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text-generation
|
transformers
|
<!-- 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. -->
# distilgpt2-sgnews
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1516
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.3558 | 1.0 | 23769 | 3.2316 |
| 3.2558 | 2.0 | 47538 | 3.1683 |
| 3.2321 | 3.0 | 71307 | 3.1516 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilgpt2", "model-index": [{"name": "distilgpt2-sgnews", "results": []}]}
|
chinhon/distilgpt2-sgnews
| null |
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-classification
|
transformers
|
{}
|
chinhon/fake_tweet_detect
| null |
[
"transformers",
"pytorch",
"safetensors",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text2text-generation
|
transformers
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 25965855
- CO2 Emissions (in grams): 114.71292762345828
## Validation Metrics
- Loss: 1.3862273693084717
- Rouge1: 52.4988
- Rouge2: 31.6973
- RougeL: 47.1727
- RougeLsum: 47.1576
- Gen Len: 17.6194
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/chinhon/autonlp-sg_headline_generator-25965855
```
|
{"language": "en", "tags": "autonlp", "datasets": ["chinhon/autonlp-data-sg_headline_generator"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 114.71292762345828}
|
chinhon/headline_writer
| null |
[
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"autonlp",
"en",
"dataset:chinhon/autonlp-data-sg_headline_generator",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text2text-generation
|
transformers
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 25965856
- CO2 Emissions (in grams): 396.629376395644
## Validation Metrics
- Loss: 1.4130597114562988
- Rouge1: 51.7922
- Rouge2: 30.8259
- RougeL: 46.4585
- RougeLsum: 46.4807
- Gen Len: 15.8411
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/chinhon/autonlp-sg_headline_generator-25965856
```
|
{"language": "en", "tags": "autonlp", "datasets": ["chinhon/autonlp-data-sg_headline_generator"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 396.629376395644}
|
chinhon/headline_writer2
| null |
[
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"autonlp",
"en",
"dataset:chinhon/autonlp-data-sg_headline_generator",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text2text-generation
|
transformers
|
<!-- 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. -->
# pegasus-large-commentaries_hd
This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5453
- Rouge1: 26.3475
- Rouge2: 9.5095
- Rougel: 22.6367
- Rougelsum: 22.8127
- Gen Len: 14.4789
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.5718 | 1.0 | 4710 | 2.5277 | 25.1384 | 8.6528 | 21.3443 | 21.5289 | 15.3268 |
| 2.4034 | 2.0 | 9420 | 2.4973 | 25.9298 | 9.2238 | 22.3192 | 22.4817 | 14.2243 |
| 2.2093 | 3.0 | 14130 | 2.5013 | 26.6036 | 9.7482 | 22.8409 | 23.0077 | 14.2263 |
| 2.0518 | 4.0 | 18840 | 2.5272 | 26.4723 | 9.6599 | 22.7439 | 22.9201 | 14.38 |
| 1.9906 | 5.0 | 23550 | 2.5453 | 26.3475 | 9.5095 | 22.6367 | 22.8127 | 14.4789 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/pegasus-large", "model-index": [{"name": "pegasus-large-commentaries_hd", "results": []}]}
|
chinhon/pegasus-large-commentaries_hd
| null |
[
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-large",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text2text-generation
|
transformers
|
<!-- 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. -->
# pegasus-multi_news-commentaries_hdwriter
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pegasus-multi_news) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7259
- Rouge1: 21.3899
- Rouge2: 6.2409
- Rougel: 16.6172
- Rougelsum: 17.808
- Gen Len: 34.7016
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.847 | 1.0 | 4710 | 2.7513 | 20.5559 | 5.9762 | 16.1223 | 17.2872 | 35.81 |
| 2.6399 | 2.0 | 9420 | 2.6890 | 21.2052 | 6.0104 | 16.5753 | 17.6517 | 34.5242 |
| 2.3811 | 3.0 | 14130 | 2.6904 | 21.2358 | 6.1416 | 16.6053 | 17.7067 | 34.6157 |
| 2.2388 | 4.0 | 18840 | 2.7112 | 21.3806 | 6.1895 | 16.6909 | 17.7504 | 34.5227 |
| 2.1589 | 5.0 | 23550 | 2.7259 | 21.3899 | 6.2409 | 16.6172 | 17.808 | 34.7016 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/pegasus-multi_news", "model-index": [{"name": "pegasus-multi_news-commentaries_hdwriter", "results": []}]}
|
chinhon/pegasus-multi_news-commentaries_hdwriter
| null |
[
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-multi_news",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text2text-generation
|
transformers
|
<!-- 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. -->
# pegasus-multi_news-headline
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pegasus-multi_news) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4421
- Rouge1: 41.616
- Rouge2: 22.922
- Rougel: 35.2189
- Rougelsum: 35.3561
- Gen Len: 33.9532
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.6637 | 1.0 | 31200 | 1.4877 | 41.0996 | 22.579 | 34.9311 | 35.0611 | 34.3431 |
| 1.4395 | 2.0 | 62400 | 1.4388 | 41.6075 | 22.8274 | 35.2051 | 35.3526 | 33.7965 |
| 1.3137 | 3.0 | 93600 | 1.4421 | 41.616 | 22.922 | 35.2189 | 35.3561 | 33.9532 |
### Framework versions
- Transformers 4.12.2
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/pegasus-multi_news", "model-index": [{"name": "pegasus-multi_news-headline", "results": []}]}
|
chinhon/pegasus-multi_news-headline
| null |
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-multi_news",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text2text-generation
|
transformers
|
<!-- 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. -->
# pegasus-multi_news-malay_headlines_02
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pegasus-multi_news) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9295
- Rouge1: 39.9859
- Rouge2: 20.1943
- Rougel: 36.1927
- Rougelsum: 36.2105
- Gen Len: 35.6062
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 2.0943 | 1.0 | 53582 | 1.9295 | 39.9859 | 20.1943 | 36.1927 | 36.2105 | 35.6062 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/pegasus-multi_news", "model-index": [{"name": "pegasus-multi_news-malay_headlines_02", "results": []}]}
|
chinhon/pegasus-multi_news-malay_headlines_02
| null |
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-multi_news",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text2text-generation
|
transformers
|
<!-- 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. -->
# pegasus-multi_news-summarizer_01
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pegasus-multi_news) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2794
- Rouge1: 52.1693
- Rouge2: 34.8989
- Rougel: 41.2385
- Rougelsum: 48.4365
- Gen Len: 98.6433
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 1.3936 | 1.0 | 16113 | 1.2972 | 51.5747 | 34.2062 | 40.7279 | 47.7783 | 95.0004 |
| 1.3664 | 2.0 | 32226 | 1.2817 | 52.1077 | 34.8189 | 41.1614 | 48.3894 | 100.3265 |
| 1.3002 | 3.0 | 48339 | 1.2794 | 52.1693 | 34.8989 | 41.2385 | 48.4365 | 98.6433 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/pegasus-multi_news", "model-index": [{"name": "pegasus-multi_news-summarizer_01", "results": []}]}
|
chinhon/pegasus-multi_news-summarizer_01
| null |
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-multi_news",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text2text-generation
|
transformers
|
<!-- 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. -->
# pegasus-newsroom-commentaries_hdwriter
This model is a fine-tuned version of [google/pegasus-newsroom](https://huggingface.co/google/pegasus-newsroom) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5316
- Rouge1: 21.4079
- Rouge2: 6.2399
- Rougel: 16.6644
- Rougelsum: 17.8501
- Gen Len: 34.4111
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.6327 | 1.0 | 4710 | 2.5474 | 20.9392 | 6.1702 | 16.3859 | 17.5963 | 35.6626 |
| 2.4322 | 2.0 | 9420 | 2.5198 | 21.4026 | 6.1811 | 16.5874 | 17.8207 | 34.5976 |
| 2.2703 | 3.0 | 14130 | 2.5316 | 21.4079 | 6.2399 | 16.6644 | 17.8501 | 34.4111 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "pegasus-newsroom-commentaries_hdwriter", "results": []}]}
|
chinhon/pegasus-newsroom-commentaries_hdwriter
| null |
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text2text-generation
|
transformers
|
<!-- 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. -->
# pegasus-newsroom-headline_writer
This model is a fine-tuned version of [google/pegasus-newsroom](https://huggingface.co/google/pegasus-newsroom) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3988
- Rouge1: 41.8748
- Rouge2: 23.1947
- Rougel: 35.6263
- Rougelsum: 35.7355
- Gen Len: 34.1266
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.5784 | 1.0 | 31200 | 1.4287 | 41.4257 | 22.9355 | 35.3299 | 35.4648 | 34.4677 |
| 1.3501 | 2.0 | 62400 | 1.3955 | 41.9119 | 23.1912 | 35.6698 | 35.7479 | 33.8672 |
| 1.2417 | 3.0 | 93600 | 1.3988 | 41.8748 | 23.1947 | 35.6263 | 35.7355 | 34.1266 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/pegasus-newsroom", "model-index": [{"name": "pegasus-newsroom-headline_writer", "results": []}]}
|
chinhon/pegasus-newsroom-headline_writer
| null |
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-newsroom",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text2text-generation
|
transformers
|
<!-- 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. -->
# pegasus-newsroom-malay_headlines
This model is a fine-tuned version of [google/pegasus-newsroom](https://huggingface.co/google/pegasus-newsroom) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6603
- Rouge1: 42.6667
- Rouge2: 22.8739
- Rougel: 38.6684
- Rougelsum: 38.6928
- Gen Len: 34.7995
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.9713 | 1.0 | 15310 | 1.8121 | 41.1469 | 21.5262 | 37.3081 | 37.3377 | 35.0939 |
| 1.7917 | 2.0 | 30620 | 1.6913 | 42.4027 | 22.6089 | 38.4471 | 38.4699 | 34.8149 |
| 1.7271 | 3.0 | 45930 | 1.6603 | 42.6667 | 22.8739 | 38.6684 | 38.6928 | 34.7995 |
### Framework versions
- Transformers 4.12.2
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/pegasus-newsroom", "model-index": [{"name": "pegasus-newsroom-malay_headlines", "results": []}]}
|
chinhon/pegasus-newsroom-malay_headlines
| null |
[
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-newsroom",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text2text-generation
|
transformers
|
<!-- 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. -->
# pegasus-newsroom-summarizer_02
This model is a fine-tuned version of [google/pegasus-newsroom](https://huggingface.co/google/pegasus-newsroom) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2204
- Rouge1: 52.4459
- Rouge2: 35.2568
- Rougel: 41.6213
- Rougelsum: 48.7859
- Gen Len: 98.0627
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.3231 | 1.0 | 16113 | 1.2305 | 52.1565 | 34.8681 | 41.3189 | 48.4258 | 95.9049 |
| 1.3001 | 2.0 | 32226 | 1.2186 | 52.4921 | 35.2661 | 41.6264 | 48.8168 | 98.9241 |
| 1.2372 | 3.0 | 48339 | 1.2204 | 52.4459 | 35.2568 | 41.6213 | 48.7859 | 98.0627 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/pegasus-newsroom", "model-index": [{"name": "pegasus-newsroom-summarizer_02", "results": []}]}
|
chinhon/pegasus-newsroom-summarizer_02
| null |
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-newsroom",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-generation
|
transformers
|
Chizuru Ichinose DialoGPT Model.
|
{"tags": ["conversational"]}
|
chip/DialoGPT-small-chizuru
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
chipmooon/test_model
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
chirag2706/AudioClassifier
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null |
keras
|
{}
|
chirag2706/FlowerClassifier
| null |
[
"keras",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-generation
|
transformers
|
{}
|
chirag2706/gpt2_code_generation_model
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-classification
|
transformers
|
### Distibert model finetuned on the task of classifying product descriptions to one of 45 broad [NICE classifications](https://www.wipo.int/classifications/nice/en/)
|
{}
|
chisadi/nice-distilbert-v2
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-classification
|
transformers
|
{}
|
chisadi/nice-distilbert
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null |
transformers
|
{"license": "mit"}
|
chitanda/merit-albert-v2-xxlarge-v1
| null |
[
"transformers",
"pytorch",
"albert",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null |
transformers
|
{"license": "mit"}
|
chitanda/merit-deberta-v2-xlarge-v1
| null |
[
"transformers",
"pytorch",
"deberta-v2",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null |
transformers
|
{"license": "mit"}
|
chitanda/merit-deberta-v2-xxlarge-v1
| null |
[
"transformers",
"pytorch",
"deberta-v2",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null |
transformers
|
{"license": "mit"}
|
chitanda/merit-roberta-large-v1
| null |
[
"transformers",
"pytorch",
"roberta",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null |
transformers
|
{"license": "mit"}
|
chitanda/merit-roberta-large-v2
| null |
[
"transformers",
"pytorch",
"roberta",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-classification
|
transformers
|
{}
|
chitra/distilbert-negation
| null |
[
"transformers",
"pytorch",
"tf",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-classification
|
transformers
|
<!-- 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. -->
# finetune-paraphrase-model
This model is a fine-tuned version of [coderpotter/adversarial-paraphrasing-detector](https://huggingface.co/coderpotter/adversarial-paraphrasing-detector) on an unknown 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: 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: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.1 | 200 | 3.0116 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "model-index": [{"name": "finetune-paraphrase-model", "results": []}]}
|
chitra/finetune-paraphrase-model
| null |
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-classification
|
transformers
|
{}
|
chitra/finetuned-adversarial-paraphrase-model-test
| null |
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-classification
|
transformers
|
<!-- 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-adversarial-paraphrase-model
This model is a fine-tuned version of [coderpotter/adversarial-paraphrasing-detector](https://huggingface.co/coderpotter/adversarial-paraphrasing-detector) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 7.5680
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0848 | 1.0 | 2000 | 5.4633 |
| 0.0495 | 2.0 | 4000 | 6.0352 |
| 0.0121 | 3.0 | 6000 | 7.5680 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "model-index": [{"name": "finetuned-adversarial-paraphrase-model", "results": []}]}
|
chitra/finetuned-adversarial-paraphrase-model
| null |
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-classification
|
transformers
|
{}
|
chitra/finetuned-adversarial-paraphrase-modell
| null |
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-classification
|
transformers
|
{}
|
chitra/finetuned-adversarial-paraphrasing-detector
| null |
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-classification
|
transformers
|
{}
|
chitra/finetuned-adversial-paraphrase-model
| null |
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-classification
|
transformers
|
### Welcome to RoBERTArg!
π€ **Model description**
This model was trained on ~25k heterogeneous manually annotated sentences (π [Stab et al. 2018](https://www.aclweb.org/anthology/D18-1402/)) of controversial topics to classify text into one of two labels: π· **NON-ARGUMENT** (0) and **ARGUMENT** (1).
π **Dataset**
The dataset (π Stab et al. 2018) consists of **ARGUMENTS** (\~11k) that either support or oppose a topic if it includes a relevant reason for supporting or opposing the topic, or as a **NON-ARGUMENT** (\~14k) if it does not include reasons. The authors focus on controversial topics, i.e., topics that include "an obvious polarity to the possible outcomes" and compile a final set of eight controversial topics: _abortion, school uniforms, death penalty, marijuana legalization, nuclear energy, cloning, gun control, and minimum wage_.
| TOPIC | ARGUMENT | NON-ARGUMENT |
|----|----|----|
| abortion | 2213 | 2,427 |
| school uniforms | 325 | 1,734 |
| death penalty | 325 | 2,083 |
| marijuana legalization | 325 | 1,262 |
| nuclear energy | 325 | 2,118 |
| cloning | 325 | 1,494 |
| gun control | 325 | 1,889 |
| minimum wage | 325 | 1,346 |
ππΌββοΈ**Model training**
**RoBERTArg** was fine-tuned on a RoBERTA (base) pre-trained model from HuggingFace using the HuggingFace trainer with the following hyperparameters:
```
training_args = TrainingArguments(
num_train_epochs=2,
learning_rate=2.3102e-06,
seed=8,
per_device_train_batch_size=64,
per_device_eval_batch_size=64,
)
```
π **Evaluation**
The model was evaluated on an evaluation set (20%):
| Model | Acc | F1 | R arg | R non | P arg | P non |
|----|----|----|----|----|----|----|
| RoBERTArg | 0.8193 | 0.8021 | 0.8463 | 0.7986 | 0.7623 | 0.8719 |
Showing the **confusion matrix** using again the evaluation set:
| | ARGUMENT | NON-ARGUMENT |
|----|----|----|
| ARGUMENT | 2213 | 558 |
| NON-ARGUMENT | 325 | 1790 |
β οΈ **Intended Uses & Potential Limitations**
The model can only be a starting point to dive into the exciting field of argument mining. But be aware. An argument is a complex structure, with multiple dependencies. Therefore, the model may perform less well on different topics and text types not included in the training set.
Enjoy and stay tuned! π
π¦ Twitter: [@chklamm](http://twitter.com/chklamm)
|
{"language": "en", "widget": [{"text": "It has been determined that the amount of greenhouse gases have decreased by almost half because of the prevalence in the utilization of nuclear power."}]}
|
chkla/roberta-argument
| null |
[
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"roberta",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
chloeh13q/wav2vec2-base-finetuned-ks
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
chloem38/Chloe
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
chmanoj/kenlm_te
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
chmanoj/wav2vec2-large-xls-r-300m-turkish-demo-colab
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
automatic-speech-recognition
|
transformers
|
<!-- 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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the OPENSLR_SLR66 - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3119
- Wer: 0.2613
### Evaluation metrics
| Metric | Split | Decode with LM | Value |
|:------:|:------:|:--------------:|:---------:|
| WER | Train | No | 5.36 |
| CER | Train | No | 1.11 |
| WER | Test | No | 26.14 |
| CER | Test | No | 4.93 |
| WER | Train | Yes | 5.04 |
| CER | Train | Yes | 1.07 |
| WER | Test | Yes | 20.69 |
| CER | Test | Yes | 3.986 |
## 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: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 150.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 2.9038 | 4.8 | 500 | 3.0125 | 1.0 |
| 1.3777 | 9.61 | 1000 | 0.8681 | 0.8753 |
| 1.1436 | 14.42 | 1500 | 0.6256 | 0.7961 |
| 1.0997 | 19.23 | 2000 | 0.5244 | 0.6875 |
| 1.0363 | 24.04 | 2500 | 0.4585 | 0.6276 |
| 0.7996 | 28.84 | 3000 | 0.4072 | 0.5295 |
| 0.825 | 33.65 | 3500 | 0.3590 | 0.5222 |
| 0.8018 | 38.46 | 4000 | 0.3678 | 0.4671 |
| 0.7545 | 43.27 | 4500 | 0.3474 | 0.3962 |
| 0.7375 | 48.08 | 5000 | 0.3224 | 0.3869 |
| 0.6198 | 52.88 | 5500 | 0.3233 | 0.3630 |
| 0.6608 | 57.69 | 6000 | 0.3029 | 0.3308 |
| 0.645 | 62.5 | 6500 | 0.3195 | 0.3722 |
| 0.5249 | 67.31 | 7000 | 0.3004 | 0.3202 |
| 0.4875 | 72.11 | 7500 | 0.2826 | 0.2992 |
| 0.5171 | 76.92 | 8000 | 0.2962 | 0.2976 |
| 0.4974 | 81.73 | 8500 | 0.2990 | 0.2933 |
| 0.4387 | 86.54 | 9000 | 0.2834 | 0.2755 |
| 0.4511 | 91.34 | 9500 | 0.2886 | 0.2787 |
| 0.4112 | 96.15 | 10000 | 0.3093 | 0.2976 |
| 0.4064 | 100.96 | 10500 | 0.3123 | 0.2863 |
| 0.4047 | 105.77 | 11000 | 0.2968 | 0.2719 |
| 0.3519 | 110.57 | 11500 | 0.3106 | 0.2832 |
| 0.3719 | 115.38 | 12000 | 0.3030 | 0.2737 |
| 0.3669 | 120.19 | 12500 | 0.2964 | 0.2714 |
| 0.3386 | 125.0 | 13000 | 0.3101 | 0.2714 |
| 0.3137 | 129.8 | 13500 | 0.3063 | 0.2710 |
| 0.3008 | 134.61 | 14000 | 0.3082 | 0.2617 |
| 0.301 | 139.42 | 14500 | 0.3121 | 0.2628 |
| 0.3291 | 144.23 | 15000 | 0.3105 | 0.2612 |
| 0.3133 | 149.04 | 15500 | 0.3114 | 0.2624 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
{"language": ["te"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "openslr_SLR66", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["openslr", "SLR66"], "metrics": ["wer"], "model-index": [{"name": "xls-r-1B-te", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Open SLR", "type": "openslr", "args": "SLR66"}, "metrics": [{"type": "wer", "value": 20.624, "name": "Test WER"}, {"type": "cer", "value": 3.979, "name": "Test CER"}, {"type": "wer", "value": 26.14777618364419, "name": "Test WER (without LM)"}, {"type": "cer", "value": 4.932543184970369, "name": "Test CER (without LM)"}]}]}]}
|
chmanoj/xls-r-1B-te
| null |
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"openslr_SLR66",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"te",
"dataset:openslr",
"dataset:SLR66",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
<!-- 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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-2b](https://huggingface.co/facebook/wav2vec2-xls-r-2b) on the OPENSLR_SLR66 - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4253
- Wer: 0.5109
### Evaluation metrics
| Metric | Split | Decode with LM | Value |
|:------:|:------:|:--------------:|:---------:|
| WER | Train | No | |
| CER | Train | No | |
| WER | Test | No | |
| CER | Test | No | |
| WER | Train | Yes | |
| CER | Train | Yes | |
| WER | Test | Yes | |
| CER | Test | Yes | |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 12
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- learning_rate: 3e-6
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 150.0
- hidden_dropout: 0.15
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
{"language": ["te"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "openslr_SLR66", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["openslr", "SLR66"], "metrics": ["wer"], "model-index": [{"name": "xls-r-1B-te", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Open SLR", "type": "openslr", "args": "SLR66"}, "metrics": [{"type": "wer", "value": 0.51, "name": "Test WER"}, {"type": "cer", "value": 0.097, "name": "Test CER"}]}]}]}
|
chmanoj/xls-r-2B-te
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"openslr_SLR66",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"te",
"dataset:openslr",
"dataset:SLR66",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
<!-- 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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - SV-SE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8004
- Wer: 0.7139
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.6683 | 1.45 | 500 | 1.7698 | 1.0041 |
| 1.9548 | 2.91 | 1000 | 1.0890 | 0.8602 |
| 1.9568 | 4.36 | 1500 | 1.0878 | 0.8680 |
| 1.9497 | 5.81 | 2000 | 1.1501 | 0.8838 |
| 1.8453 | 7.27 | 2500 | 1.0452 | 0.8418 |
| 1.6952 | 8.72 | 3000 | 0.9153 | 0.7823 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu113
- Datasets 1.18.1.dev0
- Tokenizers 0.10.3
|
{"language": ["sv-SE"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]}
|
chmanoj/xls-r-300m-sv
| null |
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
{}
|
chmanoj/xls-r-300m-ta
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
automatic-speech-recognition
|
transformers
|
<!-- 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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the OPENSLR_SLR66 - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2680
- Wer: 0.3467
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.0304 | 4.81 | 500 | 1.5676 | 1.0554 |
| 1.5263 | 9.61 | 1000 | 0.4693 | 0.8023 |
| 1.5299 | 14.42 | 1500 | 0.4368 | 0.7311 |
| 1.5063 | 19.23 | 2000 | 0.4360 | 0.7302 |
| 1.455 | 24.04 | 2500 | 0.4213 | 0.6692 |
| 1.4755 | 28.84 | 3000 | 0.4329 | 0.5943 |
| 1.352 | 33.65 | 3500 | 0.4074 | 0.5765 |
| 1.3122 | 38.46 | 4000 | 0.3866 | 0.5630 |
| 1.2799 | 43.27 | 4500 | 0.3860 | 0.5480 |
| 1.212 | 48.08 | 5000 | 0.3590 | 0.5317 |
| 1.1645 | 52.88 | 5500 | 0.3283 | 0.4757 |
| 1.0854 | 57.69 | 6000 | 0.3162 | 0.4687 |
| 1.0292 | 62.5 | 6500 | 0.3126 | 0.4416 |
| 0.9607 | 67.31 | 7000 | 0.2990 | 0.4066 |
| 0.9156 | 72.12 | 7500 | 0.2870 | 0.4009 |
| 0.8329 | 76.92 | 8000 | 0.2791 | 0.3909 |
| 0.7979 | 81.73 | 8500 | 0.2770 | 0.3670 |
| 0.7144 | 86.54 | 9000 | 0.2841 | 0.3661 |
| 0.6997 | 91.35 | 9500 | 0.2721 | 0.3485 |
| 0.6568 | 96.15 | 10000 | 0.2681 | 0.3437 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
{"language": ["te"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "openslr_SLR66", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["openslr", "SLR66"], "metrics": ["wer"], "model-index": [{"name": "xls-r-300m-te", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Open SLR", "type": "openslr", "args": "SLR66"}, "metrics": [{"type": "wer", "value": 24.695121951219512, "name": "Test WER"}, {"type": "cer", "value": 4.861934182322532, "name": "Test CER"}]}]}]}
|
chmanoj/xls-r-300m-te
| null |
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"openslr_SLR66",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"te",
"dataset:openslr",
"dataset:SLR66",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
<!-- 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. -->
#
This model is a fine-tuned version of [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.
It achieves the following results on the evaluation set:
- Loss: 156.8786
- Wer: 1.3460
## 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: 0.0003
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu113
- Datasets 1.18.1.dev0
- Tokenizers 0.10.3
|
{"language": ["ab"], "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]}
|
chmanoj/xls-r-demo-test
| null |
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"ab",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
chntl/b3t5-pmid-removed
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
chnyangs/bert-base-uncased-finetuned-cola
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
chnyangs/distilbert-base-uncased-finetuned-cola
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
cho/distilbert-base-uncased-finetuned-squad
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
chochan/tan
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
choil/testFineTune
| null |
[
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
automatic-speech-recognition
|
transformers
|
# Wav2Vec2-Large-XLSR-53 in Thai Language (Train with deepcut tokenizer)
|
{"language": "th", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning"], "datasets": ["common_voice"]}
|
chompk/wav2vec2-large-xlsr-thai-tokenized
| null |
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning",
"th",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-classification
|
transformers
|
{}
|
choondrise/emolve
| null |
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text2text-generation
|
transformers
|
Test English-Dhivehi/Dhivehi-English NMT
Would need a lot more data to get accurate translations.
|
{}
|
chopey/testmntdv
| null |
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
These models were made for my course project in NLP and AI special course at the University of Latvia during my first semester of study.
|
{}
|
chrisAS12/specseminars
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
chrisevan/68
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
automatic-speech-recognition
|
transformers
|
# Wav2Vec2-Large-XLSR-53-Fon
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on [Fon (or Fongbe)](https://en.wikipedia.org/wiki/Fon_language) using the [Fon Dataset](https://github.com/laleye/pyFongbe/tree/master/data).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import json
import random
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
#Load test_dataset from saved files in folder
from datasets import load_dataset, load_metric
#for test
for root, dirs, files in os.walk(test/):
test_dataset= load_dataset("json", data_files=[os.path.join(root,i) for i in files],split="train")
#Remove unnecessary chars
chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\β\\%\\β\\β]'
def remove_special_characters(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
return batch
test_dataset = test_dataset.map(remove_special_characters)
processor = Wav2Vec2Processor.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon")
model = Wav2Vec2ForCTC.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon")
#No need for resampling because audio dataset already at 16kHz
#resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"]=speech_array.squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on our unique Fon test data.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
for root, dirs, files in os.walk(test/):
test_dataset = load_dataset("json", data_files=[os.path.join(root,i) for i in files],split="train")
chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\β\\%\\β\\β]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
return batch
test_dataset = test_dataset.map(remove_special_characters)
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon")
model = Wav2Vec2ForCTC.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon")
model.to("cuda")
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = speech_array[0].numpy()
batch["sampling_rate"] = sampling_rate
batch["target_text"] = batch["sentence"]
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
#Evaluation on test dataset
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 14.97 %
## Training
The [Fon dataset](https://github.com/laleye/pyFongbe/tree/master/data) was split into `train`(8235 samples), `validation`(1107 samples), and `test`(1061 samples).
The script used for training can be found [here](https://colab.research.google.com/drive/11l6qhJCYnPTG1TQZ8f3EvKB9z12TQi4g?usp=sharing)
# Collaborators on this project
- Chris C. Emezue ([Twitter](https://twitter.com/ChrisEmezue))|([email protected])
- Bonaventure F.P. Dossou (HuggingFace Username: [bonadossou](https://huggingface.co/bonadossou))|([Twitter](https://twitter.com/bonadossou))|([email protected])
## This is a joint project continuing our research on [OkwuGbΓ©: End-to-End Speech Recognition for Fon and Igbo](https://arxiv.org/abs/2103.07762)
|
{"language": "fon", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week", "hf-asr-leaderboard"], "datasets": ["fon_dataset"], "metrics": ["wer"], "model-index": [{"name": "Fon XLSR Wav2Vec2 Large 53", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "fon", "type": "fon_dataset", "args": "fon"}, "metrics": [{"type": "wer", "value": 14.97, "name": "Test WER"}]}]}]}
|
chrisjay/fonxlsr
| null |
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"hf-asr-leaderboard",
"fon",
"dataset:fon_dataset",
"arxiv:2103.07762",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
# Interacting with the Masakhane Benchmark Models
I created this demo for very easy interaction with the [benchmark models on Masakhane](https://github.com/masakhane-io/masakhane-mt/tree/master/benchmarks) which were trained with [JoeyNMT](https://github.com/chrisemezue/joeynmt)(my forked version).
To access the space click [here](https://huggingface.co/spaces/chrisjay/masakhane-benchmarks).
To include your language, all you need to do is:
1. Create a folder in the format *src-tgt/main* for your language pair, if it does not exist.
2. Inside the *main* folder put the following files:
1. model checkpoint. Rename it to `best.ckpt`.
2. `config.yaml` file. This is the JoeyNMT config file which loads the model an pre-processing parameters.
3. `src_vocab.txt` file.
4. `trg_vocab.txt` file.
The space currently supports these languages:
| source language | target language |
|:---------------:|:---------------:|
| English | Swahili |
| English | Afrikaans |
| English | Arabic |
| English | Urhobo |
| English | αΊΈΜdΓ³ |
| Efik | English |
| English | Hausa |
| English | Igbo |
| English | Fon |
| English | Twi |
| English | Dendi |
| English | αΊΈΜsΓ‘n |
| English | Isoko |
| English | Kamba |
| English | Luo |
| English | Southern Ndebele |
| English | Tshivenda |
| Shona | English |
| Swahili | English |
| Yoruba | English |
TO DO:
1. Include more languages from the benchmark.
|
{"language": "african-languages", "license": "apache-2.0", "tags": ["african-languages", "machine-translation", "text"]}
|
chrisjay/masakhane_benchmarks
| null |
[
"african-languages",
"machine-translation",
"text",
"license:apache-2.0",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
fill-mask
|
transformers
|
{}
|
chriskhanhtran/spanberta
| null |
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-classification
|
spacy
|
Text statistics including readability and formality.
| Feature | Description |
| --- | --- |
| **Name** | `en_statistics` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.1.1,<3.2.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `syllables`, `formality`, `readability` |
| **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `syllables`, `formality`, `readability` |
| **Vectors** | 684830 keys, 20000 unique vectors (300 dimensions) |
| **Sources** | [OntoNotes 5](https://catalog.ldc.upenn.edu/LDC2013T19) (Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston)<br />[ClearNLP Constituent-to-Dependency Conversion](https://github.com/clir/clearnlp-guidelines/blob/master/md/components/dependency_conversion.md) (Emory University)<br />[WordNet 3.0](https://wordnet.princeton.edu/) (Princeton University)<br />[GloVe Common Crawl](https://nlp.stanford.edu/projects/glove/) (Jeffrey Pennington, Richard Socher, and Christopher D. Manning) |
| **License** | `MIT` |
| **Author** | [Chris Knowles](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (96 labels for 3 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, ```` |
| **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `csubj`, `csubjpass`, `dative`, `dep`, `det`, `dobj`, `expl`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `oprd`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` |
| **`senter`** | `I`, `S` |
</details>
|
{"language": ["en"], "license": "mit", "tags": ["spacy", "text-classification"], "model-index": [{"name": "en_statistics", "results": []}]}
|
chrisknowles/en_statistics
| null |
[
"spacy",
"text-classification",
"en",
"license:mit",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
token-classification
|
spacy
|
Check style on English text (currently passive text).
| Feature | Description |
| --- | --- |
| **Name** | `en_stylecheck` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.1.1,<3.2.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner`, `stylecheck` |
| **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner`, `stylecheck` |
| **Vectors** | 684830 keys, 20000 unique vectors (300 dimensions) |
| **Sources** | n/a |
| **License** | `MIT` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (115 labels for 5 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, ```` |
| **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `csubj`, `csubjpass`, `dative`, `dep`, `det`, `dobj`, `expl`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `oprd`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` |
| **`senter`** | `I`, `S` |
| **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` |
| **`entity_ruler`** | `PASSIVE` |
</details>
|
{"language": ["en"], "license": "mit", "tags": ["spacy", "token-classification"], "model-index": [{"name": "en_stylecheck", "results": []}]}
|
chrisknowles/en_stylecheck
| null |
[
"spacy",
"token-classification",
"en",
"license:mit",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
chrisl/gpt-neo-2.7B
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
christima/DialogGPT-small-harrypotter
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
christina/decoder-only-transformer-small
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
christina/decoder-only-transformer-x2small
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
christina/decoder-only-transformer-x3small
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
christina/decoder-only-transformer-x4small
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
christina/decoder-only-transformer-x5small
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
christina/decoder-only-transformer-x6small
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null |
transformers
|
{}
|
christophalt/test-model
| null |
[
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-generation
|
transformers
|
[DistilGPT2](https://huggingface.co/distilgpt2) English language model fine-tuned on mathematical proofs extracted from [arXiv.org](https://arxiv.org) LaTeX sources from 1992 to 2020.
Proofs have been cleaned up a bit. In particular, they use
* `CITE` for any citation
* `REF` for any reference
* `MATH` for any LaTeX mathematical formula
* `CASE:` for any `\item` or labeled subcase.
|
{"widget": [{"text": "Let MATH be given."}, {"text": "If MATH is a nonempty"}, {"text": "By the inductive hypothesis,"}]}
|
christopherastone/distilgpt2-proofs
| null |
[
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
christopherlaangsell/model_name
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-classification
|
transformers
|
<!-- 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-multilingual-cased-finetuned-cola
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1729
- Accuracy: 0.9755
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5119 | 1.0 | 625 | 0.2386 | 0.922 |
| 0.2536 | 2.0 | 1250 | 0.2055 | 0.949 |
| 0.1718 | 3.0 | 1875 | 0.1733 | 0.969 |
| 0.0562 | 4.0 | 2500 | 0.1661 | 0.974 |
| 0.0265 | 5.0 | 3125 | 0.1729 | 0.9755 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model_index": [{"name": "bert-base-multilingual-cased-finetuned-cola", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9755}}]}]}
|
chrommium/bert-base-multilingual-cased-finetuned-news-headlines
| null |
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-classification
|
transformers
|
{}
|
chrommium/helper-model
| null |
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-classification
|
transformers
|
<!-- 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. -->
# rubert-base-cased-sentence-finetuned-headlines_X
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased-sentence](https://huggingface.co/DeepPavlov/rubert-base-cased-sentence) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2535
- Accuracy: 0.952
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 157 | 0.2759 | 0.912 |
| No log | 2.0 | 314 | 0.2538 | 0.936 |
| No log | 3.0 | 471 | 0.2556 | 0.945 |
| 0.1908 | 4.0 | 628 | 0.2601 | 0.95 |
| 0.1908 | 5.0 | 785 | 0.2535 | 0.952 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "metrics": ["accuracy"]}
|
chrommium/rubert-base-cased-sentence-finetuned-headlines_X
| null |
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-classification
|
transformers
|
<!-- 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. -->
# rubert-base-cased-sentence-finetuned-sent_in_news_sents
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased-sentence](https://huggingface.co/DeepPavlov/rubert-base-cased-sentence) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9506
- Accuracy: 0.7224
- F1: 0.5137
## 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: 14
- eval_batch_size: 14
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 81 | 1.0045 | 0.6690 | 0.1388 |
| No log | 2.0 | 162 | 0.9574 | 0.6228 | 0.2980 |
| No log | 3.0 | 243 | 1.0259 | 0.6477 | 0.3208 |
| No log | 4.0 | 324 | 1.1262 | 0.6619 | 0.4033 |
| No log | 5.0 | 405 | 1.3377 | 0.6299 | 0.3909 |
| No log | 6.0 | 486 | 1.5716 | 0.6868 | 0.3624 |
| 0.6085 | 7.0 | 567 | 1.6286 | 0.6762 | 0.4130 |
| 0.6085 | 8.0 | 648 | 1.6450 | 0.6940 | 0.4775 |
| 0.6085 | 9.0 | 729 | 1.7108 | 0.7224 | 0.4920 |
| 0.6085 | 10.0 | 810 | 1.8792 | 0.7046 | 0.5028 |
| 0.6085 | 11.0 | 891 | 1.8670 | 0.7153 | 0.4992 |
| 0.6085 | 12.0 | 972 | 1.8856 | 0.7153 | 0.4934 |
| 0.0922 | 13.0 | 1053 | 1.9506 | 0.7224 | 0.5137 |
| 0.0922 | 14.0 | 1134 | 2.0363 | 0.7189 | 0.4761 |
| 0.0922 | 15.0 | 1215 | 2.0601 | 0.7224 | 0.5053 |
| 0.0922 | 16.0 | 1296 | 2.0813 | 0.7153 | 0.5038 |
| 0.0922 | 17.0 | 1377 | 2.0960 | 0.7189 | 0.5065 |
| 0.0922 | 18.0 | 1458 | 2.1060 | 0.7224 | 0.5098 |
| 0.0101 | 19.0 | 1539 | 2.1153 | 0.7260 | 0.5086 |
| 0.0101 | 20.0 | 1620 | 2.1187 | 0.7260 | 0.5086 |
### Framework versions
- Transformers 4.10.3
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"]}
|
chrommium/rubert-base-cased-sentence-finetuned-sent_in_news_sents
| null |
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-classification
|
transformers
|
<!-- 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. -->
# rubert-base-cased-sentence-finetuned-sent_in_ru
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased-sentence](https://huggingface.co/DeepPavlov/rubert-base-cased-sentence) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3503
- Accuracy: 0.6884
- F1: 0.6875
## 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: 15
- eval_batch_size: 15
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 441 | 0.7397 | 0.6630 | 0.6530 |
| 0.771 | 2.0 | 882 | 0.7143 | 0.6909 | 0.6905 |
| 0.5449 | 3.0 | 1323 | 0.8385 | 0.6897 | 0.6870 |
| 0.3795 | 4.0 | 1764 | 0.8851 | 0.6939 | 0.6914 |
| 0.3059 | 5.0 | 2205 | 1.0728 | 0.6933 | 0.6953 |
| 0.2673 | 6.0 | 2646 | 1.0673 | 0.7060 | 0.7020 |
| 0.2358 | 7.0 | 3087 | 1.5200 | 0.6830 | 0.6829 |
| 0.2069 | 8.0 | 3528 | 1.3439 | 0.7024 | 0.7016 |
| 0.2069 | 9.0 | 3969 | 1.3545 | 0.6830 | 0.6833 |
| 0.1724 | 10.0 | 4410 | 1.5591 | 0.6927 | 0.6902 |
| 0.1525 | 11.0 | 4851 | 1.6425 | 0.6818 | 0.6823 |
| 0.131 | 12.0 | 5292 | 1.8999 | 0.6836 | 0.6775 |
| 0.1253 | 13.0 | 5733 | 1.6959 | 0.6884 | 0.6877 |
| 0.1132 | 14.0 | 6174 | 1.9561 | 0.6776 | 0.6803 |
| 0.0951 | 15.0 | 6615 | 2.0356 | 0.6763 | 0.6754 |
| 0.1009 | 16.0 | 7056 | 1.7995 | 0.6842 | 0.6741 |
| 0.1009 | 17.0 | 7497 | 2.0638 | 0.6884 | 0.6811 |
| 0.0817 | 18.0 | 7938 | 2.1686 | 0.6884 | 0.6859 |
| 0.0691 | 19.0 | 8379 | 2.0874 | 0.6878 | 0.6889 |
| 0.0656 | 20.0 | 8820 | 2.1772 | 0.6854 | 0.6817 |
| 0.0652 | 21.0 | 9261 | 2.4018 | 0.6872 | 0.6896 |
| 0.0608 | 22.0 | 9702 | 2.2074 | 0.6770 | 0.6656 |
| 0.0677 | 23.0 | 10143 | 2.2101 | 0.6848 | 0.6793 |
| 0.0559 | 24.0 | 10584 | 2.2920 | 0.6848 | 0.6835 |
| 0.0524 | 25.0 | 11025 | 2.3503 | 0.6884 | 0.6875 |
### Framework versions
- Transformers 4.11.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "rubert-base-cased-sentence-finetuned-sent_in_ru", "results": []}]}
|
chrommium/rubert-base-cased-sentence-finetuned-sent_in_ru
| null |
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-classification
|
transformers
|
<!-- 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. -->
# sbert_large-finetuned-sent_in_news_sents
This model is a fine-tuned version of [sberbank-ai/sbert_large_nlu_ru](https://huggingface.co/sberbank-ai/sbert_large_nlu_ru) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7056
- Accuracy: 0.7301
- F1: 0.5210
## Model examples
Model responds to label X in news text. For exaple:
For 'ΠΠ°Π·ΠΏΡΠΎΠΌ ΠΎΡΠΎΠ·Π²Π°Π» Π»ΠΈΡΠ΅Π½Π·ΠΈΡ Ρ X, ΡΠΎΠΎΠ±ΡΠ°Π΅Ρ Π€ΠΈΠ½Π°ΠΌ' the model will return negative label -3
For 'X ΠΎΡΠΎΠ·Π²Π°Π» Π»ΠΈΡΠ΅Π½Π·ΠΈΡ Ρ Π‘Π±Π΅ΡΠ±Π°Π½ΠΊΠ°, ΡΠΎΠΎΠ±ΡΠ°Π΅Ρ Π€ΠΈΠ½Π°ΠΌ' the model will return neutral label 0
For 'ΠΠ°Π·ΠΏΡΠΎΠΌ ΠΎΡΠΎΠ·Π²Π°Π» Π»ΠΈΡΠ΅Π½Π·ΠΈΡ Ρ Π‘Π±Π΅ΡΠ±Π°Π½ΠΊΠ°, ΡΠΎΠΎΠ±ΡΠ°Π΅Ρ X' the model will return neutral label 0
For 'X Π΄Π΅ΠΌΠΎΠ½ΡΡΡΠΈΡΡΠ΅Ρ Π²ΡΡΠΎΠΊΡΡ ΠΏΡΠΈΠ±ΡΠ»Ρ, ΡΠΎΠΎΠ±ΡΠ°Π΅Ρ Π€ΠΈΠ½Π°ΠΌ' the model will return positive label 1
## Simple example of News preprocessing for Russian before BERT
```
from natasha import (
Segmenter,
MorphVocab,
NewsEmbedding,
NewsMorphTagger,
NewsSyntaxParser,
NewsNERTagger,
PER,
NamesExtractor,
Doc
)
segmenter = Segmenter()
emb = NewsEmbedding()
morph_tagger = NewsMorphTagger(emb)
syntax_parser = NewsSyntaxParser(emb)
morph_vocab = MorphVocab()
### ----------------------------- key sentences block -----------------------------
def find_synax_tokens_with_order(doc, start, tokens, text_arr, full_str):
''' ΠΠ°Ρ
ΠΎΠ΄ΠΈΡ Π²ΡΠ΅ ΡΠΈΠ½ΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠΎΠΊΠ΅Π½Ρ, ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΡΡΡΠΈΠ΅ Π·Π°Π΄Π°Π½Π½ΠΎΠΌΡ Π½Π°Π±ΠΎΡΡ ΠΏΡΠΎΡΡΡΡ
ΡΠΎΠΊΠ΅Π½ΠΎΠ² (Π½Π°ΠΉΠ΄Π΅Π½Π½ΡΠ΅
Π΄Π»Ρ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΠΉ NER Π΄ΡΡΠ³ΠΈΠΌΠΈ ΡΡΠ½ΠΊΡΠΈΡΠΌΠΈ).
ΠΠΎΠ·Π²ΡΠ°ΡΠ°Π΅Ρ ΡΠ»ΠΎΠ²Π°ΡΡ Π½Π°ΠΉΠ΄Π΅Π½Π½ΡΡ
ΡΠΈΠ½ΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΎΠΊΠ΅Π½ΠΎΠ² (ΠΊΠ»ΡΡ - ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΎΡ ΡΠΎΠΊΠ΅Π½Π°, ΡΠΎΡΡΠΎΡΡΠΈΠΉ
ΠΈΠ· Π½ΠΎΠΌΠ΅ΡΠ° ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΈΡ ΠΈ Π½ΠΎΠΌΠ΅ΡΠ° ΡΠΎΠΊΠ΅Π½Π° Π²Π½ΡΡΡΠΈ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΈΡ).
ΠΠ°ΡΠΈΠ½Π°Π΅Ρ ΠΏΠΎΠΈΡΠΊ Ρ ΡΠΊΠ°Π·Π°Π½Π½ΠΎΠΉ ΠΏΠΎΠ·ΠΈΡΠΈΠΈ Π² ΡΠΏΠΈΡΠΊΠ΅ ΡΠΈΠ½ΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΎΠΊΠ΅Π½ΠΎΠ², Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎ Π²ΠΎΠ·Π²ΡΠ°ΡΠ°Π΅Ρ
ΠΏΠΎΠ·ΠΈΡΠΈΡ ΠΎΡΡΠ°Π½ΠΎΠ²ΠΊΠΈ, Ρ ΠΊΠΎΡΠΎΡΠΎΠΉ Π½ΡΠΆΠ½ΠΎ ΠΏΡΠΎΠ΄ΠΎΠ»ΠΆΠΈΡΡ ΠΏΠΎΠΈΡΠΊ ΡΠ»Π΅Π΄ΡΡΡΠ΅ΠΉ NER.
'''
found = []
in_str = False
str_candidate = ''
str_counter = 0
if len(text_arr) == 0:
return [], start
for i in range(start, len(doc.syntax.tokens)):
t = doc.syntax.tokens[i]
if in_str:
str_counter += 1
if str_counter < len(text_arr) and t.text == text_arr[str_counter]:
str_candidate += t.text
found.append(t)
if str_candidate == full_str:
return found, i+1
else:
in_str = False
str_candidate = ''
str_counter = 0
found = []
if t.text == text_arr[0]:
found.append(t)
str_candidate = t.text
if str_candidate == full_str:
return found, i+1
in_str = True
return [], len(doc.syntax.tokens)
def find_tokens_in_diap_with_order(doc, start_token, diap):
''' ΠΠ°Ρ
ΠΎΠ΄ΠΈΡ Π²ΡΠ΅ ΠΏΡΠΎΡΡΡΠ΅ ΡΠΎΠΊΠ΅Π½Ρ (Π±Π΅Π· ΡΠΈΠ½ΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ), ΠΊΠΎΡΠΎΡΡΠ΅ ΠΏΠΎΠΏΠ°Π΄Π°ΡΡ Π²
ΡΠΊΠ°Π·Π°Π½Π½ΡΠΉ Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½. ΠΡΠΈ Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½Ρ ΠΌΡ ΠΏΠΎΠ»ΡΡΠ°Π΅ΠΌ ΠΈΠ· ΡΠ°Π·ΠΌΠ΅ΡΠΊΠΈ NER.
ΠΠΎΠ·Π²ΡΠ°ΡΠ°Π΅Ρ Π½Π°Π±ΠΎΡ Π½Π°ΠΉΠ΄Π΅Π½Π½ΡΡ
ΡΠΎΠΊΠ΅Π½ΠΎΠ² ΠΈ Π² Π²ΠΈΠ΄Π΅ ΠΌΠ°ΡΡΠΈΠ²Π° ΡΠΎΠΊΠ΅Π½ΠΎΠ², ΠΈ Π² Π²ΠΈΠ΄Π΅ ΠΌΠ°ΡΡΠΈΠ²Π° ΡΡΡΠΎΡΠ΅ΠΊ.
ΠΠ°ΡΠΈΠ½Π°Π΅Ρ ΠΏΠΎΠΈΡΠΊ Ρ ΡΠΊΠ°Π·Π°Π½Π½ΠΎΠΉ ΠΏΠΎΠ·ΠΈΡΠΈΠΈ Π² ΡΡΡΠΎΠΊΠ΅ ΠΈ Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎ Π²ΠΎΠ·Π²ΡΠ°ΡΠ°Π΅Ρ ΠΏΠΎΠ·ΠΈΡΠΈΡ ΠΎΡΡΠ°Π½ΠΎΠ²ΠΊΠΈ.
'''
found_tokens = []
found_text = []
full_str = ''
next_i = 0
for i in range(start_token, len(doc.tokens)):
t = doc.tokens[i]
if t.start > diap[-1]:
next_i = i
break
if t.start in diap:
found_tokens.append(t)
found_text.append(t.text)
full_str += t.text
return found_tokens, found_text, full_str, next_i
def add_found_arr_to_dict(found, dict_dest):
for synt in found:
dict_dest.update({synt.id: synt})
return dict_dest
def make_all_syntax_dict(doc):
all_syntax = {}
for synt in doc.syntax.tokens:
all_syntax.update({synt.id: synt})
return all_syntax
def is_consiquent(id_1, id_2):
''' ΠΡΠΎΠ²Π΅ΡΡΠ΅Ρ ΠΈΠ΄ΡΡ Π»ΠΈ ΡΠΎΠΊΠ΅Π½Ρ Π΄ΡΡΠ³ Π·Π° Π΄ΡΡΠ³ΠΎΠΌ Π±Π΅Π· ΠΏΡΠΎΠΌΠ΅ΠΆΡΡΠΊΠ° ΠΏΠΎ ΠΊΠ»ΡΡΠ°ΠΌ. '''
id_1_list = id_1.split('_')
id_2_list = id_2.split('_')
if id_1_list[0] != id_2_list[0]:
return False
return int(id_1_list[1]) + 1 == int(id_2_list[1])
def replace_found_to(found, x_str):
''' ΠΠ°ΠΌΠ΅Π½ΡΠ΅Ρ ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΡ ΡΠΎΠΊΠ΅Π½ΠΎΠ² NER Π½Π° Β«Π·Π°Π³Π»ΡΡΠΊΡΒ». '''
prev_id = '0_0'
for synt in found:
if is_consiquent(prev_id, synt.id):
synt.text = ''
else:
synt.text = x_str
prev_id = synt.id
def analyze_doc(text):
''' ΠΠ°ΠΏΡΡΠΊΠ°Π΅Ρ Natasha Π΄Π»Ρ Π°Π½Π°Π»ΠΈΠ·Π° Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ°. '''
doc = Doc(text)
doc.segment(segmenter)
doc.tag_morph(morph_tagger)
doc.parse_syntax(syntax_parser)
ner_tagger = NewsNERTagger(emb)
doc.tag_ner(ner_tagger)
return doc
def find_non_sym_syntax_short(entity_name, doc, add_X=False, x_str='X'):
''' ΠΡΡΡΠΊΠΈΠ²Π°Π΅Ρ Π·Π°Π΄Π°Π½Π½ΡΡ ΡΡΡΠ½ΠΎΡΡΡ Π² ΡΠ΅ΠΊΡΡΠ΅, ΡΡΠ΅Π΄ΠΈ Π²ΡΠ΅Ρ
NER (Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎ, Π² Π΄ΡΡΠ³ΠΎΠΉ Π³ΡΠ°ΠΌΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΎΡΠΌΠ΅).
entity_name - ΡΡΡΠ½ΠΎΡΡΡ, ΠΊΠΎΡΠΎΡΡΡ ΠΈΡΠ΅ΠΌ;
doc - Π΄ΠΎΠΊΡΠΌΠ΅Π½Ρ, Π² ΠΊΠΎΡΠΎΡΠΎΠΌ ΡΠ΄Π΅Π»Π°Π½ ΠΏΡΠ΅ΠΏΡΠΎΡΠ΅ΡΡΠΈΠ½Π³ Natasha;
add_X - ΡΠ΄Π΅Π»Π°ΡΡ Π»ΠΈ Π·Π°ΠΌΠ΅Π½Ρ ΡΡΡΠ½ΠΎΡΡΠΈ Π½Π° Β«Π·Π°Π³Π»ΡΡΠΊΡΒ»;
x_str - ΡΠ΅ΠΊΡΡ Π·Π°ΠΌΠ΅Π½Ρ.
ΠΠΎΠ·Π²ΡΠ°ΡΠ°Π΅Ρ:
all_found_syntax - ΡΠ»ΠΎΠ²Π°ΡΡ Π²ΡΠ΅Ρ
ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΡΡΠΈΡ
ΡΠΎΠΊΠ΅Π½ΠΎΠ² ΠΎΠ±ΡΠ°Π·ΡΡΡΠΈΡ
ΠΈΡΠΊΠΎΠΌΡΠ΅ ΡΡΡΠ½ΠΎΡΡΠΈ, Π² ΠΊΠΎΡΠΎΡΠΎΠΌ
Π² ΡΠ»ΡΡΠ°Π΅ Π½Π°Π΄ΠΎΠ±Π½ΠΎΡΡΠΈ ΠΏΡΠΎΠΈΠ·Π²Π΅Π΄Π΅Π½Π° Π·Π°ΠΌΠ΅Π½Π° NER Π½Π° Β«Π·Π°Π³Π»ΡΡΠΊΡΒ»;
all_syntax - ΡΠ»ΠΎΠ²Π°ΡΡ Π²ΡΠ΅Ρ
ΡΠΎΠΊΠ΅Π½ΠΎΠ².
'''
all_found_syntax = {}
current_synt_number = 0
current_tok_number = 0
# ΠΈΠ΄Π΅ΠΌ ΠΏΠΎ Π²ΡΠ΅ΠΌ Π½Π°ΠΉΠ΄Π΅Π½Π½ΡΠΌ NER
for span in doc.spans:
span.normalize(morph_vocab)
if span.type != 'ORG':
continue
diap = range(span.start, span.stop)
# ΡΠΎΠ·Π΄Π°Π΅ΠΌ ΡΠ»ΠΎΠ²Π°ΡΡ Π²ΡΠ΅Ρ
ΡΠΈΠ½ΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ»Π΅ΠΌΠ΅Π½ΡΠΎΠ² (ΠΊΠ»ΡΡ -- id ΠΈΠ· Π½ΠΎΠΌΠ΅ΡΠ° ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΈΡ ΠΈ Π½ΠΎΠΌΠ΅ΡΠ° Π²Π½ΡΡΡΠΈ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΈΡ)
all_syntax = make_all_syntax_dict(doc)
# Π½Π°Ρ
ΠΎΠ΄ΠΈΠΌ Π²ΡΠ΅ ΠΏΡΠΎΡΡΡΠ΅ ΡΠΎΠΊΠ΅Π½Ρ Π²Π½ΡΡΡΠΈ NER
found_tokens, found_text, full_str, current_tok_number = find_tokens_in_diap_with_order(doc, current_tok_number,
diap)
# ΠΏΠΎ Π½Π°ΠΉΠ΄Π΅Π½Π½ΡΠΌ ΠΏΡΠΎΡΡΡΠΌ ΡΠΎΠΊΠ΅Π½Π°ΠΌ Π½Π°Ρ
ΠΎΠ΄ΠΈΠΌ Π²ΡΠ΅ ΡΠΈΠ½ΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠΎΠΊΠ΅Π½Ρ Π²Π½ΡΡΡΠΈ Π΄Π°Π½Π½ΠΎΠ³ΠΎ NER
found, current_synt_number = find_synax_tokens_with_order(doc, current_synt_number, found_tokens, found_text,
full_str)
# Π΅ΡΠ»ΠΈ ΡΠ΅ΠΊΡΡ NER ΡΠΎΠ²ΠΏΠ°Π΄Π°Π΅Ρ Ρ ΡΠΊΠ°Π·Π°Π½Π½ΠΎΠΉ ΡΡΡΠ½ΠΎΡΡΡΡ, ΡΠΎ Π΄Π΅Π»Π°Π΅ΠΌ Π·Π°ΠΌΠ΅Π½Ρ
if entity_name.find(span.normal) >= 0 or span.normal.find(entity_name) >= 0:
if add_X:
replace_found_to(found, x_str)
all_found_syntax = add_found_arr_to_dict(found, all_found_syntax)
return all_found_syntax, all_syntax
def key_sentences(all_found_syntax):
''' ΠΠ°Ρ
ΠΎΠ΄ΠΈΡ Π½ΠΎΠΌΠ΅ΡΠ° ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΈΠΉ Ρ ΠΈΡΠΊΠΎΠΌΠΎΠΉ NER. '''
key_sent_numb = {}
for synt in all_found_syntax.keys():
key_sent_numb.update({synt.split('_')[0]: 1})
return key_sent_numb
def openinig_punct(x):
opennings = ['Β«', '(']
return x in opennings
def key_sentences_str(entitiy_name, doc, add_X=False, x_str='X', return_all=True):
''' Π‘ΠΎΡΡΠ°Π²Π»ΡΠ΅Ρ ΠΎΠΊΠΎΠ½ΡΠ°ΡΠ΅Π»ΡΠ½ΡΠΉ ΡΠ΅ΠΊΡΡ, Π² ΠΊΠΎΡΠΎΡΠΎΠΌ Π΅ΡΡΡ ΡΠΎΠ»ΡΠΊΠΎ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΈΡ, Π³Π΄Π΅ Π΅ΡΡΡ ΠΊΠ»ΡΡΠ΅Π²Π°Ρ ΡΡΡΠ½ΠΎΡΡΡ,
ΡΡΠ° ΡΡΡΠ½ΠΎΡΡΡ, Π΅ΡΠ»ΠΈ ΡΠΊΠ°Π·Π°Π½ΠΎ, Π·Π°ΠΌΠ΅Π½ΡΠ΅ΡΡΡ Π½Π° Β«Π·Π°Π³Π»ΡΡΠΊΡΒ».
'''
all_found_syntax, all_syntax = find_non_sym_syntax_short(entitiy_name, doc, add_X, x_str)
key_sent_numb = key_sentences(all_found_syntax)
str_ret = ''
for s in all_syntax.keys():
if (s.split('_')[0] in key_sent_numb.keys()) or (return_all):
to_add = all_syntax[s]
if s in all_found_syntax.keys():
to_add = all_found_syntax[s]
else:
if to_add.rel == 'punct' and not openinig_punct(to_add.text):
str_ret = str_ret.rstrip()
str_ret += to_add.text
if (not openinig_punct(to_add.text)) and (to_add.text != ''):
str_ret += ' '
return str_ret
### ----------------------------- key entities block -----------------------------
def find_synt(doc, synt_id):
for synt in doc.syntax.tokens:
if synt.id == synt_id:
return synt
return None
def is_subj(doc, synt, recursion_list=[]):
''' Π‘ΠΎΠΎΠ±ΡΠ°Π΅Ρ ΡΠ²Π»ΡΠ΅ΡΡΡ Π»ΠΈ ΡΠ»ΠΎΠ²ΠΎ ΠΏΠΎΠ΄Π»Π΅ΠΆΠ°ΡΠΈΠΌ ΠΈΠ»ΠΈ ΡΠ°ΡΡΡΡ ΡΠ»ΠΎΠΆΠ½ΠΎΠ³ΠΎ ΠΏΠΎΠ΄Π»Π΅ΠΆΠ°ΡΠ΅Π³ΠΎ. '''
if synt.rel == 'nsubj':
return True
if synt.rel == 'appos':
found_head = find_synt(doc, synt.head_id)
if found_head.id in recursion_list:
return False
return is_subj(doc, found_head, recursion_list + [synt.id])
return False
def find_subjects_in_syntax(doc):
''' ΠΡΠ΄Π°Π΅Ρ ΡΠ»ΠΎΠ²Π°ΡΠΈΠΊ, Π² ΠΊΠΎΡΠΎΡΠΎΠΌ Π΄Π»Ρ ΠΊΠ°ΠΆΠ΄ΠΎΠΉ NER Π½Π°ΠΏΠΈΡΠ°Π½ΠΎ, ΡΠ²Π»ΡΠ΅ΡΡΡ Π»ΠΈ ΠΎΠ½
ΠΏΠΎΠ΄Π»Π΅ΠΆΠ°ΡΠΈΠΌ Π² ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΈΠΈ.
ΠΡΠ΄Π°Π΅Ρ ΡΡΠ°ΡΡΠΎΠ²ΡΡ ΠΏΠΎΠ·ΠΈΡΠΈΡ NER ΠΈ Π±ΡΠ»ΠΎ Π»ΠΈ ΠΎΠ½ΠΎ ΠΏΠΎΠ΄Π»Π΅ΠΆΠ°ΡΠΈΠΌ (ΠΈΠ»ΠΈ appos)
'''
found_subjects = {}
current_synt_number = 0
current_tok_number = 0
for span in doc.spans:
span.normalize(morph_vocab)
if span.type != 'ORG':
continue
found_subjects.update({span.start: 0})
diap = range(span.start, span.stop)
found_tokens, found_text, full_str, current_tok_number = find_tokens_in_diap_with_order(doc,
current_tok_number,
diap)
found, current_synt_number = find_synax_tokens_with_order(doc, current_synt_number, found_tokens,
found_text, full_str)
found_subjects.update({span.start: 0})
for synt in found:
if is_subj(doc, synt):
found_subjects.update({span.start: 1})
return found_subjects
def entity_weight(lst, c=1):
return c*lst[0]+lst[1]
def determine_subject(found_subjects, doc, new_agency_list, return_best=True, threshold=0.75):
''' ΠΠΏΡΠ΅Π΄Π΅Π»ΡΠ΅Ρ ΠΊΠ»ΡΡΠ΅Π²ΡΡ NER ΠΈ ΡΠΏΠΈΡΠΎΠΊ ΡΠ°ΠΌΡΡ
Π²Π°ΠΆΠ½ΡΡ
NER, ΠΎΡΠ½ΠΎΠ²ΡΠ²Π°ΡΡΡ Π½Π° ΡΠΎΠΌ, ΡΠΊΠΎΠ»ΡΠΊΠΎ
ΡΠ°Π· ΠΊΠ°ΠΆΠ΄Π°Ρ ΠΈΠ· Π½ΠΈΡ
Π²ΡΡΡΠ΅ΡΠ°Π΅ΡΡΡ Π² ΡΠ΅ΠΊΡΡΠ° Π²ΠΎΠΎΠ±ΡΠ΅ ΠΈ ΡΠΊΠΎΠ»ΡΠΊΠΎ ΡΠ°Π· Π² ΡΠΎΠ»ΠΈ ΠΏΠΎΠ΄Π»Π΅ΠΆΠ°ΡΠ΅Π³ΠΎ '''
objects_arr = []
objects_arr_ners = []
should_continue = False
for span in doc.spans:
should_continue = False
span.normalize(morph_vocab)
if span.type != 'ORG':
continue
if span.normal in new_agency_list:
continue
for i in range(len(objects_arr)):
t, lst = objects_arr[i]
if t.find(span.normal) >= 0:
lst[0] += 1
lst[1] += found_subjects[span.start]
should_continue = True
break
if span.normal.find(t) >= 0:
objects_arr[i] = (span.normal, [lst[0]+1, lst[1]+found_subjects[span.start]])
should_continue = True
break
if should_continue:
continue
objects_arr.append((span.normal, [1, found_subjects[span.start]]))
objects_arr_ners.append(span.normal)
max_weight = 0
opt_ent = 0
for obj in objects_arr:
t, lst = obj
w = entity_weight(lst)
if max_weight < w:
max_weight = w
opt_ent = t
if not return_best:
return opt_ent, objects_arr_ners
bests = []
for obj in objects_arr:
t, lst = obj
w = entity_weight(lst)
if max_weight*threshold < w:
bests.append(t)
return opt_ent, bests
text = '''Π ΠΎΡΠΈΡΠ°Ρ
Π‘Π±Π΅ΡΠ° Π½Π°ΡΠ°Π»ΠΈ ΡΠ΅ΡΡΠΈΡΠΎΠ²Π°ΡΡ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡ ΠΏΠΎΠΌΠΎΡΠΈ ΠΏΠΎΡΠ΅ΡΠΈΡΠ΅Π»ΡΠΌ Π² ΡΠΊΡΡΡΠ΅Π½Π½ΡΡ
ΡΠΈΡΡΠ°ΡΠΈΡΡ
. Β«ΠΠ΅Π»Π΅Π½Π°Ρ ΠΊΠ½ΠΎΠΏΠΊΠ°Β» Π±ΡΠ΄Π΅Ρ
Π² Π·ΠΎΠ½Π°Ρ
ΠΊΡΡΠ³Π»ΠΎΡΡΡΠΎΡΠ½ΠΎΠ³ΠΎ ΠΎΠ±ΡΠ»ΡΠΆΠΈΠ²Π°Π½ΠΈΡ ΠΎΡΠΈΡΠΎΠ² Π±Π°Π½ΠΊΠ° Π² ΠΠΎΡΠΎΠ½Π΅ΠΆΠ΅, Π‘Π°Π½ΠΊΡ-ΠΠ΅ΡΠ΅ΡΠ±ΡΡΠ³Π΅, ΠΠΎΠ΄ΠΎΠ»ΡΡΠΊΠ΅, ΠΡΠΊΠΎΠ²Π΅, ΠΡΠ»Π΅ ΠΈ Π―ΡΠΎΡΠ»Π°Π²Π»Π΅.
Π Π½ΠΈΡ
Π½Π°Ρ
ΠΎΠ΄ΡΡΡΡ ΡΡΠ΅Π½Π΄Ρ Ρ ΡΠ΅Π½ΡΠΎΡΠ½ΡΠΌΠΈ ΠΊΠ½ΠΎΠΏΠΊΠ°ΠΌΠΈ, ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°ΡΡΠΈΠ΅ ΡΠ²ΡΠ·Ρ Ρ ΠΎΠΏΠ΅ΡΠ°ΡΠΎΡΠ°ΠΌΠΈ ΡΠ΅Π½ΡΡΠ° ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π° ΡΠ»ΡΠΆΠ±Ρ Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΠΎΡΡΠΈ
Π±Π°Π½ΠΊΠ°. ΠΠΎΠ»ΡΡΠΈΠ² ΡΠΈΠ³Π½Π°Π» ΠΎ ΠΏΠΎΠΌΠΎΡΠΈ, ΠΎΠΏΠ΅ΡΠ°ΡΠΎΡ ΡΠ΅Π½ΡΡΠ° ΠΌΠΎΠΆΠ΅Ρ ΠΏΠΎΠ΄ΠΊΠ»ΡΡΠΈΡΡΡΡ ΠΊ ΠΎΠ±ΡΠ΅ΠΊΡΡ ΠΏΠΎ Π³ΠΎΠ»ΠΎΡΠΎΠ²ΠΎΠΉ ΡΠ²ΡΠ·ΠΈ. Π‘ ΠΏΠΎΠΌΠΎΡΡΡ ΠΊΠ°ΠΌΠ΅Ρ
Π²ΠΈΠ΄Π΅ΠΎΠ½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΡ ΠΎΠ½ ΠΎΡΠ΅Π½ΠΈΡ ΠΎΠ±ΡΡΠ°Π½ΠΎΠ²ΠΊΡ ΠΈ ΠΏΡΠΈ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΠΈ Π²ΡΠ·ΠΎΠ²Π΅Ρ ΠΏΠΎΠ»ΠΈΡΠΈΡ ΠΈΠ»ΠΈ ΡΠΊΠΎΡΡΡ ΠΏΠΎΠΌΠΎΡΡ. Β«ΠΠ΅Π»Π΅Π½ΠΎΠΉ ΠΊΠ½ΠΎΠΏΠΊΠΎΠΉΒ» ΠΌΠΎΠΆΠ½ΠΎ
Π²ΠΎΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΡΡΡ Π² Π½Π΅ΡΠ°Π±ΠΎΡΠ΅Π΅ Π΄Π»Ρ ΠΎΡΠ΄Π΅Π»Π΅Π½ΠΈΡ Π²ΡΠ΅ΠΌΡ, Π΅ΡΠ»ΠΈ Π²ΠΎΠ·Π½ΠΈΠΊΠ»Π° ΡΠ³ΡΠΎΠ·Π° ΠΆΠΈΠ·Π½ΠΈ ΠΈΠ»ΠΈ Π·Π΄ΠΎΡΠΎΠ²ΡΡ. Π ΠΎΡΡΠ°Π»ΡΠ½ΡΡ
ΡΠ»ΡΡΠ°ΡΡ
ΠΏΠΎΠΌΠΎΡΡ
ΠΊΠ»ΠΈΠ΅Π½ΡΠ°ΠΌ Π³ΠΎΡΠΎΠ²Ρ ΡΠΎΡΡΡΠ΄Π½ΠΈΠΊΠΈ ΠΎΡΠ΄Π΅Π»Π΅Π½ΠΈΡ Π±Π°Π½ΠΊΠ°. Β«ΠΠ΄Π½ΠΎ ΠΈΠ· Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠΉ Π½Π°ΡΠ΅ΠΉ ΡΠ°Π±ΠΎΡΡ Π² ΠΎΠ±Π»Π°ΡΡΠΈ ESG ΠΈ ΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΠ³ΠΎ ΡΠ°Π·Π²ΠΈΡΠΈΡ
β ΡΡΠΎ Π·Π°Π±ΠΎΡΠ° ΠΎΠ± ΠΎΠ±ΡΠ΅ΡΡΠ²Π΅. Π Π·Π΄ΠΎΡΠΎΠ²ΡΠ΅ Π»ΡΠ΄Π΅ΠΉ ΠΊΠ°ΠΊ Π²ΡΡΡΠ°Ρ ΡΠ΅Π½Π½ΠΎΡΡΡ ΡΠ²Π»ΡΠ΅ΡΡΡ Π΅Π³ΠΎ ΠΎΡΠ½ΠΎΠ²ΠΎΠΉ. ΠΠΎΡΡΠΎΠΌΡ Π·Π°Π΄Π°ΡΠ° Π±Π°Π½ΠΊΠ° Π² ΠΎΠ±Π»Π°ΡΡΠΈ
Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΠΎΡΡΠΈ Π³ΠΎΡΠ°Π·Π΄ΠΎ ΠΌΠ°ΡΡΡΠ°Π±Π½Π΅Π΅, ΡΠ΅ΠΌ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΠ΅ ΡΠΎΠ»ΡΠΊΠΎ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎΠΉ Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΠΎΡΡΠΈ ΠΊΠ»ΠΈΠ΅Π½ΡΠΎΠ². ΠΡΠΎΡ ΠΏΠΈΠ»ΠΎΡΠ½ΡΠΉ ΠΏΡΠΎΠ΅ΠΊΡ
ΠΏΡΠΈΡΡΠΎΡΠ΅Π½ ΠΊ 180-Π»Π΅ΡΠΈΡ Π‘Π±Π΅ΡΠ±Π°Π½ΠΊΠ°: ΠΌΡ Ρ
ΠΎΡΠΈΠΌ, ΡΡΠΎΠ±Ρ, ΠΏΡΠΈΡ
ΠΎΠ΄Ρ Π² Π±Π°Π½ΠΊ, ΠΊΠ»ΠΈΠ΅Π½Ρ ΡΡΠ²ΡΡΠ²ΠΎΠ²Π°Π», ΡΡΠΎ Π΅Π³ΠΎ ΠΆΠΈΠ·Π½Ρ ΠΈ Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΠΎΡΡΡ
β Π½Π°ΡΠ° ΡΠ΅Π½Π½ΠΎΡΡΡΒ», β ΠΎΡΠΌΠ΅ΡΠΈΠ» Π·Π°ΠΌΠ΅ΡΡΠΈΡΠ΅Π»Ρ ΠΏΡΠ΅Π΄ΡΠ΅Π΄Π°ΡΠ΅Π»Ρ ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ Π‘Π±Π΅ΡΠ±Π°Π½ΠΊΠ° Π‘ΡΠ°Π½ΠΈΡΠ»Π°Π² ΠΡΠ·Π½Π΅ΡΠΎΠ².'''
doc = analyze_doc(text)
key_entity = determine_subject(find_subjects_in_syntax(doc), doc, [])[0]
text_for_model = key_sentences_str(key_entity, doc, add_X=True, x_str='X', return_all=False)
```
## 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: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 176 | 0.9504 | 0.6903 | 0.2215 |
| No log | 2.0 | 352 | 0.9065 | 0.7159 | 0.4760 |
| 0.8448 | 3.0 | 528 | 0.9687 | 0.7045 | 0.4774 |
| 0.8448 | 4.0 | 704 | 1.2436 | 0.7045 | 0.4686 |
| 0.8448 | 5.0 | 880 | 1.4809 | 0.7273 | 0.4630 |
| 0.2074 | 6.0 | 1056 | 1.5866 | 0.7330 | 0.5185 |
| 0.2074 | 7.0 | 1232 | 1.7056 | 0.7301 | 0.5210 |
| 0.2074 | 8.0 | 1408 | 1.6982 | 0.7415 | 0.5056 |
| 0.0514 | 9.0 | 1584 | 1.8088 | 0.7273 | 0.5203 |
| 0.0514 | 10.0 | 1760 | 1.9250 | 0.7102 | 0.4879 |
### Framework versions
- Transformers 4.11.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "sbert_large-finetuned-sent_in_news_sents", "results": []}]}
|
chrommium/sbert_large-finetuned-sent_in_news_sents
| null |
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-classification
|
transformers
|
<!-- 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. -->
# sbert_large-finetuned-sent_in_news_sents_3lab
This model is a fine-tuned version of [sberbank-ai/sbert_large_nlu_ru](https://huggingface.co/sberbank-ai/sbert_large_nlu_ru) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9443
- Accuracy: 0.8580
- F1: 0.6199
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 17
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 264 | 0.6137 | 0.8608 | 0.3084 |
| 0.524 | 2.0 | 528 | 0.6563 | 0.8722 | 0.4861 |
| 0.524 | 3.0 | 792 | 0.7110 | 0.8494 | 0.4687 |
| 0.2225 | 4.0 | 1056 | 0.7323 | 0.8608 | 0.6015 |
| 0.2225 | 5.0 | 1320 | 0.9604 | 0.8551 | 0.6185 |
| 0.1037 | 6.0 | 1584 | 0.8801 | 0.8523 | 0.5535 |
| 0.1037 | 7.0 | 1848 | 0.9443 | 0.8580 | 0.6199 |
| 0.0479 | 8.0 | 2112 | 1.0048 | 0.8608 | 0.6168 |
| 0.0479 | 9.0 | 2376 | 0.9757 | 0.8551 | 0.6097 |
| 0.0353 | 10.0 | 2640 | 1.0743 | 0.8580 | 0.6071 |
| 0.0353 | 11.0 | 2904 | 1.1216 | 0.8580 | 0.6011 |
### Framework versions
- Transformers 4.11.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "sbert_large-finetuned-sent_in_news_sents_3lab", "results": []}]}
|
chrommium/sbert_large-finetuned-sent_in_news_sents_3lab
| null |
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-classification
|
transformers
|
{}
|
chrommium/two-step-finetuning-sbert
| null |
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-classification
|
transformers
|
<!-- 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-large-finetuned-sent_in_news
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8872
- Accuracy: 0.7273
- F1: 0.5125
## Model description
ΠΠΎΠ΄Π΅Π»Ρ Π°ΡΡΠΈΠΌΠ΅ΡΡΠΈΡΠ½Π°, ΡΠ΅Π°Π³ΠΈΡΡΠ΅Ρ Π½Π° ΠΌΠ΅ΡΠΊΡ X Π² ΡΠ΅ΠΊΡΡΠ΅ Π½ΠΎΠ²ΠΎΡΡΠΈ.
ΠΠΎΠΏΡΠΎΠ±ΡΠΉΡΠ΅ ΡΠ»Π΅Π΄ΡΡΡΠΈΠ΅ ΠΏΡΠΈΠΌΠ΅ΡΡ:
a) ΠΠ³Π΅Π½ΡΡΡΠ²ΠΎ X ΠΏΠΎΠ½ΠΈΠ·ΠΈΠ»ΠΎ ΡΠ΅ΠΉΡΠΈΠ½Π³ Π±Π°Π½ΠΊΠ° Fitch.
b) ΠΠ³Π΅Π½ΡΡΡΠ²ΠΎ Fitch ΠΏΠΎΠ½ΠΈΠ·ΠΈΠ»ΠΎ ΡΠ΅ΠΉΡΠΈΠ½Π³ Π±Π°Π½ΠΊΠ° X.
a) ΠΠΎΠΌΠΏΠ°Π½ΠΈΡ Π€ΠΈΠ½Π°ΠΌ ΠΏΠΎΠΊΠ°Π·Π°Π»Π° ΡΠ΅ΠΊΠΎΡΠ΄Π½ΡΡ ΠΏΡΠΈΠ±ΡΠ»Ρ, Π³ΠΎΠ²ΠΎΡΡΡ Π°Π½Π°Π»ΠΈΡΠΈΠΊΠΈ ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΈ X.
b) ΠΠΎΠΌΠΏΠ°Π½ΠΈΡ X ΠΏΠΎΠΊΠ°Π·Π°Π»Π° ΡΠ΅ΠΊΠΎΡΠ΄Π½ΡΡ ΠΏΡΠΈΠ±ΡΠ»Ρ, Π³ΠΎΠ²ΠΎΡΡΡ Π°Π½Π°Π»ΠΈΡΠΈΠΊΠΈ ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΈ Π€ΠΈΠ½Π°ΠΌ.
## 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: 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: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 106 | 1.2526 | 0.6108 | 0.1508 |
| No log | 2.0 | 212 | 1.1553 | 0.6648 | 0.1141 |
| No log | 3.0 | 318 | 1.1150 | 0.6591 | 0.1247 |
| No log | 4.0 | 424 | 1.0007 | 0.6705 | 0.1383 |
| 1.1323 | 5.0 | 530 | 0.9267 | 0.6733 | 0.2027 |
| 1.1323 | 6.0 | 636 | 1.0869 | 0.6335 | 0.4084 |
| 1.1323 | 7.0 | 742 | 1.1224 | 0.6932 | 0.4586 |
| 1.1323 | 8.0 | 848 | 1.2535 | 0.6307 | 0.3424 |
| 1.1323 | 9.0 | 954 | 1.4288 | 0.6932 | 0.4881 |
| 0.5252 | 10.0 | 1060 | 1.5856 | 0.6932 | 0.4739 |
| 0.5252 | 11.0 | 1166 | 1.7101 | 0.6733 | 0.4530 |
| 0.5252 | 12.0 | 1272 | 1.7330 | 0.6903 | 0.4750 |
| 0.5252 | 13.0 | 1378 | 1.8872 | 0.7273 | 0.5125 |
| 0.5252 | 14.0 | 1484 | 1.8797 | 0.7301 | 0.5033 |
| 0.1252 | 15.0 | 1590 | 1.9339 | 0.7330 | 0.5024 |
| 0.1252 | 16.0 | 1696 | 1.9632 | 0.7301 | 0.4967 |
### Framework versions
- Transformers 4.11.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "xlm-roberta-large-finetuned-sent_in_news", "results": []}]}
|
chrommium/xlm-roberta-large-finetuned-sent_in_news
| null |
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
chu/KCdot
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
chuangzhidian/electra-small-spanish
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-generation
|
transformers
|
[blenderbot-400M-distill](https://huggingface.co/facebook/blenderbot-400M-distill) fine-tuned on the [ESConv dataset](https://github.com/thu-coai/Emotional-Support-Conversation). Usage example:
```python
import torch
from transformers import AutoTokenizer
from transformers.models.blenderbot import BlenderbotTokenizer, BlenderbotForConditionalGeneration
def _norm(x):
return ' '.join(x.strip().split())
tokenizer = BlenderbotTokenizer.from_pretrained('thu-coai/blenderbot-400M-esconv')
model = BlenderbotForConditionalGeneration.from_pretrained('thu-coai/blenderbot-400M-esconv')
model.eval()
utterances = [
"I am having a lot of anxiety about quitting my current job. It is too stressful but pays well",
"What makes your job stressful for you?",
"I have to deal with many people in hard financial situations and it is upsetting",
"Do you help your clients to make it to a better financial situation?",
"I do, but often they are not going to get back to what they want. Many people are going to lose their home when safeguards are lifted",
]
input_sequence = ' '.join([' ' + e for e in utterances]) + tokenizer.eos_token # add space prefix and separate utterances with two spaces
input_ids = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(input_sequence))[-128:]
input_ids = torch.LongTensor([input_ids])
model_output = model.generate(input_ids, num_beams=1, do_sample=True, top_p=0.9, num_return_sequences=5, return_dict=False)
generation = tokenizer.batch_decode(model_output, skip_special_tokens=True)
generation = [_norm(e) for e in generation]
print(generation)
utterances.append(generation[0]) # for future loop
```
Please kindly cite the [original paper](https://arxiv.org/abs/2106.01144) if you use this model:
```bib
@inproceedings{liu-etal-2021-towards,
title={Towards Emotional Support Dialog Systems},
author={Liu, Siyang and
Zheng, Chujie and
Demasi, Orianna and
Sabour, Sahand and
Li, Yu and
Yu, Zhou and
Jiang, Yong and
Huang, Minlie},
booktitle={Proceedings of the 59th annual meeting of the Association for Computational Linguistics},
year={2021}
}
```
|
{"language": ["en"], "tags": ["pytorch", "coai"], "pipeline_tag": "conversational"}
|
thu-coai/blenderbot-400M-esconv
| null |
[
"transformers",
"pytorch",
"safetensors",
"blenderbot",
"text2text-generation",
"coai",
"conversational",
"en",
"arxiv:2106.01144",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
chulin/distilbert-base-uncased-finetuned-squad
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
chymaks/IgboBert-finetuned-ner
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
chymaks/bert-base-multilingual-cased-finetuned-ner
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
chymaks/distilbert-base-uncased-finetuned-ner
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
chymaks/roberta-base-finetuned-ner
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
cicuq/Nea
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null |
transformers
|
## EnDR-BERT
EnDR-BERT - Multilingual, Cased, which pretrained on the english collection of consumer comments on drug administration from [2]. Pre-training was based on the [original BERT code](https://github.com/google-research/bert) provided by Google. In particular, Multi-BERT was for used for initialization and all the parameters are the same as in Multi-BERT. Training details are described in our paper. \
link: https://yadi.sk/d/-PTn0xhk1PqvgQ
## Citing & Authors
If you find this repository helpful, feel free to cite our publication:
[1] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews.//Bioinformatics. - 2020.
preprint: https://arxiv.org/abs/2004.03659
```
@article{10.1093/bioinformatics/btaa675,
author = {Tutubalina, Elena and Alimova, Ilseyar and Miftahutdinov, Zulfat and Sakhovskiy, Andrey and Malykh, Valentin and Nikolenko, Sergey},
title = "{The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews}",
journal = {Bioinformatics},
year = {2020},
month = {07},
issn = {1367-4803},
doi = {10.1093/bioinformatics/btaa675},
url = {https://doi.org/10.1093/bioinformatics/btaa675},
note = {btaa675},
eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa675/33539752/btaa675.pdf},
}
```
[2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects.//Russian Chemical Bulletin. β 2017. β Π’. 66. β β. 11. β Π‘. 2180-2189.
[link to paper](https://www.researchgate.net/profile/Elena_Tutubalina/publication/323751823_Using_semantic_analysis_of_texts_for_the_identification_of_drugs_with_similar_therapeutic_effects/links/5bf7cfc3299bf1a0202cbc1f/Using-semantic-analysis-of-texts-for-the-identification-of-drugs-with-similar-therapeutic-effects.pdf)
```
@article{tutubalina2017using,
title={Using semantic analysis of texts for the identification of drugs with similar therapeutic effects},
author={Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE},
journal={Russian Chemical Bulletin},
volume={66},
number={11},
pages={2180--2189},
year={2017},
publisher={Springer}
}
```
|
{"language": ["ru", "en"], "tags": ["bio", "med", "biomedical"]}
|
cimm-kzn/endr-bert
| null |
[
"transformers",
"pytorch",
"bio",
"med",
"biomedical",
"ru",
"en",
"arxiv:2004.03659",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null |
transformers
|
## EnRuDR-BERT
EnRuDR-BERT - Multilingual, Cased, which pretrained on the raw part of the RuDReC corpus (1.4M reviews) and english collection of consumer comments on drug administration from [2]. Pre-training was based on the [original BERT code](https://github.com/google-research/bert) provided by Google. In particular, Multi-BERT was for used for initialization; vocabulary of Russian subtokens and parameters are the same as in Multi-BERT. Training details are described in our paper. \
link: https://yadi.sk/d/-PTn0xhk1PqvgQ
## Citing & Authors
If you find this repository helpful, feel free to cite our publication:
[1] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews.//Bioinformatics. - 2020.
preprint: https://arxiv.org/abs/2004.03659
```
@article{10.1093/bioinformatics/btaa675,
author = {Tutubalina, Elena and Alimova, Ilseyar and Miftahutdinov, Zulfat and Sakhovskiy, Andrey and Malykh, Valentin and Nikolenko, Sergey},
title = "{The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews}",
journal = {Bioinformatics},
year = {2020},
month = {07},
issn = {1367-4803},
doi = {10.1093/bioinformatics/btaa675},
url = {https://doi.org/10.1093/bioinformatics/btaa675},
note = {btaa675},
eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa675/33539752/btaa675.pdf},
}
```
[2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects.//Russian Chemical Bulletin. β 2017. β Π’. 66. β β. 11. β Π‘. 2180-2189.
[link to paper](https://www.researchgate.net/profile/Elena_Tutubalina/publication/323751823_Using_semantic_analysis_of_texts_for_the_identification_of_drugs_with_similar_therapeutic_effects/links/5bf7cfc3299bf1a0202cbc1f/Using-semantic-analysis-of-texts-for-the-identification-of-drugs-with-similar-therapeutic-effects.pdf)
```
@article{tutubalina2017using,
title={Using semantic analysis of texts for the identification of drugs with similar therapeutic effects},
author={Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE},
journal={Russian Chemical Bulletin},
volume={66},
number={11},
pages={2180--2189},
year={2017},
publisher={Springer}
}
```
|
{"language": ["ru", "en"], "tags": ["bio", "med", "biomedical"]}
|
cimm-kzn/enrudr-bert
| null |
[
"transformers",
"pytorch",
"bio",
"med",
"biomedical",
"ru",
"en",
"arxiv:2004.03659",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null |
transformers
|
## RuDR-BERT
RuDR-BERT - Multilingual, Cased, which pretrained on the raw part of the RuDReC corpus (1.4M reviews). Pre-training was based on the [original BERT code](https://github.com/google-research/bert) provided by Google. In particular, Multi-BERT was for used for initialization; vocabulary of Russian subtokens and parameters are the same as in Multi-BERT. Training details are described in our paper. \
link: https://yadi.sk/d/-PTn0xhk1PqvgQ
## Citing & Authors
If you find this repository helpful, feel free to cite our publication:
[1] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews.
preprint: https://arxiv.org/abs/2004.03659
```
@article{10.1093/bioinformatics/btaa675,
author = {Tutubalina, Elena and Alimova, Ilseyar and Miftahutdinov, Zulfat and Sakhovskiy, Andrey and Malykh, Valentin and Nikolenko, Sergey},
title = "{The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews}",
journal = {Bioinformatics},
year = {2020},
month = {07},
issn = {1367-4803},
doi = {10.1093/bioinformatics/btaa675},
url = {https://doi.org/10.1093/bioinformatics/btaa675},
note = {btaa675},
eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa675/33539752/btaa675.pdf},
}
```
[2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects.
[link to paper](https://www.researchgate.net/profile/Elena_Tutubalina/publication/323751823_Using_semantic_analysis_of_texts_for_the_identification_of_drugs_with_similar_therapeutic_effects/links/5bf7cfc3299bf1a0202cbc1f/Using-semantic-analysis-of-texts-for-the-identification-of-drugs-with-similar-therapeutic-effects.pdf)
```
@article{tutubalina2017using,
title={Using semantic analysis of texts for the identification of drugs with similar therapeutic effects},
author={Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE},
journal={Russian Chemical Bulletin},
volume={66},
number={11},
pages={2180--2189},
year={2017},
publisher={Springer}
}
```
|
{"language": ["ru"], "tags": ["bio", "med", "biomedical"]}
|
cimm-kzn/rudr-bert
| null |
[
"transformers",
"pytorch",
"bio",
"med",
"biomedical",
"ru",
"arxiv:2004.03659",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
End-2-End with english
|
{}
|
cjcu/End2End-asr
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
question-answering
|
transformers
|
<!-- 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. -->
# afriberta_base-finetuned-tydiqa
This model is a fine-tuned version of [castorini/afriberta_base](https://huggingface.co/castorini/afriberta_base) on the tydiqa dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3728
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 192 | 2.1359 |
| No log | 2.0 | 384 | 2.3409 |
| 0.8353 | 3.0 | 576 | 2.3728 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"language": ["sw"], "tags": ["generated_from_trainer"], "datasets": ["tydiqa"], "model-index": [{"name": "afriberta_base-finetuned-tydiqa", "results": []}]}
|
cjrowe/afriberta_base-finetuned-tydiqa
| null |
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"sw",
"dataset:tydiqa",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
fill-mask
|
transformers
|
{}
|
ck46/camembert-base
| null |
[
"transformers",
"pytorch",
"camembert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text2text-generation
|
transformers
|
{}
|
ck46/t5-base-hotpot-qa-qg
| null |
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text2text-generation
|
transformers
|
{}
|
ck46/t5-base-qg-prefix
| null |
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text2text-generation
|
transformers
|
{}
|
ck46/t5-base-squad-qa-qg
| null |
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text2text-generation
|
transformers
|
{}
|
ck46/t5-small-hotpot-qa-qg
| null |
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text2text-generation
|
transformers
|
{}
|
ck46/t5-small-squad-qa-qg
| null |
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
token-classification
|
transformers
|
{}
|
ckauth/ck-ner-disease
| null |
[
"transformers",
"tf",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
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