modelId stringlengths 6 107 | label list | readme stringlengths 0 56.2k | readme_len int64 0 56.2k |
|---|---|---|---|
adamlin/ml999_power_stacker | [
"0",
"1"
] | Entry not found | 15 |
addy88/perceiver_imdb | [
"neg",
"pos"
] | ### How to use
Here is how to use this model in PyTorch:
```python
from transformers import PerceiverTokenizer, PerceiverForMaskedLM
tokenizer = PerceiverTokenizer.from_pretrained("addy88/perceiver_imdb")
model = PerceiverForMaskedLM.from_pretrained("addy88/perceiver_imdb")
text = "This is an incomplete sentence where some words are missing."
# prepare input
encoding = tokenizer(text, padding="max_length", return_tensors="pt")
# mask " missing.". Note that the model performs much better if the masked span starts with a space.
encoding.input_ids[0, 52:61] = tokenizer.mask_token_id
inputs, input_mask = encoding.input_ids.to(device), encoding.attention_mask.to(device)
# forward pass
outputs = model(inputs=inputs, attention_mask=input_mask)
logits = outputs.logits
masked_tokens_predictions = logits[0, 51:61].argmax(dim=-1)
print(tokenizer.decode(masked_tokens_predictions))
>>> should print " missing."
``` | 914 |
aditeyabaral/finetuned-iitp_pdt_review-additionalpretrained-xlm-roberta-base | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
aditeyabaral/finetuned-iitp_pdt_review-bert-hinglish-small | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
aditeyabaral/finetuned-iitp_pdt_review-xlm-roberta-large | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
aditeyabaral/finetuned-sail2017-additionalpretrained-bert-base-cased | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
aditeyabaral/finetuned-sail2017-additionalpretrained-roberta-base | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
aditeyabaral/finetuned-sail2017-indic-bert | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
aloxatel/AVG | null | Entry not found | 15 |
alvp/autonlp-alberti-stanza-names-34318169 | [
"cantar",
"chamberga",
"copla_arte_mayor",
"copla_arte_menor",
"copla_castellana",
"copla_mixta",
"copla_real",
"couplet",
"cuaderna_vía",
"cuarteta",
"cuarteto",
"cuarteto_lira",
"décima_antigua",
"endecha_real",
"espinela",
"estrofa_francisco_de_la_torre",
"estrofa_manriqueña",
"... | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- alvp/autonlp-data-alberti-stanza-names
co2_eq_emissions: 8.612473981829835
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 34318169
- CO2 Emissions (in grams): 8.612473981829835
## Validation Metrics
- Loss: 1.3520570993423462
- Accuracy: 0.6083916083916084
- Macro F1: 0.5420169617715481
- Micro F1: 0.6083916083916084
- Weighted F1: 0.5963328136975058
- Macro Precision: 0.5864033493660455
- Micro Precision: 0.6083916083916084
- Weighted Precision: 0.6364793882921277
- Macro Recall: 0.5545405576555766
- Micro Recall: 0.6083916083916084
- Weighted Recall: 0.6083916083916084
## 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/alvp/autonlp-alberti-stanza-names-34318169
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("alvp/autonlp-alberti-stanza-names-34318169", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("alvp/autonlp-alberti-stanza-names-34318169", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 1,397 |
anditya/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: 0.8885
- Mae: 0.4390
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1089 | 1.0 | 235 | 0.9027 | 0.4756 |
| 0.9674 | 2.0 | 470 | 0.8885 | 0.4390 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
| 1,429 |
anirudh21/bert-base-uncased-finetuned-mrpc | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert-base-uncased-finetuned-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.7916666666666666
- name: F1
type: f1
value: 0.8590381426202321
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-mrpc
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6645
- Accuracy: 0.7917
- F1: 0.8590
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 63 | 0.5387 | 0.7402 | 0.8349 |
| No log | 2.0 | 126 | 0.5770 | 0.7696 | 0.8513 |
| No log | 3.0 | 189 | 0.5357 | 0.7574 | 0.8223 |
| No log | 4.0 | 252 | 0.6645 | 0.7917 | 0.8590 |
| No log | 5.0 | 315 | 0.6977 | 0.7721 | 0.8426 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.10.3
| 1,987 |
anirudh21/bert-base-uncased-finetuned-rte | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-base-uncased-finetuned-rte
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: rte
metrics:
- name: Accuracy
type: accuracy
value: 0.6642599277978339
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-rte
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8075
- Accuracy: 0.6643
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 63 | 0.6777 | 0.5668 |
| No log | 2.0 | 126 | 0.6723 | 0.6282 |
| No log | 3.0 | 189 | 0.7238 | 0.6318 |
| No log | 4.0 | 252 | 0.7993 | 0.6354 |
| No log | 5.0 | 315 | 0.8075 | 0.6643 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.10.3
| 1,841 |
anirudh21/distilbert-base-uncased-finetuned-mrpc | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8455882352941176
- name: F1
type: f1
value: 0.8958677685950412
---
<!-- 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-mrpc
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: 0.3830
- Accuracy: 0.8456
- F1: 0.8959
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 230 | 0.3826 | 0.8186 | 0.8683 |
| No log | 2.0 | 460 | 0.3830 | 0.8456 | 0.8959 |
| 0.4408 | 3.0 | 690 | 0.3835 | 0.8382 | 0.8866 |
| 0.4408 | 4.0 | 920 | 0.5036 | 0.8431 | 0.8919 |
| 0.1941 | 5.0 | 1150 | 0.5783 | 0.8431 | 0.8930 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
| 2,011 |
anirudh21/electra-base-discriminator-finetuned-rte | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: electra-base-discriminator-finetuned-rte
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: rte
metrics:
- name: Accuracy
type: accuracy
value: 0.8231046931407943
---
<!-- 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. -->
# electra-base-discriminator-finetuned-rte
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4793
- Accuracy: 0.8231
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 156 | 0.6076 | 0.6570 |
| No log | 2.0 | 312 | 0.4824 | 0.7762 |
| No log | 3.0 | 468 | 0.4793 | 0.8231 |
| 0.4411 | 4.0 | 624 | 0.7056 | 0.7906 |
| 0.4411 | 5.0 | 780 | 0.6849 | 0.8159 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
| 1,891 |
anirudh21/electra-base-discriminator-finetuned-wnli | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: electra-base-discriminator-finetuned-wnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: wnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5633802816901409
---
<!-- 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. -->
# electra-base-discriminator-finetuned-wnli
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6893
- Accuracy: 0.5634
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 0.6893 | 0.5634 |
| No log | 2.0 | 80 | 0.7042 | 0.4225 |
| No log | 3.0 | 120 | 0.7008 | 0.3803 |
| No log | 4.0 | 160 | 0.6998 | 0.5634 |
| No log | 5.0 | 200 | 0.7016 | 0.5352 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
| 1,894 |
anirudh21/xlnet-base-cased-finetuned-wnli | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: xlnet-base-cased-finetuned-wnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: wnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5633802816901409
---
<!-- 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. -->
# xlnet-base-cased-finetuned-wnli
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6874
- Accuracy: 0.5634
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 0.7209 | 0.5352 |
| No log | 2.0 | 80 | 0.6874 | 0.5634 |
| No log | 3.0 | 120 | 0.6908 | 0.5634 |
| No log | 4.0 | 160 | 0.6987 | 0.4930 |
| No log | 5.0 | 200 | 0.6952 | 0.5634 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
| 1,833 |
arjunth2001/priv_ftc | [
"Data Retention",
"Data Security",
"Do Not Track",
"First Party Collection/Use",
"International and Specific Audiences",
"Other",
"Policy Change",
"Third Party Sharing/Collection",
"User Access, Edit and Deletion",
"User Choice/Control"
] | Entry not found | 15 |
benjaminbeilharz/distilbert-base-uncased-empatheticdialogues-sentiment-classifier | [
"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_26",
"LABEL_27",
"LABEL_28",
"LABEL_29",... | Entry not found | 15 |
beomi/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: 0.5552849676135797
---
<!-- 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: 0.7525
- Matthews Correlation: 0.5553
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.523 | 1.0 | 535 | 0.5024 | 0.4160 |
| 0.3437 | 2.0 | 1070 | 0.5450 | 0.4965 |
| 0.2326 | 3.0 | 1605 | 0.6305 | 0.5189 |
| 0.177 | 4.0 | 2140 | 0.7525 | 0.5553 |
| 0.1354 | 5.0 | 2675 | 0.8630 | 0.5291 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
| 1,999 |
blizrys/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa-2 | [
"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-2
results:
- task:
name: Text Classification
type: text-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.54
---
<!-- 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-2
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: 1.0005
- Accuracy: 0.54
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.003
- 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 57 | 1.3510 | 0.54 |
| No log | 2.0 | 114 | 0.9606 | 0.54 |
| No log | 3.0 | 171 | 0.9693 | 0.54 |
| No log | 4.0 | 228 | 1.0445 | 0.54 |
| No log | 5.0 | 285 | 1.0005 | 0.54 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
| 1,922 |
blizrys/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: 0.5373623427702773
---
<!-- 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: 0.6223
- Matthews Correlation: 0.5374
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5275 | 1.0 | 535 | 0.5456 | 0.3973 |
| 0.3481 | 2.0 | 1070 | 0.5401 | 0.5006 |
| 0.242 | 3.0 | 1605 | 0.6223 | 0.5374 |
| 0.1725 | 4.0 | 2140 | 0.7934 | 0.5229 |
| 0.1346 | 5.0 | 2675 | 0.8478 | 0.5367 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
| 1,999 |
blizrys/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: 0.8205807437595517
---
<!-- 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: 0.6753
- Accuracy: 0.8206
## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.5146 | 1.0 | 24544 | 0.4925 | 0.8049 |
| 0.4093 | 2.0 | 49088 | 0.5090 | 0.8164 |
| 0.3122 | 3.0 | 73632 | 0.5299 | 0.8185 |
| 0.2286 | 4.0 | 98176 | 0.6753 | 0.8206 |
| 0.182 | 5.0 | 122720 | 0.8372 | 0.8195 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
| 1,881 |
boronbrown48/sentiment_neutral_from_other_v2 | null | Entry not found | 15 |
boronbrown48/sentiment_others_v1 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | Entry not found | 15 |
boychaboy/SNLI_bert-base-uncased | [
"contradiction",
"entailment",
"neutral"
] | Entry not found | 15 |
bshlgrs/autonlp-old-data-trained-10022181 | [
"No",
"Unsure",
"Yes"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- bshlgrs/autonlp-data-old-data-trained
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 10022181
## Validation Metrics
- Loss: 0.369505375623703
- Accuracy: 0.8706206896551724
- Macro F1: 0.5410226656476808
- Micro F1: 0.8706206896551724
- Weighted F1: 0.8515634683886795
- Macro Precision: 0.5159711665622992
- Micro Precision: 0.8706206896551724
- Weighted Precision: 0.8346991124101657
- Macro Recall: 0.5711653346601209
- Micro Recall: 0.8706206896551724
- Weighted Recall: 0.8706206896551724
## 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/bshlgrs/autonlp-old-data-trained-10022181
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("bshlgrs/autonlp-old-data-trained-10022181", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("bshlgrs/autonlp-old-data-trained-10022181", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 1,309 |
candra/indo-headline-similarity | null | Entry not found | 15 |
chenqian/bert_finetuning_test | [
"LABEL_0",
"LABEL_1"
] | Entry not found | 15 |
chisadi/nice-distilbert-v2 | [
"NICE_1",
"NICE_10",
"NICE_11",
"NICE_12",
"NICE_13",
"NICE_14",
"NICE_15",
"NICE_16",
"NICE_17",
"NICE_18",
"NICE_19",
"NICE_2",
"NICE_20",
"NICE_21",
"NICE_22",
"NICE_23",
"NICE_24",
"NICE_25",
"NICE_26",
"NICE_27",
"NICE_28",
"NICE_29",
"NICE_3",
"NICE_30",
"NICE_3... | ### Distibert model finetuned on the task of classifying product descriptions to one of 45 broad [NICE classifications](https://www.wipo.int/classifications/nice/en/)
| 167 |
clem/autonlp-test3-2101782 | [
"not_urgent",
"urgent"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- clem/autonlp-data-test3
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 2101782
## Validation Metrics
- Loss: 0.015991805121302605
- Accuracy: 1.0
- Precision: 1.0
- Recall: 1.0
- AUC: 1.0
- F1: 1.0
## 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/clem/autonlp-test3-2101782
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("clem/autonlp-test3-2101782", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("clem/autonlp-test3-2101782", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 962 |
d4niel92/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: 0.8976
- Mae: 0.4268
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.092 | 1.0 | 235 | 0.9514 | 0.5122 |
| 0.9509 | 2.0 | 470 | 0.8976 | 0.4268 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
| 1,429 |
diegozs97/finetuned-sciie-seed-0-100k | [
"COMPARE",
"CONJUNCTION",
"EVALUATE-FOR",
"FEATURE-OF",
"HYPONYM-OF",
"PART-OF",
"USED-FOR"
] | Entry not found | 15 |
diegozs97/finetuned-sciie-seed-4-100k | [
"COMPARE",
"CONJUNCTION",
"EVALUATE-FOR",
"FEATURE-OF",
"HYPONYM-OF",
"PART-OF",
"USED-FOR"
] | Entry not found | 15 |
dtam/autonlp-covid-fake-news-36839110 | [
"0",
"1"
] | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- dtam/autonlp-data-covid-fake-news
co2_eq_emissions: 123.79523392848652
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 36839110
- CO2 Emissions (in grams): 123.79523392848652
## Validation Metrics
- Loss: 0.17188367247581482
- Accuracy: 0.9714953271028037
- Precision: 0.9917948717948718
- Recall: 0.9480392156862745
- AUC: 0.9947452731092438
- F1: 0.9694235588972432
## 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/dtam/autonlp-covid-fake-news-36839110
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("dtam/autonlp-covid-fake-news-36839110", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("dtam/autonlp-covid-fake-news-36839110", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 1,165 |
edwardgowsmith/bert-base-cased-best | null | Entry not found | 15 |
gbade786/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: 0.923
- name: F1
type: f1
value: 0.9233262687967644
---
<!-- 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: 0.2180
- Accuracy: 0.923
- F1: 0.9233
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8217 | 1.0 | 250 | 0.3137 | 0.903 | 0.8999 |
| 0.2484 | 2.0 | 500 | 0.2180 | 0.923 | 0.9233 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
| 1,805 |
google/tapas-mini-finetuned-tabfact | null | ---
language: en
tags:
- tapas
- sequence-classification
license: apache-2.0
datasets:
- tab_fact
---
# TAPAS mini model fine-tuned on Tabular Fact Checking (TabFact)
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_tabfact_inter_masklm_mini_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training, and then fine-tuned on [TabFact](https://github.com/wenhuchen/Table-Fact-Checking). It uses relative position embeddings by default (i.e. resetting the position index at every cell of the table).
The other (non-default) version which can be used is the one with absolute position embeddings:
- `no_reset`, which corresponds to `tapas_tabfact_inter_masklm_mini`
Disclaimer: The team releasing TAPAS did not write a model card for this model so this model card has been written by
the Hugging Face team and contributors.
## Model description
TAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion.
This means it was pretrained on the raw tables and associated texts only, with no humans labelling them in any way (which is why it
can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
was pretrained with two objectives:
- Masked language modeling (MLM): taking a (flattened) table and associated context, the model randomly masks 15% of the words in
the input, then runs the entire (partially masked) sequence through the model. The model then has to predict the masked words.
This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other,
or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional
representation of a table and associated text.
- Intermediate pre-training: to encourage numerical reasoning on tables, the authors additionally pre-trained the model by creating
a balanced dataset of millions of syntactically created training examples. Here, the model must predict (classify) whether a sentence
is supported or refuted by the contents of a table. The training examples are created based on synthetic as well as counterfactual statements.
This way, the model learns an inner representation of the English language used in tables and associated texts, which can then be used
to extract features useful for downstream tasks such as answering questions about a table, or determining whether a sentence is entailed
or refuted by the contents of a table. Fine-tuning is done by adding a classification head on top of the pre-trained model, and then
jointly train this randomly initialized classification head with the base model on TabFact.
## Intended uses & limitations
You can use this model for classifying whether a sentence is supported or refuted by the contents of a table.
For code examples, we refer to the documentation of TAPAS on the HuggingFace website.
## Training procedure
### Preprocessing
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
then of the form:
```
[CLS] Sentence [SEP] Flattened table [SEP]
```
### Fine-tuning
The model was fine-tuned on 32 Cloud TPU v3 cores for 80,000 steps with maximum sequence length 512 and batch size of 512.
In this setup, fine-tuning takes around 14 hours. The optimizer used is Adam with a learning rate of 2e-5, and a warmup
ratio of 0.05. See the [paper](https://arxiv.org/abs/2010.00571) for more details (appendix A2).
### BibTeX entry and citation info
```bibtex
@misc{herzig2020tapas,
title={TAPAS: Weakly Supervised Table Parsing via Pre-training},
author={Jonathan Herzig and Paweł Krzysztof Nowak and Thomas Müller and Francesco Piccinno and Julian Martin Eisenschlos},
year={2020},
eprint={2004.02349},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
```
```bibtex
@misc{eisenschlos2020understanding,
title={Understanding tables with intermediate pre-training},
author={Julian Martin Eisenschlos and Syrine Krichene and Thomas Müller},
year={2020},
eprint={2010.00571},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@inproceedings{2019TabFactA,
title={TabFact : A Large-scale Dataset for Table-based Fact Verification},
author={Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou and William Yang Wang},
booktitle = {International Conference on Learning Representations (ICLR)},
address = {Addis Ababa, Ethiopia},
month = {April},
year = {2020}
}
``` | 4,867 |
gooohjy/suicidal-electra | null | # Suicidal-ELECTRA
This text classification model predicts whether a sequence of words are suicidal (1) or non-suicidal (0).
## Data
The model was trained on the [Suicide and Depression Dataset](https://www.kaggle.com/nikhileswarkomati/suicide-watch) obtained from Kaggle. The dataset was scraped from Reddit and consists of 232,074 rows equally distributed between 2 classes - suicide and non-suicide.
## Parameters
The model fine-tuning was conducted on 1 epoch, with batch size of 6, and learning rate of 0.00001. Due to limited computing resources and time, we were unable to scale up the number of epochs and batch size.
## Performance
The model has achieved the following results after fine-tuning on the aforementioned dataset:
- Accuracy: 0.9792
- Recall: 0.9788
- Precision: 0.9677
- F1 Score: 0.9732
## How to Use
Load the model via the transformers library:
```
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("gooohjy/suicidal-electra")
model = AutoModel.from_pretrained("gooohjy/suicidal-electra")
```
## Resources
For more resources, including the source code, please refer to the GitHub repository [gohjiayi/suicidal-text-detection](https://github.com/gohjiayi/suicidal-text-detection/). | 1,251 |
hiiamsid/BETO_es_binary_classification | null | ---
language:
- es
tags:
- es
- ticket classification
license: "apache-2.0"
datasets:
- self made to classify whether text is related to technology or not.
metrics:
- fscore
- accuracy
- precision
- recall
---
# BETO(cased)
This model was built using pytorch.
## Model description
Input for the model: Any spanish text
Output for the model: Sentiment. (0 - Negative, 1 - Positive(i.e. technology relate))
#### How to use
Here is how to use this model to get the features of a given text in *PyTorch*:
```python
# You can include sample code which will be formatted
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("hiiamsid/BETO_es_binary_classification")
model = AutoModelForSequenceClassification.from_pretrained("hiiamsid/BETO_es_binary_classification")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
## Training procedure
I trained on the dataset on the [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased).
| 1,115 |
ikevin98/bert-base-uncased-finetuned-sst2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model_index:
- name: bert-base-uncased-finetuned-sst2
results:
- dataset:
name: glue
type: glue
args: sst2
metric:
name: Accuracy
type: accuracy
value: 0.926605504587156
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-sst2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2716
- Accuracy: 0.9266
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1666 | 1.0 | 2105 | 0.2403 | 0.9232 |
| 0.1122 | 2.0 | 4210 | 0.2716 | 0.9266 |
| 0.0852 | 3.0 | 6315 | 0.3150 | 0.9232 |
| 0.056 | 4.0 | 8420 | 0.3209 | 0.9163 |
| 0.0344 | 5.0 | 10525 | 0.3740 | 0.9243 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.8.1
- Datasets 1.11.0
- Tokenizers 0.10.1
| 1,767 |
l3cube-pune/hate-roberta-hasoc-hindi | null | ---
language: hi
tags:
- roberta
license: cc-by-4.0
datasets:
- HASOC 2021
widget:
- text: "I like you. </s></s> I love you."
---
## hate-roberta-hasoc-hindi
hate-roberta-hasoc-hindi is a binary hate speech model fine-tuned on Hindi Hasoc Hate Speech Dataset 2021.
The label mappings are 0 -> None, 1 -> Hate.
More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2110.12200)
```
@article{velankar2021hate,
title={Hate and Offensive Speech Detection in Hindi and Marathi},
author={Velankar, Abhishek and Patil, Hrushikesh and Gore, Amol and Salunke, Shubham and Joshi, Raviraj},
journal={arXiv preprint arXiv:2110.12200},
year={2021}
}
``` | 707 |
lewtun/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: 0.8850
- Mae: 0.4390
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1589 | 1.0 | 235 | 0.9769 | 0.5122 |
| 0.974 | 2.0 | 470 | 0.8850 | 0.4390 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
| 1,429 |
lvargas/distilbert-base-uncased-finetuned-emotion2 | [
"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-emotion2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.903
- name: F1
type: f1
value: 0.9003235459489749
---
<!-- 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-emotion2
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: 0.3623
- Accuracy: 0.903
- F1: 0.9003
## 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: 128
- eval_batch_size: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 125 | 0.5960 | 0.8025 | 0.7750 |
| 0.7853 | 2.0 | 250 | 0.3623 | 0.903 | 0.9003 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.10.3
| 1,809 |
m3hrdadfi/albert-fa-base-v2-clf-digimag | [
"بازی ویدیویی",
"راهنمای خرید",
"سلامت و زیبایی",
"علم و تکنولوژی",
"عمومی",
"هنر و سینما",
"کتاب و ادبیات"
] | ---
language: fa
license: apache-2.0
---
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Version 2.0 over various writing styles from numerous subjects (e.g., scientific, novels, news) with more than 3.9M documents, 73M sentences, and 1.3B words, like the way we did for ParsBERT.
Please follow the [ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) repo for the latest information about previous and current models.
## Persian Text Classification [DigiMag, Persian News]
The task target is labeling texts in a supervised manner in both existing datasets `DigiMag` and `Persian News`.
### DigiMag
A total of 8,515 articles scraped from [Digikala Online Magazine](https://www.digikala.com/mag/). This dataset includes seven different classes.
1. Video Games
2. Shopping Guide
3. Health Beauty
4. Science Technology
5. General
6. Art Cinema
7. Books Literature
| Label | # |
|:------------------:|:----:|
| Video Games | 1967 |
| Shopping Guide | 125 |
| Health Beauty | 1610 |
| Science Technology | 2772 |
| General | 120 |
| Art Cinema | 1667 |
| Books Literature | 254 |
**Download**
You can download the dataset from [here](https://drive.google.com/uc?id=1YgrCYY-Z0h2z0-PfWVfOGt1Tv0JDI-qz)
## Results
The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.
| Dataset | ALBERT-fa-base-v2 | ParsBERT-v1 | mBERT |
|:-----------------:|:-----------------:|:-----------:|:-----:|
| Digikala Magazine | 92.33 | 93.59 | 90.72 |
### BibTeX entry and citation info
Please cite in publications as the following:
```bibtex
@misc{ALBERTPersian,
author = {Mehrdad Farahani},
title = {ALBERT-Persian: A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/m3hrdadfi/albert-persian}},
}
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Questions?
Post a Github issue on the [ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) repo. | 2,648 |
m3hrdadfi/albert-fa-base-v2-sentiment-binary | [
"Negative",
"Positive"
] | ---
language: fa
license: apache-2.0
---
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Version 2.0 over various writing styles from numerous subjects (e.g., scientific, novels, news) with more than 3.9M documents, 73M sentences, and 1.3B words, like the way we did for ParsBERT.
Please follow the [ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) repo for the latest information about previous and current models.
## Persian Sentiment [Digikala, SnappFood, DeepSentiPers]
It aims to classify text, such as comments, based on their emotional bias. We tested three well-known datasets for this task: `Digikala` user comments, `SnappFood` user comments, and `DeepSentiPers` in two binary-form and multi-form types.
## Results
The model obtained an F1 score of 87.56% for a composition of all three datasets into a binary-labels `Negative` and `Positive`.
### BibTeX entry and citation info
Please cite in publications as the following:
```bibtex
@misc{ALBERTPersian,
author = {Mehrdad Farahani},
title = {ALBERT-Persian: A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/m3hrdadfi/albert-persian}},
}
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Questions?
Post a Github issue on the [ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) repo. | 1,928 |
mrm8488/bert-tiny-finetuned-fake-news-detection | null | ---
language: en
widget:
- text: "It s official the inmates are running the asylum A police department in Northampton, Massachusetts is ending its High-Five Friday program at local elementary schools due to concerns that undocumented children and others may feel uncomfortable seeing an officer at school.The program, started by the Northampton Police Department in December, had officers stand outside of a school each Friday morning to high-five students as they walked in to begin the day. WFBToday was High-5 Friday at Bridge St School! Thanks to everyone who participated! The kids and officers all had fun! #highfiveHere are a few tweets that were sent out by the NPD highlighting their high-five program with kids:Today was High-5 Friday at Bridge St School! Thanks to everyone who participated! The kids and officers all had fun! #highfive pic.twitter.com/Trz0yoW3Qh Northampton Police (@NorthamptonPD) December 9, 2016Today was High-Five Friday! Thanks to Jackson St School for hosting! We hope that everyone had a great time! Happy Friday!! #highfive pic.twitter.com/MWY6JBlHlK Northampton Police (@NorthamptonPD) January 6, 2017Here is part of their Facebook explanation for doing away with the high-five program:This is the same Northampton Police Department by the way, that celebrated the great turn-out for the nasty women march that was really about protesting Trump and defending abortion. Does it make you feel any safer when you see a police department bragging about their promotion of lawless liberal politics?"
---
# BERT Tiny fine-tuned for fake news detection | 1,597 |
mujeensung/albert-base-v2_mnli_bc | [
"contradiction",
"entailment",
"neutral"
] | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: albert-base-v2_mnli_bc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.9398776667163956
---
<!-- 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_mnli_bc
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2952
- Accuracy: 0.9399
## 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: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2159 | 1.0 | 16363 | 0.2268 | 0.9248 |
| 0.1817 | 2.0 | 32726 | 0.2335 | 0.9347 |
| 0.0863 | 3.0 | 49089 | 0.3014 | 0.9401 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.1+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
| 1,764 |
navteca/quora-roberta-large | [
"LABEL_0"
] | ---
datasets:
- quora
language: en
license: mit
pipeline_tag: text-classification
tags:
- roberta
- text-classification
---
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
This model uses [roberta-large](https://huggingface.co/roberta-large).
## Training Data
This model was trained on the [Quora Duplicate Questions](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) dataset.
The model will predict a score between 0 and 1: How likely the two given questions are duplicates.
Note: The model is not suitable to estimate the similarity of questions, e.g. the two questions "How to learn Java" and "How to learn Python" will result in a rahter low score, as these are not duplicates.
## Usage and Performance
The trained model can be used like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
scores = model.predict([('Question 1', 'Question 2'), ('Question 3', 'Question 4')])
print(scores)
```
| 1,155 |
nbouali/flaubert-base-uncased-finetuned-cooking | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7"
] | ---
language: fr
tags:
- text-classification
- flaubert
- french
- flaubert-base-uncased
widget:
- text: "Lasagnes à la bolognaise"
---
# FlauBERT finetuned on French cooking recipes
This model is finetuned on a sequence classification task that associates each sequence with the appropriate recipe category.
### How to use it?
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import TextClassificationPipeline
loaded_tokenizer = AutoTokenizer.from_pretrained("nbouali/flaubert-base-uncased-finetuned-cooking")
loaded_model = AutoModelForSequenceClassification.from_pretrained("nbouali/flaubert-base-uncased-finetuned-cooking")
nlp = TextClassificationPipeline(model=loaded_model,tokenizer=loaded_tokenizer,task="Recipe classification")
print(nlp("Lasagnes à la bolognaise"))
```
```
[{'label': 'LABEL_6', 'score': 0.9921900033950806}]
```
### Label encoding:
| label | Recipe Category |
|:------:|:--------------:|
| 0 |'Accompagnement' |
| 1 | 'Amuse-gueule' |
| 2 | 'Boisson' |
| 3 | 'Confiserie' |
| 4 | 'Dessert'|
| 5 | 'Entrée' |
| 6 |'Plat principal' |
| 7 | 'Sauce' |
<br/>
<br/>
> If you would like to know more about this model you can refer to [our blog post](https://medium.com/unify-data-office/a-cooking-language-model-fine-tuned-on-dozens-of-thousands-of-french-recipes-bcdb8e560571) | 1,399 |
persiannlp/mbert-base-parsinlu-entailment | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
language:
- fa
- multilingual
thumbnail: https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg
tags:
- entailment
- parsbert
- persian
- farsi
license: cc-by-nc-sa-4.0
datasets:
- parsinlu
metrics:
- accuracy
---
# Textual Entailment (مدل برای پاسخ به استلزام منطقی)
This is a model for textual entailment problems.
Here is an example of how you can run this model:
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import numpy as np
labels = ["entails", "contradicts", "neutral"]
model_name_or_path = "persiannlp/mbert-base-parsinlu-entailment"
model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path,)
def model_predict(text_a, text_b):
features = tokenizer( [(text_a, text_b)], padding="max_length", truncation=True, return_tensors='pt')
output = model(**features)
logits = output[0]
probs = torch.nn.functional.softmax(logits, dim=1).tolist()
idx = np.argmax(np.array(probs))
print(labels[idx], probs)
model_predict(
"این مسابقات بین آوریل و دسامبر در هیپودروم ولیفندی در نزدیکی باکرکی ، ۱۵ کیلومتری (۹ مایل) غرب استانبول برگزار می شود.",
"در ولیفندی هیپودروم، مسابقاتی از آوریل تا دسامبر وجود دارد."
)
model_predict(
"آیا کودکانی وجود دارند که نیاز به سرگرمی دارند؟",
"هیچ کودکی هرگز نمی خواهد سرگرم شود.",
)
model_predict(
"ما به سفرهایی رفته ایم که در نهرهایی شنا کرده ایم",
"علاوه بر استحمام در نهرها ، ما به اسپا ها و سونا ها نیز رفته ایم."
)
```
For more details, visit this page: https://github.com/persiannlp/parsinlu/
| 1,636 |
sackoh/bert-base-multilingual-cased-nsmc | null | Entry not found | 15 |
sismetanin/rubert_conversational-ru-sentiment-sentirueval2016 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
socialmediaie/TRAC2020_IBEN_B_bert-base-multilingual-uncased | null | # Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.
Our trained models as well as evaluation metrics during traing are available at: https://databank.illinois.edu/datasets/IDB-8882752#
We also make a few of our models available in HuggingFace's models repository at https://huggingface.co/socialmediaie/, these models can be further fine-tuned on your dataset of choice.
Our approach is described in our paper titled:
> Mishra, Sudhanshu, Shivangi Prasad, and Shubhanshu Mishra. 2020. "Multilingual Joint Fine-Tuning of Transformer Models for Identifying Trolling, Aggression and Cyberbullying at TRAC 2020." In Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying (TRAC-2020).
The source code for training this model and more details can be found on our code repository: https://github.com/socialmediaie/TRAC2020
NOTE: These models are retrained for uploading here after our submission so the evaluation measures may be slightly different from the ones reported in the paper.
If you plan to use the dataset please cite the following resources:
* Mishra, Sudhanshu, Shivangi Prasad, and Shubhanshu Mishra. 2020. "Multilingual Joint Fine-Tuning of Transformer Models for Identifying Trolling, Aggression and Cyberbullying at TRAC 2020." In Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying (TRAC-2020).
* Mishra, Shubhanshu, Shivangi Prasad, and Shubhanshu Mishra. 2020. “Trained Models for Multilingual Joint Fine-Tuning of Transformer Models for Identifying Trolling, Aggression and Cyberbullying at TRAC 2020.” University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-8882752_V1.
```
@inproceedings{Mishra2020TRAC,
author = {Mishra, Sudhanshu and Prasad, Shivangi and Mishra, Shubhanshu},
booktitle = {Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying (TRAC-2020)},
title = {{Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020}},
year = {2020}
}
@data{illinoisdatabankIDB-8882752,
author = {Mishra, Shubhanshu and Prasad, Shivangi and Mishra, Shubhanshu},
doi = {10.13012/B2IDB-8882752_V1},
publisher = {University of Illinois at Urbana-Champaign},
title = {{Trained models for Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020}},
url = {https://doi.org/10.13012/B2IDB-8882752{\_}V1},
year = {2020}
}
```
## Usage
The models can be used via the following code:
```python
from transformers import AutoModel, AutoTokenizer, AutoModelForSequenceClassification
import torch
from pathlib import Path
from scipy.special import softmax
import numpy as np
import pandas as pd
TASK_LABEL_IDS = {
"Sub-task A": ["OAG", "NAG", "CAG"],
"Sub-task B": ["GEN", "NGEN"],
"Sub-task C": ["OAG-GEN", "OAG-NGEN", "NAG-GEN", "NAG-NGEN", "CAG-GEN", "CAG-NGEN"]
}
model_version="databank" # other option is hugging face library
if model_version == "databank":
# Make sure you have downloaded the required model file from https://databank.illinois.edu/datasets/IDB-8882752
# Unzip the file at some model_path (we are using: "databank_model")
model_path = next(Path("databank_model").glob("./*/output/*/model"))
# Assuming you get the following type of structure inside "databank_model"
# 'databank_model/ALL/Sub-task C/output/bert-base-multilingual-uncased/model'
lang, task, _, base_model, _ = model_path.parts
tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForSequenceClassification.from_pretrained(model_path)
else:
lang, task, base_model = "ALL", "Sub-task C", "bert-base-multilingual-uncased"
base_model = f"socialmediaie/TRAC2020_{lang}_{lang.split()[-1]}_{base_model}"
tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForSequenceClassification.from_pretrained(base_model)
# For doing inference set model in eval mode
model.eval()
# If you want to further fine-tune the model you can reset the model to model.train()
task_labels = TASK_LABEL_IDS[task]
sentence = "This is a good cat and this is a bad dog."
processed_sentence = f"{tokenizer.cls_token} {sentence}"
tokens = tokenizer.tokenize(sentence)
indexed_tokens = tokenizer.convert_tokens_to_ids(tokens)
tokens_tensor = torch.tensor([indexed_tokens])
with torch.no_grad():
logits, = model(tokens_tensor, labels=None)
logits
preds = logits.detach().cpu().numpy()
preds_probs = softmax(preds, axis=1)
preds = np.argmax(preds_probs, axis=1)
preds_labels = np.array(task_labels)[preds]
print(dict(zip(task_labels, preds_probs[0])), preds_labels)
"""You should get an output as follows:
({'CAG-GEN': 0.06762535,
'CAG-NGEN': 0.03244293,
'NAG-GEN': 0.6897794,
'NAG-NGEN': 0.15498641,
'OAG-GEN': 0.034373745,
'OAG-NGEN': 0.020792078},
array(['NAG-GEN'], dtype='<U8'))
"""
``` | 5,074 |
sukhendrasingh/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: 0.8733333333333333
- name: F1
type: f1
value: 0.879746835443038
---
<!-- 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: 0.3323
- Accuracy: 0.8733
- F1: 0.8797
## 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 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
| 1,521 |
textattack/distilbert-base-uncased-SST-2 | null | Entry not found | 15 |
timtarusov/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: 0.9211076096482195
---
<!-- 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: 0.2274
- Accuracy: 0.921
- F1: 0.9211
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8308 | 1.0 | 250 | 0.3319 | 0.8955 | 0.8897 |
| 0.2516 | 2.0 | 500 | 0.2274 | 0.921 | 0.9211 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,805 |
yigitbekir/turkish-bert-uncased-sentiment | null | Entry not found | 15 |
yoshitomo-matsubara/bert-large-uncased-mnli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
language: en
tags:
- bert
- mnli
- ax
- glue
- torchdistill
license: apache-2.0
datasets:
- mnli
- ax
metrics:
- accuracy
---
`bert-large-uncased` fine-tuned on MNLI dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb).
The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available [here](https://github.com/yoshitomo-matsubara/torchdistill/blob/main/configs/sample/glue/mnli/ce/bert_large_uncased.yaml).
I submitted prediction files to [the GLUE leaderboard](https://gluebenchmark.com/leaderboard), and the overall GLUE score was **80.2**.
| 838 |
DoyyingFace/bert-asian-hate-tweets-self-clean | null | Entry not found | 15 |
inovex/multi2convai-corona-en-bert | [
"corona.traffic",
"corona.supplies",
"corona.quarantine",
"corona.masks",
"corona.illness",
"corona.package",
"corona.vaccine",
"corona.rumors",
"corona.risk",
"corona.course",
"corona.symptoms",
"corona.patients",
"corona.deathRate",
"corona.infect",
"corona.protect",
"corona.definiti... | ---
tags:
- text-classification
- pytorch
- transformers
widget:
- text: "Do I need to wear a mask?"
license: mit
language: en
---
# Multi2ConvAI-Corona: finetuned Bert for English
This model was developed in the [Multi2ConvAI](https://multi2conv.ai) project:
- domain: Corona (more details about our use cases: ([en](https://multi2convai/en/blog/use-cases), [de](https://multi2convai/en/blog/use-cases)))
- language: English (en)
- model type: finetuned Bert
## How to run
Requires:
- Huggingface transformers
### Run with Huggingface Transformers
````python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("inovex/multi2convai-logistics-en-bert")
model = AutoModelForSequenceClassification.from_pretrained("inovex/multi2convai-logistics-en-bert")
````
## Further information on Multi2ConvAI:
- https://multi2conv.ai
- https://github.com/inovex/multi2convai
- mailto: info@multi2conv.ai | 1,002 |
DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-12 | null | Entry not found | 15 |
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-100 | null | Entry not found | 15 |
DoyyingFace/bert-asian-hate-tweets-asian-unclean-slanted | null | Entry not found | 15 |
DoyyingFace/bert-asian-hate-tweets-self-unclean-small | null | Entry not found | 15 |
DoyyingFace/bert-asian-hate-tweets-self-clean-small-epoch5-freeze4 | null | Entry not found | 15 |
ali2066/finetuned_sentence_itr0_3e-05_essays_27_02_2022-19_35_56 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_3e-05_essays_27_02_2022-19_35_56
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. -->
# finetuned_sentence_itr0_3e-05_essays_27_02_2022-19_35_56
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3767
- Accuracy: 0.8638
- F1: 0.9165
## 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: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 81 | 0.4489 | 0.8309 | 0.8969 |
| No log | 2.0 | 162 | 0.4429 | 0.8272 | 0.8915 |
| No log | 3.0 | 243 | 0.5154 | 0.8529 | 0.9083 |
| No log | 4.0 | 324 | 0.5552 | 0.8309 | 0.8925 |
| No log | 5.0 | 405 | 0.5896 | 0.8309 | 0.8940 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
| 1,794 |
BAHIJA/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: 0.5481326292844919
---
<!-- 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: 0.7371
- Matthews Correlation: 0.5481
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5298 | 1.0 | 535 | 0.5333 | 0.4142 |
| 0.3619 | 2.0 | 1070 | 0.5174 | 0.5019 |
| 0.2449 | 3.0 | 1605 | 0.6394 | 0.4921 |
| 0.1856 | 4.0 | 2140 | 0.7371 | 0.5481 |
| 0.133 | 5.0 | 2675 | 0.8600 | 0.5327 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
| 2,000 |
DoyyingFace/bert-asian-hate-tweets-self-unclean-large-epoch5 | null | Entry not found | 15 |
DoyyingFace/bert-asian-hate-tweets-self-unclean-focus | null | Entry not found | 15 |
daisyxie21/bert-base-uncased-8-200-0.01 | null | Entry not found | 15 |
Someshfengde/autonlp-kaggledays-625717992 | [
"association",
"disagreement",
"unbiased"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Someshfengde/autonlp-data-kaggledays
co2_eq_emissions: 28.622267513847273
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 625717992
- CO2 Emissions (in grams): 28.622267513847273
## Validation Metrics
- Loss: 0.8782362937927246
- Accuracy: 0.6022282660559214
- Macro F1: 0.6024258279848015
- Micro F1: 0.6022282660559214
- Weighted F1: 0.6024299908624371
- Macro Precision: 0.604093172183357
- Micro Precision: 0.6022282660559214
- Weighted Precision: 0.6041166306778806
- Macro Recall: 0.6022424576798522
- Micro Recall: 0.6022282660559214
- Weighted Recall: 0.6022282660559214
## 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/Someshfengde/autonlp-kaggledays-625717992
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Someshfengde/autonlp-kaggledays-625717992", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Someshfengde/autonlp-kaggledays-625717992", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 1,393 |
waboucay/camembert-base-finetuned-xnli_fr | [
"contradiction",
"non-contradiction"
] | ---
language:
- fr
tags:
- nli
metrics:
- f1
---
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 89.2 | 87.6 |
| test | 88.9 | 87.4 |
| 368 |
quincyqiang/distilbert-base-uncased-finetuned-emotion | [
"sadness",
"joy",
"love",
"anger",
"fear",
"surprise"
] | ---
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: 0.927
- name: F1
type: f1
value: 0.927273630943427
---
<!-- 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: 0.2106
- Accuracy: 0.927
- F1: 0.9273
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8007 | 1.0 | 250 | 0.2955 | 0.914 | 0.9117 |
| 0.2417 | 2.0 | 500 | 0.2106 | 0.927 | 0.9273 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.8.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
| 1,803 |
BAHIJA/bert-base-uncased-finetuned-sst2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-base-uncased-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.9346330275229358
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-sst2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2745
- Accuracy: 0.9346
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1778 | 1.0 | 4210 | 0.3553 | 0.9060 |
| 0.1257 | 2.0 | 8420 | 0.2745 | 0.9346 |
| 0.0779 | 3.0 | 12630 | 0.3272 | 0.9300 |
| 0.0655 | 4.0 | 16840 | 0.3412 | 0.9323 |
| 0.0338 | 5.0 | 21050 | 0.3994 | 0.9300 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
| 1,851 |
moralstories/roberta-large_action-norm | null | ---
license: afl-3.0
---
| 28 |
Rustem/roberta-base-trained-50k-docs | null | ---
license: apache-2.0
---
| 31 |
aytugkaya/distilbert-base-uncased-finetuned-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-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9148387096774193
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7760
- Accuracy: 0.9148
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2994 | 1.0 | 318 | 3.3016 | 0.7442 |
| 2.6387 | 2.0 | 636 | 1.8892 | 0.8339 |
| 1.5535 | 3.0 | 954 | 1.1602 | 0.8948 |
| 1.0139 | 4.0 | 1272 | 0.8619 | 0.9084 |
| 0.7936 | 5.0 | 1590 | 0.7760 | 0.9148 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.6
| 1,890 |
clisi2000/distilbert-base-uncased-finetuned-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-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9158064516129032
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7796
- Accuracy: 0.9158
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2883 | 1.0 | 318 | 3.2778 | 0.7390 |
| 2.6185 | 2.0 | 636 | 1.8740 | 0.8232 |
| 1.5423 | 3.0 | 954 | 1.1579 | 0.8890 |
| 1.0131 | 4.0 | 1272 | 0.8629 | 0.9077 |
| 0.7964 | 5.0 | 1590 | 0.7796 | 0.9158 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.2+cpu
- Datasets 1.18.4
- Tokenizers 0.10.3
| 1,888 |
dannyvas23/electricidad-small-discriminator-finetuned-clasificacion-texto-suicida | null | ---
license: afl-3.0
language: "es"
tags:
- generated_from_trainer
- sentiment
- emotion
widget:
- text: "La vida no merece la pena"
example_title: "Ejemplo 1"
- text: "Para vivir así lo mejor es estar muerto"
example_title: "Ejemplo 2"
- text: "me siento triste por no poder viajar"
example_title: "Ejemplo 3"
- text: "Quiero terminar con todo"
example_title: "Ejemplo 4"
- text: "Disfruto de la vista"
example_title: "Ejemplo 5"
metrics:
- accuracy
model-index:
- name: electricidad-small-discriminator-finetuned-clasificacion-texto-suicida
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. -->
# electricidad-small-discriminator-finetuned-clasificacion-texto-suicida
This model is a fine-tuned version of [mrm8488/electricidad-small-discriminator](https://huggingface.co/mrm8488/electricidad-small-discriminator) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0458
- Accuracy: 0.9916
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Validation Loss | Accuracy |
|:-------------:|:-----:|:---------------:|:--------:|
| 0.161100 | 1.0 | 0.133057 | 0.952718 |
| 0.134500 | 2.0 | 0.110966 | 0.960804 |
| 0.108500 | 3.0 | 0.086417 | 0.970835 |
| 0.099400 | 4.0 | 0.073618 | 0.974856 |
| 0.090500 | 5.0 | 0.065231 | 0.979629 |
| 0.080700 | 6.0 | 0.060849 | 0.982324 |
| 0.069200 | 7.0 | 0.054718 | 0.986125 |
| 0.060400 | 8.0 | 0.051153 | 0.985948 |
| 0.048200 | 9.0 | 0.045747 | 0.989748 |
| 0.045500 | 10.0 | 0.049992 | 0.988069 |
| 0.043400 | 11.0 | 0.046325 | 0.990234 |
| 0.034300 | 12.0 | 0.050746 | 0.989792 |
| 0.032900 | 13.0 | 0.043434 | 0.991737 |
| 0.028400 | 14.0 | 0.045003 | 0.991869 |
| 0.022300 | 15.0 | 0.045819 | 0.991648 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
| 2,639 |
imyday/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: 0.923
- name: F1
type: f1
value: 0.9233039604362318
---
<!-- 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: 0.2282
- Accuracy: 0.923
- F1: 0.9233
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8344 | 1.0 | 250 | 0.3317 | 0.8995 | 0.8953 |
| 0.2606 | 2.0 | 500 | 0.2282 | 0.923 | 0.9233 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,805 |
SergeyKamenshchikov/nsi_tuned | [
"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_26",
"LABEL_27",
"LABEL_28",
"LABEL_29",... | Entry not found | 15 |
hackathon-pln-es/electricidad-base-generator-fake-news | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: electricidad-base-generator-fake-news
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. -->
# electricidad-base-generator-fake-news
This model is a fine-tuned version of [mrm8488/electricidad-base-generator](https://huggingface.co/mrm8488/electricidad-base-generator) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0067
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1136 | 1.0 | 180 | 0.0852 | 1.0 |
| 0.0267 | 2.0 | 360 | 0.0219 | 1.0 |
| 0.0132 | 3.0 | 540 | 0.0108 | 1.0 |
| 0.0091 | 4.0 | 720 | 0.0075 | 1.0 |
| 0.0077 | 5.0 | 900 | 0.0067 | 1.0 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
| 1,629 |
neibla/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: 0.9255
- name: F1
type: f1
value: 0.9254917237562972
---
<!-- 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: 0.2187
- Accuracy: 0.9255
- F1: 0.9255
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.855 | 1.0 | 250 | 0.3211 | 0.905 | 0.9017 |
| 0.2561 | 2.0 | 500 | 0.2187 | 0.9255 | 0.9255 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,807 |
anjandash/JavaBERT-small | null | ---
language:
- java
license: mit
datasets:
- anjandash/java-8m-methods-v1
--- | 89 |
efederici/cross-encoder-umberto-stsb | [
"LABEL_0"
] | ---
pipeline_tag: text-classification
language:
- it
datasets:
- stsb_multi_mt
tags:
- cross-encoder
- sentence-similarity
- transformers
---
# Cross-Encoder
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
<p align="center">
<img src="https://user-images.githubusercontent.com/7140210/72913702-d55a8480-3d3d-11ea-99fc-f2ef29af4e72.jpg" width="700"> </br>
Marco Lodola, Monument to Umberto Eco, Alessandria 2019
</p>
## Training Data
This model was trained on [stsb](https://huggingface.co/datasets/stsb_multi_mt/viewer/it/train). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.
## Usage and Performance
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('efederici/cross-encoder-umberto-stsb')
scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')])
```
The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`. | 1,106 |
efederici/cross-encoder-bert-base-stsb | [
"LABEL_0"
] | ---
pipeline_tag: text-classification
language:
- it
datasets:
- stsb_multi_mt
tags:
- cross-encoder
- sentence-similarity
- transformers
---
# Cross-Encoder
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
<p align="center">
<img src="https://upload.wikimedia.org/wikipedia/commons/f/f6/Edouard_Vuillard%2C_1920c_-_Sunlit_Interior.jpg" width="400"> </br>
Edouard Vuillard, Sunlit Interior
</p>
## Training Data
This model was trained on [stsb](https://huggingface.co/datasets/stsb_multi_mt/viewer/it/train). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.
## Usage and Performance
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('efederici/cross-encoder-umberto-stsb')
scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')])
```
The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.
| 1,084 |
dapang/distilbert-base-uncased-finetuned-truthful | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-truthful
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-truthful
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4660
- Accuracy: 0.87
- F1: 0.8697
## 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: 9.910294163459086e-05
- train_batch_size: 400
- eval_batch_size: 400
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 9
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 5 | 0.6509 | 0.59 | 0.5780 |
| No log | 2.0 | 10 | 0.4950 | 0.77 | 0.7701 |
| No log | 3.0 | 15 | 0.4787 | 0.81 | 0.8099 |
| No log | 4.0 | 20 | 0.4936 | 0.81 | 0.8096 |
| No log | 5.0 | 25 | 0.4443 | 0.82 | 0.82 |
| No log | 6.0 | 30 | 0.4547 | 0.85 | 0.8497 |
| No log | 7.0 | 35 | 0.4268 | 0.85 | 0.8500 |
| No log | 8.0 | 40 | 0.4790 | 0.87 | 0.8697 |
| No log | 9.0 | 45 | 0.4660 | 0.87 | 0.8697 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.1
- Datasets 2.0.0
- Tokenizers 0.11.0
| 2,018 |
cammiemw/marco-cw09 | [
"LABEL_0"
] | Entry not found | 15 |
btjiong/robbert-twitter-sentiment | [
"NEGATIEF",
"NEUTRAAL",
"POSITIEF"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- dutch_social
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: robbert-twitter-sentiment
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: dutch_social
type: dutch_social
args: dutch_social
metrics:
- name: Accuracy
type: accuracy
value: 0.749
- name: F1
type: f1
value: 0.7491844724992662
- name: Precision
type: precision
value: 0.7493911755249737
- name: Recall
type: recall
value: 0.749
---
<!-- 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. -->
# robbert-twitter-sentiment
This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the dutch_social dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6818
- Accuracy: 0.749
- F1: 0.7492
- Precision: 0.7494
- Recall: 0.749
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.7485 | 1.0 | 188 | 0.7670 | 0.692 | 0.6915 | 0.6920 | 0.692 |
| 0.5202 | 2.0 | 376 | 0.6818 | 0.749 | 0.7492 | 0.7494 | 0.749 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cpu
- Datasets 2.0.0
- Tokenizers 0.12.0
| 2,080 |
Stremie/xlm-roberta-base-clickbait-keywords | null | This model classifies whether a tweet is clickbait or not. It has been trained using [Webis-Clickbait-17](https://webis.de/data/webis-clickbait-17.html) dataset. Input is composed of 'postText' + '[SEP]' + 'targetKeywords'. Achieved ~0.7 F1-score on test data. | 261 |
GioReg/AlbertoBertsentipol | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | ---
tags:
- generated_from_trainer
model-index:
- name: AlbertoBertsentipol
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. -->
# AlbertoBertsentipol
This model is a fine-tuned version of [m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0](https://huggingface.co/m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0) 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: 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 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
| 1,103 |
afbudiman/distilled-indobert-classification | [
"negative",
"neutral",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- accuracy
- f1
model-index:
- name: distilled-indobert-classification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: indonlu
type: indonlu
args: smsa
metrics:
- name: Accuracy
type: accuracy
value: 0.9015873015873016
- name: F1
type: f1
value: 0.9014926755197933
---
<!-- 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. -->
# distilled-indobert-classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the indonlu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6015
- Accuracy: 0.9016
- F1: 0.9015
## 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: 6e-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
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.0427 | 1.0 | 688 | 0.6306 | 0.8683 | 0.8684 |
| 0.5332 | 2.0 | 1376 | 0.5621 | 0.8794 | 0.8779 |
| 0.3021 | 3.0 | 2064 | 0.6785 | 0.8905 | 0.8896 |
| 0.1851 | 4.0 | 2752 | 0.6085 | 0.8968 | 0.8959 |
| 0.1152 | 5.0 | 3440 | 0.6015 | 0.9016 | 0.9015 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
| 2,012 |
optimum/roberta-large-finetuned-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: mit
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: roberta-large-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9729032258064516
---
<!-- 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-large-finetuned-clinc
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1574
- Accuracy: 0.9729
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 239 | 0.8113 | 0.9035 |
| No log | 2.0 | 478 | 0.2364 | 0.9548 |
| 1.7328 | 3.0 | 717 | 0.1760 | 0.9684 |
| 1.7328 | 4.0 | 956 | 0.1565 | 0.9723 |
| 0.0976 | 5.0 | 1195 | 0.1574 | 0.9729 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,837 |
lewtun/sagemaker-distilbert-emotion | [
"anger",
"fear",
"joy",
"love",
"sadness",
"surprise"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: sagemaker-distilbert-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.921
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
config: default
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.921
verified: true
- name: Precision Macro
type: precision
value: 0.8870419502496194
verified: true
- name: Precision Micro
type: precision
value: 0.921
verified: true
- name: Precision Weighted
type: precision
value: 0.9208079974712109
verified: true
- name: Recall Macro
type: recall
value: 0.8688429370077566
verified: true
- name: Recall Micro
type: recall
value: 0.921
verified: true
- name: Recall Weighted
type: recall
value: 0.921
verified: true
- name: F1 Macro
type: f1
value: 0.87642650638535
verified: true
- name: F1 Micro
type: f1
value: 0.9209999999999999
verified: true
- name: F1 Weighted
type: f1
value: 0.9203938811554648
verified: true
- name: loss
type: loss
value: 0.23216550052165985
verified: true
---
<!-- 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. -->
# sagemaker-distilbert-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: 0.2322
- Accuracy: 0.921
## 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: 32
- eval_batch_size: 64
- 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9306 | 1.0 | 500 | 0.2322 | 0.921 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.1
- Datasets 1.15.1
- Tokenizers 0.10.3
| 2,851 |
luquesky/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: 0.934
- name: F1
type: f1
value: 0.9337817808480242
---
<!-- 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: 0.2155
- Accuracy: 0.934
- F1: 0.9338
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.1768 | 1.0 | 250 | 0.1867 | 0.924 | 0.9235 |
| 0.1227 | 2.0 | 500 | 0.1588 | 0.934 | 0.9346 |
| 0.1031 | 3.0 | 750 | 0.1656 | 0.931 | 0.9306 |
| 0.0843 | 4.0 | 1000 | 0.1662 | 0.9395 | 0.9392 |
| 0.0662 | 5.0 | 1250 | 0.1714 | 0.9325 | 0.9326 |
| 0.0504 | 6.0 | 1500 | 0.1821 | 0.934 | 0.9338 |
| 0.0429 | 7.0 | 1750 | 0.2038 | 0.933 | 0.9324 |
| 0.0342 | 8.0 | 2000 | 0.2054 | 0.938 | 0.9379 |
| 0.0296 | 9.0 | 2250 | 0.2128 | 0.9345 | 0.9345 |
| 0.0211 | 10.0 | 2500 | 0.2155 | 0.934 | 0.9338 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
| 2,373 |
dbounds/roberta-large-finetuned-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: mit
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: roberta-large-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9741935483870968
---
<!-- 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-large-finetuned-clinc
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1594
- Accuracy: 0.9742
## 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
- distributed_type: sagemaker_data_parallel
- num_devices: 8
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 5.0651 | 1.0 | 120 | 5.0213 | 0.0065 |
| 4.2482 | 2.0 | 240 | 2.5682 | 0.7997 |
| 1.694 | 3.0 | 360 | 0.6019 | 0.9445 |
| 0.4594 | 4.0 | 480 | 0.2330 | 0.9655 |
| 0.1599 | 5.0 | 600 | 0.1594 | 0.9742 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6
| 2,035 |
lewtun/MiniLMv2-L12-H384-distilled-finetuned-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... | ---
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: MiniLMv2-L12-H384-distilled-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
revision: b189f1fa78f41282a748b673231c21dfb07182b5
metrics:
- name: Accuracy
type: accuracy
value: 0.9529032258064516
verified: false
---
<!-- 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. -->
# MiniLMv2-L12-H384-distilled-finetuned-clinc
This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3058
- Accuracy: 0.9529
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9908 | 1.0 | 239 | 1.6816 | 0.3910 |
| 1.5212 | 2.0 | 478 | 1.2365 | 0.7697 |
| 1.129 | 3.0 | 717 | 0.9209 | 0.8706 |
| 0.8462 | 4.0 | 956 | 0.6978 | 0.9152 |
| 0.6497 | 5.0 | 1195 | 0.5499 | 0.9342 |
| 0.5124 | 6.0 | 1434 | 0.4447 | 0.9445 |
| 0.4196 | 7.0 | 1673 | 0.3797 | 0.9455 |
| 0.3587 | 8.0 | 1912 | 0.3358 | 0.95 |
| 0.3228 | 9.0 | 2151 | 0.3133 | 0.9513 |
| 0.3052 | 10.0 | 2390 | 0.3058 | 0.9529 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6
| 2,398 |
cj-mills/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: 0.9467741935483871
---
<!-- 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: 0.2525
- Accuracy: 0.9468
## 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2246 | 1.0 | 318 | 3.1584 | 0.7545 |
| 2.4033 | 2.0 | 636 | 1.5656 | 0.8652 |
| 1.1684 | 3.0 | 954 | 0.7795 | 0.9161 |
| 0.5693 | 4.0 | 1272 | 0.4653 | 0.9329 |
| 0.3042 | 5.0 | 1590 | 0.3412 | 0.9406 |
| 0.1794 | 6.0 | 1908 | 0.2912 | 0.9403 |
| 0.1184 | 7.0 | 2226 | 0.2654 | 0.9461 |
| 0.0873 | 8.0 | 2544 | 0.2557 | 0.9439 |
| 0.0719 | 9.0 | 2862 | 0.2549 | 0.9465 |
| 0.0646 | 10.0 | 3180 | 0.2525 | 0.9468 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.0.0
- Tokenizers 0.12.1
| 2,233 |
crcb/emo_nojoylove | [
"anger",
"fear",
"sadness",
"surprise"
] | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- crcb/autotrain-data-emo_carer_nojoylove
co2_eq_emissions: 12.236769332727217
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 751422966
- CO2 Emissions (in grams): 12.236769332727217
## Validation Metrics
- Loss: 0.1358409821987152
- Accuracy: 0.9397905759162304
- Macro F1: 0.9096049124431982
- Micro F1: 0.9397905759162304
- Weighted F1: 0.9395954853807672
- Macro Precision: 0.919807346649452
- Micro Precision: 0.9397905759162304
- Weighted Precision: 0.9407259082357824
- Macro Recall: 0.9024000547645126
- Micro Recall: 0.9397905759162304
- Weighted Recall: 0.9397905759162304
## 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/crcb/autotrain-emo_carer_nojoylove-751422966
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("crcb/autotrain-emo_carer_nojoylove-751422966", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("crcb/autotrain-emo_carer_nojoylove-751422966", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,415 |
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