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Jeevesh8/bert_ft_cola-58 | 9825801765da82244a893061954a15c7678b6801 | 2022-05-09T14:32:45.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-58 | 4 | null | transformers | 19,600 | Entry not found |
Jeevesh8/bert_ft_cola-60 | 05f59beb61c1b6fa201e715f7d57e3302662eade | 2022-05-09T14:34:04.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-60 | 4 | null | transformers | 19,601 | Entry not found |
Jeevesh8/bert_ft_cola-61 | 8671685f288aef5ad01085ed22551f89d4dcbcd1 | 2022-05-09T14:34:43.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-61 | 4 | null | transformers | 19,602 | Entry not found |
Jeevesh8/bert_ft_cola-64 | c7bbade797097eb8c1e8ded44e02eeffd75a3eb5 | 2022-05-09T14:36:45.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-64 | 4 | null | transformers | 19,603 | Entry not found |
Jeevesh8/bert_ft_cola-66 | 7c3e3b55c0c8b8ca9b94f18e0f0c7cad5c19311c | 2022-05-09T14:38:02.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-66 | 4 | null | transformers | 19,604 | Entry not found |
Jeevesh8/bert_ft_cola-67 | 95573875d0c1826df02bf07c99f12c286851e5ae | 2022-05-09T14:38:41.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-67 | 4 | null | transformers | 19,605 | Entry not found |
Jeevesh8/bert_ft_cola-68 | 51be95d214d794b2419c11369c2469446f6ee6c1 | 2022-05-09T14:39:21.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-68 | 4 | null | transformers | 19,606 | Entry not found |
Jeevesh8/bert_ft_cola-69 | 0e3441b44c00b317e636b8348a491faa8740aa46 | 2022-05-09T14:40:02.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-69 | 4 | null | transformers | 19,607 | Entry not found |
Jeevesh8/bert_ft_cola-70 | 607e45eef25e00e75f73abd7f2a903b657c9db44 | 2022-05-09T14:40:41.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-70 | 4 | null | transformers | 19,608 | Entry not found |
Jeevesh8/bert_ft_cola-71 | f214326f9e8aed81406c3cad1f0553a537151d32 | 2022-05-09T14:41:20.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-71 | 4 | null | transformers | 19,609 | Entry not found |
Jeevesh8/bert_ft_cola-72 | 2fc48260cd02842e12e62ec985c7eb69c9ca3202 | 2022-05-09T14:42:00.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-72 | 4 | null | transformers | 19,610 | Entry not found |
Jeevesh8/bert_ft_cola-73 | a93505942d739a6cd73c8d96e46af00f393cd2a8 | 2022-05-09T14:42:38.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-73 | 4 | null | transformers | 19,611 | Entry not found |
Jeevesh8/bert_ft_cola-74 | 3e754b5b3a4c8982c278dffd881cd82dd9a526a4 | 2022-05-09T14:43:17.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-74 | 4 | null | transformers | 19,612 | Entry not found |
Jeevesh8/bert_ft_cola-75 | 8d8ebc76ced4b65f6ce3b90edc0fb6f5d03ed052 | 2022-05-09T14:44:04.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-75 | 4 | null | transformers | 19,613 | Entry not found |
Jeevesh8/bert_ft_cola-76 | 13963ff0689a8e1cc060ea2936ea7abca3febd20 | 2022-05-09T14:44:44.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-76 | 4 | null | transformers | 19,614 | Entry not found |
Jeevesh8/bert_ft_cola-77 | bfd657ed6cb16616322a01331ed4308ec6b48fea | 2022-05-09T14:45:25.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-77 | 4 | null | transformers | 19,615 | Entry not found |
Jeevesh8/bert_ft_cola-78 | 27a61af402038bd9c4fb7f642f62da90233d0013 | 2022-05-09T14:46:05.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-78 | 4 | null | transformers | 19,616 | Entry not found |
Jeevesh8/bert_ft_cola-80 | d1faf92fdf9d6e4a49db1ae284e7fad494f8f3e9 | 2022-05-09T14:47:26.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-80 | 4 | null | transformers | 19,617 | Entry not found |
Jeevesh8/bert_ft_cola-81 | 3de5a7c700fb9387d5dbed8222355c2867928074 | 2022-05-09T14:48:07.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-81 | 4 | null | transformers | 19,618 | Entry not found |
Jeevesh8/bert_ft_cola-82 | 48e3ef8751336745a772b3d9c8914940bc9e8397 | 2022-05-09T14:48:49.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-82 | 4 | null | transformers | 19,619 | Entry not found |
Jeevesh8/bert_ft_cola-84 | 4f03d1dea5bbae496c7315f77972f613b3f3908e | 2022-05-09T14:50:09.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-84 | 4 | null | transformers | 19,620 | Entry not found |
Jeevesh8/bert_ft_cola-86 | ec53173f9e56965e93054a07b15a1c0278250969 | 2022-05-09T14:51:26.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-86 | 4 | null | transformers | 19,621 | Entry not found |
Jeevesh8/bert_ft_cola-87 | 34c9fc3c83831e4f4de39c98e43db4ea7cdb8b5d | 2022-05-09T14:52:05.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-87 | 4 | null | transformers | 19,622 | Entry not found |
Jeevesh8/bert_ft_cola-88 | 20f9bb6cb1b278a92f91f151aa8187c3c2ae7d9b | 2022-05-09T14:52:43.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-88 | 4 | null | transformers | 19,623 | Entry not found |
Jeevesh8/bert_ft_cola-89 | fd0c63dfe5b38d4e14445dc726315f255281df38 | 2022-05-09T14:53:23.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-89 | 4 | null | transformers | 19,624 | Entry not found |
Jeevesh8/bert_ft_cola-90 | 0d35b0b4bf7bfc49ce55e1cade5961cb271e4dd2 | 2022-05-09T14:54:05.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-90 | 4 | null | transformers | 19,625 | Entry not found |
Jeevesh8/bert_ft_cola-91 | 583c5dfe590171f9c3afdc5e079c93b6e0b05371 | 2022-05-09T14:54:44.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-91 | 4 | null | transformers | 19,626 | Entry not found |
Jeevesh8/bert_ft_cola-92 | 8606fd3d4ab72bb757800ab0bfe7c1c06f1fd47f | 2022-05-09T14:55:22.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-92 | 4 | null | transformers | 19,627 | Entry not found |
Jeevesh8/bert_ft_cola-93 | 6e547f322de23a64947a8fad9d442a9630634258 | 2022-05-09T14:56:03.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-93 | 4 | null | transformers | 19,628 | Entry not found |
Jeevesh8/bert_ft_cola-94 | aace9d57803b3237ad75c2306abc4c6d1371cc8f | 2022-05-09T14:56:42.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-94 | 4 | null | transformers | 19,629 | Entry not found |
Jeevesh8/bert_ft_cola-95 | 80fddea6c7aca0911e1a82ce2e9adbc2f4a29d79 | 2022-05-09T14:57:20.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-95 | 4 | null | transformers | 19,630 | Entry not found |
Jeevesh8/bert_ft_cola-96 | c5ffb904728773b5293600a8e77fca9e1243a718 | 2022-05-09T14:57:59.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-96 | 4 | null | transformers | 19,631 | Entry not found |
Jeevesh8/bert_ft_cola-97 | 7c14abc0ea62ae17e1b8b6e3f78aba3cb44c2cba | 2022-05-09T14:58:38.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-97 | 4 | null | transformers | 19,632 | Entry not found |
Jeevesh8/bert_ft_cola-98 | 1aeb601ccbe5cb1ebaeaaae5c5704900dd256130 | 2022-05-09T14:59:18.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-98 | 4 | null | transformers | 19,633 | Entry not found |
Jeevesh8/bert_ft_cola-99 | ad99fa918f92f2daa0f3aa4daf61d86ac1b59eff | 2022-05-09T14:59:57.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/bert_ft_cola-99 | 4 | null | transformers | 19,634 | Entry not found |
princeton-nlp/CoFi-MRPC-s60 | 7197cf0174c250b91fdab08dfacc7310589627ba | 2022-05-09T15:24:25.000Z | [
"pytorch",
"bert",
"text-classification",
"arxiv:2204.00408",
"transformers"
] | text-classification | false | princeton-nlp | null | princeton-nlp/CoFi-MRPC-s60 | 4 | null | transformers | 19,635 | 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 60% 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. |
zhiguoxu/xlm-roberta-base-finetuned-token-clasify | cbebabfeadfe15e6ae905f3fbd49134e40774826 | 2022-05-12T02:25:02.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | zhiguoxu | null | zhiguoxu/xlm-roberta-base-finetuned-token-clasify | 4 | null | transformers | 19,636 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-token-clasify
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.zh
metrics:
- name: F1
type: f1
value: 0.6841680129240711
---
<!-- 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-token-clasify
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4777
- F1: 0.6842
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7235 | 1.0 | 500 | 0.4621 | 0.4872 |
| 0.4398 | 2.0 | 1000 | 0.4605 | 0.5648 |
| 0.326 | 3.0 | 1500 | 0.3910 | 0.6019 |
| 0.2421 | 4.0 | 2000 | 0.4549 | 0.6173 |
| 0.1589 | 5.0 | 2500 | 0.4725 | 0.676 |
| 0.1166 | 6.0 | 3000 | 0.4777 | 0.6842 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
moshew/MiniLM-L6-clinc-distilled | c2d0b44da9baea70e9abaae15795aac8b79f0a90 | 2022-05-10T17:58:23.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | moshew | null | moshew/MiniLM-L6-clinc-distilled | 4 | null | transformers | 19,637 | Entry not found |
liyijing024/covid-twitter-bert-v2-mnli-NLI-STS-CrossEncoder-Covid-HeRA | 848b4c2bc1f004ab907476f47c2ff2a1608b0698 | 2022-05-09T23:32:42.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | liyijing024 | null | liyijing024/covid-twitter-bert-v2-mnli-NLI-STS-CrossEncoder-Covid-HeRA | 4 | null | transformers | 19,638 | Entry not found |
mrm8488/electricidad-small-finetuned-politices-binary | fde495ce16bddf03cce7eef874d0fd9fd43c94b5 | 2022-05-10T09:18:15.000Z | [
"pytorch",
"tensorboard",
"electra",
"text-classification",
"transformers"
] | text-classification | false | mrm8488 | null | mrm8488/electricidad-small-finetuned-politices-binary | 4 | null | transformers | 19,639 | Entry not found |
SreyanG-NVIDIA/bert-base-cased-finetuned-cola | 9ad01bbbe5b6885e775bc11f368729355c34a5ea | 2022-05-10T10:54:43.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | SreyanG-NVIDIA | null | SreyanG-NVIDIA/bert-base-cased-finetuned-cola | 4 | null | transformers | 19,640 | Entry not found |
choondrise/emolve_basic | 7d3876b758ced813df6f5c57685766b750d430fa | 2022-05-10T10:57:23.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | choondrise | null | choondrise/emolve_basic | 4 | null | transformers | 19,641 | Entry not found |
FabianWillner/distilbert-base-uncased-finetuned-triviaqa | b015557f0b9eb6f57dc357656210f9d08bc80656 | 2022-06-08T12:22:36.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | FabianWillner | null | FabianWillner/distilbert-base-uncased-finetuned-triviaqa | 4 | null | transformers | 19,642 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-triviaqa
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-triviaqa
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9949
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0391 | 1.0 | 11195 | 1.0133 |
| 0.8425 | 2.0 | 22390 | 0.9949 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
akozlo/lib_gpt_med | b1c594a98e63b081daadc1bb08d5ea088b81c384 | 2022-05-10T13:02:41.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | akozlo | null | akozlo/lib_gpt_med | 4 | null | transformers | 19,643 | hello
|
chancar/distilbert-base-uncased-finetuned-ner | ff4a38cac25d3688593082081700daad0b82085a | 2022-07-16T14:11:56.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | chancar | null | chancar/distilbert-base-uncased-finetuned-ner | 4 | null | transformers | 19,644 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
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: 0.9780
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.7891
## 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: 0.002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| 0.9591 | 1.0 | 878 | 0.9780 | 0.0 | 0.0 | 0.0 | 0.7891 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Tokenizers 0.12.1
|
reallycarlaost/finetuning-tut-model | aa5e36b9aef916e901a5e63115619929b19424e3 | 2022-05-10T17:21:47.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | reallycarlaost | null | reallycarlaost/finetuning-tut-model | 4 | null | transformers | 19,645 | Entry not found |
cmcmorrow/distilbert-rater | b40aaf26e4f3846e97a5a2042d229e44bbb1b999 | 2022-05-10T17:52:42.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | cmcmorrow | null | cmcmorrow/distilbert-rater | 4 | null | transformers | 19,646 | ---
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
|
TinySuitStarfish/distilbert-base-uncased-finetuned-emotion | ac03691a026cc0dee97a0600ee38369acfe1beee | 2022-05-10T19:11:10.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | TinySuitStarfish | null | TinySuitStarfish/distilbert-base-uncased-finetuned-emotion | 4 | null | transformers | 19,647 | Entry not found |
dreamerdeo/da-large | c185259f2fe357a62ad24555eef8916baad30489 | 2022-05-11T03:00:10.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | dreamerdeo | null | dreamerdeo/da-large | 4 | null | transformers | 19,648 | Entry not found |
Yarn/autotrain-Traimn-853827191 | 1bd2fe60700de4333b6a28ecb77329af5a892958 | 2022-05-11T18:47:41.000Z | [
"pytorch",
"bert",
"text-classification",
"unk",
"dataset:Yarn/autotrain-data-Traimn",
"transformers",
"autotrain",
"co2_eq_emissions"
] | text-classification | false | Yarn | null | Yarn/autotrain-Traimn-853827191 | 4 | 1 | transformers | 19,649 | ---
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)
``` |
EhsanAghazadeh/bert-base-uncased-random-weights-S42 | 5f53fe85fe70c03370f29d532bb4c1763b05daae | 2022-05-15T14:23:49.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | EhsanAghazadeh | null | EhsanAghazadeh/bert-base-uncased-random-weights-S42 | 4 | null | transformers | 19,650 | Entry not found |
Pablo94/roberta-base-bne-finetuned-detests | 1cac7a3dbce257ac5dae87c6f5be6d9e08c09690 | 2022-05-14T09:14:36.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | Pablo94 | null | Pablo94/roberta-base-bne-finetuned-detests | 4 | null | transformers | 19,651 | ---
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
|
subhasisj/es-finetuned-squad-qa-minilmv2-16 | 2c406ab54397a0c39b6ec786adb65ff9c8b22c24 | 2022-05-12T22:52:07.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | question-answering | false | subhasisj | null | subhasisj/es-finetuned-squad-qa-minilmv2-16 | 4 | null | transformers | 19,652 | ---
tags:
- generated_from_trainer
model-index:
- name: es-finetuned-squad-qa-minilmv2-16
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. -->
# es-finetuned-squad-qa-minilmv2-16
This model is a fine-tuned version of [subhasisj/es-TAPT-MLM-MiniLM](https://huggingface.co/subhasisj/es-TAPT-MLM-MiniLM) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2304
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.485 | 1.0 | 711 | 1.7377 |
| 1.6984 | 2.0 | 1422 | 1.3005 |
| 1.0772 | 3.0 | 2133 | 1.2348 |
| 0.9997 | 4.0 | 2844 | 1.2231 |
| 0.8976 | 5.0 | 3555 | 1.2304 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
aajrami/bert-mlm-base | 6346beb3e7d49d2337da786788145932dc35497c | 2022-06-01T11:52:59.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"arxiv:2203.10415",
"transformers",
"bert",
"license:cc-by-4.0"
] | feature-extraction | false | aajrami | null | aajrami/bert-mlm-base | 4 | null | transformers | 19,653 | ---
tags:
- bert
license: cc-by-4.0
---
## bert-mlm-base
is a BERT base Language Model with an **MLM** pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to [How does the pre-training objective affect what large language models learn about linguistic properties?](https://arxiv.org/abs/2203.10415)
## License
CC BY 4.0
## Citation
If you use this model, please cite the following paper:
```
@inproceedings{alajrami2022does,
title={How does the pre-training objective affect what large language models learn about linguistic properties?},
author={Alajrami, Ahmed and Aletras, Nikolaos},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
pages={131--147},
year={2022}
}
``` |
manthan40/wav2vec2-base-finetuned-manthan-gujarati-digits | cf60c222e1cbe695174860a060703088c4808929 | 2022-05-13T02:03:31.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"dataset:new_dataset",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | audio-classification | false | manthan40 | null | manthan40/wav2vec2-base-finetuned-manthan-gujarati-digits | 4 | null | transformers | 19,654 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- new_dataset
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-manthan-gujarati-digits
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. -->
# wav2vec2-base-finetuned-manthan-gujarati-digits
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the new_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5613
- Accuracy: 0.9923
## 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: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3392 | 0.98 | 12 | 1.1315 | 0.9665 |
| 1.2319 | 1.98 | 24 | 0.9487 | 0.9716 |
| 1.0824 | 2.98 | 36 | 0.8338 | 0.9820 |
| 0.9995 | 3.98 | 48 | 0.7533 | 0.9845 |
| 0.8175 | 4.98 | 60 | 0.6759 | 0.9923 |
| 0.8015 | 5.98 | 72 | 0.6425 | 0.9845 |
| 0.7417 | 6.98 | 84 | 0.6048 | 0.9871 |
| 0.7181 | 7.98 | 96 | 0.5850 | 0.9923 |
| 0.6907 | 8.98 | 108 | 0.5687 | 0.9897 |
| 0.6511 | 9.98 | 120 | 0.5613 | 0.9923 |
### Framework versions
- Transformers 4.19.0
- Pytorch 1.11.0+cu113
- Datasets 1.14.0
- Tokenizers 0.12.1
|
jkhan447/language-detection-RoBert-base | ec88e39e4344a1dd1ff216a2e8ff3bb3df059d99 | 2022-05-13T10:19:59.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | jkhan447 | null | jkhan447/language-detection-RoBert-base | 4 | null | transformers | 19,655 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: language-detection-RoBert-base
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. -->
# language-detection-RoBert-base
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1398
- Accuracy: 0.9865
## 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: 50
### Training results
### Framework versions
- Transformers 4.19.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
AnonymousSub/rule_based_hier_triplet_epochs_1_shard_1_kldiv_squad2.0 | db84367d42f51a48cda97f45983d9cd2293c5efe | 2022-05-13T11:13:22.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | AnonymousSub | null | AnonymousSub/rule_based_hier_triplet_epochs_1_shard_1_kldiv_squad2.0 | 4 | null | transformers | 19,656 | Entry not found |
anwesham/autotrain-imdb-sentiment-analysis-864927559 | d47528c6e56b8350e6a852567c94bb6dd30d4029 | 2022-05-14T03:56:56.000Z | [
"pytorch",
"distilbert",
"text-classification",
"unk",
"dataset:anwesham/autotrain-data-imdb-sentiment-analysis",
"transformers",
"co2_eq_emissions"
] | text-classification | false | anwesham | null | anwesham/autotrain-imdb-sentiment-analysis-864927559 | 4 | null | transformers | 19,657 | ---
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)
``` |
Jeevesh8/6ep_bert_ft_cola-0 | a318b8af390a9f43f5bdf866aa787f48a197bdcd | 2022-05-14T11:33:10.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-0 | 4 | null | transformers | 19,658 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-1 | def8f49e3ecd850dbb74246603cda2f32eccfaf6 | 2022-05-14T11:34:48.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-1 | 4 | null | transformers | 19,659 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-3 | 68026f431dc4717398a94cf25b93439330a2c5e4 | 2022-05-14T11:38:02.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-3 | 4 | null | transformers | 19,660 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-4 | 9b6fe552f322ef96bc2a2689bb5525de41c8d62b | 2022-05-14T11:39:37.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-4 | 4 | null | transformers | 19,661 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-6 | 79da704a6a67d2b31afab2111bcbe8e2b07d1ec9 | 2022-05-14T11:42:50.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-6 | 4 | null | transformers | 19,662 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-7 | 76af61abd7169b155c8f9264caee5dd3e7f4577a | 2022-05-14T11:44:35.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-7 | 4 | null | transformers | 19,663 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-8 | 6b4a11ff895e27ad73eaf838af02f82bc59ca17d | 2022-05-14T11:46:15.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-8 | 4 | null | transformers | 19,664 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-9 | 95deb94b68e28d9e680562d83b4c4e0047d578d5 | 2022-05-14T11:47:57.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-9 | 4 | null | transformers | 19,665 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-10 | 74ef00d835710690cfc8773ef489f2afe7a7b88d | 2022-05-14T11:49:38.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-10 | 4 | null | transformers | 19,666 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-11 | 0c49145264fd114685a356ed400b16fbb6306ea6 | 2022-05-14T11:51:17.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-11 | 4 | null | transformers | 19,667 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-12 | 743d13de5a27b775ce31f6cb7a16413ebfce2dda | 2022-05-14T11:52:56.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-12 | 4 | null | transformers | 19,668 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-14 | 3e8046ff994a2e8d7e0f1a9a2fdc2a8f99c9abc3 | 2022-05-14T12:21:10.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-14 | 4 | null | transformers | 19,669 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-15 | 6b45083f63549918f67bafddd9105881c540131c | 2022-05-14T12:22:57.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-15 | 4 | null | transformers | 19,670 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-16 | 1b513e92afd57f25a5369d10d93c354cf7293a3e | 2022-05-14T12:24:42.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-16 | 4 | null | transformers | 19,671 | Entry not found |
Jeevesh8/6ep_bert_ft_cola-17 | 51924767a4082b8da4c500ba02ae6e53921c16b0 | 2022-05-14T12:26:21.000Z | [
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Jeevesh8/6ep_bert_ft_cola-18 | f51f5a3145950f3f35b047e75228b62a9d58f349 | 2022-05-14T12:28:09.000Z | [
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Jeevesh8/6ep_bert_ft_cola-19 | b2f57c9973f2f83e614fac2fffb12d1ebbc04f35 | 2022-05-14T12:29:53.000Z | [
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Jeevesh8/6ep_bert_ft_cola-20 | 81a13ef65fd267125254002a26c192a6f8fcf4c5 | 2022-05-14T12:31:32.000Z | [
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Jeevesh8/6ep_bert_ft_cola-21 | 4d03151b47c71102114d84cea5a275ce6a817428 | 2022-05-14T12:33:14.000Z | [
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Jeevesh8/6ep_bert_ft_cola-22 | e06189ba56903b5e2f3649cd6f4b55097ecd44fc | 2022-05-14T12:34:53.000Z | [
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Jeevesh8/6ep_bert_ft_cola-24 | ef9779f13088c0c6774a6bef448aec2487295b93 | 2022-05-14T12:38:12.000Z | [
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Jeevesh8/6ep_bert_ft_cola-25 | 355bb8e038c46ce6fe03ca2ffd8f57764942d3af | 2022-05-14T12:39:54.000Z | [
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Jeevesh8/6ep_bert_ft_cola-26 | 3a63b14fe6c583efa9b6dff23407cb473e711636 | 2022-05-14T12:41:32.000Z | [
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Jeevesh8/6ep_bert_ft_cola-27 | c8e463c92e7a95119cded437a5d55b585d62c36e | 2022-05-14T12:43:13.000Z | [
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Jeevesh8/6ep_bert_ft_cola-29 | ff171dcd2f29931157cee36ec106b96587ea8a86 | 2022-05-14T12:46:38.000Z | [
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Jeevesh8/6ep_bert_ft_cola-30 | 902047a576a3f08b7e6bb7eaf1837d401fc5320d | 2022-05-14T12:48:24.000Z | [
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Jeevesh8/6ep_bert_ft_cola-31 | b9bdebd0c6b0bda5619b418f2467de56d7e1b144 | 2022-05-14T12:50:33.000Z | [
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Jeevesh8/6ep_bert_ft_cola-32 | ade6a9f0de7322fb4ef950e6755150670f909baf | 2022-05-14T12:52:14.000Z | [
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Jeevesh8/6ep_bert_ft_cola-33 | 68037cbd6f05720082d9d6281d0c4ffb04914078 | 2022-05-14T12:54:02.000Z | [
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Jeevesh8/6ep_bert_ft_cola-34 | 74f6f51dd5a502667d9a389bb87eb796c1b77bbc | 2022-05-14T12:55:43.000Z | [
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Jeevesh8/6ep_bert_ft_cola-35 | 14344dc4efd99b6ddf4fc0bdb29426613332e5b0 | 2022-05-14T12:57:26.000Z | [
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Jeevesh8/6ep_bert_ft_cola-36 | 5e097c528198c27e54baf32b8a2a8dcc225b5afd | 2022-05-14T12:59:06.000Z | [
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Jeevesh8/6ep_bert_ft_cola-37 | 8505c6a4de9ffcc9cb37a8e6b75517cdb4062477 | 2022-05-14T13:00:47.000Z | [
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Jeevesh8/6ep_bert_ft_cola-38 | e570bb9e9f191764b7add13c5fe9ed9665010b4f | 2022-05-14T13:02:26.000Z | [
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Jeevesh8/6ep_bert_ft_cola-39 | 95df5a0948d81923b5c8eebac275f429273289dd | 2022-05-14T13:04:07.000Z | [
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Jeevesh8/6ep_bert_ft_cola-40 | 774cbfe5b59f8b59c7ce0ad5eb0fc9f351b498db | 2022-05-14T13:05:45.000Z | [
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Jeevesh8/6ep_bert_ft_cola-41 | 18198e953f275ad247769302b3315d7dd9a8c045 | 2022-05-14T13:07:28.000Z | [
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Jeevesh8/6ep_bert_ft_cola-42 | ff5a7fddc1cb3ff07fe663dc4077f967ec22ba8a | 2022-05-14T13:09:08.000Z | [
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Jeevesh8/6ep_bert_ft_cola-43 | 272981ee30f45c9fa59b998147ba49eb3e17ab92 | 2022-05-14T13:10:48.000Z | [
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Jeevesh8/6ep_bert_ft_cola-44 | 65cbb1e3bd49efa32c7084d43f7ef9188bc753cf | 2022-05-14T13:12:29.000Z | [
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Jeevesh8/6ep_bert_ft_cola-45 | 777a4c2fe1600a7b2fdd692bd3626a67f8f5a3d5 | 2022-05-14T13:14:07.000Z | [
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Jeevesh8/6ep_bert_ft_cola-46 | ac762bb89e739ce23b9d30fc517253b76e01492c | 2022-05-14T13:15:48.000Z | [
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] | text-classification | false | Jeevesh8 | null | Jeevesh8/6ep_bert_ft_cola-46 | 4 | null | transformers | 19,699 | Entry not found |
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