use the model:
```python
from transformers import XLMRobertaTokenizer, XLMRobertaForSequenceClassification
model = XLMRobertaForSequenceClassification.from_pretrained("DaNLP/da-xlmr-ned")
tokenizer = XLMRobertaTokenizer.from_pretrained("DaNLP/da-xlmr-ned")
```
The tokenizer takes 2 strings has input: the sentence and the knowledge graph (KG) context.
Here is an example:
```python
sentence = "Karen Blixen vendte tilbage til Danmark, hvor hun boede resten af sit liv på Rungstedlund, som hun arvede efter sin mor i 1939"
kg_context = "udmærkelser modtaget Kritikerprisen udmærkelser modtaget Tagea Brandts Rejselegat udmærkelser modtaget Ingenio et arti udmærkelser modtaget Holbergmedaljen udmærkelser modtaget De Gyldne Laurbær mor Ingeborg Dinesen ægtefælle Bror von Blixen-Finecke køn kvinde Commons-kategori Karen Blixen LCAuth no95003722 VIAF 90663542 VIAF 121643918 GND-identifikator 118637878 ISNI 0000 0001 2096 6265 ISNI 0000 0003 6863 4408 ISNI 0000 0001 1891 0457 fødested Rungstedlund fødested Rungsted dødssted Rungstedlund dødssted København statsborgerskab Danmark NDL-nummer 00433530 dødsdato +1962-09-07T00:00:00Z dødsdato +1962-01-01T00:00:00Z fødselsdato +1885-04-17T00:00:00Z fødselsdato +1885-01-01T00:00:00Z AUT NKC jn20000600905 AUT NKC jo2015880827 AUT NKC xx0196181 emnets hovedkategori Kategori:Karen Blixen tilfælde af menneske billede Karen Blixen cropped from larger original.jpg IMDb-identifikationsnummer nm0227598 Freebase-ID /m/04ymd8w BNF 118857710 beskæftigelse skribent beskæftigelse selvbiograf beskæftigelse novelleforfatter ..."
```
A KG context, for a specific entity, can be generated from its Wikidata page.
In the previous example, the KG context is a string representation of the Wikidata page of [Karen Blixen (QID=Q182804)](https://www.wikidata.org/wiki/Q182804).
See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/ned.html#xlmr) for more details about how to generate a KG context.
## Training Data
The model has been trained on the [DaNED](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#daned) and [DaWikiNED](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#dawikined) datasets.
| 2,732 |
DoyyingFace/bert-cola-finetuned | null | Entry not found | 15 |
DoyyingFace/bert-wiki-comments-finetuned | null | Entry not found | 15 |
EMBEDDIA/bertic-tweetsentiment | [
"Negative",
"Neutral",
"Positive"
] | Entry not found | 15 |
EhsanAghazadeh/bert-based-uncased-sst2-e3 | [
"0",
"1"
] | Entry not found | 15 |
EhsanAghazadeh/bert-based-uncased-sst2-e5 | [
"0",
"1"
] | Entry not found | 15 |
EhsanAghazadeh/xlm-roberta-base-lcc-fa-2e-5-42 | null | Entry not found | 15 |
EthanChen0418/domain-cls-nine-classes | [
"contradiction",
"entailment",
"neutral"
] | Entry not found | 15 |
Fauzan/autonlp-judulberita-32517788 | [
"0.0",
"1.0"
] | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Fauzan/autonlp-data-judulberita
co2_eq_emissions: 0.9413042739759596
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 32517788
- CO2 Emissions (in grams): 0.9413042739759596
## Validation Metrics
- Loss: 0.32112351059913635
- Accuracy: 0.8641304347826086
- Precision: 0.8055555555555556
- Recall: 0.8405797101449275
- AUC: 0.9493383742911153
- F1: 0.8226950354609929
## 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/Fauzan/autonlp-judulberita-32517788
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Fauzan/autonlp-judulberita-32517788", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Fauzan/autonlp-judulberita-32517788", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 1,157 |
Fiona99/distilbert-base-uncased-finetuned-cola | null | Entry not found | 15 |
Hyeon/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: 0.5442538936990396
---
<!-- 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: 0.8575
- Matthews Correlation: 0.5443
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5242 | 1.0 | 535 | 0.5258 | 0.4391 |
| 0.346 | 2.0 | 1070 | 0.5264 | 0.5074 |
| 0.2334 | 3.0 | 1605 | 0.6808 | 0.5074 |
| 0.1711 | 4.0 | 2140 | 0.7737 | 0.5373 |
| 0.1205 | 5.0 | 2675 | 0.8575 | 0.5443 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
| 2,000 |
IMSyPP/hate_speech_targets_slo | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_11",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8",
"LABEL_9"
] | ---
language:
- sl
license: mit
--- | 38 |
ItuThesis2022MlviNikw/bert-base-uncased | [
"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 |
Jeska/VaccinChatSentenceClassifierDutch_fromBERTje | [
"chitchat_ask_bye",
"chitchat_ask_hi",
"chitchat_ask_hi_de",
"chitchat_ask_hi_en",
"chitchat_ask_hi_fr",
"chitchat_ask_hoe_gaat_het",
"chitchat_ask_name",
"chitchat_ask_thanks",
"faq_ask_aantal_gevaccineerd",
"faq_ask_aantal_gevaccineerd_wereldwijd",
"faq_ask_afspraak_afzeggen",
"faq_ask_afspr... | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: VaccinChatSentenceClassifierDutch_fromBERTje
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. -->
# VaccinChatSentenceClassifierDutch_fromBERTje
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6223
- Accuracy: 0.9068
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 3.4666 | 1.0 | 1320 | 2.3355 | 0.5768 |
| 1.5293 | 2.0 | 2640 | 1.1118 | 0.8144 |
| 0.8031 | 3.0 | 3960 | 0.6362 | 0.8803 |
| 0.2985 | 4.0 | 5280 | 0.5119 | 0.8958 |
| 0.1284 | 5.0 | 6600 | 0.5023 | 0.8931 |
| 0.0842 | 6.0 | 7920 | 0.5246 | 0.9022 |
| 0.0414 | 7.0 | 9240 | 0.5581 | 0.9013 |
| 0.0372 | 8.0 | 10560 | 0.5721 | 0.9004 |
| 0.0292 | 9.0 | 11880 | 0.5469 | 0.9141 |
| 0.0257 | 10.0 | 13200 | 0.5871 | 0.9059 |
| 0.0189 | 11.0 | 14520 | 0.6181 | 0.9049 |
| 0.0104 | 12.0 | 15840 | 0.6184 | 0.9068 |
| 0.009 | 13.0 | 17160 | 0.6013 | 0.9049 |
| 0.0051 | 14.0 | 18480 | 0.6205 | 0.9059 |
| 0.0035 | 15.0 | 19800 | 0.6223 | 0.9068 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.10.3
| 2,270 |
Jeska/autonlp-vaccinfaq-22144706 | [
"chitchat_ask_bye",
"chitchat_ask_hi",
"chitchat_ask_hi_de",
"chitchat_ask_hi_en",
"chitchat_ask_hi_fr",
"chitchat_ask_hoe_gaat_het",
"chitchat_ask_name",
"chitchat_ask_thanks",
"faq_ask_aantal_gevaccineerd",
"faq_ask_aantal_gevaccineerd_wereldwijd",
"faq_ask_afspraak_afzeggen",
"faq_ask_afspr... | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Jeska/autonlp-data-vaccinfaq
co2_eq_emissions: 27.135492487925884
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 22144706
- CO2 Emissions (in grams): 27.135492487925884
## Validation Metrics
- Loss: 1.81697416305542
- Accuracy: 0.6377269139700079
- Macro F1: 0.5181293370145044
- Micro F1: 0.6377269139700079
- Weighted F1: 0.631117826235572
- Macro Precision: 0.5371452512845428
- Micro Precision: 0.6377269139700079
- Weighted Precision: 0.6655055695465463
- Macro Recall: 0.5609328178925124
- Micro Recall: 0.6377269139700079
- Weighted Recall: 0.6377269139700079
## 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/Jeska/autonlp-vaccinfaq-22144706
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Jeska/autonlp-vaccinfaq-22144706", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Jeska/autonlp-vaccinfaq-22144706", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 1,356 |
LysandreJik/testing | [
"equivalent",
"not_equivalent"
] | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: testing
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.6813725490196079
- name: F1
type: f1
value: 0.8104956268221574
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# testing
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6644
- Accuracy: 0.6814
- F1: 0.8105
- Combined Score: 0.7459
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10
### Training results
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.11.0
- Tokenizers 0.10.3
| 1,509 |
bush/autonlp-bp-29016523 | [
"command",
"greeting",
"information",
"other",
"question"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Jush/autonlp-data-bp
co2_eq_emissions: 3.273303707756322
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 29016523
- CO2 Emissions (in grams): 3.273303707756322
## Validation Metrics
- Loss: 0.6093757748603821
- Accuracy: 0.8333333333333334
- Macro F1: 0.7937936978656889
- Micro F1: 0.8333333333333334
- Weighted F1: 0.8239843785760546
- Macro Precision: 0.8988882462566673
- Micro Precision: 0.8333333333333334
- Weighted Precision: 0.8404982541824647
- Macro Recall: 0.7805142534864643
- Micro Recall: 0.8333333333333334
- Weighted Recall: 0.8333333333333334
## 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/Jush/autonlp-bp-29016523
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Jush/autonlp-bp-29016523", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Jush/autonlp-bp-29016523", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 1,324 |
Katsiaryna/distilbert-base-uncased-finetuned_9th_auc | [
"LABEL_0"
] | Entry not found | 15 |
Kieran/distilbert-base-uncased-finetuned-cola | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model_index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
metric:
name: Matthews Correlation
type: matthews_correlation
value: 0.9719066462260881
---
<!-- 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 an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1037
- Matthews Correlation: 0.9719
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.2094 | 1.0 | 525 | 0.1069 | 0.9607 |
| 0.0483 | 2.0 | 1050 | 0.0878 | 0.9719 |
| 0.0296 | 3.0 | 1575 | 0.1263 | 0.9664 |
| 0.0108 | 4.0 | 2100 | 0.1037 | 0.9719 |
| 0.0096 | 5.0 | 2625 | 0.1065 | 0.9719 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
| 1,917 |
LilaBoualili/electra-sim-pair | null | At its core it uses an ELECTRA-Base model (google/electra-base-discriminator) fine-tuned on the MS MARCO passage classification task using the Sim-Pair marking strategy that highlights exact term matches between the query and the passage via marker tokens (#). It can be loaded using the TF/AutoModelForSequenceClassification classes but it follows the same classification layer defined for BERT similarly to the TFElectraRelevanceHead in the Capreolus BERT-MaxP implementation.
Refer to our [github repository](https://github.com/BOUALILILila/ExactMatchMarking) for a usage example for ad hoc ranking. | 603 |
Lumos/imdb3 | null | Entry not found | 15 |
Lumos/imdb4 | null | Entry not found | 15 |
M-FAC/bert-tiny-finetuned-mnli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | # BERT-tiny model finetuned with M-FAC
This model is finetuned on MNLI 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 = 1024
dampening = 1e-6
```
## Results
We share the best model out of 5 runs with the following score on MNLI validation set:
```bash
matched_accuracy = 69.55
mismatched_accuracy = 70.58
```
Mean and standard deviation for 5 runs on MNLI validation set:
| | Matched Accuracy | Mismatched Accuracy |
|:----:|:-----------:|:----------:|
| Adam | 65.36 ± 0.13 | 66.78 ± 0.15 |
| M-FAC | 68.28 ± 3.29 | 68.98 ± 3.05 |
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 42 \
--model_name_or_path prajjwal1/bert-tiny \
--task_name mnli \
--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": 1024, "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}
}
```
| 2,836 |
M-FAC/bert-tiny-finetuned-mrpc | null | # BERT-tiny model finetuned with M-FAC
This model is finetuned on MRPC 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 MRPC validation set:
```bash
f1 = 83.12
accuracy = 73.52
```
Mean and standard deviation for 5 runs on MRPC validation set:
| | F1 | Accuracy |
|:----:|:-----------:|:----------:|
| Adam | 81.68 ± 0.33 | 69.90 ± 0.32 |
| M-FAC | 82.77 ± 0.22 | 72.94 ± 0.37 |
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 42 \
--model_name_or_path prajjwal1/bert-tiny \
--task_name mrpc \
--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}
}
```
| 2,785 |
M47Labs/binary_classification_arabic | [
"neg",
"pos"
] | Entry not found | 15 |
Maha/OGBV-gender-indicbert-ta-eacl_finals | null | Entry not found | 15 |
MiBo/RepML | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_11",
"LABEL_12",
"LABEL_13",
"LABEL_14",
"LABEL_15",
"LABEL_16",
"LABEL_17",
"LABEL_18",
"LABEL_19",
"LABEL_2",
"LABEL_20",
"LABEL_21",
"LABEL_22",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8",
"LABEL_9"
] | Entry not found | 15 |
MickyMike/0-GPT2SP-appceleratorstudio | [
"LABEL_0"
] | Entry not found | 15 |
MickyMike/0-GPT2SP-talendesb | [
"LABEL_0"
] | Entry not found | 15 |
MickyMike/00-GPT2SP-mesos-usergrid | [
"LABEL_0"
] | Entry not found | 15 |
MickyMike/00-GPT2SP-mule-mulestudio | [
"LABEL_0"
] | Entry not found | 15 |
MickyMike/00-GPT2SP-usergrid-mesos | [
"LABEL_0"
] | Entry not found | 15 |
MickyMike/1-GPT2SP-duracloud | [
"LABEL_0"
] | Entry not found | 15 |
MickyMike/1-GPT2SP-springxd | [
"LABEL_0"
] | Entry not found | 15 |
MickyMike/11-GPT2SP-appceleratorstudio-titanium | [
"LABEL_0"
] | Entry not found | 15 |
MickyMike/2-GPT2SP-talenddataquality | [
"LABEL_0"
] | Entry not found | 15 |
MickyMike/222-GPT2SP-appceleratorstudio-mulestudio | [
"LABEL_0"
] | Entry not found | 15 |
MickyMike/6-GPT2SP-datamanagement | [
"LABEL_0"
] | Entry not found | 15 |
MickyMike/6-GPT2SP-titanium | [
"LABEL_0"
] | Entry not found | 15 |
MickyMike/666-GPT2SP-appceleratorstudio-mulestudio | [
"LABEL_0"
] | Entry not found | 15 |
MickyMike/666-GPT2SP-talendesb-mesos | [
"LABEL_0"
] | Entry not found | 15 |
MickyMike/7-GPT2SP-clover | [
"LABEL_0"
] | Entry not found | 15 |
MickyMike/7-GPT2SP-datamanagement | [
"LABEL_0"
] | Entry not found | 15 |
MickyMike/7-GPT2SP-duracloud | [
"LABEL_0"
] | Entry not found | 15 |
MickyMike/777-GPT2SP-mulestudio-titanium | [
"LABEL_0"
] | Entry not found | 15 |
MickyMike/777-GPT2SP-talenddataquality-aptanastudio | [
"LABEL_0"
] | Entry not found | 15 |
Omar95farag/distilbert-base-uncased-distilled-clinc | [
"accept_reservations",
"account_blocked",
"alarm",
"application_status",
"apr",
"are_you_a_bot",
"balance",
"bill_balance",
"bill_due",
"book_flight",
"book_hotel",
"calculator",
"calendar",
"calendar_update",
"calories",
"cancel",
"cancel_reservation",
"car_rental",
"card_declin... | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9332258064516129
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1259
- Accuracy: 0.9332
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 0.5952 | 0.7355 |
| 0.7663 | 2.0 | 636 | 0.3130 | 0.8742 |
| 0.7663 | 3.0 | 954 | 0.2024 | 0.9206 |
| 0.3043 | 4.0 | 1272 | 0.1590 | 0.9235 |
| 0.181 | 5.0 | 1590 | 0.1378 | 0.9303 |
| 0.181 | 6.0 | 1908 | 0.1287 | 0.9329 |
| 0.1468 | 7.0 | 2226 | 0.1259 | 0.9332 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
| 2,014 |
Ritvik/nlp_model | null | Entry not found | 15 |
SCORE/claim2-distilbert-base-uncased | null | Entry not found | 15 |
SCORE/claim3b-distilbert-base-uncased | null | Entry not found | 15 |
SEISHIN/distilbert-base-uncased-finetuned-mnli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.82190524707081
---
<!-- 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-mnli
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: 0.6560
- Accuracy: 0.8219
## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.5161 | 1.0 | 24544 | 0.5025 | 0.8037 |
| 0.4176 | 2.0 | 49088 | 0.5274 | 0.8131 |
| 0.3154 | 3.0 | 73632 | 0.5348 | 0.8194 |
| 0.2294 | 4.0 | 98176 | 0.6560 | 0.8219 |
| 0.1827 | 5.0 | 122720 | 0.8190 | 0.8203 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
| 1,880 |
Sebb/german-nli-base-thesis | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
SetFit/deberta-v3-large__sst2__train-16-0 | [
"negative",
"positive"
] | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large__sst2__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. -->
# deberta-v3-large__sst2__train-16-0
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: 0.9917
- Accuracy: 0.7705
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7001 | 1.0 | 7 | 0.7327 | 0.2857 |
| 0.6326 | 2.0 | 14 | 0.6479 | 0.5714 |
| 0.5232 | 3.0 | 21 | 0.5714 | 0.5714 |
| 0.3313 | 4.0 | 28 | 0.6340 | 0.7143 |
| 0.3161 | 5.0 | 35 | 0.6304 | 0.7143 |
| 0.0943 | 6.0 | 42 | 0.4719 | 0.8571 |
| 0.0593 | 7.0 | 49 | 0.5000 | 0.7143 |
| 0.0402 | 8.0 | 56 | 0.3530 | 0.8571 |
| 0.0307 | 9.0 | 63 | 0.3499 | 0.8571 |
| 0.0033 | 10.0 | 70 | 0.3258 | 0.8571 |
| 0.0021 | 11.0 | 77 | 0.3362 | 0.8571 |
| 0.0012 | 12.0 | 84 | 0.4591 | 0.8571 |
| 0.0036 | 13.0 | 91 | 0.4661 | 0.8571 |
| 0.001 | 14.0 | 98 | 0.5084 | 0.8571 |
| 0.0017 | 15.0 | 105 | 0.5844 | 0.8571 |
| 0.0005 | 16.0 | 112 | 0.6645 | 0.8571 |
| 0.002 | 17.0 | 119 | 0.7422 | 0.8571 |
| 0.0006 | 18.0 | 126 | 0.7354 | 0.8571 |
| 0.0005 | 19.0 | 133 | 0.7265 | 0.8571 |
| 0.0005 | 20.0 | 140 | 0.7207 | 0.8571 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
| 2,588 |
SetFit/deberta-v3-large__sst2__train-32-1 | [
"negative",
"positive"
] | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large__sst2__train-32-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-large__sst2__train-32-1
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: 0.4201
- Accuracy: 0.8759
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7162 | 1.0 | 13 | 0.6832 | 0.5385 |
| 0.6561 | 2.0 | 26 | 0.7270 | 0.4615 |
| 0.4685 | 3.0 | 39 | 1.0674 | 0.5385 |
| 0.2837 | 4.0 | 52 | 1.0841 | 0.5385 |
| 0.1129 | 5.0 | 65 | 0.3502 | 0.9231 |
| 0.0118 | 6.0 | 78 | 0.4829 | 0.9231 |
| 0.0022 | 7.0 | 91 | 0.7430 | 0.8462 |
| 0.0007 | 8.0 | 104 | 0.8219 | 0.8462 |
| 0.0005 | 9.0 | 117 | 0.8787 | 0.8462 |
| 0.0003 | 10.0 | 130 | 0.8713 | 0.8462 |
| 0.0003 | 11.0 | 143 | 0.8473 | 0.8462 |
| 0.0002 | 12.0 | 156 | 0.8482 | 0.8462 |
| 0.0002 | 13.0 | 169 | 0.8494 | 0.8462 |
| 0.0002 | 14.0 | 182 | 0.8638 | 0.8462 |
| 0.0002 | 15.0 | 195 | 0.8492 | 0.8462 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
| 2,278 |
SetFit/deberta-v3-large__sst2__train-8-1 | [
"negative",
"positive"
] | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large__sst2__train-8-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-large__sst2__train-8-1
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: 0.7020
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6773 | 1.0 | 3 | 0.7822 | 0.25 |
| 0.6587 | 2.0 | 6 | 0.8033 | 0.25 |
| 0.693 | 3.0 | 9 | 0.8101 | 0.25 |
| 0.5979 | 4.0 | 12 | 1.1235 | 0.25 |
| 0.4095 | 5.0 | 15 | 1.3563 | 0.25 |
| 0.2836 | 6.0 | 18 | 1.5325 | 0.5 |
| 0.1627 | 7.0 | 21 | 1.7786 | 0.25 |
| 0.0956 | 8.0 | 24 | 2.0067 | 0.5 |
| 0.0535 | 9.0 | 27 | 2.3351 | 0.5 |
| 0.0315 | 10.0 | 30 | 2.6204 | 0.5 |
| 0.0182 | 11.0 | 33 | 2.8483 | 0.5 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
| 2,028 |
SetFit/deberta-v3-large__sst2__train-8-5 | [
"negative",
"positive"
] | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large__sst2__train-8-5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-large__sst2__train-8-5
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: 1.3078
- Accuracy: 0.6930
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6813 | 1.0 | 3 | 0.7842 | 0.25 |
| 0.6617 | 2.0 | 6 | 0.7968 | 0.25 |
| 0.6945 | 3.0 | 9 | 0.7746 | 0.25 |
| 0.5967 | 4.0 | 12 | 0.7557 | 0.25 |
| 0.4824 | 5.0 | 15 | 0.6920 | 0.25 |
| 0.3037 | 6.0 | 18 | 0.6958 | 0.5 |
| 0.2329 | 7.0 | 21 | 0.6736 | 0.5 |
| 0.1441 | 8.0 | 24 | 0.3749 | 1.0 |
| 0.0875 | 9.0 | 27 | 0.3263 | 0.75 |
| 0.0655 | 10.0 | 30 | 0.3525 | 0.75 |
| 0.0373 | 11.0 | 33 | 0.1993 | 1.0 |
| 0.0173 | 12.0 | 36 | 0.1396 | 1.0 |
| 0.0147 | 13.0 | 39 | 0.0655 | 1.0 |
| 0.0084 | 14.0 | 42 | 0.0343 | 1.0 |
| 0.0049 | 15.0 | 45 | 0.0225 | 1.0 |
| 0.004 | 16.0 | 48 | 0.0167 | 1.0 |
| 0.003 | 17.0 | 51 | 0.0134 | 1.0 |
| 0.0027 | 18.0 | 54 | 0.0114 | 1.0 |
| 0.002 | 19.0 | 57 | 0.0104 | 1.0 |
| 0.0015 | 20.0 | 60 | 0.0099 | 1.0 |
| 0.0014 | 21.0 | 63 | 0.0095 | 1.0 |
| 0.0013 | 22.0 | 66 | 0.0095 | 1.0 |
| 0.0012 | 23.0 | 69 | 0.0091 | 1.0 |
| 0.0011 | 24.0 | 72 | 0.0085 | 1.0 |
| 0.0009 | 25.0 | 75 | 0.0081 | 1.0 |
| 0.001 | 26.0 | 78 | 0.0077 | 1.0 |
| 0.0008 | 27.0 | 81 | 0.0074 | 1.0 |
| 0.0009 | 28.0 | 84 | 0.0071 | 1.0 |
| 0.0007 | 29.0 | 87 | 0.0068 | 1.0 |
| 0.0008 | 30.0 | 90 | 0.0064 | 1.0 |
| 0.0007 | 31.0 | 93 | 0.0062 | 1.0 |
| 0.0007 | 32.0 | 96 | 0.0059 | 1.0 |
| 0.0007 | 33.0 | 99 | 0.0056 | 1.0 |
| 0.0005 | 34.0 | 102 | 0.0054 | 1.0 |
| 0.0006 | 35.0 | 105 | 0.0053 | 1.0 |
| 0.0008 | 36.0 | 108 | 0.0051 | 1.0 |
| 0.0007 | 37.0 | 111 | 0.0050 | 1.0 |
| 0.0007 | 38.0 | 114 | 0.0049 | 1.0 |
| 0.0006 | 39.0 | 117 | 0.0048 | 1.0 |
| 0.0005 | 40.0 | 120 | 0.0048 | 1.0 |
| 0.0005 | 41.0 | 123 | 0.0048 | 1.0 |
| 0.0005 | 42.0 | 126 | 0.0047 | 1.0 |
| 0.0005 | 43.0 | 129 | 0.0047 | 1.0 |
| 0.0005 | 44.0 | 132 | 0.0047 | 1.0 |
| 0.0006 | 45.0 | 135 | 0.0047 | 1.0 |
| 0.0005 | 46.0 | 138 | 0.0047 | 1.0 |
| 0.0005 | 47.0 | 141 | 0.0047 | 1.0 |
| 0.0006 | 48.0 | 144 | 0.0047 | 1.0 |
| 0.0005 | 49.0 | 147 | 0.0047 | 1.0 |
| 0.0005 | 50.0 | 150 | 0.0047 | 1.0 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
| 4,446 |
SetFit/distilbert-base-uncased__sst2__train-32-3 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__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__sst2__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: 0.5694
- Accuracy: 0.7073
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7118 | 1.0 | 13 | 0.6844 | 0.5385 |
| 0.6587 | 2.0 | 26 | 0.6707 | 0.6154 |
| 0.6067 | 3.0 | 39 | 0.6295 | 0.5385 |
| 0.4714 | 4.0 | 52 | 0.5811 | 0.6923 |
| 0.2444 | 5.0 | 65 | 0.5932 | 0.7692 |
| 0.1007 | 6.0 | 78 | 0.7386 | 0.6923 |
| 0.0332 | 7.0 | 91 | 0.6962 | 0.6154 |
| 0.0147 | 8.0 | 104 | 0.8200 | 0.7692 |
| 0.0083 | 9.0 | 117 | 0.9250 | 0.7692 |
| 0.0066 | 10.0 | 130 | 0.9345 | 0.7692 |
| 0.005 | 11.0 | 143 | 0.9313 | 0.7692 |
| 0.0036 | 12.0 | 156 | 0.9356 | 0.7692 |
| 0.0031 | 13.0 | 169 | 0.9395 | 0.7692 |
| 0.0029 | 14.0 | 182 | 0.9504 | 0.7692 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
| 2,231 |
SetFit/distilbert-base-uncased__sst2__train-8-4 | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__sst2__train-8-4
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: 0.6921
- Accuracy: 0.5107
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7163 | 1.0 | 3 | 0.7100 | 0.25 |
| 0.6785 | 2.0 | 6 | 0.7209 | 0.25 |
| 0.6455 | 3.0 | 9 | 0.7321 | 0.25 |
| 0.6076 | 4.0 | 12 | 0.7517 | 0.25 |
| 0.5593 | 5.0 | 15 | 0.7780 | 0.25 |
| 0.5202 | 6.0 | 18 | 0.7990 | 0.25 |
| 0.4967 | 7.0 | 21 | 0.8203 | 0.25 |
| 0.4158 | 8.0 | 24 | 0.8497 | 0.25 |
| 0.3997 | 9.0 | 27 | 0.8638 | 0.25 |
| 0.3064 | 10.0 | 30 | 0.8732 | 0.25 |
| 0.2618 | 11.0 | 33 | 0.8669 | 0.25 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
| 2,043 |
SetFit/distilbert-base-uncased__subj__train-8-5 | [
"objective",
"subjective"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__subj__train-8-5
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: 0.6927
- Accuracy: 0.506
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7102 | 1.0 | 3 | 0.6790 | 0.75 |
| 0.6693 | 2.0 | 6 | 0.6831 | 0.75 |
| 0.6438 | 3.0 | 9 | 0.6876 | 0.75 |
| 0.6047 | 4.0 | 12 | 0.6970 | 0.75 |
| 0.547 | 5.0 | 15 | 0.7065 | 0.75 |
| 0.4885 | 6.0 | 18 | 0.7114 | 0.75 |
| 0.4601 | 7.0 | 21 | 0.7147 | 0.5 |
| 0.4017 | 8.0 | 24 | 0.7178 | 0.5 |
| 0.3474 | 9.0 | 27 | 0.7145 | 0.5 |
| 0.2624 | 10.0 | 30 | 0.7153 | 0.5 |
| 0.2175 | 11.0 | 33 | 0.7158 | 0.5 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
| 2,042 |
Shuvam/autonlp-college_classification-164469 | [
"0",
"1"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Shuvam/autonlp-data-college_classification
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 164469
## Validation Metrics
- Loss: 0.05527503043413162
- Accuracy: 0.9853049228508449
- Precision: 0.991044776119403
- Recall: 0.9793510324483776
- AUC: 0.9966895139869654
- F1: 0.9851632047477745
## 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/Shuvam/autonlp-college_classification-164469
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Shuvam/autonlp-college_classification-164469", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Shuvam/autonlp-college_classification-164469", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 1,107 |
TehranNLP-org/bert-base-uncased-avg-sst2-2e-5-21 | null | Entry not found | 15 |
TehranNLP-org/bert-base-uncased-avg-sst2-2e-5-63 | null | Entry not found | 15 |
TehranNLP-org/electra-base-avg-mnli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Tejas3/distillbert_base_uncased_80_all | [
"NEGATIVE",
"NEUTRAL",
"POSITIVE"
] | Entry not found | 15 |
TransQuest/monotransquest-da-et_en-wiki | [
"LABEL_0"
] | ---
language: et-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-et_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 |
TransQuest/monotransquest-hter-en_de-it-smt | [
"LABEL_0"
] | ---
language: en-de
tags:
- Quality Estimation
- monotransquest
- hter
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-hter-en_de-it-smt", 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}
}
```
| 5,407 |
Vasanth/tamil-sentiment-distilbert | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tamilmixsentiment
metrics:
- accuracy
model_index:
- name: tamil-sentiment-distilbert
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tamilmixsentiment
type: tamilmixsentiment
args: default
metric:
name: Accuracy
type: accuracy
value: 0.665
---
<!-- 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. -->
# tamil-sentiment-distilbert
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tamilmixsentiment dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0230
- Accuracy: 0.665
## Dataset Information
- text: Tamil-English code-mixed comment.
- label: list of the possible sentiments
- LABEL_0: "Positive",
- LABEL_1: "Negative",
- LABEL_2: "Mixed_feelings",
- LABEL_3: "unknown_state",
- LABEL_4: "not-Tamil"
## Intended uses & limitations
This model was just created for doing classification task on tamilmixsentiment dataset
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0442 | 1.0 | 250 | 0.9883 | 0.674 |
| 0.9227 | 2.0 | 500 | 0.9782 | 0.673 |
| 0.7591 | 3.0 | 750 | 1.0230 | 0.665 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
| 1,936 |
VincentC12/sentiment_analysis_kara | null | ---
language:
- en
library_name: pytorch
metrics:
- negative
- positive
tags:
- sentiment-analysis
widget:
- text: "Thank you for listening to the recommendations of the telephone team for teleworking. we have a strong expertise in this field and accurate listening to Our management!!!!"
example_title: "Exemple positif"
- text: "working conditions and wages are less than average more part of the time it is not a hierarchical system Our opinion counts"
example_title: "Exemple négatif"
---
Ce modèle est développé pour KARA.
Ce modèle est :
- Un outil d'analyse de sentiment associé à un commentaire de sondage RH
- Entrainé pour être utilisé en ANGLAIS (les commentaires doivent êtres traduits)
- Spécialisé pour des commentaires entre 10 et 512 charactères
Ce modèle n'est pas :
- Utilisable pour détecter un discours haineux ou bien une lettre de suicide
Étiquettes :
- Label_0 = Négatif
- Label_1 = Positif
version 1.1.0
Performances sur le jeux de données du HRM : 91.5% de précision
| 1,028 |
XSY/albert-base-v2-fakenews-discriminator | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: albert-base-v2-fakenews-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. -->
# albert-base-v2-fakenews-discriminator
The dataset: Fake and real news dataset https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset
I use title and label to train the classifier
label_0 : Fake news
label_1 : Real news
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0910
- Accuracy: 0.9758
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0452 | 1.0 | 1768 | 0.0910 | 0.9758 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
| 1,590 |
XSY/albert-base-v2-scarcasm-discriminator | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: albert-base-v2-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. -->
# albert-base-v2-scarcasm-discriminator
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2379
- Accuracy: 0.8996
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2111 | 1.0 | 2179 | 0.2379 | 0.8996 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Tokenizers 0.10.3
| 1,377 |
aXhyra/demo_emotion_1234567 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: demo_emotion_1234567
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: emotion
metrics:
- name: F1
type: f1
value: 0.7348035780583043
---
<!-- 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_emotion_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: 0.9818
- F1: 0.7348
## 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: 7.551070618629693e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 | 204 | 0.7431 | 0.6530 |
| No log | 2.0 | 408 | 0.6943 | 0.7333 |
| 0.5176 | 3.0 | 612 | 0.8456 | 0.7326 |
| 0.5176 | 4.0 | 816 | 0.9818 | 0.7348 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,769 |
aXhyra/demo_sentiment_31415 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: demo_sentiment_31415
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: sentiment
metrics:
- name: F1
type: f1
value: 0.7113620044371958
---
<!-- 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_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: 0.6332
- 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
| 1,770 |
aXhyra/irony_trained_42 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: irony_trained_42
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: irony
metrics:
- name: F1
type: f1
value: 0.6785912258473235
---
<!-- 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_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: 1.5669
- F1: 0.6786
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6669 | 1.0 | 716 | 0.6291 | 0.6198 |
| 0.5655 | 2.0 | 1432 | 0.7332 | 0.6771 |
| 0.3764 | 3.0 | 2148 | 1.4193 | 0.6554 |
| 0.229 | 4.0 | 2864 | 1.5669 | 0.6786 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,759 |
aXhyra/presentation_hate_1234567 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: presentation_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: 0.7679568806891273
---
<!-- 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_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: 0.8438
- F1: 0.7680
## 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: 5.436235805743952e-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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6027 | 1.0 | 282 | 0.5186 | 0.7209 |
| 0.3537 | 2.0 | 564 | 0.4989 | 0.7619 |
| 0.0969 | 3.0 | 846 | 0.6405 | 0.7697 |
| 0.0514 | 4.0 | 1128 | 0.8438 | 0.7680 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,782 |
aXhyra/presentation_sentiment_1234567 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: presentation_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: 0.71829420028644
---
<!-- 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_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: 1.0860
- F1: 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: 7.2792011721188e-06
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.3747 | 1.0 | 11404 | 0.6515 | 0.7045 |
| 0.6511 | 2.0 | 22808 | 0.7334 | 0.7188 |
| 0.0362 | 3.0 | 34212 | 0.9498 | 0.7195 |
| 1.0576 | 4.0 | 45616 | 1.0860 | 0.7183 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,791 |
aXhyra/presentation_sentiment_31415 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: presentation_sentiment_31415
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: sentiment
metrics:
- name: F1
type: f1
value: 0.71829420028644
---
<!-- 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_sentiment_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: 1.0860
- F1: 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: 7.2792011721188e-06
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.3747 | 1.0 | 11404 | 0.6515 | 0.7045 |
| 0.6511 | 2.0 | 22808 | 0.7334 | 0.7188 |
| 0.0362 | 3.0 | 34212 | 0.9498 | 0.7195 |
| 1.0576 | 4.0 | 45616 | 1.0860 | 0.7183 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,787 |
aXhyra/sentiment_temp | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
aXhyra/sentiment_trained_1234567 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: sentiment_trained_1234567
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: sentiment
metrics:
- name: F1
type: f1
value: 0.7165064254565859
---
<!-- 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_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: 1.2854
- F1: 0.7165
## 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.6603 | 1.0 | 11404 | 0.7020 | 0.6992 |
| 0.5978 | 2.0 | 22808 | 0.8024 | 0.7151 |
| 0.5495 | 3.0 | 34212 | 1.0837 | 0.7139 |
| 0.4026 | 4.0 | 45616 | 1.2854 | 0.7165 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,792 |
aXhyra/sentiment_trained_42 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: sentiment_trained_42
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: sentiment
metrics:
- name: F1
type: f1
value: 0.7131935389791447
---
<!-- 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_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: 1.3194
- F1: 0.7132
## 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.6405 | 1.0 | 11404 | 0.6631 | 0.7046 |
| 0.5998 | 2.0 | 22808 | 0.8429 | 0.7102 |
| 0.5118 | 3.0 | 34212 | 1.0906 | 0.7155 |
| 0.3745 | 4.0 | 45616 | 1.3194 | 0.7132 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,777 |
aXhyra/test-model | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | Entry not found | 15 |
adamlin/ml999_grinding_wheel | [
"0",
"1"
] | Entry not found | 15 |
aditeyabaral/finetuned-iitp_pdt_review-additionalpretrained-bert-base-cased | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
aditeyabaral/finetuned-iitp_pdt_review-additionalpretrained-distilbert-base-cased | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
aditeyabaral/finetuned-iitp_pdt_review-bert-hinglish-big | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
aditeyabaral/finetuned-iitp_pdt_review-distilbert-hinglish-big | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
aditeyabaral/finetuned-iitp_pdt_review-indic-bert | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
aditeyabaral/finetuned-sail2017-bert-base-cased | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
aditeyabaral/finetuned-sail2017-xlm-roberta-base | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
ageron/distilbert-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | Entry not found | 15 |
ardauzunoglu/gp-classification | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: gp-classification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gp-classification
This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0013
- Accuracy: 0.9997
- F1: 0.9997
## 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.0215 | 1.0 | 956 | 0.0051 | 0.9987 | 0.9987 |
| 0.0033 | 2.0 | 1912 | 0.0088 | 0.9984 | 0.9985 |
| 0.001 | 3.0 | 2868 | 0.0036 | 0.9995 | 0.9995 |
| 0.0005 | 4.0 | 3824 | 0.0012 | 0.9997 | 0.9997 |
| 0.0 | 5.0 | 4780 | 0.0013 | 0.9997 | 0.9997 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
| 1,675 |
ashraq/dv-electra-small-news-classification | [
"ރާއްޖެ",
"ކުޅިވަރު",
"ވިޔަފާރި",
"މުނިފޫހިފިލުވުން",
"ދީނީ",
"ދުނިޔެ",
"ސިޔާސީ",
"ޓެކްނޮލޮޖީ"
] | ---
widget:
- text: 'ގޫގަލް ޕިކްސަލް 6 ގެ ކެމެރާ، އޭއައި ގެ ޖާދޫއިން ފުރިފައި'
---
# The [ELECTRA-small](https://huggingface.co/ashraq/dv-electra-small) fine-tuned for news classification in Dhivehi | 199 |
astarostap/autonlp-antisemitism-2-21194454 | [
"0",
"1"
] | ---
tags: autonlp
language: en
widget:
- text: "the jews have a lot of power"
datasets:
- astarostap/autonlp-data-antisemitism-2
co2_eq_emissions: 2.0686690092905224
---
# Description
This model takes a tweet with the word "jew" in it, and determines if it's antisemitic.
Training data:
This model was trained on 4k tweets, where ~50% were labeled as antisemitic.
I labeled them myself based on personal experience and knowledge about common antisemitic tropes.
Note:
The goal for this model is not to be used as a final say on what is or is not antisemitic, but rather as a first pass on what might be antisemitic and should be reviewed by human experts.
Please keep in mind that I'm not an expert on antisemitism or hatespeech.
Whether something is antisemitic or not depends on the context, as for any hate speech, and everyone has a different definition for what is hate speech.
If you would like to collaborate on antisemitism detection, please feel free to contact me at starosta@alumni.stanford.edu
This model is not ready for production, it needs more evaluation and more training data.
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 21194454
- CO2 Emissions (in grams): 2.0686690092905224
- Dataset: https://huggingface.co/datasets/astarostap/autonlp-data-antisemitism-2
## Validation Metrics
- Loss: 0.5291365385055542
- Accuracy: 0.7572692793931732
- Precision: 0.7126948775055679
- Recall: 0.835509138381201
- AUC: 0.8185826549941126
- F1: 0.7692307692307693
## 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/astarostap/autonlp-antisemitism-2-21194454
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("astarostap/autonlp-antisemitism-2-21194454", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("astarostap/autonlp-antisemitism-2-21194454", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 2,212 |
avneet/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: 0.42176824452830747
---
<!-- 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: 0.4981
- Matthews Correlation: 0.4218
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5248 | 1.0 | 535 | 0.4981 | 0.4218 |
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
| 1,702 |
benjaminbeilharz/distilbert-dailydialog-turn-classifier | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | Entry not found | 15 |
beomi/beep-KR-Medium-hate | [
"hate",
"none",
"offensive"
] | Entry not found | 15 |
berkergurcay/finetuned-bert-base-uncased | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
blizrys/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa-1 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- null
metrics:
- accuracy
model-index:
- name: BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa-1
results:
- task:
name: Text Classification
type: text-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.7
---
<!-- 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. -->
# BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa-1
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6660
- Accuracy: 0.7
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 57 | 0.8471 | 0.58 |
| No log | 2.0 | 114 | 0.8450 | 0.58 |
| No log | 3.0 | 171 | 0.7846 | 0.58 |
| No log | 4.0 | 228 | 0.8649 | 0.58 |
| No log | 5.0 | 285 | 0.7220 | 0.68 |
| No log | 6.0 | 342 | 0.7395 | 0.66 |
| No log | 7.0 | 399 | 0.7198 | 0.72 |
| No log | 8.0 | 456 | 0.6417 | 0.72 |
| 0.7082 | 9.0 | 513 | 0.6265 | 0.74 |
| 0.7082 | 10.0 | 570 | 0.6660 | 0.7 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.0
- Tokenizers 0.10.3
| 2,231 |
bob1966/distilbert-base-uncased-finetuned-cola | null | Entry not found | 15 |
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