modelId stringlengths 6 107 | label list | readme stringlengths 0 56.2k | readme_len int64 0 56.2k |
|---|---|---|---|
Jeevesh8/bert_ft_cola-14 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-15 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-16 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-17 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-18 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-19 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-22 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-23 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-24 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-25 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-26 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-28 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-29 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-30 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-32 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-33 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-34 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-36 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-37 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-41 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-43 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-44 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-47 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-48 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-50 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-51 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-52 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-53 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-54 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-57 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-58 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-59 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-60 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-61 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-66 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-69 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-72 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-74 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-76 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-78 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-79 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-82 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-84 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-86 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-88 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-89 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-90 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-93 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-95 | null | Entry not found | 15 |
Jeevesh8/bert_ft_cola-98 | null | Entry not found | 15 |
princeton-nlp/CoFi-MRPC-s95 | [
"0",
"1"
] | This is a model checkpoint for "[Structured Pruning Learns Compact and Accurate Models](https://arxiv.org/pdf/2204.00408.pdf)". The model is pruned from `bert-base-uncased` to a 95% sparsity on dataset MRPC. Please go to [our repository](https://github.com/princeton-nlp/CoFiPruning) for more details on how to use the model for inference. Note that you would have to use the model class specified in our repository to load the model. | 434 |
Nakul24/RoBERTa-Goemotions-6 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | Entry not found | 15 |
SreyanG-NVIDIA/bert-base-cased-finetuned-cola | null | Entry not found | 15 |
choondrise/emolve_basic | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7"
] | Entry not found | 15 |
cmcmorrow/distilbert-rater | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-rater
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-rater
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.11.6
| 1,031 |
TinySuitStarfish/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | Entry not found | 15 |
Yarn/autotrain-Traimn-853827191 | [
"business",
"entertainment",
"politics",
"sport",
"tech"
] | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Yarn/autotrain-data-Traimn
co2_eq_emissions: 1.712176860015081
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 853827191
- CO2 Emissions (in grams): 1.712176860015081
## Validation Metrics
- Loss: 0.10257730633020401
- Accuracy: 0.973421926910299
- Macro F1: 0.9735224586288418
- Micro F1: 0.973421926910299
- Weighted F1: 0.9735187934099364
- Macro Precision: 0.9738505933839127
- Micro Precision: 0.973421926910299
- Weighted Precision: 0.9738995774527256
- Macro Recall: 0.9734994306470444
- Micro Recall: 0.973421926910299
- Weighted Recall: 0.973421926910299
## 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/Yarn/autotrain-Traimn-853827191
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Yarn/autotrain-Traimn-853827191", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Yarn/autotrain-Traimn-853827191", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,359 |
EhsanAghazadeh/bert-base-uncased-random-weights-S42 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Pablo94/roberta-base-bne-finetuned-detests | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-bne-finetuned-detests
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned-detests
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0052
- Accuracy: 0.8674
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2876 | 1.0 | 153 | 0.3553 | 0.8445 |
| 0.3309 | 2.0 | 306 | 0.4247 | 0.8216 |
| 0.0679 | 3.0 | 459 | 0.6958 | 0.8494 |
| 0.0007 | 4.0 | 612 | 0.8027 | 0.8445 |
| 0.0003 | 5.0 | 765 | 0.9791 | 0.8511 |
| 0.0002 | 6.0 | 918 | 0.9495 | 0.8642 |
| 0.0002 | 7.0 | 1071 | 0.9742 | 0.8642 |
| 0.0001 | 8.0 | 1224 | 0.9913 | 0.8658 |
| 0.0001 | 9.0 | 1377 | 1.0017 | 0.8674 |
| 0.0001 | 10.0 | 1530 | 1.0052 | 0.8674 |
### Framework versions
- Transformers 4.19.1
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
| 1,935 |
anwesham/autotrain-imdb-sentiment-analysis-864927559 | [
"0",
"1"
] | ---
language: unk
datasets:
- anwesham/autotrain-data-imdb-sentiment-analysis
co2_eq_emissions: 0.2033402242358345
---
- Problem type: Binary Classification
- Model ID: 864927559
- CO2 Emissions (in grams): 0.2033402242358345
## Validation Metrics
- Loss: 0.18383920192718506
- Accuracy: 0.9318
- Precision: 0.9560625264047318
- Recall: 0.9052
- AUC: 0.98281574
- F1: 0.9299363057324841
## 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/anwesham/autotrain-imdb-sentiment-analysis-864927559
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("anwesham/autotrain-imdb-sentiment-analysis-864927559", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("anwesham/autotrain-imdb-sentiment-analysis-864927559", use_auth_token=True)
inputs = tokenizer("I love to eat food", return_tensors="pt")
outputs = model(**inputs)
``` | 1,119 |
reallycarlaost/emobert-valence-5 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-0 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-3 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-4 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-5 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-6 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-7 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-8 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-11 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-12 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-13 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-15 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-16 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-17 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-18 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-19 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-20 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-21 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-22 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-24 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-25 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-26 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-27 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-29 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-30 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-32 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-36 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-38 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-39 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-40 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-41 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-43 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-44 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-46 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-47 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-56 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-57 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-58 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-64 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-74 | null | Entry not found | 15 |
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