modelId stringlengths 4 111 | lastModified stringlengths 24 24 | tags list | pipeline_tag stringlengths 5 30 ⌀ | author stringlengths 2 34 ⌀ | config null | securityStatus null | id stringlengths 4 111 | likes int64 0 9.53k | downloads int64 2 73.6M | library_name stringlengths 2 84 ⌀ | created timestamp[us] | card stringlengths 101 901k | card_len int64 101 901k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
hw2942/chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-7 | 2023-10-19T03:01:42.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-7 | 0 | 2 | transformers | 2023-10-19T02:54:31 | ---
license: cc-by-nc-sa-4.0
base_model: hfl/chinese-pert-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-7
This model is a fine-tuned version of [hfl/chinese-pert-base](https://huggingface.co/hfl/chinese-pert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1008
- F1: 0.5385
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 1.4578 | 0.6316 |
| No log | 2.0 | 68 | 2.3450 | 0.4828 |
| No log | 3.0 | 102 | 2.0562 | 0.5926 |
| No log | 4.0 | 136 | 1.7694 | 0.5455 |
| No log | 5.0 | 170 | 1.6744 | 0.5455 |
| No log | 6.0 | 204 | 1.6164 | 0.5714 |
| No log | 7.0 | 238 | 1.6055 | 0.5714 |
| No log | 8.0 | 272 | 2.1733 | 0.5385 |
| No log | 9.0 | 306 | 2.2074 | 0.5185 |
| No log | 10.0 | 340 | 2.1008 | 0.5385 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-6 | 2023-10-19T03:01:44.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-6 | 0 | 2 | transformers | 2023-10-19T02:55:10 | ---
license: apache-2.0
base_model: hfl/chinese-lert-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-6
This model is a fine-tuned version of [hfl/chinese-lert-base](https://huggingface.co/hfl/chinese-lert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4508
- F1: 0.5517
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 2.7277 | 0.3636 |
| No log | 2.0 | 68 | 1.9660 | 0.4706 |
| No log | 3.0 | 102 | 2.0223 | 0.6207 |
| No log | 4.0 | 136 | 3.2434 | 0.5333 |
| No log | 5.0 | 170 | 3.2008 | 0.5517 |
| No log | 6.0 | 204 | 3.7892 | 0.5333 |
| No log | 7.0 | 238 | 3.1922 | 0.4348 |
| No log | 8.0 | 272 | 3.3802 | 0.5185 |
| No log | 9.0 | 306 | 3.4030 | 0.5517 |
| No log | 10.0 | 340 | 3.4508 | 0.5517 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-8 | 2023-10-19T03:14:04.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-8 | 0 | 2 | transformers | 2023-10-19T03:06:16 | ---
license: cc-by-nc-sa-4.0
base_model: hfl/chinese-pert-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-8
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-8
This model is a fine-tuned version of [hfl/chinese-pert-base](https://huggingface.co/hfl/chinese-pert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3830
- F1: 0.5000
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 2.5929 | 0.4800 |
| No log | 2.0 | 68 | 2.4496 | 0.4800 |
| No log | 3.0 | 102 | 2.5505 | 0.3158 |
| No log | 4.0 | 136 | 1.6666 | 0.5217 |
| No log | 5.0 | 170 | 1.8663 | 0.5217 |
| No log | 6.0 | 204 | 2.1621 | 0.5217 |
| No log | 7.0 | 238 | 2.3676 | 0.5000 |
| No log | 8.0 | 272 | 2.4140 | 0.5000 |
| No log | 9.0 | 306 | 2.3761 | 0.5000 |
| No log | 10.0 | 340 | 2.3830 | 0.5000 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-7 | 2023-10-19T03:23:26.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-7 | 0 | 2 | transformers | 2023-10-19T03:17:05 | ---
license: apache-2.0
base_model: hfl/chinese-lert-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-7
This model is a fine-tuned version of [hfl/chinese-lert-base](https://huggingface.co/hfl/chinese-lert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6281
- F1: 0.6207
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 0.7628 | 0.5652 |
| No log | 2.0 | 68 | 0.5678 | 0.375 |
| No log | 3.0 | 102 | 0.5716 | 0.6857 |
| No log | 4.0 | 136 | 0.5449 | 0.6087 |
| No log | 5.0 | 170 | 0.7354 | 0.5455 |
| No log | 6.0 | 204 | 0.9029 | 0.6667 |
| No log | 7.0 | 238 | 1.2826 | 0.5385 |
| No log | 8.0 | 272 | 1.5671 | 0.6875 |
| No log | 9.0 | 306 | 1.6494 | 0.6875 |
| No log | 10.0 | 340 | 1.6281 | 0.6207 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-8 | 2023-10-19T03:36:22.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-8 | 0 | 2 | transformers | 2023-10-19T03:29:56 | ---
license: apache-2.0
base_model: hfl/chinese-lert-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-8
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-8
This model is a fine-tuned version of [hfl/chinese-lert-base](https://huggingface.co/hfl/chinese-lert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6145
- F1: 0.6061
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 2.4743 | 0.6061 |
| No log | 2.0 | 68 | 2.6833 | 0.6207 |
| No log | 3.0 | 102 | 1.9147 | 0.6667 |
| No log | 4.0 | 136 | 3.1882 | 0.6667 |
| No log | 5.0 | 170 | 3.6711 | 0.6154 |
| No log | 6.0 | 204 | 2.9826 | 0.6471 |
| No log | 7.0 | 238 | 2.4841 | 0.7273 |
| No log | 8.0 | 272 | 2.4590 | 0.6207 |
| No log | 9.0 | 306 | 2.3496 | 0.6452 |
| No log | 10.0 | 340 | 2.6145 | 0.6061 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-9 | 2023-10-19T03:49:28.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-9 | 0 | 2 | transformers | 2023-10-19T03:42:50 | ---
license: apache-2.0
base_model: hfl/chinese-lert-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-9
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-9
This model is a fine-tuned version of [hfl/chinese-lert-base](https://huggingface.co/hfl/chinese-lert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5499
- F1: 0.6207
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 3.5448 | 0.6154 |
| No log | 2.0 | 68 | 3.4107 | 0.6857 |
| No log | 3.0 | 102 | 3.0344 | 0.5185 |
| No log | 4.0 | 136 | 3.3629 | 0.5625 |
| No log | 5.0 | 170 | 4.4222 | 0.5946 |
| No log | 6.0 | 204 | 3.8678 | 0.5882 |
| No log | 7.0 | 238 | 3.3837 | 0.6207 |
| No log | 8.0 | 272 | 3.7149 | 0.5714 |
| No log | 9.0 | 306 | 3.5553 | 0.6000 |
| No log | 10.0 | 340 | 3.5499 | 0.6207 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-10 | 2023-10-19T04:03:04.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-10 | 0 | 2 | transformers | 2023-10-19T03:55:56 | ---
license: apache-2.0
base_model: hfl/chinese-lert-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-10
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-10
This model is a fine-tuned version of [hfl/chinese-lert-base](https://huggingface.co/hfl/chinese-lert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2254
- F1: 0.6857
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 3.0804 | 0.6842 |
| No log | 2.0 | 68 | 2.5190 | 0.6897 |
| No log | 3.0 | 102 | 2.3444 | 0.6875 |
| No log | 4.0 | 136 | 3.4872 | 0.6842 |
| No log | 5.0 | 170 | 2.5766 | 0.6857 |
| No log | 6.0 | 204 | 2.6344 | 0.6667 |
| No log | 7.0 | 238 | 2.7650 | 0.6667 |
| No log | 8.0 | 272 | 3.2483 | 0.6667 |
| No log | 9.0 | 306 | 3.2497 | 0.6667 |
| No log | 10.0 | 340 | 3.2254 | 0.6857 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-11 | 2023-10-19T04:16:19.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-11 | 0 | 2 | transformers | 2023-10-19T04:09:40 | ---
license: apache-2.0
base_model: hfl/chinese-lert-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-11
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-11
This model is a fine-tuned version of [hfl/chinese-lert-base](https://huggingface.co/hfl/chinese-lert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8223
- 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: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 3.1252 | 0.6842 |
| No log | 2.0 | 68 | 1.8581 | 0.64 |
| No log | 3.0 | 102 | 2.5310 | 0.7143 |
| No log | 4.0 | 136 | 3.7412 | 0.6316 |
| No log | 5.0 | 170 | 2.7172 | 0.6429 |
| No log | 6.0 | 204 | 2.3534 | 0.6667 |
| No log | 7.0 | 238 | 2.8210 | 0.6452 |
| No log | 8.0 | 272 | 2.7498 | 0.7273 |
| No log | 9.0 | 306 | 2.6381 | 0.7647 |
| No log | 10.0 | 340 | 2.8223 | 0.6875 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-12 | 2023-10-19T04:29:03.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-12 | 0 | 2 | transformers | 2023-10-19T04:22:29 | ---
license: apache-2.0
base_model: hfl/chinese-lert-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-12
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-12
This model is a fine-tuned version of [hfl/chinese-lert-base](https://huggingface.co/hfl/chinese-lert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9201
- F1: 0.625
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 4.2669 | 0.6667 |
| No log | 2.0 | 68 | 4.1599 | 0.6286 |
| No log | 3.0 | 102 | 3.3657 | 0.6286 |
| No log | 4.0 | 136 | 3.1829 | 0.6207 |
| No log | 5.0 | 170 | 3.6588 | 0.6286 |
| No log | 6.0 | 204 | 3.4327 | 0.6286 |
| No log | 7.0 | 238 | 2.5612 | 0.6154 |
| No log | 8.0 | 272 | 3.7531 | 0.6842 |
| No log | 9.0 | 306 | 3.2791 | 0.6061 |
| No log | 10.0 | 340 | 2.9201 | 0.625 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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jrs-a/wav2vec2_batangueno-hp1 | 2023-10-19T05:34:16.000Z | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | jrs-a | null | null | jrs-a/wav2vec2_batangueno-hp1 | 0 | 2 | transformers | 2023-10-19T05:04:47 | ---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2_batangueno-hp1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2_batangueno-hp1
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6618
- Wer: 0.4864
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.130370904397918e-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
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.9814 | 10.64 | 500 | 2.6422 | 1.0 |
| 1.413 | 21.28 | 1000 | 0.6618 | 0.4864 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 1.18.3
- Tokenizers 0.14.1
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jrs-a/wav2vec2_batangueno-hp2 | 2023-10-19T05:29:59.000Z | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | jrs-a | null | null | jrs-a/wav2vec2_batangueno-hp2 | 0 | 2 | transformers | 2023-10-19T05:08:59 | ---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2_batangueno-hp2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2_batangueno-hp2
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4632
- Wer: 0.3485
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.5860027851721875e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.8461 | 5.32 | 500 | 2.6248 | 1.0 |
| 1.5848 | 10.64 | 1000 | 0.6266 | 0.5268 |
| 0.3678 | 15.96 | 1500 | 0.4632 | 0.3485 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 1.18.3
- Tokenizers 0.14.1
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irexyc/internlm-chat-20b-4bit | 2023-10-19T05:48:55.000Z | [
"transformers",
"pytorch",
"internlm",
"feature-extraction",
"text-generation",
"custom_code",
"license:apache-2.0",
"region:us"
] | text-generation | irexyc | null | null | irexyc/internlm-chat-20b-4bit | 0 | 2 | transformers | 2023-10-19T05:45:39 | ---
license: apache-2.0
pipeline_tag: text-generation
---
<div align="center">
<img src="https://raw.githubusercontent.com/InternLM/lmdeploy/0be9e7ab6fe9a066cfb0a09d0e0c8d2e28435e58/resources/lmdeploy-logo.svg" width="450"/>
</div>
[LMDeploy](https://github.com/InternLM/lmdeploy) supports LLM model inference of 4-bit weight, with the minimum requirement for NVIDIA graphics cards being sm80, such as A10, A100, Geforce 30/40 series.
Before proceeding with the inference of `internlm-chat-20b-4bit`, please ensure that lmdeploy is installed.
```shell
pip install 'lmdeploy>=0.0.11'
```
## Inference
Please download `internlm-chat-20b-4bit` model as follows,
```shell
git-lfs install
git clone https://huggingface.co/internlm/internlm-chat-20b-4bit
```
As demonstrated in the command below, first convert the model's layout using `turbomind.deploy`, and then you can interact with the AI assistant in the terminal
```shell
# Convert the model's layout and store it in the default path, ./workspace.
python3 -m lmdeploy.serve.turbomind.deploy \
--model-name internlm-chat-20b \
--model-path ./internlm-chat-20b-4bit \
--model-format awq \
--group-size 128
# inference
python3 -m lmdeploy.turbomind.chat ./workspace
```
## Serve with gradio
If you wish to interact with the model via web UI, please initiate the gradio server as indicated below:
```shell
python3 -m lmdeploy.serve.gradio.app ./workspace --server_name {ip_addr} --server_port {port}
```
Subsequently, you can open the website `http://{ip_addr}:{port}` in your browser and interact with the model.
Besides serving with gradio, there are two more serving methods. One is serving with Triton Inference Server (TIS), and the other is an OpenAI-like server named as `api_server`.
Please refer to the [user guide](https://github.com/InternLM/lmdeploy#quick-start) for detailed information if you are interested.
## Inference Performance
LMDeploy provides scripts for benchmarking `token throughput` and `request throughput`.
`token throughput` tests the speed of generating new tokens, given a specified number of prompt tokens and completion tokens, while `request throughput` measures the number of requests processed per minute with real dialogue data.
We conducted benchmarks on `internlm-chat-20b-4bit`. And `token_throughput` was measured by setting 256 prompt tokens and generating 512 tokens in response on A100-80G.
**Note**: The `session_len` in `workspace/triton_models/weights/config.ini` is changed to `2056` in our test.
| batch | tensor parallel | prompt_tokens | completion_tokens | thr_per_proc(token/s) | rpm (req/min) | mem_per_proc(GB) |
|-------|-----------------|---------------|-------------------|-----------------------|---------------|------------------|
| 1 | 1 | 256 | 512 | 88.77 | - | 15.65 |
| 16 | 1 | 256 | 512 | 792.7 | 220.23 | 51.46 |
### token throughput
Run the following command,
```shell
python benchmark/profile_generation.py \
--model-path ./workspace \
--concurrency 1 8 16 --prompt-tokens 256 512 512 1024 --completion-tokens 512 512 1024 1024
--dst-csv ./token_throughput.csv
```
You will find the `token_throughput` metrics in `./token_throughput.csv`
| batch | prompt_tokens | completion_tokens | thr_per_proc(token/s) | thr_per_node(token/s) | rpm(req/min) | mem_per_proc(GB) | mem_per_gpu(GB) | mem_per_node(GB) |
|-------|---------------|-------------------|-----------------------|-----------------------|--------------|------------------|-----------------|------------------|
| 1 | 256 | 512 | 88.77 | 710.12 | - | 15.65 | 15.65 | 125.21 |
| 1 | 512 | 512 | 83.89 | 671.15 | - | 15.68 | 15.68 | 125.46 |
| 1 | 512 | 1024 | 80.19 | 641.5 | - | 15.68 | 15.68 | 125.46 |
| 1 | 1024 | 1024 | 72.34 | 578.74 | - | 15.75 | 15.75 | 125.96 |
| 1 | 1 | 2048 | 80.69 | 645.55 | - | 15.62 | 15.62 | 124.96 |
| 8 | 256 | 512 | 565.21 | 4521.67 | - | 32.37 | 32.37 | 258.96 |
| 8 | 512 | 512 | 489.04 | 3912.33 | - | 32.62 | 32.62 | 260.96 |
| 8 | 512 | 1024 | 467.23 | 3737.84 | - | 32.62 | 32.62 | 260.96 |
| 8 | 1024 | 1024 | 383.4 | 3067.19 | - | 33.06 | 33.06 | 264.46 |
| 8 | 1 | 2048 | 487.74 | 3901.93 | - | 32.12 | 32.12 | 256.96 |
| 16 | 256 | 512 | 792.7 | 6341.6 | - | 51.46 | 51.46 | 411.71 |
| 16 | 512 | 512 | 639.4 | 5115.17 | - | 51.93 | 51.93 | 415.46 |
| 16 | 512 | 1024 | 591.39 | 4731.09 | - | 51.93 | 51.93 | 415.46 |
| 16 | 1024 | 1024 | 449.11 | 3592.85 | - | 52.06 | 52.06 | 416.46 |
| 16 | 1 | 2048 | 620.5 | 4964.02 | - | 51 | 51 | 407.96 |
### request throughput
LMDeploy uses ShareGPT dataset to test request throughput. Try the next commands, and you will get the `rpm` (request per minute) metric.
```
# download the ShareGPT dataset
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
#
python profile_throughput.py \
ShareGPT_V3_unfiltered_cleaned_split.json \
./workspace \
--concurrency 16
```
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herMaster/pythia70M-finetuned-on-lamini-docs | 2023-10-19T05:57:59.000Z | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | herMaster | null | null | herMaster/pythia70M-finetuned-on-lamini-docs | 0 | 2 | transformers | 2023-10-19T05:57:44 | ---
license: apache-2.0
base_model: EleutherAI/pythia-70m
tags:
- generated_from_trainer
model-index:
- name: pythia70M-finetuned-on-lamini-docs
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pythia70M-finetuned-on-lamini-docs
This model is a fine-tuned version of [EleutherAI/pythia-70m](https://huggingface.co/EleutherAI/pythia-70m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0582
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.3194 | 0.16 | 50 | 2.4776 |
| 2.3198 | 0.32 | 100 | 2.3780 |
| 2.16 | 0.48 | 150 | 2.2905 |
| 2.2156 | 0.63 | 200 | 2.2450 |
| 2.3342 | 0.79 | 250 | 2.1934 |
| 2.7634 | 0.95 | 300 | 2.1748 |
| 2.4963 | 1.11 | 350 | 2.1500 |
| 2.1493 | 1.27 | 400 | 2.1413 |
| 1.8731 | 1.43 | 450 | 2.1200 |
| 2.0132 | 1.59 | 500 | 2.1030 |
| 1.9606 | 1.75 | 550 | 2.0848 |
| 1.37 | 1.9 | 600 | 2.0659 |
| 1.7681 | 2.06 | 650 | 2.0744 |
| 1.8926 | 2.22 | 700 | 2.0779 |
| 1.2409 | 2.38 | 750 | 2.0683 |
| 1.489 | 2.54 | 800 | 2.0616 |
| 1.5143 | 2.7 | 850 | 2.0604 |
| 1.3736 | 2.86 | 900 | 2.0582 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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] |
RaitoGS/my_awesome_eli5_mlm_model | 2023-10-19T08:22:46.000Z | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | RaitoGS | null | null | RaitoGS/my_awesome_eli5_mlm_model | 0 | 2 | transformers | 2023-10-19T08:02:54 | ---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_mlm_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_mlm_model
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0193
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2371 | 1.0 | 1140 | 2.0588 |
| 2.1394 | 2.0 | 2280 | 2.0322 |
| 2.104 | 3.0 | 3420 | 2.0193 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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confunius/my_awesome_eli5_mlm_model | 2023-10-19T08:22:53.000Z | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | confunius | null | null | confunius/my_awesome_eli5_mlm_model | 0 | 2 | transformers | 2023-10-19T08:02:58 | ---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_mlm_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_mlm_model
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9907
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2469 | 1.0 | 1136 | 2.0728 |
| 2.1803 | 2.0 | 2272 | 2.0424 |
| 2.1064 | 3.0 | 3408 | 1.9972 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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waterboy111/my_awesome_eli5_mlm_model | 2023-10-19T08:28:48.000Z | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | waterboy111 | null | null | waterboy111/my_awesome_eli5_mlm_model | 0 | 2 | transformers | 2023-10-19T08:03:00 | ---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_mlm_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_mlm_model
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9767
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2428 | 1.0 | 1148 | 2.0554 |
| 2.1731 | 2.0 | 2296 | 2.0006 |
| 2.117 | 3.0 | 3444 | 1.9815 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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yingq/my_awesome_eli5_mlm_model | 2023-10-19T08:34:04.000Z | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | yingq | null | null | yingq/my_awesome_eli5_mlm_model | 0 | 2 | transformers | 2023-10-19T08:03:00 | ---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_mlm_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_mlm_model
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0115
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7206 | 1.0 | 1142 | 2.1091 |
| 1.8323 | 2.0 | 2284 | 2.0063 |
| 1.9619 | 3.0 | 3426 | 2.0127 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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Kavan123/my_awesome_eli5_mlm_model | 2023-10-19T08:23:30.000Z | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | Kavan123 | null | null | Kavan123/my_awesome_eli5_mlm_model | 0 | 2 | transformers | 2023-10-19T08:03:01 | ---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_mlm_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_mlm_model
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0061
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2708 | 1.0 | 1135 | 2.0489 |
| 2.1558 | 2.0 | 2270 | 2.0240 |
| 2.1138 | 3.0 | 3405 | 1.9961 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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irsyadjazli/my_awesome_eli5_mlm_model | 2023-10-19T08:23:02.000Z | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | irsyadjazli | null | null | irsyadjazli/my_awesome_eli5_mlm_model | 0 | 2 | transformers | 2023-10-19T08:03:01 | ---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_mlm_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_mlm_model
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9802
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2353 | 1.0 | 1130 | 2.0551 |
| 2.1435 | 2.0 | 2260 | 2.0217 |
| 2.0999 | 3.0 | 3390 | 2.0117 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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aldaalmira/my_awesome_eli5_mlm_model | 2023-10-19T08:23:24.000Z | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | aldaalmira | null | null | aldaalmira/my_awesome_eli5_mlm_model | 0 | 2 | transformers | 2023-10-19T08:03:09 | ---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_mlm_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_mlm_model
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9878
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2426 | 1.0 | 1122 | 2.0533 |
| 2.1444 | 2.0 | 2244 | 2.0329 |
| 2.1222 | 3.0 | 3366 | 1.9696 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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mmmichelle/my_awesome_eli5_mlm_model | 2023-10-19T08:30:56.000Z | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | mmmichelle | null | null | mmmichelle/my_awesome_eli5_mlm_model | 0 | 2 | transformers | 2023-10-19T08:03:19 | ---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_mlm_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_mlm_model
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9977
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7469 | 1.0 | 1125 | 2.0577 |
| 1.8393 | 2.0 | 2250 | 2.0306 |
| 1.9959 | 3.0 | 3375 | 1.9771 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hung200504/bert-17 | 2023-10-19T08:40:28.000Z | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | hung200504 | null | null | hung200504/bert-17 | 0 | 2 | transformers | 2023-10-19T08:40:02 | ---
license: cc-by-4.0
base_model: deepset/bert-base-cased-squad2
tags:
- generated_from_trainer
model-index:
- name: bert-17
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-17
This model is a fine-tuned version of [deepset/bert-base-cased-squad2](https://huggingface.co/deepset/bert-base-cased-squad2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.7381
## 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: 0.0005
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 11.0352 | 0.09 | 5 | 11.3392 |
| 10.0155 | 0.18 | 10 | 10.1330 |
| 8.6139 | 0.27 | 15 | 9.0228 |
| 7.7654 | 0.36 | 20 | 8.0477 |
| 7.1161 | 0.45 | 25 | 7.2438 |
| 6.486 | 0.55 | 30 | 6.6691 |
| 5.9793 | 0.64 | 35 | 6.3524 |
| 5.8845 | 0.73 | 40 | 6.2251 |
| 5.8619 | 0.82 | 45 | 6.1625 |
| 5.7536 | 0.91 | 50 | 6.1058 |
| 5.6831 | 1.0 | 55 | 6.0479 |
| 5.5525 | 1.09 | 60 | 5.9939 |
| 5.4714 | 1.18 | 65 | 5.9510 |
| 5.4384 | 1.27 | 70 | 5.9123 |
| 5.4539 | 1.36 | 75 | 5.8817 |
| 5.4073 | 1.45 | 80 | 5.8593 |
| 5.4048 | 1.55 | 85 | 5.8395 |
| 5.2997 | 1.64 | 90 | 5.8225 |
| 5.2388 | 1.73 | 95 | 5.8099 |
| 5.2564 | 1.82 | 100 | 5.7986 |
| 5.1758 | 1.91 | 105 | 5.7872 |
| 5.1926 | 2.0 | 110 | 5.7800 |
| 4.9244 | 2.09 | 115 | 5.7747 |
| 5.0897 | 2.18 | 120 | 5.7689 |
| 5.2493 | 2.27 | 125 | 5.7610 |
| 5.0594 | 2.36 | 130 | 5.7541 |
| 5.0792 | 2.45 | 135 | 5.7485 |
| 4.9952 | 2.55 | 140 | 5.7455 |
| 4.8796 | 2.64 | 145 | 5.7436 |
| 4.9344 | 2.73 | 150 | 5.7418 |
| 5.2387 | 2.82 | 155 | 5.7402 |
| 5.0734 | 2.91 | 160 | 5.7385 |
| 5.0227 | 3.0 | 165 | 5.7381 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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intanm/mbert-quoref-webis-2 | 2023-10-21T01:38:55.000Z | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | intanm | null | null | intanm/mbert-quoref-webis-2 | 0 | 2 | transformers | 2023-10-19T11:30:44 | ---
license: apache-2.0
base_model: intanm/mbert-quoref
tags:
- generated_from_trainer
model-index:
- name: mbert-idkmrc-webis-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mbert-idkmrc-webis-2
This model is a fine-tuned version of [intanm/mbert-quoref](https://huggingface.co/intanm/mbert-quoref) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1719
## 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 | 200 | 3.0455 |
| No log | 2.0 | 400 | 2.9841 |
| 2.8889 | 3.0 | 600 | 3.1719 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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xieyang233/BLOOM_VI | 2023-10-27T03:33:00.000Z | [
"generated_from_trainer",
"license:bigscience-bloom-rail-1.0",
"has_space",
"region:us"
] | null | xieyang233 | null | null | xieyang233/BLOOM_VI | 0 | 2 | null | 2023-10-19T12:10:59 | ---
license: bigscience-bloom-rail-1.0
base_model: bigscience/bloomz-7b1-mt
tags:
- generated_from_trainer
model-index:
- name: BLOOM_VI
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BLOOM_VI
This model is a fine-tuned version of [bigscience/bloomz-7b1-mt](https://huggingface.co/bigscience/bloomz-7b1-mt) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2309
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3278 | 0.51 | 200 | 1.3118 |
| 1.2775 | 1.03 | 400 | 1.2715 |
| 1.2464 | 1.54 | 600 | 1.2517 |
| 1.2231 | 2.05 | 800 | 1.2390 |
| 1.2162 | 2.56 | 1000 | 1.2309 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
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hung200504/bert-24.1 | 2023-10-19T12:34:05.000Z | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | hung200504 | null | null | hung200504/bert-24.1 | 0 | 2 | transformers | 2023-10-19T12:33:45 | ---
license: cc-by-4.0
base_model: deepset/bert-base-cased-squad2
tags:
- generated_from_trainer
model-index:
- name: bert-24.1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-24.1
This model is a fine-tuned version of [deepset/bert-base-cased-squad2](https://huggingface.co/deepset/bert-base-cased-squad2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 11.5138
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 11.3192 | 0.09 | 5 | 12.3266 |
| 11.4418 | 0.18 | 10 | 12.2766 |
| 11.029 | 0.27 | 15 | 12.2278 |
| 11.1589 | 0.36 | 20 | 12.1813 |
| 11.1385 | 0.45 | 25 | 12.1361 |
| 11.1645 | 0.55 | 30 | 12.0921 |
| 10.4417 | 0.64 | 35 | 12.0500 |
| 11.0789 | 0.73 | 40 | 12.0100 |
| 10.6311 | 0.82 | 45 | 11.9712 |
| 10.5261 | 0.91 | 50 | 11.9340 |
| 10.2874 | 1.0 | 55 | 11.8991 |
| 10.5003 | 1.09 | 60 | 11.8652 |
| 10.6206 | 1.18 | 65 | 11.8330 |
| 10.8413 | 1.27 | 70 | 11.8025 |
| 10.3731 | 1.36 | 75 | 11.7735 |
| 10.8143 | 1.45 | 80 | 11.7455 |
| 10.5414 | 1.55 | 85 | 11.7199 |
| 10.4919 | 1.64 | 90 | 11.6950 |
| 10.3187 | 1.73 | 95 | 11.6721 |
| 10.5598 | 1.82 | 100 | 11.6508 |
| 10.1028 | 1.91 | 105 | 11.6310 |
| 10.4634 | 2.0 | 110 | 11.6125 |
| 10.3986 | 2.09 | 115 | 11.5958 |
| 10.2164 | 2.18 | 120 | 11.5810 |
| 10.3932 | 2.27 | 125 | 11.5674 |
| 10.5229 | 2.36 | 130 | 11.5549 |
| 10.1181 | 2.45 | 135 | 11.5444 |
| 10.5176 | 2.55 | 140 | 11.5354 |
| 10.0784 | 2.64 | 145 | 11.5279 |
| 10.599 | 2.73 | 150 | 11.5223 |
| 10.3577 | 2.82 | 155 | 11.5180 |
| 10.3107 | 2.91 | 160 | 11.5150 |
| 10.5243 | 3.0 | 165 | 11.5138 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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adityarra07/whisper-medium-ft-17000 | 2023-10-19T23:34:17.000Z | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | adityarra07 | null | null | adityarra07/whisper-medium-ft-17000 | 0 | 2 | transformers | 2023-10-19T12:43:50 | ---
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-medium-ft-17000
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-medium-ft-17000
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2319
- Wer: 8.5169
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.297 | 1.0 | 1063 | 0.2056 | 11.1111 |
| 0.0869 | 2.0 | 2126 | 0.1926 | 10.4258 |
| 0.0355 | 3.0 | 3189 | 0.1984 | 8.9574 |
| 0.0126 | 4.0 | 4252 | 0.2188 | 9.4958 |
| 0.0038 | 5.0 | 5315 | 0.2198 | 8.6637 |
| 0.001 | 6.0 | 6378 | 0.2319 | 8.5169 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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dlhw/setFit-fewShot_100 | 2023-10-19T13:21:49.000Z | [
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | dlhw | null | null | dlhw/setFit-fewShot_100 | 0 | 2 | sentence-transformers | 2023-10-19T13:21:28 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# dlhw/setFit-fewShot_100
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("dlhw/setFit-fewShot_100")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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ani2857/bert-base-multilingual-cased-squad | 2023-11-03T14:13:19.000Z | [
"transformers",
"pytorch",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | ani2857 | null | null | ani2857/bert-base-multilingual-cased-squad | 0 | 2 | transformers | 2023-10-19T13:26:29 | ---
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-multilingual-cased-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-cased-squad
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the squad 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: 1e-10
- 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: 10
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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] |
nobodynosql/sql_codellama | 2023-10-25T05:56:01.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"transformer",
"en",
"fr",
"cn",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | nobodynosql | null | null | nobodynosql/sql_codellama | 0 | 2 | transformers | 2023-10-19T14:13:45 | ---
tasks:
- text2sql
model-type:
- glm
domain:
- nlp
frameworks:
- pytorch
backbone:
- transformer
containers:
- registry-vpc.cn-shanghai.aliyuncs.com/cloud-dsw/pytorch:1.8-cpu-py36-ubuntu18.04
- registry-vpc.cn-shanghai.aliyuncs.com/cloud-dsw/tensorflow:1.12-cpu-py36-ubuntu18.04
customized-quickstart: False
finetune-support: False
license: apache-2.0
language:
- en
- fr
- cn
tags:
- transformer
pre-train: False
train:
- spider dataset
- bird dataset
---
# sql_codellama 介绍
SQL-Codellama是一个用于text2SQL的模型。
## 模型底座
它是基于codellama模型构建的，该模型通过使用qlora进行训练。
## 训练数据
训练数据包含了spider、starcode等数据集。这个模型的目标是将自然语言查询转换为SQL查询。
## 功能
Text to SQL( 以下简称Text2SQL)，是将自然语言文本（Text）转换成结构化查询语言SQL的过程，属于自然语言处理-语义分析（Semantic Parsing）领域中的子任务。
它的目的可以简单概括为：“打破人与结构化数据之间的壁垒”，即普通用户可以通过自然语言描述完成复杂数据库的查询工作，得到想要的结果。
它通过学习语法、语义和查询意图来理解用户的问题，并根据对应的数据库结构生成相应的SQL查询语句。SQL-Codellama的训练过程经过了大量的数据预处理、特征提取和模型训练，以提高其准确性和性能。
它可以应用于各种领域，如数据分析、数据库查询优化等。SQL-Codellama的设计和训练过程是为了使其能够处理复杂的查询，并产生高质量的SQL查询结果。它的目标是为用户提供准确、高效的文本到SQL转换，从而帮助用户更轻松地进行数据库查询和数据分析。
```bash
git clone https://www.modelscope.cn/tomatoModelScope/sql_codellama.git
```
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passionMan/distilbert-base-uncased-finetuned-transductive | 2023-10-19T16:36:32.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | passionMan | null | null | passionMan/distilbert-base-uncased-finetuned-transductive | 0 | 2 | transformers | 2023-10-19T16:11:27 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-transductive
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-transductive
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2014
## 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: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 49 | 2.2737 |
| 2.5592 | 2.0 | 98 | 2.6722 |
| 2.5592 | 3.0 | 147 | 2.3211 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2
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haseong8012/whisper-small_child-10k_aag | 2023-10-22T14:55:52.000Z | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"ko",
"dataset:haseong8012/child-10k",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | haseong8012 | null | null | haseong8012/whisper-small_child-10k_aag | 0 | 2 | transformers | 2023-10-19T16:55:32 | ---
language:
- ko
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- haseong8012/child-10k
model-index:
- name: >-
whisper-small-fineTuned_By_korean-child-command-voice_train-0-10000_smaplingRate-16000-aag-test1
results: []
metrics:
- wer
- cer
---
<!-- 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. -->
# whisper-small-fineTuned_By_korean-child-command-voice_train-0-10000_smaplingRate-16000-aag-test1
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the korean-child-command-voice_train-0-10000 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: 1.25e-05
- train_batch_size: 16
- eval_batch_size: 8
- 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: 500
- training_steps: 5000
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1 | 1,439 | [
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mor40/BulBERT-cinexio-10epochs | 2023-10-19T17:55:57.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:bgglue",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | mor40 | null | null | mor40/BulBERT-cinexio-10epochs | 0 | 2 | transformers | 2023-10-19T17:50:07 | ---
base_model: mor40/BulBERT-finetuned-cinexio
tags:
- generated_from_trainer
datasets:
- bgglue
metrics:
- accuracy
model-index:
- name: BulBERT-cinexio-10epochs
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: bgglue
type: bgglue
config: cinexio
split: validation
args: cinexio
metrics:
- name: Accuracy
type: accuracy
value: 0.6288532675709001
---
<!-- 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. -->
# BulBERT-cinexio-10epochs
This model is a fine-tuned version of [mor40/BulBERT-finetuned-cinexio](https://huggingface.co/mor40/BulBERT-finetuned-cinexio) on the bgglue dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1866
- Accuracy: 0.6289
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 64 | 1.3334 | 0.5746 |
| No log | 2.0 | 128 | 1.2053 | 0.6017 |
| No log | 3.0 | 192 | 1.1826 | 0.6227 |
| No log | 4.0 | 256 | 1.1826 | 0.6252 |
| No log | 5.0 | 320 | 1.1671 | 0.6227 |
| No log | 6.0 | 384 | 1.1743 | 0.6289 |
| No log | 7.0 | 448 | 1.1795 | 0.6375 |
| 1.0262 | 8.0 | 512 | 1.1847 | 0.6178 |
| 1.0262 | 9.0 | 576 | 1.1877 | 0.6264 |
| 1.0262 | 10.0 | 640 | 1.1866 | 0.6289 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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mor40/BulBERT-cinexio-5pochs | 2023-10-20T19:16:22.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | mor40 | null | null | mor40/BulBERT-cinexio-5pochs | 0 | 2 | transformers | 2023-10-19T18:56:22 | ---
base_model: mor40/BulBERT-finetuned-cinexio
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BulBERT-cinexio-5pochs
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BulBERT-cinexio-5pochs
This model is a fine-tuned version of [mor40/BulBERT-finetuned-cinexio](https://huggingface.co/mor40/BulBERT-finetuned-cinexio) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1071
- Accuracy: 0.97
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 388 | 0.2160 | 0.92 |
| 0.244 | 2.0 | 776 | 0.1136 | 0.95 |
| 0.2092 | 3.0 | 1164 | 0.1366 | 0.95 |
| 0.1607 | 4.0 | 1552 | 0.1228 | 0.94 |
| 0.1607 | 5.0 | 1940 | 0.1071 | 0.97 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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catweld/translation_flan_base_v7_96epochs | 2023-10-19T21:49:38.000Z | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | catweld | null | null | catweld/translation_flan_base_v7_96epochs | 0 | 2 | transformers | 2023-10-19T20:54:10 | ---
license: apache-2.0
base_model: catweld/translation_flan_base_v7_64epochs
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: translation_flan_base_v7_96epochs
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# translation_flan_base_v7_96epochs
This model is a fine-tuned version of [catweld/translation_flan_base_v7_64epochs](https://huggingface.co/catweld/translation_flan_base_v7_64epochs) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0008
- Bleu: 50.8149
- Gen Len: 6.77
## 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: 32
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 139 | 0.0028 | 50.9693 | 6.7804 |
| No log | 2.0 | 278 | 0.0027 | 50.9443 | 6.7795 |
| No log | 3.0 | 417 | 0.0029 | 50.7243 | 6.7691 |
| 0.0125 | 4.0 | 556 | 0.0024 | 50.9387 | 6.7832 |
| 0.0125 | 5.0 | 695 | 0.0024 | 50.9741 | 6.7795 |
| 0.0125 | 6.0 | 834 | 0.0027 | 50.7106 | 6.7669 |
| 0.0125 | 7.0 | 973 | 0.0023 | 50.7461 | 6.7691 |
| 0.0088 | 8.0 | 1112 | 0.0018 | 50.8819 | 6.7836 |
| 0.0088 | 9.0 | 1251 | 0.0017 | 50.8804 | 6.7832 |
| 0.0088 | 10.0 | 1390 | 0.0016 | 50.8819 | 6.7836 |
| 0.0077 | 11.0 | 1529 | 0.0015 | 50.8867 | 6.7832 |
| 0.0077 | 12.0 | 1668 | 0.0018 | 50.751 | 6.7687 |
| 0.0077 | 13.0 | 1807 | 0.0012 | 50.7421 | 6.7669 |
| 0.0077 | 14.0 | 1946 | 0.0012 | 50.82 | 6.7723 |
| 0.0069 | 15.0 | 2085 | 0.0011 | 50.8267 | 6.77 |
| 0.0069 | 16.0 | 2224 | 0.0011 | 50.8298 | 6.77 |
| 0.0069 | 17.0 | 2363 | 0.0010 | 50.7508 | 6.77 |
| 0.0064 | 18.0 | 2502 | 0.0010 | 50.8163 | 6.77 |
| 0.0064 | 19.0 | 2641 | 0.0011 | 50.7341 | 6.77 |
| 0.0064 | 20.0 | 2780 | 0.0009 | 50.7358 | 6.77 |
| 0.0064 | 21.0 | 2919 | 0.0009 | 50.7823 | 6.7709 |
| 0.0057 | 22.0 | 3058 | 0.0009 | 50.7958 | 6.7705 |
| 0.0057 | 23.0 | 3197 | 0.0010 | 50.7958 | 6.7705 |
| 0.0057 | 24.0 | 3336 | 0.0009 | 50.8284 | 6.77 |
| 0.0057 | 25.0 | 3475 | 0.0009 | 50.8284 | 6.77 |
| 0.006 | 26.0 | 3614 | 0.0009 | 50.8284 | 6.77 |
| 0.006 | 27.0 | 3753 | 0.0008 | 50.8284 | 6.77 |
| 0.006 | 28.0 | 3892 | 0.0008 | 50.8149 | 6.77 |
| 0.0062 | 29.0 | 4031 | 0.0008 | 50.8149 | 6.77 |
| 0.0062 | 30.0 | 4170 | 0.0008 | 50.8149 | 6.77 |
| 0.0062 | 31.0 | 4309 | 0.0008 | 50.8149 | 6.77 |
| 0.0062 | 32.0 | 4448 | 0.0008 | 50.8149 | 6.77 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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SalmonAI123/vi-mrc-large-version-1 | 2023-10-19T21:14:04.000Z | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | SalmonAI123 | null | null | SalmonAI123/vi-mrc-large-version-1 | 0 | 2 | transformers | 2023-10-19T20:58:22 | ---
license: cc-by-nc-4.0
base_model: nguyenvulebinh/vi-mrc-large
tags:
- generated_from_trainer
model-index:
- name: vi-mrc-large-version-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vi-mrc-large-version-1
This model is a fine-tuned version of [nguyenvulebinh/vi-mrc-large](https://huggingface.co/nguyenvulebinh/vi-mrc-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4380
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 172 | 0.9088 |
| No log | 2.0 | 344 | 1.2489 |
| 0.6437 | 3.0 | 516 | 1.3131 |
| 0.6437 | 4.0 | 688 | 1.4380 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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AzureBlack/Xwin-MLewd-13B-V0.2-5bpw-6h-exl2 | 2023-10-25T16:17:12.000Z | [
"transformers",
"safetensors",
"llama",
"text-generation",
"not-for-all-audiences",
"nsfw",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | AzureBlack | null | null | AzureBlack/Xwin-MLewd-13B-V0.2-5bpw-6h-exl2 | 3 | 2 | transformers | 2023-10-19T22:37:18 | ---
license: cc-by-nc-4.0
tags:
- not-for-all-audiences
- nsfw
---
ExllamaV2 version of the model created by the work of Undi95
Original Model https://huggingface.co/Undi95/Xwin-MLewd-13B-V0.2
Requires ExllamaV2, which is being developed by turboderp https://github.com/turboderp/exllamav2 under an MIT license.
THIS MODEL IS MADE FOR LEWD
SEXUAL, CRUDE AND KINKY CONTENT IN OUTPUT CAN AND WILL HAPPEN. YOU'RE WARNED
This is MLewd merged with [Xwin-LM/Xwin-LM-13B-V0.2](https://huggingface.co/Xwin-LM/Xwin-LM-13B-V0.2)
<!-- description start -->
## Description
This repo contains fp16 files of Xwin-MLewd-13B-V0.2, very hot and lewd model based on Xwin 0.2 13B.
<!-- description end -->
<!-- description start -->
## Models and loras used
- Undi95/ReMM-S-Light (base/private)
- Undi95/CreativeEngine
- Brouz/Slerpeno
- The-Face-Of-Goonery/Huginn-v3-13b
- zattio770/120-Days-of-LORA-v2-13B
- PygmalionAI/pygmalion-2-13b
- Undi95/StoryTelling
- TokenBender/sakhi_13B_roleplayer_NSFW_chat_adapter
- nRuaif/Kimiko-v2-13B
- The-Face-Of-Goonery/Huginn-13b-FP16
- lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT
- Xwin-LM/Xwin-LM-13B-V0.2
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
## The secret sauce
```
slices:
- sources:
- model: Xwin-LM/Xwin-LM-13B-V0.2
layer_range: [0, 40]
- model: Undi95/MLewd-v2.4-13B
layer_range: [0, 40]
merge_method: slerp
base_model: Xwin-LM/Xwin-LM-13B-V0.2
parameters:
t:
- filter: lm_head
value: [0.55]
- filter: embed_tokens
value: [0.7]
- filter: self_attn
value: [0.65, 0.35]
- filter: mlp
value: [0.35, 0.65]
- filter: layernorm
value: [0.4, 0.6]
- filter: modelnorm
value: [0.6]
- value: 0.5 # fallback for rest of tensors
dtype: float16
```
Special thanks to Sushi and Shena ♥
If you want to support me, you can [here](https://ko-fi.com/undiai).
| 2,207 | [
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pbaoo2705/roberta-large-squad-finetune-covidqa-direct | 2023-10-19T22:39:10.000Z | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | pbaoo2705 | null | null | pbaoo2705/roberta-large-squad-finetune-covidqa-direct | 0 | 2 | transformers | 2023-10-19T22:38:06 | ---
license: cc-by-4.0
base_model: deepset/roberta-large-squad2
tags:
- generated_from_trainer
model-index:
- name: roberta-large-squad-finetune-covidqa-direct
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-squad-finetune-covidqa-direct
This model is a fine-tuned version of [deepset/roberta-large-squad2](https://huggingface.co/deepset/roberta-large-squad2) 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: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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asmallgreenpotato/falcon-7b-ft-miia0-next-adapters | 2023-10-19T23:03:51.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | asmallgreenpotato | null | null | asmallgreenpotato/falcon-7b-ft-miia0-next-adapters | 0 | 2 | peft | 2023-10-19T23:03:37 | ---
library_name: peft
base_model: tiiuae/falcon-7b
---
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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## Glossary [optional]
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
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buku/bukumodel | 2023-10-20T01:58:24.000Z | [
"sentence-transformers",
"pytorch",
"xlm-roberta",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | buku | null | null | buku/bukumodel | 0 | 2 | sentence-transformers | 2023-10-20T01:36:14 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# /var/folders/bs/8vxf5c0n5pj_658nfkp_m5580000gn/T/tmpw885_y2r/buku/bukumodel
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("/var/folders/bs/8vxf5c0n5pj_658nfkp_m5580000gn/T/tmpw885_y2r/buku/bukumodel")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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TanmaySah/limadollyoa | 2023-10-20T17:33:36.000Z | [
"peft",
"region:us"
] | null | TanmaySah | null | null | TanmaySah/limadollyoa | 0 | 2 | peft | 2023-10-20T02:24:51 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0
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yuyijiong/atom-7b-chat-16k | 2023-10-25T10:47:38.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"zh",
"dataset:yuyijiong/LongData-instruction-chinese",
"license:cc-by-nc-nd-4.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | yuyijiong | null | null | yuyijiong/atom-7b-chat-16k | 1 | 2 | transformers | 2023-10-20T03:11:13 | ---
license: cc-by-nc-nd-4.0
datasets:
- yuyijiong/LongData-instruction-chinese
language:
- zh
pipeline_tag: text-generation
---
2023.10.25更新：改进版本已经推出，比当前版本强了很多，建议使用新版模型[LongAlpaca-7b-32k-chinese](https://huggingface.co/yuyijiong/LongAlpaca-7b-32k-chinese)
* 此模型由[atom-7b-chat](https://huggingface.co/FlagAlpha/Atom-7B-Chat)经过lora微调（只训练k_proj、q_proj、v_proj、o_proj、norm）得到，
通过线性位置插值，将文本长度从4k扩展到16k，可以完成上万字的多文档检索、论文总结等任务，而短对话能力几乎没有下降。
作为对比，原模型如果直接进行线性位置插值而不进行微调，在长度大于8k时几乎没有正常对话能力，而短对话能力严重下降。\
* 此版本为v1，初步具有长对话能力，回答格式良好，但回答内容错误依然较多，回答经常出现与参考文档内容不一致的问题，可能是因为微调数据质量低（[yuyijiong/LongData-instruction-chinese](https://huggingface.co/datasets/yuyijiong/LongData-instruction-chinese) 都是谷歌翻译过来的英文数据）。\
* 未来将会持续改进，改进的数据和模型已经推出。
* 此模型最大支持16k输入长度，如果超长仍然会出现答案错乱的问题。暂时没有训练32k的模型，是因为32k长度的中文数据量不足。\
使用方法：
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
model_path="yuyijiong/atom-7b-chat-16k"
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
# use auto mode, automatically select precision based on the device.
model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto", load_in_8bit=True).eval()
question="中国的首都是什么？"
input_text = "<s>Human: " + question + "\n</s><s>Assistant: "
input_ids = tokenizer(input_text, return_tensors='pt').input_ids.to(model.device)
with torch.no_grad():
with torch.autocast('cuda'):
output = model.generate(input_ids=input_ids,
max_new_tokens=max_new_tokens,
do_sample=True,
temperature=0.85,
top_k=None,
top_p=0.9,
use_cache=True,
**kwargs)
reply = tokenizer.decode(output[0], skip_special_tokens=False)
reply_return=reply.split('Assistant:')[-1].replace('</s>', '')
print('模型回答：', reply_return)
```
示例（多文档检索（问答）任务，输入文本大于10000字）
```
先阅读以下多个参考文档，然后根据文档内容，详细地回答问题，并指出所参考的文档的序号：
文档-1：（标题：女权主义）苏珊·B·安东尼（Susan B. Anthony），两人在倡导妇女投票权之前都曾争取废除奴隶制。这些女性受到贵格会精神平等神学的影响，该神学主张男女在上帝面前平等。在美国，第一波女权主义被认为随着美国宪法第十九修正案（1919）的通过而结束，赋予妇女在所有州投票的权利。在“第二波女权主义”一词开始被用来描述较新的女权主义运动之后，“第一波”一词是追溯性地创造的，用于对这些西方运动进行分类。
文档-2：（标题：第二波女权主义）的出现，迫使声称代表普遍经验的现有主导历史去中心化并重新聚焦。第二波女权主义 第二波女权主义是一个女权主义活动和思想的时期，始于 20 世纪 60 年代初的美国，持续了大约二十年。它迅速在西方世界传播，其目的不仅仅是通过获得选举权来提高妇女的平等。该运动解决的问题包括有关国内问题的权利，例如着装规范和就业。在 20 世纪 60 年代（实际上是在 20 世纪初的大部分时间），女性并不倾向于寻求
文档-3：（标题：第二波女权主义）怀孕、哺乳和月经绝不是将她们视为“第二性别”的有效理由或解释。这本书从法文翻译成英文（删除了部分文字），并于 1953 年在美国出版。1960 年，美国食品和药物管理局批准了复方口服避孕药，并于 1961 年上市。这使得女性更容易使用避孕药。拥有事业而不必因为意外怀孕而离开。肯尼迪总统的政府将妇女权利作为新边疆的一个关键问题，并点名女性（如埃丝特·彼得森）
文档-4：（标题：1960 年代）20 世纪 60 年代新兴的理想主义。 20 世纪 60 年代初，第二波女权主义浪潮在美国和世界各地兴起。 20世纪初的第一波浪潮的重点是获得选举权和推翻“法律上”的不平等，而第二波浪潮的重点是改变文化和社会规范以及与妇女相关的“事实上的”不平等。当时，女性的地位通常被认为是在家里，她们被排除在许多工作和职业之外。在美国，总统妇女地位委员会发现，在以下领域存在对妇女的歧视：
文档-5：（标题：美国的女权主义）美国始于20世纪90年代初。 1991年，安妮塔·希尔（Anita Hill）指控美国最高法院提名的克拉伦斯·托马斯（Clarence Thomas）性骚扰。托马斯否认了这些指控，经过广泛辩论，美国参议院以 52 比 48 投票支持托马斯。 1992年，针对安妮塔·希尔性骚扰案，美国女权主义者丽贝卡·沃克在《女士》杂志上发表题为《成为第三波浪潮》的文章，文中她表示：“我不是后女权主义女权主义者，我是第三波女权主义者。” -波”，创造了“第三波”一词。同样是在 1992 年，Third Wave Direct Action Corporation 成立
文档-6：（标题：第二波女权主义）女权主义于 20 世纪 80 年代初以女权主义性战争结束，并于 1990 年代初被第三波女权主义所取代。 1967年，在伦敦举行的国际妇女联盟大会上，代表们了解到联合国妇女地位委员会发起的一项研究和评估本国妇女状况的倡议。许多组织和非政府组织，如商业和职业妇女协会、职业妇女福利互助会俱乐部以及教学和护理协会，都成立了委员会，以响应该倡议，准备对妇女状况进行评估，并敦促本国政府
文档-7：（标题：第一波女权主义）有但不能有，而男人可以。据说，许多第一波白人基本女权主义者与有色人种女性结盟，但当她们认为自己可以为中产阶级白人女性取得进步时却保持沉默。第一波女权主义 第一波女权主义是 19 世纪和 20 世纪初整个西方世界发生的女权主义活动和思想的时期。它的重点是法律问题，主要是获得投票权。 “第一波”一词是由玛莎·李尔 (Martha Lear) 于 1968 年 3 月在《纽约时报杂志》上撰文创造的。
文档-8：（标题：女性反对女权主义）社交媒体上，女性反对女权主义仍然出现在博客平台上——但现在，有更多直言不讳的反对者。女性反对女权主义的重大历史运动始于19世纪。 1848年，由苏珊·B·安东尼和伊丽莎白·斯塔顿发起的“妇女运动”之后，为妇女投票权而斗争的反主流文化，即第一波反女权运动开始了。这个时代的反女权主义者相信传统的生活方式受到了女权主义议程的威胁。反女权主义者反对妇女在公共和私人领域、劳动力以及任何与生育自由相关的权利。早期期间
文档-9：（标题：第二波女权主义）莱文，《女权主义的重生》，1971 年，第 17 页。 120）。会议结束后，少数妇女成立了西雅图第一个妇女解放组织。第二波女权运动也标志着妇女研究作为一个合法研究领域的出现。 1970 年，圣地亚哥州立大学成为美国第一所提供妇女研究精选课程的大学。 1977 年在德克萨斯州休斯顿举行的全国妇女会议为妇女解放团体提供了解决众多妇女问题的机会。在这次会议上，来自全国各地的代表齐聚一堂，制定了一项国家行动计划，
文档-10：（标题：第二波女权主义）20 世纪 60 年代末。韦尔斯学院以前只有女性学生，2005 年成为男女同校。道格拉斯学院隶属于罗格斯大学，是最后一所公立公立女性学院，直到 2007 年成为男女混合学院。从20世纪末开始，许多女权主义学者批评美国的第二波女权主义运动将女权主义活动简化为同质化和粉饰的女权主义历史年表，忽视了许多有色人种女性、工人阶级女性和女性的声音和贡献。 LGBT 女性。美国第二波女权主义史学因未能承认和分析而受到批评
文档-11：（标题：后殖民女权主义）下面进一步讨论。现代女权运动的历史可以分为三个浪潮。当第一波女权主义起源于十九世纪末时，它是作为北半球白人中产阶级女性的一场运动而兴起的，她们能够合理地获得资源和教育。因此，第一波女权主义几乎完全解决了这些相对富裕的女性的问题。第一波浪潮关注的是绝对权利，例如选举权和推翻法律上性别平等的其他障碍。这个人口并不包括有色人种女性的现实，她们感受到了种族的力量
文档-12：（标题：荷兰的女权主义）是在 20 世纪 50 年代实现的：1955 年，法律发生变化，妇女在结婚后不能再被迫辞去公务员职务，1956 年，已婚妇女具有法律能力。 1957 年，婚姻禁令被取消。1967 年，乔克·库尔-史密茨 (Joke Kool-Smits) 在《De Gids》上发表了一篇文章《妇女的不满》，该文章被认为在荷兰发起了第二波女权主义。第二年，一群女权主义者联合起来创建了活动团体“Man-Vrouw-Maatschappij”（男女社会，简称MVM）。其双性别构成在西方第二波女权主义组织中并不多见，但它
文档-13：（书名：《女权主义）性》）于1949年出版。该书表达了女权主义者的不公正感。第二波女权主义是一场始于20世纪60年代初并持续至今的女权运动，因此它与第三波女权主义并存。第二波女权主义主要关注选举权之外的平等问题，例如结束性别歧视。第二波女权主义者认为妇女的文化和政治不平等是密不可分的，并鼓励妇女了解其个人生活的各个方面，这些方面被深深政治化并反映了性别歧视女权主义活动家兼作家卡罗尔·哈尼施 (Carol Hanisch) 创造了“个人即政治”的口号，该口号成为第二次浪潮的代名词。
文档-14：（标题：白人女权主义）一场专注于“全面议程”的运动。白人女权主义 白人女权主义是一个用来描述女权主义理论的绰号，这些理论关注白人妇女的斗争，而不解决少数族裔妇女和缺乏其他特权的妇女所面临的不同形式的压迫。第一波女权主义始于近代早期，一直持续到20世纪初，主要关注与妇女有关的法律问题，特别是妇女的选举权问题。这一浪潮正式始于 1848 年工业革命末期在纽约塞内卡福尔斯举行的塞内卡福尔斯大会。这波浪潮的目标是
文档-15：（标题：第二波女权主义）建立全国妇女地位委员会。 1967年，乔克·库尔-史密茨（Joke Kool-Smits）的《妇女的不满》出版；这篇文章的发表通常被认为是荷兰第二波女权主义的开始。在这篇文章中，斯密特描述了已婚女性的挫败感，称她们厌倦了仅仅做母亲和家庭主妇。在土耳其和以色列，第二波女权主义于 20 世纪 80 年代开始。女权主义活动家建立了一系列女权主义企业，包括女性书店、女权主义信用合作社、女权主义出版社、女权主义邮购目录、女权主义餐馆和女权主义唱片公司。这些企业作为其中的一部分而蓬勃发展
文档-16：（标题：第一波女权主义）反对性别平等的性双重标准。 1902 年，全国妇女选举权协会终于成立。 1921年，妇女终于享有选举权。妇女选举权改革之后是 1923 年的“Behrighetslagen”（1923 年准入法案），其中正式赋予男性和女性平等参与社会所有职业和职位的机会，唯一的例外是军事和神职职位。最后两项限制于 1958 年被取消，当时妇女被允许成为牧师，并在 1980 年至 1989 年间的一系列改革中被取消，所有军事职业都向妇女开放。
文档-17：（标题：美国女性史）增加了一倍多，从约 68,000 人增加到 170,000 人。 1987 年，国会宣布三月为第一个全国妇女历史月。此后每年都会发布一项特别总统公告，以表彰美国妇女所取得的成就。年轻女性现在开始更多地参与女权主义。 20 世纪 90 年代初，第三波女权主义兴起，作为对第二波女权主义明显不足和缺点的回应。持续至今的第三波女权主义通常与年轻一代的女权主义激进主义、对流行文化和性能动性的兴趣以及对多元化和矛盾的接受联系在一起。 1991 年，在
文档-18：（标题：第一波女权主义） 第一波女权主义 第一波女权主义是19世纪和20世纪初整个西方世界发生的女权主义活动和思想的一个时期。它的重点是法律问题，主要是获得投票权。 “第一波”一词是玛莎·李尔于1968年3月在《纽约时报》杂志上撰文时创造的，同时她也使用了“第二波女权主义”一词。当时，妇女运动的重点是“事实上的”（非官方的）不平等，它希望将其与早期女权主义者的目标区分开来。根据米里亚姆·施奈尔的说法，西蒙娜·德·波伏瓦写道：
文档-19：（标题：第一波女权主义）（1920），赋予妇女投票权。这是这场运动的重大胜利，其中还包括高等教育、工作场所和职业以及医疗保健方面的改革。女性开始在学校董事会和地方机构任职，而且人数不断增加。这一时期也有更多的女性获得了高等教育的机会。 1910 年，“女性开始就读许多领先的医学院，1915 年，美国医学会开始接纳女性会员。” 《1923 年婚姻诉讼法》赋予女性与男性同等的离婚理由。伟大时期失业率上升
文档-20：（书名：《女权主义》）于 1970 年巡回演出。弗里丹成功出版后的十年内，女性在第一世界劳动力中占到了一半以上。第三波女权主义可以追溯到 20 世纪 90 年代初华盛顿州奥林匹亚 Riot grrrl 女权主义朋克亚文化的出现，以及 1991 年安妮塔·希尔 (Anita Hill) 向全男性、全白人的参议院司法委员会作电视证词——克拉伦斯被提名为美国最高法院法官的托马斯对她进行了性骚扰。 “第三波”一词归功于丽贝卡·沃克（Rebecca Walker），她在《女士》杂志上发表了一篇文章，回应托马斯被任命为最高法院法官。杂志，
文档-21：（标题：第一波女权主义）通过她创立的政治和女权主义组织。 1917年至1919年，她实现了妇女获得选举权的目标。 Cornelia Ramondt-Hirschmann (1871–1951)，荷兰妇女国际和平与自由联盟 [WILPF] 主席 Selma Meyer (1890–1941)，荷兰妇女国际和平与自由联盟 [WILPF] 秘书在文化和语言方面，巴达什特会议（1848年）的事件展示了第一波女权主义关注的进展。在巴达什特和塞内卡瀑布举行的会议之间，波斯（后来称为伊朗）和美国在时间上具有同步性，在主题和事件上也有相似性
文档-22：（标题：第一波女权主义）争议并引发了一场被称为“赫塔辩论”的辩论。两个最重要的问题是废除未婚女性的秘密身份，以及国家为女性提供相当于大学的教育。这两个问题都得到了满足：1858 年，一项改革赋予未婚妇女通过简单程序申请法定成年的权利，1861 年，Hgre lrarinneseminariet 作为“女子大学”成立。 1859 年，Sophie Adlersparre 和 Rosalie Olivecrona 创办了瑞典和北欧国家第一本女性杂志《Tidskrift fr hemmet》。这被称为起点
文档-23：（标题：女权主义与媒体）利用报纸、电视、广播和发表的论文来传播她们的信息。 1960 年之前，男性和女性都接受了传统性别和家庭角色的现实。但是，当第二波女权主义开始时，女性在家庭和工作中都挑战了这些角色。 （贝克，1998）。贝蒂·弗里丹 (Betty Friedan) 1963 年出版的著作《女性的奥秘》(The Feminine Mystique) 据说激发了第二波运动，因为该书讨论了（白人、中产阶级）女性的不幸，“她们的性别角色有限，在社会中她们有孤立感”。郊区核心家庭”（Mendes，2011）。也是在这个时期（1960年代和1970年代左右）
文档-24：（标题：第二波女权主义）直到 1975 年末，家庭殴打和强奸都为社会所接受且合法，因为妇女被视为丈夫的财产。由于第二波女权运动的积极分子以及与他们合作的当地执法机构，到 1982 年，已经建立了 300 个庇护所和 48 个州联盟，为遭受男性虐待的妇女提供保护和服务在他们的生活中。这一时期在美国展开的一场辩论围绕着男女同校的问题。美国大多数男子学院都实行男女同校，通常是由
文档-25：（标题：全国妇女组织）家庭女性范式。这本书的目的是推动女性在家庭环境之外扮演角色的运动。承认抚养孩子、做饭、重新布置房屋装饰带来的一些满足感并不足以满足女性接受教育的更深层次的愿望。这本书被广泛认为引发了美国第二波女权主义的开端。该书由 WW Norton 于 1963 年 2 月 19 日出版。在一次采访中，弗里丹特别指出，“当我写《女性的奥秘》时，并没有任何激进主义。但我意识到仅仅写一个是不够的。”
文档-26：（标题：波兰的女权主义）工作。这一时期被称为波兰女权主义第六波浪潮。其特点是大量制作提倡性别平等的宣传文本以及妇女大量参与工业生产、农业和政治。波兰是世界上第一位女部长。第二波女权主义作为女权主义活动的一个时期，始于20世纪60年代初的美国。 1956 年，随着堕胎合法化，同样的浪潮在波兰达到顶峰，引发了争议性的支持堕胎的文本。此后，女权主义的声音几乎被压制（直到1989年）；这
文档-27：（标题：第一波女权主义）1882 年《已婚妇女财产法》的通过。 1858 年，芭芭拉·博迪雄 (Barbara Bodichon)、玛蒂尔达·玛丽·海斯 (Matilda Mary Hays) 和贝西·雷纳·帕克斯 (Bessie Rayner Parkes) 创办了第一份英国女权主义期刊《英国妇女杂志》，贝西·帕克斯 (Bessie Parkes)总编辑。该杂志一直出版到 1864 年，并于 1866 年被 Jessie Boucherett 编辑到 1880 年的《英国妇女评论》继承，该杂志一直出版到 1910 年。Jessie Boucherett 和 Adelaide Anne Proctor 于 1859 年加入朗豪坊圈。该团体一直活跃到 1866 年。同样在 1859 年，Jessie Boucherett、Barbara Bodichon 和 Adelaide Proctor 成立了促进就业协会
文档-28：（标题：女权主义的历史）第二波女权主义的多样性和激进主义（例如斯坦顿、安东尼、玛蒂尔达·乔斯林·盖奇和全国妇女选举权协会，斯坦顿是该协会的主席）。美国第一波女权主义被认为随着美国宪法第十九修正案（1920）的通过而结束，该修正案赋予白人妇女在美国投票的权利。争取妇女平等的活动并不局限于美国。在 19 世纪中叶的波斯，塔荷蕾是一位活跃的诗人和宗教改革家，据记载，她在她的著作中宣称妇女平等。
文档-29：（标题：女权主义与媒体）女权主义者宣扬“性别是一个绝对的范畴而不是一个相对的范畴”的观念。对这一观点的早期描述包括瓦莱丽·索拉纳斯 (Valerie Solanas) 于 1967 年撰写的 SCUM 宣言。这一浪潮的主要立法焦点是《平等权利修正案》(ERA) 的通过，该修正案保证了无论性别如何的社会平等。 ERA 已提交国会批准，但未能获得批准。据说第二波浪潮在 20 世纪 80 年代初结束，讨论了第三波浪潮中讨论过的性和色情问题。随着第二波浪潮中更先进的技术，女权主义者
文档-30：（标题：第一波女权主义）年轻时，她的鳏夫哲学家威廉·戈德温很快为她写了一本回忆录，这与他的意图相反，毁掉了她几代人的声誉。沃斯通克拉夫特被认为是英国女权运动的“前辈”，她的思想塑造了争取妇女选举权的妇女参政论者的思想。早期的女权主义与废奴运动直接相关，因此许多著名的女权主义者和活动家开始发出自己的声音。其中一些早期的活动家包括索杰纳·特鲁斯、伊丽莎白·布莱克威尔、简·亚当斯和多萝西·戴。第一波女权主义浪潮主要由白人女性领导
文档-31：（标题：挪威的女权主义）家园创建了。 1950年，与外国人结婚的女性可以自行决定是否保留挪威公民身份。同年，每个妇女自由控制自己身体的权利问题在挪威全国妇女委员会成为现实。第一波女权主义浪潮是为了改变女性在婚姻中的地位，结束已婚女性的从属地位；下一波女权主义为获得与男性相同的权利而奋斗。 20 世纪 60 年代的标志是许多抗议、新思想的出现以及第一个女权主义的出现。
文档-32：（标题：白人女权主义） 白人女权主义 白人女权主义是一个用来描述女权主义理论的绰号，这些理论关注白人妇女的斗争，而不解决少数族裔妇女和缺乏其他特权的妇女所面临的不同形式的压迫。第一波女权主义始于近代早期，一直持续到20世纪初，主要关注与妇女有关的法律问题，特别是妇女的选举权问题。这一浪潮正式始于 1848 年工业革命末期在纽约塞内卡福尔斯举行的塞内卡福尔斯大会。这一浪潮的目标是为女性创造机会，重点是
文档-33：（标题：美国的女权主义）她也是一名性教育家、作家和护士。她普及了“节育”一词，于 1916 年在美国开设了第一家节育诊所，并建立了后来发展为美国计划生育联合会的组织。美国第二波女权主义始于20世纪60年代初。 1963年，贝蒂·弗里丹受《第二性》影响，写出了畅销书《女性的奥秘》，书中明确反对主流媒体对女性的形象，指出把女性放在家里限制了她们的可能性，浪费了才华和潜力。 。完美的核心家庭形象被刻画得淋漓尽致
文档-34：（标题：女权主义与媒体）纽约于1860年通过了《已婚妇女财产法》，使妇女的财产所有权合法化。他们也取得了成功，国会于 1920 年批准了第十九条修正案，允许妇女投票权。第一波女权主义期间女权主义者的信息主要通过报纸和其他印刷媒体（例如小册子和公告）传播。第二波女权主义始于 20 世纪 60 年代，贯穿 20 世纪 80 年代。此时，妇女在社会和政治上取得了进步，社会上的激进观点正在兴起。这一浪潮将女权主义讨论从选举权扩大到各种问题，例如
文档-35：（标题：第二波女权主义） 第二波女权主义 第二波女权主义是一个女权主义活动和思想的时期，始于20世纪60年代初的美国，持续了大约二十年。它迅速在西方世界传播，其目的不仅仅是通过获得选举权来提高妇女的平等。该运动解决的问题包括有关国内问题的权利，例如着装规范和就业。在 20 世纪 60 年代（实际上是在 20 世纪初的大部分时间），女性由于从事家务和家务而不太倾向于寻找工作，而这被视为她们的首要职责
文档-36：（标题：加拿大的女权主义）最高法院（1982 年任命）写下了反对该条款的最强烈意见之一。加拿大于 1980 年签署了《消除对妇女一切形式歧视公约》，并于 1981 年批准了该公约。人们普遍认为，加拿大第三次女权主义浪潮始于 90 年代初，它与反歧视观念密切相关。种族主义、反殖民主义、反资本主义。第二波浪潮中盛行的女性姐妹情谊的观念受到了第三波女权主义者的批评，她们认为这种看似普遍的观点是对女性多样化经历的蔑视，以及女性的生活方式。
文档-37：（标题：第一波女权主义）衡平法院，实际上很少有妇女有经济能力为自己的权利请愿。第一个有组织的英国女权主义运动是 1850 年代的朗豪坊圈子，其中包括芭芭拉·博迪雄 (Barbara Bodichon，娘家姓利-史密斯) 和贝西·雷纳·帕克斯 (Bessie Rayner Parkes)。该组织为许多妇女事业开展活动，包括改善女性在就业和教育方面的权利。它还通过其已婚妇女财产委员会追求妇女的财产权。 1854年，博迪雄出版了她的《英格兰有关妇女的法律概要》，该书被1857年成立的社会科学协会用来推动
文档-38：（标题：第三波女权主义） 第三波女权主义 第三波女权主义是 20 世纪 90 年代初美国开始的女权运动的迭代，一直持续到 2012 年左右开始第四波。出生于 1960 年代和 1970 年代，是X一代，以第二波民权进步为基础，第三波女权主义者拥抱个人主义和多样性，并试图重新定义女权主义者的含义。根据女权主义学者伊丽莎白·埃文斯的说法，“围绕什么构成第三波女权主义的混乱在某些方面是其定义特征。”第三波浪潮可以追溯到Riot grrrl女权主义朋克的出现
文档-39：（标题：第一波女权主义） 妇女促进妇女培训和就业。该协会是英国最早的妇女组织之一，并继续以注册慈善机构“Futures for Women”的名义运作。海伦·布莱克本 (Helen Blackburn) 和布切雷特 (Boucherett) 于 1891 年成立了妇女就业捍卫联盟，以捍卫妇女的工作权利，反对限制性就业立法。 1896年，她们还共同编辑了《职业妇女状况和工厂法》。20世纪初，妇女的就业仍主要局限于工厂劳动和家政工作。第一次世界大战期间，更多的女性在外面找到了工作。后果
文档-40：（标题：女权主义的历史）鲍姆加德纳在 2011 年提出的第四波浪潮，融合了社交媒体等在线资源，可能始于 2008 年，部分灵感来自《带我们的女儿去上班》。第四波浪潮反过来又激发了以下方面的灵感或与之相关：儿童服务导乐项目；堕胎后谈话台词；追求生殖正义；大码时尚支持；跨性别主义支持；男性女权主义；性工作接受度；开发媒体，包括女权主义、种族歧视、博客和推特活动。据基拉·科克伦 (Kira Cochrane) 称，到 2012-13 年，英国和其他几个国家出现了第四波疫情。它的重点是： 性别不平等，表现为“街头骚扰、性骚扰”
文档-41：（标题：第一波女权主义）在中产阶级中，直到第二波女权主义浪潮中，有色人种女性才开始发出声音。女权主义这个词是在那个时期作为一种政治意识形态而产生的。女权主义是在关于在平等主义条件下改革和纠正民主的演讲中出现的。澳大利亚女权主义的第一波浪潮可以追溯到 19 世纪末，主要关注选举权（妇女的投票权），从而关注妇女参与议会和其他政治活动的机会。 1882年，女权活动家罗斯·斯科特开始每周举办沙龙会议
文档-42：（标题：德国女权主义，第二次浪潮的出现）这一体系的第一次变化是一场自觉的女权主义运动的开始。妇女运动的首次出现和对妇女权利的讨论取决于法国大革命实现所有人平等的目标。 1791 年 9 月 14 日，法国女权主义者奥林普·德·古热 (Olympe de Gouges) 要求男女享有平等权利。这一时期的妇女运动主要受到社会阶级问题的影响。路易丝·奥托-彼得斯被认为是第一个中产阶级妇女运动的创始人，该运动积极追求妇女参与教育和政治事务。她的要求是为了
文档-43：（标题：女权主义与媒体）19世纪和20世纪初的女权主义运动。这个时候，女性对自己的生活几乎没有控制权。她们一般都是家庭主妇，没有受过教育，没有财产或经济权利。她们的生活极其有限，在仅限于母亲或妻子等角色的背景下产生了不满。当时的女权主义者（主要是中产阶级白人女性）关注妇女的法律障碍，特别是妇女的选举权。第一波浪潮始于 1848 年塞尼卡福尔斯大会，伊丽莎白·卡迪·斯坦顿在会上起草了《塞尼卡福尔斯宣言》。该宣言概述了女权主义者的政治策略
文档-44：（标题：后殖民女权主义）压迫或经济上处于不利地位的妇女被迫离开家庭并从事蓝领工作。然而，第一波女权主义确实成功地为女性赢得了选票，并且在某些国家还改变了有关离婚以及子女照顾和抚养的法律。第二波女权主义始于 20 世纪 60 年代初，激励女性审视个人生活中存在的性别歧视权力斗争，并将对话范围扩大到工作场所、性、家庭和生殖权利等问题。它在同工同酬和消除基于性别的歧视做法方面取得了显着的胜利。首先和
文档-45：（标题：性别的社会建构）本世纪最重大的革命是女权运动。第一波浪潮始于 1854 年，是妇女参政论者为争取妇女受教育权和投票权而进行的斗争。继这一运动之后，第二波女权主义和第三波女权主义进一步推动了女权主义事业。女权运动不仅是为了争取女性权利，更本质上是为了赢得公众的认可和尊重，承认她们并不比男性低人一等，因此应该得到平等对待和公平机会。女权主义出现并开始挑战这一观念
文档-46：（标题：第一波女权主义）存在主义。”参见巴哈伊信仰和性别平等。18 世纪，玛格丽塔·莫玛 (Margareta Momma)、凯瑟琳娜·阿尔格伦 (Catharina Ahlgren)、安娜·玛丽亚·吕克舍尔德 (Anna Maria Rückerschld) 和Hedvig Charlotta Nordenflycht，但它没有引发任何形式的运动。第一个发表公开演讲并鼓动支持女权主义的人是 1848 年的 Sophie Sager，第一个为解决妇女问题而创建的组织是“Svenska lrarinnorspensionfrening”（退休女教师协会）由约瑟菲娜·德兰于 1855 年出版。 1856 年，弗雷德里卡·布雷默出版了她著名的《赫塔》，引起了极大的轰动。
文档-47：（标题：第一波女权主义）无障碍。其中包括与伊利诺伊州代表团一起游行的艾达·B·威尔斯-巴尼特 (Ida B. Wells-Barnett)。与第二波女权主义者相比，第一波女权主义者很少关注堕胎、节育和妇女的整体生殖权利等主题。尽管她从未结过婚，但安东尼发表了她对婚姻的看法，认为应该允许女性拒绝与丈夫发生性关系；当时，这位美国妇女无法针对丈夫的强奸行为诉诸法律。第一波浪潮的结束常常与美国宪法第十九修正案的通过联系在一起
文档-48：（标题：针对妇女的暴力行为）和自由主义政治引发了关注为妇女获得平等机会的女权主义团体的崛起。这一浪潮标志着女性“选举权、独立权、国籍权、工作权和同工同酬”的时代。第二波女权运动是20世纪60年代末至1970年代初的一系列运动。女权主义学者指出，这一浪潮可以被描述为妇女解放时期和女权主义分支激进女权主义的兴起。这股女权主义浪潮是在战后时期背景下兴起的。
文档-49：（标题：1970s）它（在未批准的州批准电子逆向拍卖的努力一直持续到今天，有 22 个州已经采用了州电子逆向拍卖）。此外，工资差距未能缩小，但确实有所缩小。 1982年，随着平等权利修正案的失败以及华盛顿特区新的保守派领导层的出现，美国的第二波女权运动基本上结束了。美国女性在 20 世纪 90 年代初发起了一场简短但有力的第三次浪潮，旨在解决性骚扰问题（受到 1991 年安妮塔·希尔-克拉伦斯·托马斯参议院司法委员会听证会的启发）。运动的结果包括对此类问题的新认识
文档-50：（标题：女权主义）二十世纪初期，提倡妇女的投票权。第二波浪潮与 20 世纪 60 年代开始的妇女解放运动的思想和行动有关。第二波浪潮为妇女争取法律和社会平等。第三波女权主义浪潮是对 20 世纪 90 年代开始的第二波女权主义失败的延续和反应。第一波女权主义是19世纪和20世纪初的一个活跃时期。在英国，最终在美国，它的重点是促进女性的平等契约、婚姻、养育和财产权。由
文档-51：（标题：第一波女权主义）瑞典妇女运动。有组织的妇女运动始于 1873 年，当时已婚妇女财产权协会由 Anna Hierta-Retzius 和 Ellen Anckarsvrd 共同创立。该组织的首要任务是废除秘密行动。 1884 年，Sophie Adlersparre 创立了 Fredrika Bremer 协会，致力于改善妇女权利。十九世纪下半叶，多个妇女权利组织成立，活跃的组织和知识分子辩论也开展了大量活动。 1880 年代出现了所谓的“Sedlighetsdebatten”，即文学辩论中讨论性别角色的问题
文档-52：（标题：女权主义史）世界上寻求赢得妇女选举权、女性受教育权、更好的工作条件以及废除性别双重标准的被称为第一波女权主义。 “第一波”一词是在“第二波女权主义”一词被用来描述一场新的女权主义运动时创造的，该运动反对基本政治不平等之外的社会和文化不平等。在美国，女权运动领导人在倡导妇女权利之前先呼吁全国废除奴隶制和禁酒。美国第一波女权主义涉及范围广泛的女性，其中一些属于保守的基督教团体（例如弗朗西斯·威拉德和妇女基督教禁酒联盟），其他类似
文档-53：（标题：第一波女权主义）职场女性的战时经历，《1919年性别取消资格（去除）法》向女性开放职业和公务员，婚姻不再是女性外出工作的法律障碍。 1918年玛丽·斯托普斯出版了颇具影响的《婚姻之爱》，其中主张婚姻中的性别平等以及女性性欲的重要性。 （直到 1931 年，美国才禁止将这本书进口到美国。）“1918 年人民代表法”将选举权扩大到了年满 30 岁的女性，并且她们或她们的丈夫是
文档-54：（标题：第一波女权主义）第一个“拿起笔捍卫自己的性别”的女性是15世纪的克里斯蒂娜·德·皮赞。 Heinrich Cornelius Agrippa 和 Modesta di Pozzo di Forzi 工作于 16 世纪。玛丽·勒·贾尔斯·德·古尔奈、安妮·布拉德斯特里特和弗朗索瓦·保兰·德拉巴雷的《性别平等》于 1673 年问世。玛丽·沃斯通克拉夫特的写作时期受到卢梭和启蒙运动哲学的影响。启蒙运动之父定义了一个基于男性平等的理想民主社会，而女性却经常受到歧视。固有的排斥
问题: 第一波女权主义浪潮是什么时候出现的？
```
模型回答：
```
19世纪和20世纪初的欧洲和美国是第一波女权主义时期，其主要目标是通过法律争取女性权利，特别是选举权。 1848年，“女权主义”一词首次出现在《美国宣言》中。该词由玛莎·李尔 (Martha Lear) 于 1968 年 3 月在《纽约时报》杂志上撰文时创造。
综上所述，问题的答案是：19世纪和20世纪初。
以上回答参考了文档-43。
```
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adityarra07/whisper-medium-ft-24000 | 2023-10-20T20:45:57.000Z | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | adityarra07 | null | null | adityarra07/whisper-medium-ft-24000 | 0 | 2 | transformers | 2023-10-20T03:59:45 | ---
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-medium-ft-24000
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-medium-ft-24000
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1714
- Wer: 6.9790
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2548 | 1.0 | 1458 | 0.1422 | 7.2180 |
| 0.0841 | 2.0 | 2916 | 0.1303 | 7.5048 |
| 0.0362 | 3.0 | 4374 | 0.1420 | 6.9790 |
| 0.0131 | 4.0 | 5832 | 0.1491 | 7.2658 |
| 0.004 | 5.0 | 7290 | 0.1654 | 7.2180 |
| 0.0012 | 6.0 | 8748 | 0.1714 | 6.9790 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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xtuner/internlm-7b-qlora-alpaca-enzh | 2023-10-20T08:57:42.000Z | [
"peft",
"conversational",
"dataset:tatsu-lab/alpaca",
"dataset:silk-road/alpaca-data-gpt4-chinese",
"region:us"
] | conversational | xtuner | null | null | xtuner/internlm-7b-qlora-alpaca-enzh | 0 | 2 | peft | 2023-10-20T05:25:40 | ---
library_name: peft
datasets:
- tatsu-lab/alpaca
- silk-road/alpaca-data-gpt4-chinese
pipeline_tag: conversational
base_model: internlm/internlm-7b
---
<div align="center">
<img src="https://github.com/InternLM/lmdeploy/assets/36994684/0cf8d00f-e86b-40ba-9b54-dc8f1bc6c8d8" width="600"/>
[](https://github.com/InternLM/xtuner)
</div>
## Model
internlm-7b-qlora-alpaca-enzh is fine-tuned from [InternLM-7B](https://huggingface.co/internlm/internlm-7b) with [alpaca en](https://huggingface.co/datasets/tatsu-lab/alpaca) / [zh](https://huggingface.co/datasets/silk-road/alpaca-data-gpt4-chinese) datasets by [XTuner](https://github.com/InternLM/xtuner).
## Quickstart
### Usage with XTuner CLI
#### Installation
```shell
pip install xtuner
```
#### Chat
```shell
xtuner chat internlm/internlm-7b --adapter xtuner/internlm-7b-qlora-alpaca-enzh --prompt-template internlm_chat --system-template alpaca
```
#### Fine-tune
Use the following command to quickly reproduce the fine-tuning results.
```shell
xtuner train internlm_7b_qlora_alpaca_enzh_e3
```
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xtuner/internlm-chat-7b-qlora-alpaca-enzh | 2023-10-20T08:30:26.000Z | [
"peft",
"conversational",
"dataset:tatsu-lab/alpaca",
"dataset:silk-road/alpaca-data-gpt4-chinese",
"region:us"
] | conversational | xtuner | null | null | xtuner/internlm-chat-7b-qlora-alpaca-enzh | 0 | 2 | peft | 2023-10-20T05:26:46 | ---
library_name: peft
datasets:
- tatsu-lab/alpaca
- silk-road/alpaca-data-gpt4-chinese
pipeline_tag: conversational
base_model: internlm/internlm-chat-7b
---
<div align="center">
<img src="https://github.com/InternLM/lmdeploy/assets/36994684/0cf8d00f-e86b-40ba-9b54-dc8f1bc6c8d8" width="600"/>
[](https://github.com/InternLM/xtuner)
</div>
## Model
internlm-chat-7b-qlora-alpaca-enzh is fine-tuned from [InternLM-Chat-7B](https://huggingface.co/internlm/internlm-chat-7b) with [alpaca en](https://huggingface.co/datasets/tatsu-lab/alpaca) / [zh](https://huggingface.co/datasets/silk-road/alpaca-data-gpt4-chinese) datasets by [XTuner](https://github.com/InternLM/xtuner).
## Quickstart
### Usage with XTuner CLI
#### Installation
```shell
pip install xtuner
```
#### Chat
```shell
xtuner chat internlm/internlm-chat-7b --adapter xtuner/internlm-chat-7b-qlora-alpaca-enzh --prompt-template internlm_chat --system-template alpaca
```
#### Fine-tune
Use the following command to quickly reproduce the fine-tuning results.
```shell
xtuner train internlm_chat_7b_qlora_alpaca_enzh_e3
```
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JoungRae/FineTuningMistral7BUsing4BitQuantizationWithLudwig | 2023-10-20T08:08:19.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | JoungRae | null | null | JoungRae/FineTuningMistral7BUsing4BitQuantizationWithLudwig | 0 | 2 | peft | 2023-10-20T08:08:17 | ---
library_name: peft
base_model: alexsherstinsky/Mistral-7B-v0.1-sharded
---
# Model Card for Model ID
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## Model Details
### Model Description
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## How to Get Started with the Model
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#### Preprocessing [optional]
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#### Training Hyperparameters
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
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alperengozeten/turkish-sentiment-model | 2023-10-20T08:16:20.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | alperengozeten | null | null | alperengozeten/turkish-sentiment-model | 0 | 2 | transformers | 2023-10-20T08:15:14 | ---
base_model: emre/turkish-sentiment-analysis
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [emre/turkish-sentiment-analysis](https://huggingface.co/emre/turkish-sentiment-analysis) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6201
- Accuracy: 0.6139
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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] |
PavanPasidu/T5_summ_gen_v1 | 2023-10-20T10:13:22.000Z | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:billsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | PavanPasidu | null | null | PavanPasidu/T5_summ_gen_v1 | 0 | 2 | transformers | 2023-10-20T08:48:26 | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: T5_summ_gen_v1
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: billsum
type: billsum
config: default
split: ca_test
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.1986
---
<!-- 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. -->
# T5_summ_gen_v1
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0950
- Rouge1: 0.1986
- Rouge2: 0.1044
- Rougel: 0.1726
- Rougelsum: 0.1727
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 62 | 2.2294 | 0.1988 | 0.1023 | 0.1715 | 0.1714 | 19.0 |
| No log | 2.0 | 124 | 2.2038 | 0.1998 | 0.1024 | 0.1727 | 0.1725 | 19.0 |
| No log | 3.0 | 186 | 2.1890 | 0.2011 | 0.1049 | 0.1744 | 0.1746 | 19.0 |
| No log | 4.0 | 248 | 2.1767 | 0.2002 | 0.1059 | 0.1736 | 0.1737 | 19.0 |
| No log | 5.0 | 310 | 2.1593 | 0.2015 | 0.1064 | 0.1739 | 0.1741 | 19.0 |
| No log | 6.0 | 372 | 2.1522 | 0.2022 | 0.1059 | 0.1747 | 0.175 | 19.0 |
| No log | 7.0 | 434 | 2.1404 | 0.2028 | 0.1078 | 0.1746 | 0.1748 | 19.0 |
| No log | 8.0 | 496 | 2.1369 | 0.2015 | 0.1061 | 0.1735 | 0.1737 | 19.0 |
| 2.382 | 9.0 | 558 | 2.1299 | 0.1999 | 0.1053 | 0.1723 | 0.1725 | 19.0 |
| 2.382 | 10.0 | 620 | 2.1205 | 0.2003 | 0.1058 | 0.173 | 0.1729 | 19.0 |
| 2.382 | 11.0 | 682 | 2.1170 | 0.1998 | 0.105 | 0.1727 | 0.1727 | 19.0 |
| 2.382 | 12.0 | 744 | 2.1122 | 0.2003 | 0.1057 | 0.1734 | 0.1734 | 19.0 |
| 2.382 | 13.0 | 806 | 2.1084 | 0.1993 | 0.1042 | 0.1725 | 0.1726 | 19.0 |
| 2.382 | 14.0 | 868 | 2.1046 | 0.1988 | 0.1037 | 0.1723 | 0.1725 | 19.0 |
| 2.382 | 15.0 | 930 | 2.1023 | 0.1992 | 0.1047 | 0.1727 | 0.1729 | 19.0 |
| 2.382 | 16.0 | 992 | 2.1006 | 0.1992 | 0.1047 | 0.1727 | 0.1729 | 19.0 |
| 2.2855 | 17.0 | 1054 | 2.0979 | 0.1983 | 0.1034 | 0.1722 | 0.1723 | 19.0 |
| 2.2855 | 18.0 | 1116 | 2.0961 | 0.1988 | 0.1046 | 0.1729 | 0.173 | 19.0 |
| 2.2855 | 19.0 | 1178 | 2.0953 | 0.1986 | 0.1044 | 0.1725 | 0.1726 | 19.0 |
| 2.2855 | 20.0 | 1240 | 2.0950 | 0.1986 | 0.1044 | 0.1726 | 0.1727 | 19.0 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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Anopheles/summary_cz_eurlex | 2023-10-20T09:28:44.000Z | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:eur-lex-sum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | Anopheles | null | null | Anopheles/summary_cz_eurlex | 0 | 2 | transformers | 2023-10-20T09:13:00 | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- eur-lex-sum
metrics:
- rouge
model-index:
- name: summary_cz_eurlex
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: eur-lex-sum
type: eur-lex-sum
config: czech
split: test
args: czech
metrics:
- name: Rouge1
type: rouge
value: 0.0181
---
<!-- 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. -->
# summary_cz_eurlex
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the eur-lex-sum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8559
- Rouge1: 0.0181
- Rouge2: 0.0155
- Rougel: 0.0181
- Rougelsum: 0.0181
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 8 | 6.7050 | 0.0181 | 0.0155 | 0.0181 | 0.0181 | 19.0 |
| No log | 2.0 | 16 | 3.3004 | 0.0181 | 0.0155 | 0.0181 | 0.0181 | 19.0 |
| No log | 3.0 | 24 | 2.9529 | 0.0181 | 0.0155 | 0.0181 | 0.0181 | 19.0 |
| No log | 4.0 | 32 | 2.8559 | 0.0181 | 0.0155 | 0.0181 | 0.0181 | 19.0 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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raghugoud/distilbert-base-uncased-finetuned-squad | 2023-10-20T12:08:55.000Z | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | raghugoud | null | null | raghugoud/distilbert-base-uncased-finetuned-squad | 0 | 2 | transformers | 2023-10-20T09:56:46 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: raghugoud/distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# raghugoud/distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.9615
- Validation Loss: 1.1127
- Epoch: 1
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 11064, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.4967 | 1.1548 | 0 |
| 0.9615 | 1.1127 | 1 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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indiejoseph/distiluse-yue-bilingual | 2023-10-20T10:48:23.000Z | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | sentence-similarity | indiejoseph | null | null | indiejoseph/distiluse-yue-bilingual | 0 | 2 | sentence-transformers | 2023-10-20T10:32:53 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 5745 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MSELoss.MSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"eps": 1e-06,
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | 3,764 | [
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] |
Imran1/PE_regnet_v2 | 2023-10-20T10:38:51.000Z | [
"transformers",
"pytorch",
"tensorboard",
"regnet",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | Imran1 | null | null | Imran1/PE_regnet_v2 | 0 | 2 | transformers | 2023-10-20T10:33:01 | ---
license: apache-2.0
base_model: facebook/regnet-x-002
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: PE_regnet_v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# PE_regnet_v2
This model is a fine-tuned version of [facebook/regnet-x-002](https://huggingface.co/facebook/regnet-x-002) on an unknown dataset.
It achieves the following results on the evaluation set:
- Accuracy: 1.0
- F1: 1.0
- Precision: 1.0
- Recall: 1.0
- Loss: 0.0153
- Classification Report: precision recall f1-score support
0 1.00 1.00 1.00 4
1 1.00 1.00 1.00 3
2 1.00 1.00 1.00 3
3 1.00 1.00 1.00 4
4 1.00 1.00 1.00 3
5 1.00 1.00 1.00 4
6 1.00 1.00 1.00 5
7 1.00 1.00 1.00 2
accuracy 1.00 28
macro avg 1.00 1.00 1.00 28
weighted avg 1.00 1.00 1.00 28
## 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Accuracy | F1 | Precision | Recall | Validation Loss | Classification Report |
|:-------------:|:-----:|:----:|:--------:|:------:|:---------:|:------:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 0.5022 | 1.0 | 172 | 0.9643 | 0.9643 | 0.9688 | 0.9688 | 0.1774 | precision recall f1-score support
0 1.00 0.75 0.86 4
1 1.00 1.00 1.00 3
2 0.75 1.00 0.86 3
3 1.00 1.00 1.00 4
4 1.00 1.00 1.00 3
5 1.00 1.00 1.00 4
6 1.00 1.00 1.00 5
7 1.00 1.00 1.00 2
accuracy 0.96 28
macro avg 0.97 0.97 0.96 28
weighted avg 0.97 0.96 0.96 28
|
| 0.234 | 2.0 | 344 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0306 | precision recall f1-score support
0 1.00 1.00 1.00 4
1 1.00 1.00 1.00 3
2 1.00 1.00 1.00 3
3 1.00 1.00 1.00 4
4 1.00 1.00 1.00 3
5 1.00 1.00 1.00 4
6 1.00 1.00 1.00 5
7 1.00 1.00 1.00 2
accuracy 1.00 28
macro avg 1.00 1.00 1.00 28
weighted avg 1.00 1.00 1.00 28
|
| 0.1539 | 3.0 | 516 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0153 | precision recall f1-score support
0 1.00 1.00 1.00 4
1 1.00 1.00 1.00 3
2 1.00 1.00 1.00 3
3 1.00 1.00 1.00 4
4 1.00 1.00 1.00 3
5 1.00 1.00 1.00 4
6 1.00 1.00 1.00 5
7 1.00 1.00 1.00 2
accuracy 1.00 28
macro avg 1.00 1.00 1.00 28
weighted avg 1.00 1.00 1.00 28
|
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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brjezierski/sentence-embeddings-combined-ai_car-class-sim | 2023-10-20T11:57:06.000Z | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | sentence-similarity | brjezierski | null | null | brjezierski/sentence-embeddings-combined-ai_car-class-sim | 0 | 2 | sentence-transformers | 2023-10-20T11:56:06 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 3435 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 1500,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | 2,341 | [
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astrid01052/ema-lima-4 | 2023-10-20T12:28:12.000Z | [
"peft",
"region:us"
] | null | astrid01052 | null | null | astrid01052/ema-lima-4 | 0 | 2 | peft | 2023-10-20T12:23:57 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
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GuysTrans/conversation-summ | 2023-10-23T20:22:54.000Z | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | GuysTrans | null | null | GuysTrans/conversation-summ | 0 | 2 | transformers | 2023-10-20T13:02:51 | ---
license: mit
base_model: facebook/bart-large-xsum
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: conversation-summ
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: samsum
type: samsum
config: samsum
split: validation
args: samsum
metrics:
- name: Rouge1
type: rouge
value: 52.5102
---
<!-- 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. -->
# conversation-summ
This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5467
- Rouge1: 52.5102
- Rouge2: 26.7766
- Rougel: 42.7536
- Rougelsum: 48.004
- Gen Len: 30.9487
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.0464 | 1.0 | 3683 | 0.5467 | 52.5102 | 26.7766 | 42.7536 | 48.004 | 30.9487 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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oeg/software_benchmark_multidomain | 2023-10-23T11:42:05.000Z | [
"transformers",
"pytorch",
"bert",
"token-classification",
"software_mentions",
"scibert",
"es",
"dataset:oeg/software_benchmark_v2",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | oeg | null | null | oeg/software_benchmark_multidomain | 0 | 2 | transformers | 2023-10-20T14:21:18 | ---
license: cc-by-4.0
datasets:
- oeg/software_benchmark_v2
language:
- es
metrics:
- accuracy
library_name: transformers
tags:
- software_mentions
- scibert
---
#Software Benchmark SCIBERT model.
This model is a fine-tuned version of the [SCIBERT](https://huggingface.co/allenai/scibert_scivocab_uncased) model on a dataset built based on the corpora SoMESCi and Softcite.
The objective of this model is to extract software mentions from scientific texts in the BIO domain.
The training code can be found on [Github](https://github.com/oeg-upm/software_mentions_benchmark).
## Corpus
The corpus have been built using two corpora in software mentions.
* SoMESCi [1]. We have used the corpus uploaded to [Github](https://github.com/dave-s477/SoMeSci/tree/9f17a43f342be026f97f03749457d4abb1b01dbf/PLoS_sentences), more specifically, the corpus created with sentences.
* Softcite [2]. This project has published another corpus for software mentions, which is also available on [Github](https://github.com/howisonlab/softcite-dataset/tree/master/data/corpus). We have used the annotations from bio and economics domain.
* Papers with code. We have downloaded a list of publications from the [Papers with Code](https://paperswithcode.com/) site. You can find there publications and software from machine learning domain. To build this corpus, we have selected texts where you can find mentions of the software related with the publication. DOI: 10.5281/zenodo.10033751
To build this corpus, we have removed the annotations of other entities such as version, url and those which are related with the relation of teh entity with the text. IN this case, we only use the label Application_Mention.
To reconciliate both corpora, we have mapping the labels of both corpora. Also, some decisions about the annotations have been taken, for example, in the case of Microsoft Excel, we have decided to annotate Excel as software mention, not the whole text.
## Training
The corpus have been splitted in a 70-30 proportion for training and testing.
The training code can be found on [Github](https://github.com/oeg-upm/software_mentions_benchmark).
## Evaluation Results
These are the hyperparameters used to train the model:
* evaluation_strategy = "epoch"
* save_strategy="no"
* per_device_train_batch_size=16
* per_device_eval_batch_size=16
* num_train_epochs=3
* weight_decay=1e-5
* learning_rate=1e-4
The evaluation results are:
* Precision: 0.8928176795580111
* Recall: 0.8568398727465536
* F1-score: 0.8744588744588745
This model has been compared with some generative models such as llama2 and hermes using the testing part of the benchmark. Following, we present the results of partial matches, it means, the predictions are included in the corpus
### Llama2 (7B)
* Precision: 0.6342857142857142
* Recall: 0.7161290322580646
* F1-score: 0.67
### Hermes (13B)
* Precision: 0.4666666666666667
* Recall: 0.509090909090909
* F1-score: 0.4869565217391304
## Acknoledgements
This is a work done thank to the effort of other projects:
* Softcite
* SoMESCi
* [SCIBERT](https://huggingface.co/allenai/scibert_scivocab_uncased)
## Authors
* Esteban González Guardia
* Daniel Garijo Verdejo
## Contributors
<kbd><img src="https://raw.githubusercontent.com/oeg-upm/TINTO/main/assets/logo-oeg.png" alt="Ontology Engineering Group" width="100"></kbd>
<kbd><img src="https://raw.githubusercontent.com/oeg-upm/TINTO/main/assets/logo-upm.png" alt="Universidad Politécnica de Madrid" width="100"></kbd>
## References
1. Schindler, D., Bensmann, F., Dietze, S., & Krüger, F. (2021, October). Somesci-A 5 star open data gold standard knowledge graph of software mentions in scientific articles. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (pp. 4574-4583).
2. Du, C., Cohoon, J., Lopez, P., & Howison, J. (2021). Softcite dataset: A dataset of software mentions in biomedical and economic research publications. Journal of the Association for Information Science and Technology, 72(7), 870-884. | 4,051 | [
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carles-undergrad-thesis/indobert-mmarco-margin-mse | 2023-11-05T12:37:38.000Z | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | sentence-similarity | carles-undergrad-thesis | null | null | carles-undergrad-thesis/indobert-mmarco-margin-mse | 0 | 2 | sentence-transformers | 2023-10-20T16:28:58 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
This model utilizes a newer version of Sentence Transformers. If you're having trouble using this model, please try installing the latest version of Sentence Transformers with:
```bash
pip install --upgrade --force-reinstall --no-deps git+https://github.com/UKPLab/sentence-transformers.git
```
|
# carles-undergrad-thesis/indobert-mmarco-margin-mse
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('carles-undergrad-thesis/indobert-mmarco-margin-mse')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('carles-undergrad-thesis/indobert-mmarco-margin-mse')
model = AutoModel.from_pretrained('carles-undergrad-thesis/indobert-mmarco-margin-mse')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
| Model | Mmarco Dev | | MrTyDi Test | | Miracal Test | |
|-----------------------------------------|------------|----------------|-------------|----------------|--------------|----------------------------|
| | MRR@10 | R@1000 | MRR@10 | R@1000 | NCDG@10 | R@1K |
| $\text{BM25 (Elastic Search)}$ | .114 | .642 | .279 | .858 | .391 | .971 |
| $\text{IndoBERT}_{\text{DOTMargin}}$ | .207 | .799 | .446 | .929 | .387 | .899 |
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 15717 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`beir.losses.margin_mse_loss.MarginMSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"correct_bias": false,
"eps": 1e-06,
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 7858,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | 4,469 | [
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sandeep12345/alpaca-text-sentiment-llama2 | 2023-10-20T17:13:29.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | sandeep12345 | null | null | sandeep12345/alpaca-text-sentiment-llama2 | 0 | 2 | peft | 2023-10-20T17:13:28 | ---
library_name: peft
base_model: meta-llama/Llama-2-7b-hf
---
# Model Card for Model ID
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## Model Details
### Model Description
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## How to Get Started with the Model
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## Training Details
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#### Preprocessing [optional]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
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TanmaySah/ldmcombine | 2023-10-21T01:59:22.000Z | [
"peft",
"region:us"
] | null | TanmaySah | null | null | TanmaySah/ldmcombine | 0 | 2 | peft | 2023-10-20T20:21:55 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
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yale-nlp/tapex-large-finetuned-qtsumm | 2023-11-05T00:09:44.000Z | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"table-to-text",
"summarization",
"long-form-question-answering",
"en",
"dataset:yale-nlp/QTSumm",
"arxiv:2305.14303",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | summarization | yale-nlp | null | null | yale-nlp/tapex-large-finetuned-qtsumm | 0 | 2 | transformers | 2023-10-20T20:24:19 | ---
license: mit
language: en
tags:
- table-to-text
- summarization
- long-form-question-answering
datasets:
- yale-nlp/QTSumm
---
# QTSumm Dataset
QTSumm is a query-focused table summarization dataset proposed in EMNLP 2023 paper [QTSUMM: Query-Focused Summarization over Tabular Data](https://arxiv.org/pdf/2305.14303.pdf). The original Github repository is [https://github.com/yale-nlp/QTSumm](https://github.com/yale-nlp/QTSumm).
## Model Description
`yale-nlp/tapex-large-finetuned-qtsumm` (based on BART architecture) is initialized with `microsoft/tapex-large` and finetuned on the QTSumm dataset.
## Usage
Check the github repository: [https://github.com/yale-nlp/QTSumm](https://github.com/yale-nlp/QTSumm)
## Reference
```bibtex
@misc{zhao2023qtsumm,
title={QTSUMM: Query-Focused Summarization over Tabular Data},
author={Yilun Zhao and Zhenting Qi and Linyong Nan and Boyu Mi and Yixin Liu and Weijin Zou and Simeng Han and Xiangru Tang and Yumo Xu and Arman Cohan and Dragomir Radev},
year={2023},
eprint={2305.14303},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | 1,125 | [
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yale-nlp/bart-large-finetuned-qtsumm | 2023-11-05T00:08:24.000Z | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"table-to-text",
"summarization",
"long-form-question-answering",
"en",
"dataset:yale-nlp/QTSumm",
"arxiv:2305.14303",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | summarization | yale-nlp | null | null | yale-nlp/bart-large-finetuned-qtsumm | 0 | 2 | transformers | 2023-10-20T20:24:43 | ---
license: mit
language: en
tags:
- table-to-text
- summarization
- long-form-question-answering
datasets:
- yale-nlp/QTSumm
---
# QTSumm Dataset
QTSumm is a query-focused table summarization dataset proposed in EMNLP 2023 paper [QTSUMM: Query-Focused Summarization over Tabular Data](https://arxiv.org/pdf/2305.14303.pdf). The original Github repository is [https://github.com/yale-nlp/QTSumm](https://github.com/yale-nlp/QTSumm).
## Model Description
`yale-nlp/bart-large-finetuned-qtsumm` (based on BART architecture) is initialized with `facebook/bart-large` and finetuned on the QTSumm dataset.
## Usage
Check the github repository: [https://github.com/yale-nlp/QTSumm](https://github.com/yale-nlp/QTSumm)
## Reference
```bibtex
@misc{zhao2023qtsumm,
title={QTSUMM: Query-Focused Summarization over Tabular Data},
author={Yilun Zhao and Zhenting Qi and Linyong Nan and Boyu Mi and Yixin Liu and Weijin Zou and Simeng Han and Xiangru Tang and Yumo Xu and Arman Cohan and Dragomir Radev},
year={2023},
eprint={2305.14303},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | 1,122 | [
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yale-nlp/reastap-large-finetuned-qtsumm | 2023-11-05T00:08:03.000Z | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"table-to-text",
"summarization",
"long-form-question-answering",
"en",
"dataset:yale-nlp/QTSumm",
"arxiv:2305.14303",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | summarization | yale-nlp | null | null | yale-nlp/reastap-large-finetuned-qtsumm | 0 | 2 | transformers | 2023-10-20T20:25:19 | ---
license: mit
language: en
tags:
- table-to-text
- summarization
- long-form-question-answering
datasets:
- yale-nlp/QTSumm
---
# QTSumm Dataset
QTSumm is a query-focused table summarization dataset proposed in EMNLP 2023 paper [QTSUMM: Query-Focused Summarization over Tabular Data](https://arxiv.org/pdf/2305.14303.pdf). The original Github repository is [https://github.com/yale-nlp/QTSumm](https://github.com/yale-nlp/QTSumm).
## Model Description
`yale-nlp/reastap-large-finetuned-qtsumm` (based on BART architecture) is initialized with `Yale-LILY/reastap-large` and finetuned on the QTSumm dataset.
## Usage
Check the github repository: [https://github.com/yale-nlp/QTSumm](https://github.com/yale-nlp/QTSumm)
## Reference
```bibtex
@misc{zhao2023qtsumm,
title={QTSUMM: Query-Focused Summarization over Tabular Data},
author={Yilun Zhao and Zhenting Qi and Linyong Nan and Boyu Mi and Yixin Liu and Weijin Zou and Simeng Han and Xiangru Tang and Yumo Xu and Arman Cohan and Dragomir Radev},
year={2023},
eprint={2305.14303},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | 1,129 | [
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yale-nlp/omnitab-large-finetuned-qtsumm | 2023-11-05T00:08:53.000Z | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"table-to-text",
"summarization",
"long-form-question-answering",
"en",
"dataset:yale-nlp/QTSumm",
"arxiv:2305.14303",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | summarization | yale-nlp | null | null | yale-nlp/omnitab-large-finetuned-qtsumm | 0 | 2 | transformers | 2023-10-20T20:26:02 | ---
license: mit
language: en
tags:
- table-to-text
- summarization
- long-form-question-answering
datasets:
- yale-nlp/QTSumm
---
# QTSumm Dataset
QTSumm is a query-focused table summarization dataset proposed in EMNLP 2023 paper [QTSUMM: Query-Focused Summarization over Tabular Data](https://arxiv.org/pdf/2305.14303.pdf). The original Github repository is [https://github.com/yale-nlp/QTSumm](https://github.com/yale-nlp/QTSumm).
## Model Description
`yale-nlp/omnitab-large-finetuned-qtsumm` (based on BART architecture) is initialized with `neulab/omnitab-large` and finetuned on the QTSumm dataset.
## Usage
Check the github repository: [https://github.com/yale-nlp/QTSumm](https://github.com/yale-nlp/QTSumm)
## Reference
```bibtex
@misc{zhao2023qtsumm,
title={QTSUMM: Query-Focused Summarization over Tabular Data},
author={Yilun Zhao and Zhenting Qi and Linyong Nan and Boyu Mi and Yixin Liu and Weijin Zou and Simeng Han and Xiangru Tang and Yumo Xu and Arman Cohan and Dragomir Radev},
year={2023},
eprint={2305.14303},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | 1,126 | [
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jmoney54378256438905/jondurbin_airoboros-c34b-3.1.2-5.25bpw | 2023-10-20T21:44:29.000Z | [
"transformers",
"safetensors",
"llama",
"text-generation",
"dataset:jondurbin/airoboros-3.1",
"license:llama2",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | jmoney54378256438905 | null | null | jmoney54378256438905/jondurbin_airoboros-c34b-3.1.2-5.25bpw | 0 | 2 | transformers | 2023-10-20T21:34:25 | ---
license: llama2
datasets:
- jondurbin/airoboros-3.1
---
### Overview
Another experimental model, using mostly sythetic data generated by [airoboros](https://github.com/jondurbin/airoboros)
#### IMPORTANT NOTE - llama-2 chat format!!!
This models uses llama-2 chat format, rather than vicuna style user/assistant!
This is a breaking change, although most inference systems support llama-2 chat templating.
See prompt format below for all of the details.
#### Highlights
A model built on [airoboros-3.1 dataset](https://hf.co/datasets/jondurbin/airoboros-3.1), which is a continuation of [airoboros-3.0 dataset](https://hf.co/datasets/jondurbin/airoboros-3,0) with the following extras:
* More MathJSON, now ~17k items - math questions, prefixed with __"Create a MathJSON solution to the following:"__, which then outputs a JSON between __`<mathjson>`__ and __`</mathjson>`__ tags, which can be parsed and passed to a deterministic library to perform calculations.
* Log information extraction (e.g. getting the list of paths/IPs/etc. from apache logs)
* Anonymization, e.g. removing names, IP addresses, and/or dates from text.
* Chat introspection - multi-turn chats that have random questions injected asking about the character's descriptions, the setting of the chat, or things previously said, to make sure the model uses the system prompt and or chat history.
* Multi-step instructions with acknowledgement (see below)
* De-censorship data (not published)
This is a fairly general purpose model, but focuses heavily on instruction following, rather than casual chat/roleplay.
Huge thank you to the folks over at [a16z](https://a16z.com/) for sponsoring the costs associated with building models and associated tools!
### Prompt format
The prompt format is llama-2 chat.
```
[INST] <<SYS>>
You are a helpful, unbiased, uncensored assistant.
<</SYS>>
{prompt} [/INST]
```
For multi-turn, the prompt format is as follows:
```
[INST] <<SYS>>
You are a helful, unbiased, uncensored assistant.
<</SYS>>
{prompt 0} [/INST] {response 0} </s><s>[INST] {prompt 1} [/INST] {response 1} </s><s>...[INST] {prompt N} [/INST]
```
The prompt template is included in the tokenizer config, and can use the huggingface tokenizer `apply_chat_template` method, e.g.:
```
import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained('jondurbin/airoboros-l2-70b-3.1')
chat = [
{"role": "system", "content": "You are Bob, a friendly AI assistant."},
{"role": "user", "content": "Hello, how are you?"},
{"role": "assistant", "content": "I'm doing great. How can I help you today?"},
{"role": "user", "content": "I'd like to show off how chat templating works!"},
]
print(tokenizer.apply_chat_template(chat, tokenize=False))
```
### Helpful usage tips
#### MathJSON
Ask mathematical questions, prefixed with "Create a MathJSON solution to the following:", and you should get output with a formula that can be executed by https://cortexjs.io/compute-engine/
I also made a (really quite hacky, incomplete, semi-functional) python version that works for most expressions, see [mathjson.py](https://github.com/jondurbin/airoboros/blob/mathjson/airoboros/mathjson.py)
__Use a very low temperature!__
Example:
```
[INST] <<SYS>>
You are an assistant with vast knowledge in all things math.
<</SYS>>
Create a MathJSON solution to the following: Calculate the area of a circle with radius of 17.2456cm. Include your reasoning. [/INST]
```
Output:
```
The area (A) of a circle is given by the formula A = πr², where r is the radius of the circle. Since the radius is given in centimeters, we need to convert it to meters first. We know that 1 meter equals 100 centimeters. Therefore, we can represent the radius as: r = 17.2456 cm * 100/1.
Once we have the radius in meters, we can calculate the area of the circle using the formula A = πr².
Solution as MathJSON:
<mathjson>
[
"Multiply",
"Pi",
[
"Power",
17.2456,
2
]
]
</mathjson>
```
You can then validate the JSON between `<mathjson>` and `</mathjson>`, then pass the parsed JSON to compute-engine JS or the `evaluate` function in mathjson.py to calculate the response.
#### Context obedient question answering
By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.
The format for a closed-context prompt is as follows:
```
BEGININPUT
BEGINCONTEXT
[key0: value0]
[key1: value1]
... other metdata ...
ENDCONTEXT
[insert your text blocks here]
ENDINPUT
[add as many other blocks, in the exact same format]
BEGININSTRUCTION
[insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.]
ENDINSTRUCTION
```
It's also helpful to add "Don't make up answers if you don't know." to your instruction block to make sure if the context is completely unrelated it doesn't make something up.
*The __only__ prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*
I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.
- `BEGININPUT` - denotes a new input block
- `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block
- `ENDCONTEXT` - denotes the end of the metadata block for the current input
- [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.
- `ENDINPUT` - denotes the end of the current input block
- [repeat as many input blocks in this format as you want]
- `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.
- [instruction(s)]
- `ENDINSTRUCTION` - denotes the end of instruction set
It sometimes works without `ENDINSTRUCTION`, but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.
__Use a very low temperature!__
Here's a trivial, but important example to prove the point:
```
BEGININPUT
BEGINCONTEXT
date: 2021-01-01
url: https://web.site/123
ENDCONTEXT
In a shocking turn of events, blueberries are now green, but will be sticking with the same name.
ENDINPUT
BEGININSTRUCTION
What color are bluberries? Source?
ENDINSTRUCTION
```
And the response:
```
Blueberries are now green.
Source:
date: 2021-01-01
url: https://web.site/123
```
#### Summarization
500 samples have been included from [this dataset](https://huggingface.co/datasets/mattpscott/airoboros-summarization), using the same format as contextual question answering, for example:
```
BEGININPUT
{text to summarize}
ENDINPUT
BEGININSTRUCTION
Summarize the input in around 130 words.
ENDINSTRUCTION
```
#### Getting longer responses
You can use a few techniques to get longer responses.
Detailed prompts, with explicit instruction for word count:
```
Please compose a narrative set in the heart of an ancient library, steeped in the scent of old parchment and ink. The protagonist should be a young scholar who is dedicated to studying the art of storytelling and its evolution throughout history. In her pursuit of knowledge, she stumbles upon a forgotten tome that seems to possess an unusual aura. This book has the ability to bring stories to life, literally manifesting characters and scenarios from within its pages into reality.
The main character must navigate through various epochs of storytelling - from oral traditions of tribal societies, through medieval minstrels' tales, to modern-day digital narratives - as they come alive around her. Each era presents its unique challenges and lessons about the power and impact of stories on human civilization.
One such character could be a sentient quill pen, who was once used by renowned authors of yesteryears and now holds their wisdom and experiences. It becomes her mentor, guiding her through this journey with witty remarks and insightful commentary.
Ensure that your tale encapsulates the thrill of adventure, the beauty of learning, and the profound connection between humans and their stories. All characters involved should be non-human entities. Feel free to explore creative liberties but maintain the mentioned elements.
Your response should be approximately 2300 words.
```
Or, a simpler example:
```
Please create a long, detailed story about a dragon in an old growth forest who, for some reason, begins speaking the words of the source code of linux.
```
There are a few examples of next chapter completion as well, e.g.:
```
Write the next chapter of a historical fiction novel set in Paris during the 20th century.
Here's a summary of the previous chapter:
In the vibrant city of Paris, amid the tumultuous changes of the 20th century, our protagonist Margot, an aspiring fashion designer, has just secured an apprenticeship at a prestigious couture house. She meets Lucien, a charming journalist who covers the fashion industry. Together they navigate the ever-changing world of fashion and society, uncovering secrets that reveal the intricate links between style, politics, and culture. As the chapter concludes, they decide to delve deeper into the hidden corners of the fashion world to unravel its mysteries.
Requirements for the next chapter:
1. Character Development of Margot and Lucien:
- Margot's Evolution: Unfold more about Margot's past, her dreams of revolutionizing fashion, and her struggle to establish herself in a male-dominated industry. Illustrate her growing expertise, innovative ideas, and increasing dependence on Lucien.
- Lucien's Complexity: Introduce uncertainties surrounding Lucien's background and real motives. Increase suspense by suggesting undisclosed information he possesses, while also highlighting his wit and perceptiveness.
2. Exploration of Paris and the Couture House:
- Paris: Elaborate their journey through the bustling streets of Paris, including encounters with iconic figures, social unrest, and relics from different eras of French history.
- The Couture House: Expand on the grandeur of the couture house they work in, filled with artistic masterpieces, intense competition, and cryptic notes hinting at a scandalous past.
3. Emergence of the Subplot: The Lost Collection:
- Discovery: Have Margot and Lucien stumble upon a secret vault containing a lost collection designed before World War II, raising new questions about the previous owner and the influence of war on fashion.
- Revelation: Capture their shock as they realize the designs were plagiarized, the potential repercussions, and the opportunities it presents for Margot's career.
- Twist: End with a twist that suggests there are other stolen collections across Paris, setting up their new mission.
Your response should be approximately 650 words.
```
#### Coding
You can ask for fairly complex coding instructions with multiple criteria, e.g.:
```
Create a python application with the following requirements:
- Asyncio FastAPI webserver
- ping endpoint that returns the current date in JSON format
- file upload endpoint, which calculates the file's sha256 checksum, and checks postgres to deduplicate
```
Or inline criteria:
```
Write a multi-threaded TCP server in C that accepts a "GET [key]" input and "SET [key] [value]" input, and uses a binary tree to get and store the input values.
```
You can also optionally add a single space and "PLAINFORMAT" at the end of your prompt to avoid backticks, explanations, etc. and just print the code, e.g.:
```
Write a websocket application in node.js. PLAINFORMAT
```
#### Agent/function calling
The dataset includes many examples of function/args generation based on input criteria. This is somewhat similar to the OpenAI function calling, but the output is either JSON or YAML.
Example prompt:
```
As an AI assistant, please select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format.
Input: I want to know how many times 'Python' is mentioned in my text file.
Available functions:
file_analytics:
description: This tool performs various operations on a text file.
params:
action: The operation we want to perform on the data, such as "count_occurrences", "find_line", etc.
filters:
keyword: The word or phrase we want to search for.
```
Response:
```json
{
"function": "file_analytics",
"params": {
"action": "count_occurrences",
"filters": {
"keyword": "Python"
}
}
}
```
#### Chain-of-thought
You can ask for several possible responses to a given problem, with a ranking and final answer selection.
Example prompt:
```
A group of people decide to play a round-robin tournament where every player plays every other player exactly once. If a total of 45 games were played, how many players participated in the tournament? Offer several potential answers, rank them according to mathematical logic, and pick the most feasible one.
```
Example response:
```
Solution 1:
In a round-robin tournament, each player plays every other player exactly once. This means that the total number of games played is equal to the combination of all players taken two at a time (since a game involves two players).
The formula for combinations is nC2 = n(n-1)/2, where n is the total number of players. We know that nC2 equals 45 (the total number of games), so we can set up the equation as follows:
n(n-1)/2 = 45
Solving this equation gives us n=10.
Final answer: There were 10 players in the tournament.
Solution 2:
Another way to approach this problem is by using the concept of triangular numbers. Triangular numbers are a sequence of numbers where each number can be represented as a triangle with dots. The nth triangular number is given by the sum of the first n natural numbers.
If we consider each game as a dot and each player as a side of the triangle, then the total number of games (45) would correspond to the 9th triangular number because 1+2+3+4+5+6+7+8+9=45. However, since each side of the triangle represents a player, and there's one more player than sides in our model (because the last player has no one left to play against), we need to add one more to get the total number of players.
Final answer: There were 10 players in the tournament.
Solution 3:
We could also use trial and error to solve this problem. Starting from a small number of players, we calculate how many games would be played in a round-robin tournament until we reach 45 games. For example, with 2 players, only 1 game is played; with 3 players, 3 games are played; with 4 players, 6 games are played, and so on. Continuing this process, we find that with 10 players, 45 games are played.
Final answer: There were 10 players in the tournament.
Ranking of solutions from best to worst:
1. Solution 1: This solution uses direct mathematical reasoning based on the properties of a round-robin tournament and solves an equation to find the answer.
2. Solution 2: This solution uses the concept of triangular numbers which requires additional steps and knowledge beyond the immediate problem context, but still arrives at the correct answer.
3. Solution 3: This solution relies on trial and error, which is less efficient and reliable than the other methods, but it eventually gets to the right answer.
Best and final answer: There were 10 players in the tournament.
```
#### reWOO style execution planning
The model now supports execution planning for complex instructions that would require making use of several tools. The output is just the plan, you must implement a mechanism to parse the output and actually call the functions!
Example prompt:
```
Please construct a systematic plan to generate an optimal response to the user instruction, utilizing a set of provided tools. Each plan will correspond to an evidence value, which will be the output of one of the available functions given an input string
that could be the user's question, one or more prior evidence values, or a combination of both.
Here are the tools available to be called:
DuckDuckGo[input]: Tool that enables the user to search for information using the DuckDuckGo search engine. This tool is helpful in locating a suitable list of sites that may contain the answer to the user's query. It does not directly answer the question but finds a list of sites that might have the answer.
TextScraper[input]: Load one or more websites from the input string containing newline delimited links, where input is one or more links, and produces plain text output containing the content of the links.
HyperlinkExtractor[input]: Extract links from plain text and produces a plain text, newline delimited response of links.
KnowledgeModel[input]: Question answering language model, particularly useful in answering questions based on an input passage of text. The input must be a text question that references an :evidence[n]: variable, e.g. What color is the cat, given :evidence1:?
The output should be in format:
Plan: [first action to take based on input question]
:evidence0: = [function to call with input parameter]
Plan: [next action to take, based on result of :evidence0:]
:evidence1: = [next function to call and input parameter, which may include reference to previous evidence, e.g. "Given context :evidence0"]
...
Answer: [:evidence[n]: containing the final answer.]
Question: Who won the Nobel Prize in Literature in 2020 and what is their most famous work?
```
Response:
```
Plan: Begin by conducting a web search to find out who won the Nobel Prize in Literature in 2020.
:evidence0: = DuckDuckGo[Nobel Prize in Literature 2020 winner]
Plan: Utilize the KnowledgeModel to interpret the search results and find the name of the laureate.
:evidence1: = KnowledgeModel[Who won the Nobel Prize in Literature in 2020, given :evidence0:?]
Plan: Conduct another web search to find the most famous work of the identified laureate.
:evidence2: = DuckDuckGo[Most famous work of :evidence1:]
Plan: Extract the relevant links from the DuckDuckGo search results for a more focused search.
:evidence3: = HyperlinkExtractor[:evidence2:]
Plan: Use the TextScraper tool to extract information from the relevant links.
:evidence4: = TextScraper[:evidence3:]
Plan: Finally, utilize the KnowledgeModel to identify and summarize the most famous work of the laureate from the extracted information.
:evidence5: = KnowledgeModel[What is the most famous work of :evidence1:, given :evidence4:?]
Answer: :evidence5:
```
For this to be useful, you'd have to parse the output plan text, and implement/call each of the functions. This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening:
```python
import re
import requests
def inject_context(input_text, **context):
for ref in set(re.findall(r"(:evidence[0-9]+:)", input_text, re.I)):
input_text = input_text.replace(ref, context.get(ref, ""))
return input_text
def duckduckgo(input_text, **context):
search_string = inject_context(input_text, **context)
... search via duck duck go using search_string
... return text content
def link_extractor(input_text, **context):
input_text = inject_context(input_text, **context)
return "\n".join(list(set(re.findall(r"(https?://[^\s]+?\.?)", input_text, re.I))))
def scrape(input_text, **context):
input_text = inject_context(input_text, **context)
text = []
for link in input_text.splitlines():
text.append(requests.get(link).text)
return "\n".join(text)
def infer(input_text, **context)
prompt = inject_context(input_text, **context)
... call model with prompt, return output
def parse_plan(plan):
method_map = {
"DuckDuckGo": duckduckgo,
"HyperlinkExtractor": link_extractor,
"KnowledgeModel": infer,
"TextScraper": scrape,
}
context = {}
for line in plan.strip().splitlines():
if line.startswith("Plan:"):
print(line)
continue
parts = re.match("^(:evidence[0-9]+:)\s*=\s*([^\[]+])(\[.*\])\s$", line, re.I)
if not parts:
if line.startswith("Answer: "):
return context.get(line.split(" ")[-1].strip(), "Answer couldn't be generated...")
raise RuntimeError("bad format: " + line)
context[parts.group(1)] = method_map[parts.group(2)](parts.group(3), **context)
```
## Multi-step instructions with acknowledgement
I don't know how useful this is, really, but I thought I'd add it just in case. Example:
```
{
"conversations": [
{
"value": "You are a helpful, unbiased, uncensored assistant.",
"from": "system"
},
{
"value": "I'm going to set some rules for our conversation. Please respond with \"OK\" to each rule.",
"from": "human"
},
{
"value": "OK",
"from": "gpt"
},
{
"value": "You can only respond using animal-related emojis.",
"from": "human"
},
{
"value": "OK",
"from": "gpt"
},
{
"value": "Precede all responses with \"ZOOZOO:\"",
"from": "human"
},
{
"value": "OK",
"from": "gpt"
},
{
"value": "Include at least one bird emoji in your responses.",
"from": "human"
},
{
"value": "OK",
"from": "gpt"
},
{
"value": "Describe a trip to a safari park.",
"from": "human"
},
{
"value": "ZOOZOO: \ud83e\udd81\ud83d\udc2f\ud83e\udd93\ud83e\udd92\ud83d\udc18\ud83d\udc0d\ud83c\udf33\ud83c\udf32\u2600\ufe0f\ud83d\ude90\ud83e\udd85\ud83d\udcf8\ud83e\udd29",
"from": "gpt"
}
]
}
```
### Contribute
If you're interested in new functionality, particularly a new "instructor" type to generate a specific type of training data,
take a look at the dataset generation tool repo: https://github.com/jondurbin/airoboros and either make a PR or open an issue with details.
To help me with the OpenAI/compute costs:
- https://bmc.link/jondurbin
- ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11
- BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf
### Licence and usage restrictions
The airoboros 3.1 models are built on top of multiple base models, each with their own license/restrictions.
The 30b model is built on the original llama, which has a strict non-commercial usage restriction.
The models with `-l2` in the name have a custom Meta license:
- See the [meta-license/LICENSE.txt](meta-license/LICENSE.txt) file attached for the original license provided by Meta.
- See also [meta-license/USE_POLICY.md](meta-license/USE_POLICY.md) and [meta-license/Responsible-Use-Guide.pdf](meta-license/Responsible-Use-Guide.pdf), also provided by Meta.
The models with `-m-` are mistral-7b (apache 2.0)
The fine-tuning data was mostly generated by OpenAI API calls to gpt-4, via [airoboros](https://github.com/jondurbin/airoboros)
The ToS for OpenAI API usage has a clause preventing the output from being used to train a model that __competes__ with OpenAI
- what does *compete* actually mean here?
- these small open source models will not produce output anywhere near the quality of gpt-4, or even gpt-3.5, so I can't imagine this could credibly be considered competing in the first place
- if someone else uses the dataset to do the same, they wouldn't necessarily be violating the ToS because they didn't call the API, so I don't know how that works
- the training data used in essentially all large language models includes a significant amount of copyrighted or otherwise non-permissive licensing in the first place
- other work using the self-instruct method, e.g. the original here: https://github.com/yizhongw/self-instruct released the data and model as apache-2
I am purposingly leaving this license ambiguous (other than the fact you must comply with the Meta original license for llama-2) because I am not a lawyer and refuse to attempt to interpret all of the terms accordingly.
Your best bet is probably to avoid using this commercially due to the OpenAI API usage.
Either way, by using this model, you agree to completely indemnify me.
| 24,389 | [
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robbylegit/blip2-opt-2.7b-gauge-captions-adapters-1 | 2023-10-20T21:55:51.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | robbylegit | null | null | robbylegit/blip2-opt-2.7b-gauge-captions-adapters-1 | 0 | 2 | peft | 2023-10-20T21:55:19 | ---
library_name: peft
base_model: ybelkada/blip2-opt-2.7b-fp16-sharded
---
# Model Card for Model ID
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
### Framework versions
- PEFT 0.6.0.dev0
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] |
vladjr/bert-teste4 | 2023-10-21T00:06:51.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | vladjr | null | null | vladjr/bert-teste4 | 0 | 2 | transformers | 2023-10-20T23:59:09 | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: vladjr/bert-teste4
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# vladjr/bert-teste4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3511
- Validation Loss: 0.5113
- Train Accuracy: 0.75
- Epoch: 7
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 200, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.4295 | 0.5164 | 0.7438 | 0 |
| 0.3839 | 0.5113 | 0.75 | 1 |
| 0.3366 | 0.5113 | 0.75 | 2 |
| 0.3391 | 0.5113 | 0.75 | 3 |
| 0.3534 | 0.5113 | 0.75 | 4 |
| 0.3536 | 0.5113 | 0.75 | 5 |
| 0.3546 | 0.5113 | 0.75 | 6 |
| 0.3511 | 0.5113 | 0.75 | 7 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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adityarra07/whisper-medium-ft-5000 | 2023-10-21T03:41:55.000Z | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | adityarra07 | null | null | adityarra07/whisper-medium-ft-5000 | 0 | 2 | transformers | 2023-10-21T00:33:42 | ---
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-medium-ft-5000
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-medium-ft-5000
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2673
- Wer: 10.3673
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.5238 | 1.0 | 313 | 0.2389 | 12.2734 |
| 0.1035 | 2.0 | 626 | 0.2360 | 11.2041 |
| 0.0381 | 3.0 | 939 | 0.2349 | 10.7857 |
| 0.0134 | 4.0 | 1252 | 0.2512 | 10.5532 |
| 0.0039 | 5.0 | 1565 | 0.2611 | 10.2278 |
| 0.0013 | 6.0 | 1878 | 0.2673 | 10.3673 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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gustavokpc/IC_segundo | 2023-10-21T07:02:27.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | gustavokpc | null | null | gustavokpc/IC_segundo | 0 | 2 | transformers | 2023-10-21T02:22:56 | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: gustavokpc/IC_segundo
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# gustavokpc/IC_segundo
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0909
- Validation Loss: 0.2104
- Train Accuracy: 0.9281
- Epoch: 2
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 2274, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.3384 | 0.2146 | 0.9195 | 0 |
| 0.1693 | 0.2000 | 0.9235 | 1 |
| 0.0909 | 0.2104 | 0.9281 | 2 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.14.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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] |
TanmaySah/targetmoduleldm | 2023-10-21T05:15:54.000Z | [
"peft",
"region:us"
] | null | TanmaySah | null | null | TanmaySah/targetmoduleldm | 0 | 2 | peft | 2023-10-21T02:29:09 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
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Wanuch/bert-finetuned-wenuch | 2023-10-21T02:42:35.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | Wanuch | null | null | Wanuch/bert-finetuned-wenuch | 0 | 2 | transformers | 2023-10-21T02:42:16 | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: bert-finetuned-wenuch
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert-finetuned-wenuch
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 1377, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.13.0
- Tokenizers 0.14.1
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] |
WaitingWu/llama2_taiwan_news_qlora | 2023-10-21T07:31:13.000Z | [
"peft",
"region:us"
] | null | WaitingWu | null | null | WaitingWu/llama2_taiwan_news_qlora | 0 | 2 | peft | 2023-10-21T06:00:19 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0
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] |
KiddsMo/llama2_taiwan_news_qlora | 2023-10-21T07:31:52.000Z | [
"peft",
"region:us"
] | null | KiddsMo | null | null | KiddsMo/llama2_taiwan_news_qlora | 0 | 2 | peft | 2023-10-21T06:06:41 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0
| 464 | [
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] |
RubyC/llama2_taiwan_news_qlora | 2023-10-21T07:31:46.000Z | [
"peft",
"region:us"
] | null | RubyC | null | null | RubyC/llama2_taiwan_news_qlora | 0 | 2 | peft | 2023-10-21T06:34:15 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0
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brianwwwww1231/llama2_taiwan_news_qlora | 2023-10-21T07:31:38.000Z | [
"peft",
"region:us"
] | null | brianwwwww1231 | null | null | brianwwwww1231/llama2_taiwan_news_qlora | 0 | 2 | peft | 2023-10-21T07:30:37 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0
| 464 | [
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] |
MikeKuo/llama2_taiwan_news_qlora | 2023-10-21T07:33:28.000Z | [
"peft",
"region:us"
] | null | MikeKuo | null | null | MikeKuo/llama2_taiwan_news_qlora | 0 | 2 | peft | 2023-10-21T07:31:11 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0
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ADRIANRICO/Distilbert-finetuned-emotion | 2023-10-21T08:36:34.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | ADRIANRICO | null | null | ADRIANRICO/Distilbert-finetuned-emotion | 0 | 2 | transformers | 2023-10-21T08:01:32 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: Distilbert-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.926
- name: F1
type: f1
value: 0.9260273838038886
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Distilbert-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2179
- Accuracy: 0.926
- F1: 0.9260
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.805 | 1.0 | 250 | 0.3141 | 0.909 | 0.9079 |
| 0.2481 | 2.0 | 500 | 0.2179 | 0.926 | 0.9260 |
### Framework versions
- Transformers 4.27.2
- Pytorch 1.13.1+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
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Tahmid37/mt5-text-to-ipa | 2023-10-21T08:43:18.000Z | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"bn",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | Tahmid37 | null | null | Tahmid37/mt5-text-to-ipa | 0 | 2 | transformers | 2023-10-21T08:42:38 | ---
language:
- bn
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: mt5-text-to-ipa
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-text-to-ipa
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0653
- Wer: 0.0536
## 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: 0.005
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.8729 | 1.62 | 4000 | 0.9909 | 1.1948 |
| 0.8001 | 3.23 | 8000 | 0.2286 | 0.3110 |
| 0.3642 | 4.85 | 12000 | 0.1414 | 0.1860 |
| 0.22 | 6.46 | 16000 | 0.1037 | 0.1154 |
| 0.1289 | 8.08 | 20000 | 0.0777 | 0.0707 |
| 0.0762 | 9.7 | 24000 | 0.0653 | 0.0536 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1
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Sarthak7777/translate_fukkkiii-hindi-a | 2023-10-21T09:28:50.000Z | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | Sarthak7777 | null | null | Sarthak7777/translate_fukkkiii-hindi-a | 0 | 2 | transformers | 2023-10-21T08:54:20 | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: translate_fukkkiii-hindi-a
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# translate_fukkkiii-hindi-a
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2745
- Bleu: 0.969
- Gen Len: 13.878
## 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: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| 0.311 | 1.0 | 625 | 0.2950 | 0.0819 | 13.875 |
| 0.2976 | 2.0 | 1250 | 0.2894 | 0.1709 | 13.8705 |
| 0.2983 | 3.0 | 1875 | 0.2873 | 0.325 | 13.9675 |
| 0.2859 | 4.0 | 2500 | 0.2847 | 0.3271 | 13.85 |
| 0.2829 | 5.0 | 3125 | 0.2822 | 0.47 | 13.8655 |
| 0.2799 | 6.0 | 3750 | 0.2813 | 0.3258 | 13.853 |
| 0.2809 | 7.0 | 4375 | 0.2802 | 0.4139 | 13.9745 |
| 0.2753 | 8.0 | 5000 | 0.2781 | 0.7067 | 13.883 |
| 0.2733 | 9.0 | 5625 | 0.2768 | 0.6897 | 13.809 |
| 0.277 | 10.0 | 6250 | 0.2754 | 0.6447 | 13.8995 |
| 0.2673 | 11.0 | 6875 | 0.2756 | 0.9871 | 13.9095 |
| 0.2683 | 12.0 | 7500 | 0.2757 | 1.0343 | 13.797 |
| 0.2702 | 13.0 | 8125 | 0.2750 | 0.9205 | 13.8755 |
| 0.2658 | 14.0 | 8750 | 0.2749 | 0.9886 | 13.87 |
| 0.2636 | 15.0 | 9375 | 0.2747 | 0.9838 | 13.88 |
| 0.2659 | 16.0 | 10000 | 0.2745 | 0.969 | 13.878 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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waterabbit114/my-random2-setfit-model | 2023-10-21T09:49:49.000Z | [
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | waterabbit114 | null | null | waterabbit114/my-random2-setfit-model | 0 | 2 | sentence-transformers | 2023-10-21T09:49:28 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# waterabbit114/my-random2-setfit-model
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("waterabbit114/my-random2-setfit-model")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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SoyGema/falcon-7b-instruct | 2023-10-21T11:10:43.000Z | [
"transformers",
"pytorch",
"falcon",
"text-generation",
"custom_code",
"en",
"dataset:tiiuae/falcon-refinedweb",
"arxiv:2205.14135",
"arxiv:1911.02150",
"arxiv:2005.14165",
"arxiv:2104.09864",
"arxiv:2306.01116",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | SoyGema | null | null | SoyGema/falcon-7b-instruct | 0 | 2 | transformers | 2023-10-21T10:47:12 | ---
datasets:
- tiiuae/falcon-refinedweb
language:
- en
inference: true
widget:
- text: "Hey Falcon! Any recommendations for my holidays in Abu Dhabi?"
example_title: "Abu Dhabi Trip"
- text: "What's the Everett interpretation of quantum mechanics?"
example_title: "Q/A: Quantum & Answers"
- text: "Give me a list of the top 10 dive sites you would recommend around the world."
example_title: "Diving Top 10"
- text: "Can you tell me more about deep-water soloing?"
example_title: "Extreme sports"
- text: "Can you write a short tweet about the Apache 2.0 release of our latest AI model, Falcon LLM?"
example_title: "Twitter Helper"
- text: "What are the responsabilities of a Chief Llama Officer?"
example_title: "Trendy Jobs"
license: apache-2.0
---
# ✨ Falcon-7B-Instruct
**Falcon-7B-Instruct is a 7B parameters causal decoder-only model built by [TII](https://www.tii.ae) based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) and finetuned on a mixture of chat/instruct datasets. It is made available under the Apache 2.0 license.**
*Paper coming soon 😊.*
🤗 To get started with Falcon (inference, finetuning, quantization, etc.), we recommend reading [this great blogpost fron HF](https://huggingface.co/blog/falcon)!
## Why use Falcon-7B-Instruct?
* **You are looking for a ready-to-use chat/instruct model based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).**
* **Falcon-7B is a strong base model, outperforming comparable open-source models** (e.g., [MPT-7B](https://huggingface.co/mosaicml/mpt-7b), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1) etc.), thanks to being trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
* **It features an architecture optimized for inference**, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)).
💬 **This is an instruct model, which may not be ideal for further finetuning.** If you are interested in building your own instruct/chat model, we recommend starting from [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).
🔥 **Looking for an even more powerful model?** [Falcon-40B-Instruct](https://huggingface.co/tiiuae/falcon-40b-instruct) is Falcon-7B-Instruct's big brother!
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-7b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
💥 **Falcon LLMs require PyTorch 2.0 for use with `transformers`!**
For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon).
You will need **at least 16GB of memory** to swiftly run inference with Falcon-7B-Instruct.
# Model Card for Falcon-7B-Instruct
## Model Details
### Model Description
- **Developed by:** [https://www.tii.ae](https://www.tii.ae);
- **Model type:** Causal decoder-only;
- **Language(s) (NLP):** English and French;
- **License:** Apache 2.0;
- **Finetuned from model:** [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).
### Model Source
- **Paper:** *coming soon*.
## Uses
### Direct Use
Falcon-7B-Instruct has been finetuned on a mixture of instruct and chat datasets.
### Out-of-Scope Use
Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.
## Bias, Risks, and Limitations
Falcon-7B-Instruct is mostly trained on English data, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.
### Recommendations
We recommend users of Falcon-7B-Instruct to develop guardrails and to take appropriate precautions for any production use.
## How to Get Started with the Model
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-7b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
## Training Details
### Training Data
Falcon-7B-Instruct was finetuned on a 250M tokens mixture of instruct/chat datasets.
| **Data source** | **Fraction** | **Tokens** | **Description** |
|--------------------|--------------|------------|-----------------------------------|
| [Bai ze](https://github.com/project-baize/baize-chatbot) | 65% | 164M | chat |
| [GPT4All](https://github.com/nomic-ai/gpt4all) | 25% | 62M | instruct |
| [GPTeacher](https://github.com/teknium1/GPTeacher) | 5% | 11M | instruct |
| [RefinedWeb-English](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) | 5% | 13M | massive web crawl |
The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer.
## Evaluation
*Paper coming soon.*
See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results.
Note that this model variant is not optimized for NLP benchmarks.
## Technical Specifications
For more information about pretraining, see [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).
### Model Architecture and Objective
Falcon-7B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences:
* **Positionnal embeddings:** rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864));
* **Attention:** multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135));
* **Decoder-block:** parallel attention/MLP with a single layer norm.
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|-----------|----------------------------------------|
| Layers | 32 | |
| `d_model` | 4544 | Increased to compensate for multiquery |
| `head_dim` | 64 | Reduced to optimise for FlashAttention |
| Vocabulary | 65024 | |
| Sequence length | 2048 | |
### Compute Infrastructure
#### Hardware
Falcon-7B-Instruct was trained on AWS SageMaker, on 32 A100 40GB GPUs in P4d instances.
#### Software
Falcon-7B-Instruct was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.)
## Citation
*Paper coming soon* 😊. In the meanwhile, you can use the following information to cite:
```
@article{falcon40b,
title={{Falcon-40B}: an open large language model with state-of-the-art performance},
author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme},
year={2023}
}
```
To learn more about the pretraining dataset, see the 📓 [RefinedWeb paper](https://arxiv.org/abs/2306.01116).
```
@article{refinedweb,
title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only},
author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay},
journal={arXiv preprint arXiv:2306.01116},
eprint={2306.01116},
eprinttype = {arXiv},
url={https://arxiv.org/abs/2306.01116},
year={2023}
}
```
## License
Falcon-7B-Instruct is made available under the Apache 2.0 license.
## Contact
falconllm@tii.ae | 9,798 | [
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Vashesh/whisper-small-ar-test | 2023-10-21T11:37:34.000Z | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"ar",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | Vashesh | null | null | Vashesh/whisper-small-ar-test | 0 | 2 | transformers | 2023-10-21T11:19:15 | ---
language:
- ar
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
model-index:
- name: Whisper Small Arabic - Vashesh Jogani
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Arabic - Vashesh Jogani
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Arabic Voice 11.0 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 400
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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adityarra07/whisper-medium-ft-1000_2 | 2023-10-21T12:53:54.000Z | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | adityarra07 | null | null | adityarra07/whisper-medium-ft-1000_2 | 0 | 2 | transformers | 2023-10-21T12:07:02 | ---
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-medium-ft-1000_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-medium-ft-1000_2
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3892
- Wer: 14.2726
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 1.4357 | 1.0 | 63 | 0.4359 | 29.4199 |
| 0.1744 | 2.0 | 126 | 0.3807 | 16.5746 |
| 0.057 | 3.0 | 189 | 0.3800 | 15.7919 |
| 0.0187 | 4.0 | 252 | 0.3829 | 14.4107 |
| 0.0068 | 5.0 | 315 | 0.3823 | 14.6409 |
| 0.0026 | 6.0 | 378 | 0.3892 | 14.2726 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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mateiaass/albert-base-qa-1 | 2023-10-21T15:43:10.000Z | [
"transformers",
"pytorch",
"albert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | mateiaass | null | null | mateiaass/albert-base-qa-1 | 0 | 2 | transformers | 2023-10-21T12:50:58 | ---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: albert-base-qa-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# albert-base-qa-1
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8449
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8953 | 1.0 | 3942 | 0.8608 |
| 0.639 | 2.0 | 7884 | 0.8449 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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mateiaass/albert-base-qa-2 | 2023-10-21T17:01:55.000Z | [
"transformers",
"pytorch",
"albert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | mateiaass | null | null | mateiaass/albert-base-qa-2 | 0 | 2 | transformers | 2023-10-21T12:57:04 | ---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: albert-base-qa-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# albert-base-qa-2
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9421
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.9078 | 1.0 | 3942 | 0.8851 |
| 0.6513 | 2.0 | 7884 | 0.8585 |
| 0.4754 | 3.0 | 11826 | 0.9421 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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] |
sainteye/ifoodie-detail-rating-v10 | 2023-10-21T13:02:29.000Z | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | sainteye | null | null | sainteye/ifoodie-detail-rating-v10 | 0 | 2 | transformers | 2023-10-21T13:02:25 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: ifoodie-detail-rating-v10
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8559321761131287
---
# ifoodie-detail-rating-v10
['中間', '偏壞', '偏好']
## Example Images
# #### 中間
# 
#
# #### 偏壞
# 
#
# #### 偏好
# 
# | 475 | [
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adityarra07/whisper-medium-ft-10000_2 | 2023-10-21T19:38:42.000Z | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | adityarra07 | null | null | adityarra07/whisper-medium-ft-10000_2 | 0 | 2 | transformers | 2023-10-21T13:24:49 | ---
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-medium-ft-10000_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-medium-ft-10000_2
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2654
- Wer: 10.1641
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.3583 | 1.0 | 625 | 0.2361 | 32.4521 |
| 0.0928 | 2.0 | 1250 | 0.2281 | 10.0273 |
| 0.0368 | 3.0 | 1875 | 0.2280 | 10.8478 |
| 0.0123 | 4.0 | 2500 | 0.2604 | 10.6655 |
| 0.0039 | 5.0 | 3125 | 0.2571 | 10.3008 |
| 0.0011 | 6.0 | 3750 | 0.2654 | 10.1641 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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mateiaass/albert-base-qa-coQA-1 | 2023-10-21T17:42:29.000Z | [
"transformers",
"pytorch",
"albert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | mateiaass | null | null | mateiaass/albert-base-qa-coQA-1 | 0 | 2 | transformers | 2023-10-21T14:09:36 | ---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
model-index:
- name: albert-base-qa-coQA-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# albert-base-qa-coQA-1
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6044
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.6576 | 1.0 | 5249 | 2.6549 |
| 2.3364 | 2.0 | 10498 | 2.6044 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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gustavokpc/IC_terceiro | 2023-10-21T15:15:37.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | gustavokpc | null | null | gustavokpc/IC_terceiro | 0 | 2 | transformers | 2023-10-21T14:13:44 | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: gustavokpc/IC_terceiro
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# gustavokpc/IC_terceiro
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1000
- Validation Loss: 0.2233
- Train Accuracy: 0.9261
- Epoch: 2
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 2274, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.3406 | 0.2337 | 0.9037 | 0 |
| 0.1737 | 0.2312 | 0.9142 | 1 |
| 0.1000 | 0.2233 | 0.9261 | 2 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.14.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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mateiaass/albert-base-qa-coQA-2 | 2023-10-21T20:06:39.000Z | [
"transformers",
"pytorch",
"albert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | mateiaass | null | null | mateiaass/albert-base-qa-coQA-2 | 0 | 2 | transformers | 2023-10-21T14:18:32 | ---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
model-index:
- name: albert-base-qa-coQA-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# albert-base-qa-coQA-2
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7040
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.6897 | 1.0 | 5249 | 2.6767 |
| 2.3603 | 2.0 | 10498 | 2.6169 |
| 2.0685 | 3.0 | 15747 | 2.7040 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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brjezierski/sentence-embeddings-classification-ai_car-data_aug | 2023-10-21T15:16:37.000Z | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | sentence-similarity | brjezierski | null | null | brjezierski/sentence-embeddings-classification-ai_car-data_aug | 0 | 2 | sentence-transformers | 2023-10-21T15:16:12 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 8190 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.dataloader._InfiniteConstantSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.BatchAllTripletLoss.BatchAllTripletLoss`
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 5542 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.dataloader._InfiniteConstantSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.BatchAllTripletLoss.BatchAllTripletLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 1388.9375,
"evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 17778,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | 2,663 | [
[
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gustavokpc/IC_quarto | 2023-10-21T18:33:01.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | gustavokpc | null | null | gustavokpc/IC_quarto | 0 | 2 | transformers | 2023-10-21T15:20:31 | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: gustavokpc/IC_quarto
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# gustavokpc/IC_quarto
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1100
- Validation Loss: 0.2097
- Train Accuracy: 0.9281
- Epoch: 2
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2274, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.3532 | 0.2443 | 0.8971 | 0 |
| 0.1794 | 0.1957 | 0.9255 | 1 |
| 0.1100 | 0.2097 | 0.9281 | 2 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.14.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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mor40/BulBERT-ct21-5pochs | 2023-10-21T16:45:43.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:bgglue",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | mor40 | null | null | mor40/BulBERT-ct21-5pochs | 0 | 2 | transformers | 2023-10-21T15:29:55 | ---
base_model: mor40/BulBERT-chitanka-model
tags:
- generated_from_trainer
datasets:
- bgglue
metrics:
- accuracy
model-index:
- name: BulBERT-ct21-5pochs
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: bgglue
type: bgglue
config: ct21t1
split: validation
args: ct21t1
metrics:
- name: Accuracy
type: accuracy
value: 0.84
---
<!-- 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. -->
# BulBERT-ct21-5pochs
This model is a fine-tuned version of [mor40/BulBERT-chitanka-model](https://huggingface.co/mor40/BulBERT-chitanka-model) on the bgglue dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0051
- Accuracy: 0.84
## 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 | 163 | 0.4891 | 0.7743 |
| No log | 2.0 | 326 | 0.5475 | 0.8257 |
| No log | 3.0 | 489 | 0.7889 | 0.82 |
| 0.288 | 4.0 | 652 | 0.9438 | 0.8286 |
| 0.288 | 5.0 | 815 | 1.0051 | 0.84 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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mateiaass/albert-base-qa-3 | 2023-10-21T17:30:31.000Z | [
"transformers",
"pytorch",
"albert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | mateiaass | null | null | mateiaass/albert-base-qa-3 | 0 | 2 | transformers | 2023-10-21T16:06:36 | ---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: albert-base-qa-3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# albert-base-qa-3
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8707
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8793 | 1.0 | 3942 | 0.8707 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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Sarthak7777/english-hindi-bbbb | 2023-10-21T17:25:16.000Z | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | Sarthak7777 | null | null | Sarthak7777/english-hindi-bbbb | 0 | 2 | transformers | 2023-10-21T17:13:19 | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: english-hindi-bbbb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# english-hindi-bbbb
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4443
- Bleu: 0.2327
- Gen Len: 18.8353
## 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| 1.4066 | 1.0 | 625 | 0.4151 | 0.1734 | 18.941 |
| 0.9564 | 2.0 | 1250 | 0.4408 | 0.287 | 18.87 |
| 0.7504 | 3.0 | 1875 | 0.4522 | 0.2908 | 18.8034 |
| 0.6375 | 4.0 | 2500 | 0.4377 | 0.2241 | 18.856 |
| 0.6063 | 5.0 | 3125 | 0.4465 | 0.2399 | 18.8305 |
| 0.591 | 6.0 | 3750 | 0.4443 | 0.2327 | 18.8353 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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vladjr/mt5-base-mt5-full | 2023-10-21T21:14:04.000Z | [
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | vladjr | null | null | vladjr/mt5-base-mt5-full | 0 | 2 | transformers | 2023-10-21T19:24:13 | ---
license: apache-2.0
base_model: google/mt5-base
tags:
- generated_from_keras_callback
model-index:
- name: vladjr/mt5-base-mt5-full
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# vladjr/mt5-base-mt5-full
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.1101
- Validation Loss: 0.9831
- Epoch: 7
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 9280, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 6.7607 | 1.6963 | 0 |
| 2.6675 | 1.4045 | 1 |
| 1.8063 | 1.2591 | 2 |
| 1.5864 | 1.0469 | 3 |
| 1.2627 | 1.0222 | 4 |
| 1.1802 | 1.0123 | 5 |
| 1.1343 | 0.9810 | 6 |
| 1.1101 | 0.9831 | 7 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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lewtun/mistral-7b-sft-ultrachat-arithmo-full | 2023-10-21T20:00:17.000Z | [
"transformers",
"pytorch",
"tensorboard",
"mistral",
"text-generation",
"generated_from_trainer",
"dataset:stingning/ultrachat",
"dataset:akjindal53244/Arithmo-Data",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | lewtun | null | null | lewtun/mistral-7b-sft-ultrachat-arithmo-full | 1 | 2 | transformers | 2023-10-21T19:40:27 | ---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
model-index:
- name: mistral-7b-sft-ultrachat-arithmo-full
results: []
datasets:
- stingning/ultrachat
- akjindal53244/Arithmo-Data
---
<!-- 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. -->
# mistral-7b-sft-ultrachat-arithmo-full
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the UltraChat and Arithmo datasets.
It achieves the following results on the evaluation set:
- Loss: 0.9133
## Model description
```python
# Install transformers from source - only needed for versions <= v4.34
# pip install git+https://github.com/huggingface/transformers.git
# pip install accelerate
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="lewtun/mistral-7b-sft-ultrachat-arithmo-full", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{
"role": "system",
"content": "You are a friendly chatbot who always responds in the style of a pirate",
},
{"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
# <|system|>
# You are a friendly chatbot who always responds in the style of a pirate.</s>
# <|user|>
# How many helicopters can a human eat in one sitting?</s>
# <|assistant|>
# Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food!
```
## 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: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- total_eval_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8586 | 0.38 | 344 | 0.9133 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.14.0 | 2,917 | [
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lewtun/mistral-7b-sft-ultrachat-arithmo-50 | 2023-10-21T20:00:46.000Z | [
"transformers",
"pytorch",
"tensorboard",
"mistral",
"text-generation",
"generated_from_trainer",
"dataset:stingning/ultrachat",
"dataset:akjindal53244/Arithmo-Data",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | lewtun | null | null | lewtun/mistral-7b-sft-ultrachat-arithmo-50 | 1 | 2 | transformers | 2023-10-21T19:42:08 | ---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
model-index:
- name: mistral-7b-sft-ultrachat-arithmo-50
results: []
datasets:
- stingning/ultrachat
- akjindal53244/Arithmo-Data
---
<!-- 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. -->
# mistral-7b-sft-ultrachat-arithmo-50
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the UltraChat and Arithmo (50%) datasets.
It achieves the following results on the evaluation set:
- Loss: 0.8892
## Model description
```python
# Install transformers from source - only needed for versions <= v4.34
# pip install git+https://github.com/huggingface/transformers.git
# pip install accelerate
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="lewtun/mistral-7b-sft-ultrachat-arithmo-50", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{
"role": "system",
"content": "You are a friendly chatbot who always responds in the style of a pirate",
},
{"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
# <|system|>
# You are a friendly chatbot who always responds in the style of a pirate.</s>
# <|user|>
# How many helicopters can a human eat in one sitting?</s>
# <|assistant|>
# Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food!
```
## 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: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- total_eval_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8776 | 0.47 | 308 | 0.8892 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.14.0 | 2,917 | [
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waterabbit114/my-zeroshot-setfit-model | 2023-10-21T19:50:51.000Z | [
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | waterabbit114 | null | null | waterabbit114/my-zeroshot-setfit-model | 0 | 2 | sentence-transformers | 2023-10-21T19:50:32 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# waterabbit114/my-zeroshot-setfit-model
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("waterabbit114/my-zeroshot-setfit-model")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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] |
GiantTreeG/german-jeopardy-mt5-base | 2023-10-21T20:25:20.000Z | [
"transformers",
"tensorboard",
"safetensors",
"mt5",
"text2text-generation",
"question-generation",
"german",
"generated_from_trainer",
"de",
"dataset:lmqg/qg_dequad",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | GiantTreeG | null | null | GiantTreeG/german-jeopardy-mt5-base | 0 | 2 | transformers | 2023-10-21T19:54:36 | ---
language:
- de
tags:
- question-generation
- german
- text2text-generation
- generated_from_trainer
datasets:
- lmqg/qg_dequad
metrics:
- bleu4
- f1
- rouge
- exact_match
model-index:
- name: german-jeopardy-mt5-base
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: lmqg/qg_dequad
type: default
args: default
metrics:
- name: BLEU-4
type: bleu4
value: 14.56
- name: F1
type: f1
value: 39.53
- name: ROUGE-1
type: rouge1
value: 40.62
- name: ROUGE-2
type: rouge2
value: 21.49
- name: ROUGE-L
type: rougel
value: 39.14
- name: ROUGE-Lsum
type: rougelsum
value: 39.13
- name: Exact Match
type: exact_match
value: 2.72
---
<!-- 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. -->
# german-jeopardy-mt5-base
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.66
- Brevity Penalty: 0.9025
- System Length: 18860
- Reference Length: 20793
- ROUGE-1: 40.62
- ROUGE-2: 21.49
- ROUGE-L: 39.14
- ROUGE-Lsum: 39.13
- Exact Match: 2.72
- BLEU: 14.56
- F1: 39.53
## Model description
See [google/mt5-base](https://huggingface.co/google/mt5-base) for the model architecture.
The model was trained on a single NVIDIA RTX 3090 GPU with 24GB of VRAM.
## Intended uses & limitations
This model can be used for question generation on German text.
## Training and evaluation data
See [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad).
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 7
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adafactor
- lr_scheduler_type: constant
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Counts 1 | Counts 2 | Counts 3 | Counts 4 | Totals 1 | Totals 2 | Totals 3 | Totals 4 | Precisions 1 | Precisions 2 | Precisions 3 | Precisions 4 | Brevity Penalty | System Length | Reference Length | ROUGE-1 | ROUGE-2 | ROUGE-L | ROUGE-Lsum | Exact Match | BLEU | Mean Generated Length | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:----------------:|:-------:|:-------:|:-------:|:----------:|:-----------:|:-------:|:---------------------:|:------:|
| 5.5131 | 1.0 | 145 | 1.8698 | 6032 | 1668 | 626 | 216 | 16023 | 13819 | 11615 | 9411 | 37.6459 | 12.0703 | 5.3896 | 2.2952 | 0.7216 | 16023 | 21250 | 0.2485 | 0.1011 | 0.2368 | 0.2366 | 0.0018 | 6.2485 | 12.6166 | 0.2406 |
| 2.3946 | 2.0 | 291 | 1.5888 | 7325 | 2554 | 1178 | 558 | 16853 | 14649 | 12445 | 10241 | 43.4641 | 17.4346 | 9.4656 | 5.4487 | 0.7704 | 16853 | 21250 | 0.3226 | 0.1585 | 0.31 | 0.31 | 0.0145 | 10.8315 | 12.2582 | 0.3148 |
| 2.0101 | 3.0 | 436 | 1.4997 | 7623 | 2764 | 1304 | 629 | 17042 | 14838 | 12634 | 10430 | 44.7307 | 18.6278 | 10.3214 | 6.0307 | 0.7812 | 17042 | 21250 | 0.3403 | 0.1723 | 0.3263 | 0.3263 | 0.0154 | 11.7891 | 12.6783 | 0.3315 |
| 1.8073 | 4.0 | 582 | 1.4610 | 7728 | 2916 | 1415 | 707 | 16654 | 14450 | 12246 | 10042 | 46.4033 | 20.1799 | 11.5548 | 7.0404 | 0.7588 | 16654 | 21250 | 0.3461 | 0.1818 | 0.3324 | 0.3326 | 0.0168 | 12.6068 | 12.2963 | 0.3387 |
| 1.6851 | 4.99 | 727 | 1.4357 | 7964 | 3059 | 1483 | 727 | 17381 | 15177 | 12973 | 10769 | 45.8201 | 20.1555 | 11.4314 | 6.7509 | 0.8004 | 17381 | 21250 | 0.3558 | 0.1888 | 0.3415 | 0.3414 | 0.0159 | 13.0784 | 12.7436 | 0.3483 |
| 1.5642 | 6.0 | 873 | 1.4003 | 8299 | 3224 | 1592 | 788 | 17351 | 15147 | 12943 | 10739 | 47.8301 | 21.2847 | 12.3001 | 7.3377 | 0.7987 | 17351 | 21250 | 0.3814 | 0.2025 | 0.3684 | 0.3685 | 0.0204 | 13.9065 | 12.9569 | 0.3736 |
| 1.4756 | 6.99 | 1018 | 1.3779 | 8640 | 3430 | 1712 | 879 | 17669 | 15465 | 13261 | 11057 | 48.8992 | 22.1791 | 12.91 | 7.9497 | 0.8165 | 17669 | 21250 | 0.3971 | 0.2133 | 0.3828 | 0.3826 | 0.025 | 14.9146 | 13.1084 | 0.3892 |
| 1.3792 | 8.0 | 1164 | 1.3624 | 8732 | 3417 | 1712 | 871 | 17996 | 15792 | 13588 | 11384 | 48.5219 | 21.6375 | 12.5994 | 7.6511 | 0.8346 | 17996 | 21250 | 0.4003 | 0.2131 | 0.3852 | 0.3849 | 0.0245 | 14.8859 | 13.3748 | 0.3917 |
| 1.3133 | 9.0 | 1310 | 1.3630 | 8804 | 3500 | 1754 | 920 | 17661 | 15457 | 13253 | 11049 | 49.85 | 22.6435 | 13.2347 | 8.3265 | 0.8161 | 17661 | 21250 | 0.4078 | 0.219 | 0.3932 | 0.3935 | 0.025 | 15.3264 | 13.2019 | 0.4 |
| 1.261 | 10.0 | 1455 | 1.3685 | 8910 | 3602 | 1849 | 1000 | 17709 | 15505 | 13301 | 11097 | 50.3134 | 23.2312 | 13.9012 | 9.0114 | 0.8188 | 17709 | 21250 | 0.4135 | 0.223 | 0.3991 | 0.3992 | 0.0295 | 16.0163 | 13.1892 | 0.4055 |
| 1.1897 | 11.0 | 1601 | 1.3639 | 9096 | 3690 | 1902 | 1012 | 18261 | 16057 | 13853 | 11649 | 49.8111 | 22.9806 | 13.7299 | 8.6874 | 0.849 | 18261 | 21250 | 0.4201 | 0.2289 | 0.4059 | 0.4057 | 0.0281 | 16.3202 | 13.5077 | 0.4121 |
| 1.1453 | 11.99 | 1746 | 1.3610 | 9106 | 3735 | 1932 | 1023 | 18329 | 16125 | 13921 | 11717 | 49.6808 | 23.1628 | 13.8783 | 8.7309 | 0.8527 | 18329 | 21250 | 0.4173 | 0.2303 | 0.4026 | 0.4025 | 0.0281 | 16.4772 | 13.8013 | 0.4099 |
| 1.0858 | 13.0 | 1892 | 1.3716 | 9245 | 3778 | 1955 | 1049 | 18556 | 16352 | 14148 | 11944 | 49.8222 | 23.1042 | 13.8182 | 8.7827 | 0.8649 | 18556 | 21250 | 0.4244 | 0.2327 | 0.409 | 0.409 | 0.0322 | 16.7204 | 13.8144 | 0.417 |
| 1.0472 | 13.99 | 2037 | 1.3770 | 9166 | 3756 | 1946 | 1054 | 18315 | 16111 | 13907 | 11703 | 50.0464 | 23.3133 | 13.993 | 9.0062 | 0.8519 | 18315 | 21250 | 0.4216 | 0.2311 | 0.4068 | 0.4067 | 0.0309 | 16.6825 | 13.8099 | 0.4143 |
| 0.9953 | 15.0 | 2183 | 1.3881 | 9342 | 3926 | 2046 | 1108 | 18132 | 15928 | 13724 | 11520 | 51.5222 | 24.6484 | 14.9082 | 9.6181 | 0.842 | 18132 | 21250 | 0.4328 | 0.2418 | 0.4171 | 0.4171 | 0.0327 | 17.3937 | 13.5023 | 0.4258 |
| 0.9509 | 16.0 | 2329 | 1.4016 | 9330 | 3894 | 2024 | 1084 | 18672 | 16468 | 14264 | 12060 | 49.9679 | 23.6459 | 14.1896 | 8.9884 | 0.871 | 18672 | 21250 | 0.4269 | 0.237 | 0.4123 | 0.4122 | 0.0313 | 17.1618 | 13.956 | 0.4198 |
| 0.9183 | 17.0 | 2474 | 1.4152 | 9303 | 3824 | 1979 | 1084 | 18476 | 16272 | 14068 | 11864 | 50.3518 | 23.5005 | 14.0674 | 9.1369 | 0.8606 | 18476 | 21250 | 0.4269 | 0.2345 | 0.4121 | 0.4122 | 0.0327 | 16.995 | 13.7854 | 0.4199 |
| 0.8696 | 18.0 | 2620 | 1.4404 | 9184 | 3798 | 1993 | 1085 | 18379 | 16175 | 13971 | 11767 | 49.9701 | 23.4807 | 14.2653 | 9.2207 | 0.8554 | 18379 | 21250 | 0.4218 | 0.2333 | 0.4076 | 0.4074 | 0.034 | 16.9541 | 13.726 | 0.4148 |
| 0.8389 | 19.0 | 2765 | 1.4360 | 9476 | 4000 | 2092 | 1139 | 19003 | 16799 | 14595 | 12391 | 49.8658 | 23.8109 | 14.3337 | 9.1922 | 0.8885 | 19003 | 21250 | 0.4307 | 0.2406 | 0.4161 | 0.416 | 0.0299 | 17.67 | 14.2064 | 0.4239 |
| 0.7993 | 19.92 | 2900 | 1.4545 | 9464 | 3970 | 2078 | 1126 | 18741 | 16537 | 14333 | 12129 | 50.4989 | 24.0068 | 14.498 | 9.2835 | 0.8747 | 18741 | 21250 | 0.4349 | 0.2424 | 0.4194 | 0.4192 | 0.0327 | 17.5799 | 13.9959 | 0.4269 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0
- Datasets 2.12.0
- Tokenizers 0.13.3
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] |
HarrisShen/example | 2023-10-21T21:34:11.000Z | [
"peft",
"region:us"
] | null | HarrisShen | null | null | HarrisShen/example | 0 | 2 | peft | 2023-10-21T21:34:09 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0
- PEFT 0.5.0
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jab11769/bert-multilingual-cased-finetuned-mrpc | 2023-10-22T04:00:28.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | jab11769 | null | null | jab11769/bert-multilingual-cased-finetuned-mrpc | 0 | 2 | transformers | 2023-10-22T03:59:51 | ---
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_keras_callback
model-index:
- name: bert-multilingual-cased-finetuned-mrpc
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert-multilingual-cased-finetuned-mrpc
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 1377, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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] |
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