modelId
stringlengths 4
81
| tags
list | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
|
|---|---|---|---|---|---|---|
Callidior/bert2bert-base-arxiv-titlegen | [
"pytorch",
"safetensors",
"encoder-decoder",
"text2text-generation",
"en",
"dataset:arxiv_dataset",
"transformers",
"summarization",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| summarization | {
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} | 145 | null | ---
language: en
license: apache-2.0
datasets:
- sst2
- glue
tags:
- openvino
---
## distilbert-base-uncased-finetuned-sst-2-english
[distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) quantized with NNCF PTQ and exported to OpenVINO IR.
**Model Description:** This model reaches an accuracy of 90.0 on the validation set. See [ov\_config.json](./ov_config.json) for the quantization config.
## Usage example
To install the requirements for using the OpenVINO backend, do:
```
pip install optimum[openvino]
```
This installs all necessary dependencies, including Transformers and OpenVINO.
*NOTE: Python 3.7-3.9 are supported. A virtualenv is recommended.*
You can use this model with a Transformers *pipeline*.
```python
from transformers import AutoTokenizer, pipeline
from optimum.intel.openvino import OVModelForSequenceClassification
model_id = "helenai/distilbert-base-uncased-finetuned-sst-2-english-ov-int8"
model = OVModelForSequenceClassification.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
cls_pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "OpenVINO is awesome!"
outputs = cls_pipe(text)
print(outputs)
```
Example output:
```sh
[{'label': 'POSITIVE', 'score': 0.9998594522476196}]
```
|
CallumRai/HansardGPT2 | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
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} | 14 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: t5-small-finetuned-en-to-it-lrs-back
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. -->
# t5-small-finetuned-en-to-it-lrs-back
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7887
- Bleu: 15.4528
- Gen Len: 52.516
## 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: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 2.8637 | 1.0 | 1125 | 2.7212 | 3.496 | 82.846 |
| 2.6665 | 2.0 | 2250 | 2.5507 | 5.4897 | 65.4087 |
| 2.5307 | 3.0 | 3375 | 2.4286 | 6.688 | 61.9687 |
| 2.4064 | 4.0 | 4500 | 2.3431 | 7.6166 | 59.5613 |
| 2.3369 | 5.0 | 5625 | 2.2779 | 8.4755 | 57.776 |
| 2.284 | 6.0 | 6750 | 2.2202 | 9.0471 | 57.1227 |
| 2.2358 | 7.0 | 7875 | 2.1728 | 9.7222 | 55.9393 |
| 2.1747 | 8.0 | 9000 | 2.1357 | 10.4908 | 54.9073 |
| 2.1555 | 9.0 | 10125 | 2.1012 | 11.0378 | 54.292 |
| 2.1215 | 10.0 | 11250 | 2.0715 | 11.2204 | 54.546 |
| 2.0882 | 11.0 | 12375 | 2.0448 | 11.6557 | 54.1687 |
| 2.0544 | 12.0 | 13500 | 2.0193 | 12.0521 | 53.604 |
| 2.0355 | 13.0 | 14625 | 1.9959 | 12.2297 | 53.3893 |
| 2.0236 | 14.0 | 15750 | 1.9755 | 12.4706 | 53.3327 |
| 1.9974 | 15.0 | 16875 | 1.9555 | 12.59 | 53.4507 |
| 1.983 | 16.0 | 18000 | 1.9400 | 12.8305 | 53.1807 |
| 1.9615 | 17.0 | 19125 | 1.9236 | 13.0549 | 53.128 |
| 1.9519 | 18.0 | 20250 | 1.9111 | 13.1942 | 53.2953 |
| 1.9408 | 19.0 | 21375 | 1.8977 | 13.3979 | 53.332 |
| 1.9203 | 20.0 | 22500 | 1.8862 | 13.5626 | 52.73 |
| 1.9134 | 21.0 | 23625 | 1.8749 | 13.8549 | 52.904 |
| 1.8981 | 22.0 | 24750 | 1.8638 | 13.9347 | 53.2787 |
| 1.8911 | 23.0 | 25875 | 1.8557 | 14.1628 | 52.946 |
| 1.8859 | 24.0 | 27000 | 1.8471 | 14.2514 | 52.744 |
| 1.8692 | 25.0 | 28125 | 1.8406 | 14.4957 | 52.9267 |
| 1.8733 | 26.0 | 29250 | 1.8324 | 14.5489 | 53.112 |
| 1.8602 | 27.0 | 30375 | 1.8268 | 14.6941 | 52.882 |
| 1.8547 | 28.0 | 31500 | 1.8202 | 14.9101 | 52.948 |
| 1.8478 | 29.0 | 32625 | 1.8151 | 14.9498 | 52.8967 |
| 1.8485 | 30.0 | 33750 | 1.8102 | 15.0763 | 52.8587 |
| 1.8401 | 31.0 | 34875 | 1.8065 | 15.1604 | 52.8513 |
| 1.8307 | 32.0 | 36000 | 1.8023 | 15.1404 | 52.6533 |
| 1.8275 | 33.0 | 37125 | 1.7994 | 15.1813 | 52.738 |
| 1.8233 | 34.0 | 38250 | 1.7964 | 15.3185 | 52.7033 |
| 1.8238 | 35.0 | 39375 | 1.7939 | 15.4693 | 52.6433 |
| 1.8253 | 36.0 | 40500 | 1.7926 | 15.4467 | 52.44 |
| 1.8169 | 37.0 | 41625 | 1.7908 | 15.4167 | 52.5907 |
| 1.8182 | 38.0 | 42750 | 1.7899 | 15.4595 | 52.5433 |
| 1.8161 | 39.0 | 43875 | 1.7890 | 15.4411 | 52.5007 |
| 1.8169 | 40.0 | 45000 | 1.7887 | 15.4528 | 52.516 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.11.0
|
CalvinHuang/mt5-small-finetuned-amazon-en-es | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"transformers",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
]
| summarization | {
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"MT5ForConditionalGeneration"
],
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} | 16 | null | ---
license: bsd-3-clause
---
Copyright 2018-2022, UT-Battelle
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
Cameron/BERT-Jigsaw | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
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}
} | 35 | null | ---
license: apache-2.0
---
https://github.com/entangledloops/slidingpuzzle |
Cameron/BERT-SBIC-targetcategory | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
],
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}
} | 30 | null | ---
library_name: fairseq
task: audio-to-audio
tags:
- fairseq
- audio
- audio-to-audio
- speech-to-speech-translation
widget:
- example_title: Common Voice sample 1
src: https://huggingface.co/facebook/xm_transformer_600m-es_en-multi_domain/resolve/main/common_voice_es_19966634.flac
---
|
Cameron/BERT-eec-emotion | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
],
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} | 36 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-Classification-kaggleEffectiveFeedback2
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-uncased-Classification-kaggleEffectiveFeedback2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9255
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.7473 | 1.0 | 3677 | 0.7181 |
| 0.6347 | 2.0 | 7354 | 0.7349 |
| 0.4244 | 3.0 | 11031 | 0.9255 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.1
|
Cameron/BERT-jigsaw-identityhate | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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} | 37 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-large-xlsr-53-intent-classification-ori
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-large-xlsr-53-intent-classification-ori
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7682
- Accuracy: 0.4167
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 45
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2017 | 1.0 | 14 | 2.2113 | 0.1042 |
| 2.2013 | 2.0 | 28 | 2.2078 | 0.0625 |
| 2.197 | 3.0 | 42 | 2.1981 | 0.0625 |
| 2.1917 | 4.0 | 56 | 2.1760 | 0.3125 |
| 2.1759 | 5.0 | 70 | 2.1534 | 0.3333 |
| 2.1713 | 6.0 | 84 | 2.1305 | 0.3333 |
| 2.1431 | 7.0 | 98 | 2.1044 | 0.3333 |
| 2.1366 | 8.0 | 112 | 2.0838 | 0.3333 |
| 2.1482 | 9.0 | 126 | 2.0649 | 0.3333 |
| 2.1074 | 10.0 | 140 | 2.0532 | 0.3333 |
| 2.1066 | 11.0 | 154 | 2.0506 | 0.3333 |
| 2.089 | 12.0 | 168 | 2.0624 | 0.3333 |
| 2.0625 | 13.0 | 182 | 2.0580 | 0.3333 |
| 2.1106 | 14.0 | 196 | 2.0419 | 0.3333 |
| 2.0714 | 15.0 | 210 | 2.0350 | 0.3333 |
| 2.0256 | 16.0 | 224 | 2.0333 | 0.3333 |
| 2.1226 | 17.0 | 238 | 2.0286 | 0.3333 |
| 2.0451 | 18.0 | 252 | 2.0195 | 0.3333 |
| 2.0822 | 19.0 | 266 | 1.9968 | 0.3333 |
| 2.0991 | 20.0 | 280 | 1.9883 | 0.3333 |
| 2.0537 | 21.0 | 294 | 1.9767 | 0.3333 |
| 1.973 | 22.0 | 308 | 1.9524 | 0.3333 |
| 2.0429 | 23.0 | 322 | 1.9432 | 0.3333 |
| 2.0091 | 24.0 | 336 | 1.9402 | 0.3333 |
| 2.0309 | 25.0 | 350 | 1.9295 | 0.3333 |
| 2.0261 | 26.0 | 364 | 1.9167 | 0.3333 |
| 2.0081 | 27.0 | 378 | 1.9083 | 0.3333 |
| 2.023 | 28.0 | 392 | 1.9013 | 0.3333 |
| 2.0 | 29.0 | 406 | 1.8623 | 0.375 |
| 1.936 | 30.0 | 420 | 1.8483 | 0.3958 |
| 1.9809 | 31.0 | 434 | 1.8344 | 0.3958 |
| 1.9645 | 32.0 | 448 | 1.8428 | 0.4167 |
| 1.9788 | 33.0 | 462 | 1.8372 | 0.3958 |
| 1.9484 | 34.0 | 476 | 1.8246 | 0.3958 |
| 1.9553 | 35.0 | 490 | 1.7941 | 0.4167 |
| 1.9321 | 36.0 | 504 | 1.7824 | 0.4167 |
| 1.9759 | 37.0 | 518 | 1.7884 | 0.3958 |
| 1.9424 | 38.0 | 532 | 1.7875 | 0.3958 |
| 1.9592 | 39.0 | 546 | 1.7901 | 0.3958 |
| 1.9425 | 40.0 | 560 | 1.7812 | 0.3958 |
| 1.8899 | 41.0 | 574 | 1.7736 | 0.3958 |
| 1.9361 | 42.0 | 588 | 1.7711 | 0.4167 |
| 1.9023 | 43.0 | 602 | 1.7711 | 0.4167 |
| 1.9203 | 44.0 | 616 | 1.7688 | 0.4167 |
| 1.8921 | 45.0 | 630 | 1.7682 | 0.4167 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Cameron/BERT-jigsaw-severetoxic | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
],
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}
} | 30 | null | ---
license: bsd-3-clause
---
Copyright 2018-2022, UT-Battelle
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
Cameron/BERT-mdgender-convai-binary | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
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}
}
} | 33 | null | Access to model Shanty/Testfdg is restricted and you are not in the authorized list. Visit https://huggingface.co/Shanty/Testfdg to ask for access. |
Cameron/BERT-mdgender-convai-ternary | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
],
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} | 38 | null | ---
license: bsd-3-clause
---
Copyright 2018-2022, UT-Battelle
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
Cameron/BERT-mdgender-wizard | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
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} | 30 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: my_awesome_billsum_model
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: 0.176
---
<!-- 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_billsum_model
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: 2.4290
- Rouge1: 0.176
- Rouge2: 0.0773
- Rougel: 0.1454
- Rougelsum: 0.1455
- 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 62 | 2.5195 | 0.1478 | 0.0528 | 0.1197 | 0.1194 | 19.0 |
| No log | 2.0 | 124 | 2.4660 | 0.1572 | 0.06 | 0.1288 | 0.1287 | 19.0 |
| No log | 3.0 | 186 | 2.4366 | 0.1691 | 0.0719 | 0.1394 | 0.1396 | 19.0 |
| No log | 4.0 | 248 | 2.4290 | 0.176 | 0.0773 | 0.1454 | 0.1455 | 19.0 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.1
|
Canadiancaleb/DialoGPT-small-jesse | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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}
} | 9 | null | ---
license: bsd-3-clause
---
Copyright 2018-2022, UT-Battelle
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
Canadiancaleb/DialoGPT-small-walter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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"max_length": 1000
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} | 13 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: BC4CHEMD-Original-128-PubMedBERT-Trial-latest-general
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. -->
# BC4CHEMD-Original-128-PubMedBERT-Trial-latest-general
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0044
- Precision: 0.9678
- Recall: 0.9892
- F1: 0.9784
## 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: 32
- eval_batch_size: 8
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.10.3
|
Canadiancaleb/jessebot | []
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} | 0 | null | ---
license: mit
---
### Nard Style on Stable Diffusion
This is the `<nard>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
CapitainData/wav2vec2-large-xlsr-turkish-demo-colab | []
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}
} | 0 | null | ---
license: bsd-3-clause
---
Copyright 2018-2022, UT-Battelle
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
Capreolus/bert-base-msmarco | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"arxiv:2008.09093",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
],
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}
}
} | 238 | null | Access to model TTian/bert-finetuned-feedback is restricted and you are not in the authorized list. Visit https://huggingface.co/TTian/bert-finetuned-feedback to ask for access. |
Capreolus/birch-bert-large-car_mb | [
"pytorch",
"tf",
"jax",
"bert",
"next-sentence-prediction",
"transformers"
]
| null | {
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"BertForNextSentencePrediction"
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}
} | 4 | null | ---
license: bsd-3-clause
---
Copyright 2018-2022, UT-Battelle
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
Capreolus/birch-bert-large-mb | [
"pytorch",
"tf",
"jax",
"bert",
"next-sentence-prediction",
"transformers"
]
| null | {
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} | 1 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.928
- name: F1
type: f1
value: 0.9280714609088352
---
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2185
- Accuracy: 0.928
- F1: 0.9281
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8374 | 1.0 | 250 | 0.3188 | 0.9045 | 0.9012 |
| 0.254 | 2.0 | 500 | 0.2185 | 0.928 | 0.9281 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cpu
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Capreolus/electra-base-msmarco | [
"pytorch",
"tf",
"electra",
"text-classification",
"arxiv:2008.09093",
"transformers"
]
| text-classification | {
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"ElectraForSequenceClassification"
],
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}
} | 110 | null | ```py
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-lms-pipe")
```
|
Captain-1337/CrudeBERT | [
"pytorch",
"bert",
"text-classification",
"arxiv:1908.10063",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
],
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}
} | 28 | null | Prompt is studio_ghibli_anime_style style
I know people will ignore this, but please don't use this to make NFTs. |
Captain272/lstm | []
| null | {
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 272.92 +/- 19.82
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Carlork314/Carlos | []
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} | 0 | null | Access to model alkzar90/skynet is restricted and you are not in the authorized list. Visit https://huggingface.co/alkzar90/skynet to ask for access. |
CarlosPR/mt5-spanish-memmories-analysis | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
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} | 7 | null | ---
tags:
- autotrain
- translation
language:
- en
- nl
datasets:
- Tritkoman/autotrain-data-kkakkakqa
co2_eq_emissions:
emissions: 96.54051975402358
---
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 1726160287
- CO2 Emissions (in grams): 96.5405
## Validation Metrics
- Loss: 0.151
- SacreBLEU: 51.859
- Gen len: 14.625 |
Cdial/hausa-asr | [
"wav2vec2",
"automatic-speech-recognition",
"ha",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
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}
} | 8 | null | Autoregressive prompt augmenter for https://medium.com/@enryu9000/anifusion-diffusion-models-for-anime-pictures-138cf1af2cbe.
|
Cedille/fr-boris | [
"pytorch",
"gptj",
"text-generation",
"fr",
"dataset:c4",
"arxiv:2202.03371",
"transformers",
"causal-lm",
"license:mit",
"has_space"
]
| text-generation | {
"architectures": [
"GPTJForCausalLM"
],
"model_type": "gptj",
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}
} | 401 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-news
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-news
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 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: 1
### Framework versions
- Transformers 4.22.0
- Pytorch 1.12.0
- Datasets 2.5.0
- Tokenizers 0.12.1
|
dccuchile/albert-large-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
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}
} | 27 | null | ---
license: apache-2.0
language: en
datasets:
- wikipedia
- bookcorpus
model-index:
- name: asi/albert-act-base
results:
- task:
type: text-classification
name: CoLA
dataset:
type: glue
name: CoLA # General Language Understanding Evaluation benchmark (GLUE)
split: cola
metrics:
- type: matthews_correlation
value: 36.7
name: Matthew's Corr
- task:
type: text-classification
name: SST-2
dataset:
type: glue
name: SST-2 # The Stanford Sentiment Treebank
split: sst2
metrics:
- type: accuracy
value: 87.8
name: Accuracy
- task:
type: text-classification
name: MRPC
dataset:
type: glue
name: MRPC # Microsoft Research Paraphrase Corpus
split: mrpc
metrics:
- type: accuracy
value: 81.4
name: Accuracy
- type: f1
value: 86.5
name: F1
- task:
type: text-similarity
name: STS-B
dataset:
type: glue
name: STS-B # Semantic Textual Similarity Benchmark
split: stsb
metrics:
- type: spearmanr
value: 83.0
name: Spearman Corr
- type: pearsonr
value: 84.2
name: Pearson Corr
- task:
type: text-classification
name: QQP
dataset:
type: glue
name: QQP # Quora Question Pairs
split: qqp
metrics:
- type: f1
value: 68.5
name: F1
- type: accuracy
value: 87.7
name: Accuracy
- task:
type: text-classification
name: MNLI-m
dataset:
type: glue
name: MNLI-m # MultiNLI Matched
split: mnli_matched
metrics:
- type: accuracy
value: 79.9
name: Accuracy
- task:
type: text-classification
name: MNLI-mm
dataset:
type: glue
name: MNLI-mm # MultiNLI Matched
split: mnli_mismatched
metrics:
- type: accuracy
value: 79.2
name: Accuracy
- task:
type: text-classification
name: QNLI
dataset:
type: glue
name: QNLI # Question NLI
split: qnli
metrics:
- type: accuracy
value: 89.0
name: Accuracy
- task:
type: text-classification
name: RTE
dataset:
type: glue
name: RTE # Recognizing Textual Entailment
split: rte
metrics:
- type: accuracy
value: 63.0
name: Accuracy
- task:
type: text-classification
name: WNLI
dataset:
type: glue
name: WNLI # Winograd NLI
split: wnli
metrics:
- type: accuracy
value: 65.1
name: Accuracy
---
# Adaptive Depth Transformers
Implementation of the paper "How Many Layers and Why? An Analysis of the Model Depth in Transformers". In this study, we investigate the role of the multiple layers in deep transformer models. We design a variant of ALBERT that dynamically adapts the number of layers for each token of the input.
## Model architecture
We augment a multi-layer transformer encoder with a halting mechanism, which dynamically adjusts the number of layers for each token.
We directly adapted this mechanism from Graves ([2016](#graves-2016)). At each iteration, we compute a probability for each token to stop updating its state.
## Model use
The architecture is not yet directly included in the Transformers library. The code used for pre-training is available in the following [github repository](https://github.com/AntoineSimoulin/adaptive-depth-transformers). So you should install the code implementation first:
```bash
!pip install git+https://github.com/AntoineSimoulin/adaptive-depth-transformers$
```
Then you can use the model directly.
```python
from act import AlbertActConfig, AlbertActModel, TFAlbertActModel
from transformers import AlbertTokenizer
tokenizer = AlbertTokenizer.from_pretrained('asi/albert-act-base')
model = AlbertActModel.from_pretrained('asi/albert-act-base')
_ = model.eval()
inputs = tokenizer("a lump in the middle of the monkeys stirred and then fell quiet .", return_tensors="pt")
outputs = model(**inputs)
outputs.updates
# tensor([[[[15., 9., 10., 7., 3., 8., 5., 7., 12., 10., 6., 8., 8., 9., 5., 8.]]]])
```
## Citations
### BibTeX entry and citation info
If you use our iterative transformer model for your scientific publication or your industrial applications, please cite the following [paper](https://aclanthology.org/2021.acl-srw.23/):
```bibtex
@inproceedings{simoulin-crabbe-2021-many,
title = "How Many Layers and Why? {A}n Analysis of the Model Depth in Transformers",
author = "Simoulin, Antoine and
Crabb{\'e}, Benoit",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-srw.23",
doi = "10.18653/v1/2021.acl-srw.23",
pages = "221--228",
}
```
### References
><div id="graves-2016">Alex Graves. 2016. <a href="https://arxiv.org/abs/1603.08983" target="_blank">Adaptive computation time for recurrent neural networks.</a> CoRR, abs/1603.08983.</div>
|
dccuchile/albert-large-spanish-finetuned-qa-mlqa | [
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"AlbertForQuestionAnswering"
],
"model_type": "albert",
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}
} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-large-960h-intent-classification-ori
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-large-960h-intent-classification-ori
This model is a fine-tuned version of [facebook/wav2vec2-large-960h](https://huggingface.co/facebook/wav2vec2-large-960h) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6013
- Accuracy: 0.7708
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 45
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1937 | 1.0 | 14 | 2.1715 | 0.3333 |
| 2.1764 | 2.0 | 28 | 2.1317 | 0.3333 |
| 2.1358 | 3.0 | 42 | 2.0794 | 0.3333 |
| 2.1227 | 4.0 | 56 | 2.0367 | 0.3333 |
| 2.1157 | 5.0 | 70 | 1.9845 | 0.3333 |
| 2.0776 | 6.0 | 84 | 1.9369 | 0.3333 |
| 2.0126 | 7.0 | 98 | 1.8021 | 0.375 |
| 1.88 | 8.0 | 112 | 1.6825 | 0.4167 |
| 1.8818 | 9.0 | 126 | 1.4638 | 0.5 |
| 1.7044 | 10.0 | 140 | 1.4188 | 0.5208 |
| 1.5442 | 11.0 | 154 | 1.3421 | 0.5625 |
| 1.5045 | 12.0 | 168 | 1.3971 | 0.5 |
| 1.3369 | 13.0 | 182 | 1.1602 | 0.5833 |
| 1.4017 | 14.0 | 196 | 1.3510 | 0.5417 |
| 1.2565 | 15.0 | 210 | 1.0978 | 0.5625 |
| 1.1056 | 16.0 | 224 | 1.0847 | 0.5833 |
| 1.2006 | 17.0 | 238 | 1.0262 | 0.625 |
| 0.9235 | 18.0 | 252 | 0.9532 | 0.7083 |
| 0.9528 | 19.0 | 266 | 1.0212 | 0.6042 |
| 0.8195 | 20.0 | 280 | 0.8442 | 0.7083 |
| 0.7518 | 21.0 | 294 | 0.8379 | 0.6875 |
| 0.6017 | 22.0 | 308 | 0.9422 | 0.7292 |
| 0.7697 | 23.0 | 322 | 0.7353 | 0.75 |
| 0.5367 | 24.0 | 336 | 0.8685 | 0.6875 |
| 0.5655 | 25.0 | 350 | 0.7440 | 0.7708 |
| 0.5116 | 26.0 | 364 | 0.7572 | 0.75 |
| 0.4297 | 27.0 | 378 | 0.7518 | 0.75 |
| 0.4928 | 28.0 | 392 | 0.5988 | 0.7917 |
| 0.4424 | 29.0 | 406 | 0.7240 | 0.75 |
| 0.3313 | 30.0 | 420 | 0.6173 | 0.7708 |
| 0.3854 | 31.0 | 434 | 0.7375 | 0.75 |
| 0.4131 | 32.0 | 448 | 0.7026 | 0.7708 |
| 0.2899 | 33.0 | 462 | 0.6516 | 0.7708 |
| 0.3644 | 34.0 | 476 | 0.6201 | 0.7917 |
| 0.2316 | 35.0 | 490 | 0.6111 | 0.7708 |
| 0.2589 | 36.0 | 504 | 0.5518 | 0.7917 |
| 0.3778 | 37.0 | 518 | 0.5512 | 0.7708 |
| 0.2426 | 38.0 | 532 | 0.5779 | 0.7917 |
| 0.304 | 39.0 | 546 | 0.7771 | 0.75 |
| 0.1833 | 40.0 | 560 | 0.5839 | 0.7708 |
| 0.1649 | 41.0 | 574 | 0.5699 | 0.7708 |
| 0.2529 | 42.0 | 588 | 0.6190 | 0.75 |
| 0.2121 | 43.0 | 602 | 0.5992 | 0.75 |
| 0.2736 | 44.0 | 616 | 0.6011 | 0.7917 |
| 0.2446 | 45.0 | 630 | 0.6013 | 0.7708 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
dccuchile/albert-large-spanish-finetuned-xnli | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
| text-classification | {
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"AlbertForSequenceClassification"
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} | 29 | null | This model is converted with https://github.com/huggingface/diffusers/blob/main/scripts/convert_original_stable_diffusion_to_diffusers.py.
However, the tokenizer in the diffuser model is wrong, for proper usage, see description at https://medium.com/@enryu9000/anifusion-diffusion-models-for-anime-pictures-138cf1af2cbe, and instructions/examples at https://github.com/enryu43/anifusion-stable-diffusion.
Also, the original checkpoint in the Latent Diffusion format is available.
Installation instructions for webui: https://gist.github.com/enryu43/858999bf69dc92b97fdad6137c3c45e6
|
dccuchile/albert-tiny-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
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},
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}
} | 32 | null | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
inference: true
---
# John Diffusion Model Card
John Diffusion (Based on Stable Diffusion) is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
For more information about how John Diffusion functions, please have a look at [🤗's Stable Diffusion with 🧨Diffusers blog](https://huggingface.co/blog/stable_diffusion).
The **John-Diffusion** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2)
checkpoint and subsequently fine-tuned on 225k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
It was then trained with Dreambooth on pictures of our savior, Little King John.
## Model Details
- **Finetuned by** Ethan Porcaro
- **Developed by:** Robin Rombach, Patrick Esser
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based.
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487).
- **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752).
- **Cite as:**
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
# Uses
## Direct Use
The model is intended for research purposes only. Possible research areas and
tasks include
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_.
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
- Faces and people in general may not be generated properly.
- The model was trained mainly with English captions and will not work as well in other languages.
- The autoencoding part of the model is lossy
- The model was trained on a large-scale dataset
[LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material
and is not fit for product use without additional safety mechanisms and
considerations.
- No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data.
The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images.
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are primarily limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
### Safety Module
The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers.
This checker works by checking model outputs against known hard-coded NSFW concepts.
The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter.
Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` *after generation* of the images.
The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept.
## Citation
```bibtex
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
```
*This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).* |
dccuchile/albert-tiny-spanish-finetuned-xnli | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
| text-classification | {
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"AlbertForSequenceClassification"
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}
}
} | 31 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-xlsr-53-espeak-cv-ft-intent-classification-ori
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-xlsr-53-espeak-cv-ft-intent-classification-ori
This model is a fine-tuned version of [facebook/wav2vec2-xlsr-53-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-xlsr-53-espeak-cv-ft) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9124
- Accuracy: 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: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 45
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1894 | 1.0 | 14 | 2.1812 | 0.3333 |
| 2.1795 | 2.0 | 28 | 2.1553 | 0.3333 |
| 2.144 | 3.0 | 42 | 2.1066 | 0.3333 |
| 2.1175 | 4.0 | 56 | 2.0283 | 0.3542 |
| 2.0542 | 5.0 | 70 | 1.9253 | 0.3958 |
| 2.0007 | 6.0 | 84 | 1.8468 | 0.4167 |
| 1.8891 | 7.0 | 98 | 1.7655 | 0.4583 |
| 1.8484 | 8.0 | 112 | 1.6695 | 0.4792 |
| 1.8256 | 9.0 | 126 | 1.5920 | 0.5 |
| 1.6832 | 10.0 | 140 | 1.5331 | 0.5 |
| 1.6149 | 11.0 | 154 | 1.4763 | 0.5 |
| 1.5853 | 12.0 | 168 | 1.4453 | 0.5 |
| 1.4357 | 13.0 | 182 | 1.3588 | 0.5 |
| 1.4789 | 14.0 | 196 | 1.3238 | 0.4792 |
| 1.3886 | 15.0 | 210 | 1.2822 | 0.4792 |
| 1.313 | 16.0 | 224 | 1.2609 | 0.5 |
| 1.3559 | 17.0 | 238 | 1.2191 | 0.5208 |
| 1.1937 | 18.0 | 252 | 1.1936 | 0.5 |
| 1.1847 | 19.0 | 266 | 1.1547 | 0.5417 |
| 1.197 | 20.0 | 280 | 1.1390 | 0.5417 |
| 1.1057 | 21.0 | 294 | 1.1310 | 0.5208 |
| 1.0291 | 22.0 | 308 | 1.1086 | 0.5417 |
| 1.0768 | 23.0 | 322 | 1.1075 | 0.5417 |
| 1.0249 | 24.0 | 336 | 1.0654 | 0.5625 |
| 1.0433 | 25.0 | 350 | 1.0390 | 0.5625 |
| 0.9974 | 26.0 | 364 | 1.0086 | 0.6458 |
| 0.9578 | 27.0 | 378 | 0.9939 | 0.625 |
| 0.916 | 28.0 | 392 | 0.9938 | 0.625 |
| 0.9187 | 29.0 | 406 | 0.9843 | 0.625 |
| 0.8759 | 30.0 | 420 | 0.9755 | 0.625 |
| 0.9199 | 31.0 | 434 | 0.9822 | 0.6042 |
| 0.8791 | 32.0 | 448 | 0.9522 | 0.6458 |
| 0.8436 | 33.0 | 462 | 0.9414 | 0.6458 |
| 0.8692 | 34.0 | 476 | 0.9510 | 0.625 |
| 0.8201 | 35.0 | 490 | 0.9208 | 0.6667 |
| 0.8284 | 36.0 | 504 | 0.9398 | 0.6458 |
| 0.8761 | 37.0 | 518 | 0.9438 | 0.6458 |
| 0.7948 | 38.0 | 532 | 0.9253 | 0.6667 |
| 0.8339 | 39.0 | 546 | 0.9250 | 0.6458 |
| 0.8002 | 40.0 | 560 | 0.9145 | 0.6458 |
| 0.7791 | 41.0 | 574 | 0.9062 | 0.6667 |
| 0.7944 | 42.0 | 588 | 0.9077 | 0.6667 |
| 0.7777 | 43.0 | 602 | 0.9069 | 0.6458 |
| 0.7943 | 44.0 | 616 | 0.9118 | 0.625 |
| 0.7573 | 45.0 | 630 | 0.9124 | 0.625 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
dccuchile/albert-xlarge-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
| text-classification | {
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"AlbertForSequenceClassification"
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}
} | 26 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- opus_books
metrics:
- bleu
model-index:
- name: my_awesome_opus_books_model
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus_books
type: opus_books
config: en-fr
split: train
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 5.9186
---
<!-- 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_opus_books_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus_books dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5550
- Bleu: 5.9186
- Gen Len: 17.5809
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| 1.7524 | 1.0 | 6355 | 1.5696 | 5.8485 | 17.5793 |
| 1.7452 | 2.0 | 12710 | 1.5550 | 5.9186 | 17.5809 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.1
|
dccuchile/albert-xlarge-spanish-finetuned-qa-mlqa | [
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"AlbertForQuestionAnswering"
],
"model_type": "albert",
"task_specific_params": {
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},
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},
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},
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},
"translation_en_to_fr": {
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}
}
} | 7 | null | ---
license: mit
---
### 27_from_Mayonnaise_SalesMen on Stable Diffusion via Dreambooth
#### model by crimsonGenocide
This your the Stable Diffusion model fine-tuned the 27_from_Mayonnaise_SalesMen concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **a drawing of 27 from Mayonnaise SalesMen**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Here are the images used for training this concept:
|
dccuchile/albert-xxlarge-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
| text-classification | {
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"AlbertForSequenceClassification"
],
"model_type": "albert",
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}
}
} | 26 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-xls-r-300m-intent-classification-ori
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-xls-r-300m-intent-classification-ori
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3107
- Accuracy: 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: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 45
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1982 | 1.0 | 14 | 2.1951 | 0.0625 |
| 2.2021 | 2.0 | 28 | 2.1847 | 0.1458 |
| 2.1819 | 3.0 | 42 | 2.1661 | 0.3333 |
| 2.1789 | 4.0 | 56 | 2.1413 | 0.3333 |
| 2.164 | 5.0 | 70 | 2.1183 | 0.3333 |
| 2.1484 | 6.0 | 84 | 2.0974 | 0.3333 |
| 2.1199 | 7.0 | 98 | 2.0939 | 0.3333 |
| 2.1343 | 8.0 | 112 | 2.0829 | 0.3333 |
| 2.1397 | 9.0 | 126 | 2.0654 | 0.3333 |
| 2.1045 | 10.0 | 140 | 2.0553 | 0.3333 |
| 2.1083 | 11.0 | 154 | 2.0255 | 0.3333 |
| 2.0914 | 12.0 | 168 | 2.0065 | 0.3333 |
| 2.0434 | 13.0 | 182 | 1.9696 | 0.3333 |
| 2.0687 | 14.0 | 196 | 1.9231 | 0.4167 |
| 2.0237 | 15.0 | 210 | 1.8679 | 0.4167 |
| 1.9562 | 16.0 | 224 | 1.8184 | 0.4167 |
| 2.0361 | 17.0 | 238 | 1.8803 | 0.3958 |
| 1.888 | 18.0 | 252 | 1.7802 | 0.4167 |
| 1.899 | 19.0 | 266 | 1.7662 | 0.4167 |
| 1.8959 | 20.0 | 280 | 1.7076 | 0.4167 |
| 1.8368 | 21.0 | 294 | 1.6566 | 0.4375 |
| 1.7358 | 22.0 | 308 | 1.6283 | 0.5 |
| 1.7877 | 23.0 | 322 | 1.6411 | 0.4583 |
| 1.7311 | 24.0 | 336 | 1.5525 | 0.5208 |
| 1.7079 | 25.0 | 350 | 1.5163 | 0.5 |
| 1.6496 | 26.0 | 364 | 1.5458 | 0.5 |
| 1.6374 | 27.0 | 378 | 1.5211 | 0.5 |
| 1.6048 | 28.0 | 392 | 1.4533 | 0.5417 |
| 1.5927 | 29.0 | 406 | 1.4319 | 0.5 |
| 1.4987 | 30.0 | 420 | 1.4579 | 0.5208 |
| 1.5745 | 31.0 | 434 | 1.4167 | 0.6042 |
| 1.4632 | 32.0 | 448 | 1.4471 | 0.5417 |
| 1.4686 | 33.0 | 462 | 1.4116 | 0.5625 |
| 1.5368 | 34.0 | 476 | 1.3872 | 0.6042 |
| 1.4327 | 35.0 | 490 | 1.3491 | 0.5833 |
| 1.3978 | 36.0 | 504 | 1.3325 | 0.5833 |
| 1.4509 | 37.0 | 518 | 1.3236 | 0.6042 |
| 1.3881 | 38.0 | 532 | 1.3426 | 0.5833 |
| 1.39 | 39.0 | 546 | 1.3137 | 0.6042 |
| 1.4153 | 40.0 | 560 | 1.3123 | 0.625 |
| 1.3635 | 41.0 | 574 | 1.3224 | 0.6042 |
| 1.403 | 42.0 | 588 | 1.3111 | 0.6042 |
| 1.3763 | 43.0 | 602 | 1.3197 | 0.5833 |
| 1.3539 | 44.0 | 616 | 1.3077 | 0.6042 |
| 1.306 | 45.0 | 630 | 1.3107 | 0.625 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
dccuchile/albert-xxlarge-spanish-finetuned-pawsx | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
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} | 26 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# copenlu/spiced
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('copenlu/spiced')
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=copenlu/spiced)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 591 with parameters:
```
{'batch_size': 8, '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": 591,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 1e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(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})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
dccuchile/bert-base-spanish-wwm-cased-finetuned-ner | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
}
} | 81 | null | Pre-trained evaluator in EMNLP 2022 paper
*[Towards a Unified Multi-Dimensional Evaluator for Text Generation](https://arxiv.org/abs/2210.07197)*
## Introduction
**Multi-dimensional evaluation** is the dominant paradigm for human evaluation in Natural Language Generation (NLG), i.e., evaluating the generated text from multiple explainable dimensions, such as coherence and fluency.
However, automatic evaluation in NLG is still dominated by similarity-based metrics (e.g., ROUGE, BLEU), but they are not sufficient to portray the difference between the advanced generation models.
Therefore, we propose **UniEval** to bridge this gap so that a more comprehensive and fine-grained evaluation of NLG systems can be achieved.
## Pre-trained Evaluator
**unieval-fact** is the pre-trained evaluator for the factual consistency detection task. It can evaluate the model output and predict a consistency score.
## Usage
Please refer to [our GitHub repository](https://github.com/maszhongming/UniEval). |
dccuchile/bert-base-spanish-wwm-cased-finetuned-pawsx | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"prefix": null
}
}
} | 25 | null | ---
language: fr
tags:
- Early Modern French
- Historical
license: apache-2.0
datasets:
- freemmax
---
<a href="https://portizs.eu/publication/2022/lrec/dalembert/">
<img width="300px" src="https://portizs.eu/publication/2022/lrec/dalembert/featured_hu18bf34d40cdc71c744bdd15e48ff0b23_61788_720x2500_fit_q100_h2_lanczos_3.webp">
</a>
# D'AlemBERT base model
This model is a [RoBERTa base model](https://huggingface.co/roberta-base) pre-trained on the [FreEMmax corpus](https://doi.org/10.5281/zenodo.6481135) for Early Modern French. It was
introduced in [this paper](https://aclanthology.org/2022.lrec-1.359/). This model is Cased and was trained with a mix of normalized and unnormalized data.
## Model description
D'AlemBERT is a transformers mode pretrained on the raw texts only with no humans labelling them in any way with an automatic process to generate inputs and labels from those texts using the RoBERTa base model. More precisely, it was pretrained
with one objective:
- Masked language modeling (MLM): this is part of the original training loss of the BERT base model. When taking a
sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the
model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that
usually see the words one after the other, or from autoregressive models like GPT which internally mask the future
tokens. It allows the model to learn a bidirectional representation of the sentence.
## Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT.
The model is primarily intended for use in Digital Humanities and Historical NLP.
### Limitations and bias
This model is trained with historical French data from starting from the 16th c., so it might produce results that seem extremely biased by today standards. It might not work well on contemporary data and it is not intended to be used on it.
This bias will also affect all fine-tuned versions of this model.
## Training data
D'AlemBERT was pretrained on the non-freely available version of the [FreEMmax corpus](https://doi.org/10.5281/zenodo.6481135), a dataset
consisting of more than 180k tokens coming from 22 different sources, and comprising French textual data going from the 16th c to the early 20th c.
### BibTeX entry and citation info
```bibtex
@inproceedings{gabay-etal-2022-freem,
title = "From {F}re{EM} to D{'}{A}lem{BERT}: a Large Corpus and a Language Model for Early {M}odern {F}rench",
author = "Gabay, Simon and
Ortiz Suarez, Pedro and
Bartz, Alexandre and
Chagu{\'e}, Alix and
Bawden, Rachel and
Gambette, Philippe and
Sagot, Beno{\^\i}t",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.359",
pages = "3367--3374",
abstract = "anguage models for historical states of language are becoming increasingly important to allow the optimal digitisation and analysis of old textual sources. Because these historical states are at the same time more complex to process and more scarce in the corpora available, this paper presents recent efforts to overcome this difficult situation. These efforts include producing a corpus, creating the model, and evaluating it with an NLP task currently used by scholars in other ongoing projects.",
}
``` |
dccuchile/bert-base-spanish-wwm-uncased-finetuned-ner | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 5 | null | ---
license: mit
---
### Kirby on Stable Diffusion
This is the `<kirby>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
dccuchile/bert-base-spanish-wwm-uncased-finetuned-pawsx | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
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}
}
} | 24 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: train
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8648740833380706
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1365
- F1: 0.8649
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2553 | 1.0 | 525 | 0.1575 | 0.8279 |
| 0.1284 | 2.0 | 1050 | 0.1386 | 0.8463 |
| 0.0813 | 3.0 | 1575 | 0.1365 | 0.8649 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
dccuchile/distilbert-base-spanish-uncased-finetuned-mldoc | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
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"prefix": null
}
}
} | 27 | null | --- "This is just a test; I'm new to hugging face." |
dccuchile/distilbert-base-spanish-uncased-finetuned-ner | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 28 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v0-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="farzeen/q-FrozenLake-v0-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
dccuchile/distilbert-base-spanish-uncased-finetuned-pawsx | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 29 | null | ---
language:
- "la"
tags:
- "latin"
- "token-classification"
- "pos"
- "dependency-parsing"
datasets:
- "universal_dependencies"
license: "cc-by-sa-4.0"
pipeline_tag: "token-classification"
widget:
- text: "deus videt te non sentientem"
---
# roberta-base-latin-ud-goeswith
## Model Description
This is a RoBERTa model pre-trained on CC-100 Latin texts for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [roberta-base-latin-v2](https://huggingface.co/ClassCat/roberta-base-latin-v2).
## How to Use
```py
class UDgoeswith(object):
def __init__(self,bert):
from transformers import AutoTokenizer,AutoModelForTokenClassification
self.tokenizer=AutoTokenizer.from_pretrained(bert)
self.model=AutoModelForTokenClassification.from_pretrained(bert)
def __call__(self,text):
import numpy,torch,ufal.chu_liu_edmonds
w=self.tokenizer(text,return_offsets_mapping=True)
v=w["input_ids"]
x=[v[0:i]+[self.tokenizer.mask_token_id]+v[i+1:]+[j] for i,j in enumerate(v[1:-1],1)]
with torch.no_grad():
e=self.model(input_ids=torch.tensor(x)).logits.numpy()[:,1:-2,:]
r=[1 if i==0 else -1 if j.endswith("|root") else 0 for i,j in sorted(self.model.config.id2label.items())]
e+=numpy.where(numpy.add.outer(numpy.identity(e.shape[0]),r)==0,0,numpy.nan)
g=self.model.config.label2id["X|_|goeswith"]
r=numpy.tri(e.shape[0])
for i in range(e.shape[0]):
for j in range(i+2,e.shape[1]):
r[i,j]=r[i,j-1] if numpy.nanargmax(e[i,j-1])==g else 1
e[:,:,g]+=numpy.where(r==0,0,numpy.nan)
m=numpy.full((e.shape[0]+1,e.shape[1]+1),numpy.nan)
m[1:,1:]=numpy.nanmax(e,axis=2).transpose()
p=numpy.zeros(m.shape)
p[1:,1:]=numpy.nanargmax(e,axis=2).transpose()
for i in range(1,m.shape[0]):
m[i,0],m[i,i],p[i,0]=m[i,i],numpy.nan,p[i,i]
h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
if [0 for i in h if i==0]!=[0]:
m[:,0]+=numpy.where(m[:,0]==numpy.nanmax(m[[i for i,j in enumerate(h) if j==0],0]),0,numpy.nan)
m[[i for i,j in enumerate(h) if j==0]]+=[0 if i==0 or j==0 else numpy.nan for i,j in enumerate(h)]
h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
u="# text = "+text+"\n"
v=[(s,e) for s,e in w["offset_mapping"] if s<e]
for i,(s,e) in enumerate(v,1):
q=self.model.config.id2label[p[i,h[i]]].split("|")
u+="\t".join([str(i),text[s:e],"_",q[0],"_","|".join(q[1:-1]),str(h[i]),q[-1],"_","_" if i<len(v) and e<v[i][0] else "SpaceAfter=No"])+"\n"
return u+"\n"
nlp=UDgoeswith("KoichiYasuoka/roberta-base-latin-ud-goeswith")
print(nlp("deus videt te non sentientem"))
```
with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/).
Or without ufal.chu-liu-edmonds:
```
from transformers import pipeline
nlp=pipeline("universal-dependencies","KoichiYasuoka/roberta-base-latin-ud-goeswith",trust_remote_code=True,aggregation_strategy="simple")
print(nlp("deus videt te non sentientem"))
```
|
dccuchile/distilbert-base-spanish-uncased-finetuned-pos | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
"task_specific_params": {
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"max_length": null
},
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},
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}
}
} | 3 | null | ---
language: zh
widget:
- text: "子墨子曰"
---
# Ancient chinese GPT2 model
## Model description
This model is a GPT2 model trained to generate ancient Chinese text, with `bert-base-chinese` as tokenizer.
## Training data
It's trained on the classic Chinese texts fetched from ctext.org. Current training data is really small scale -- all training text data together was 19Mb.
<details>
<summary> "Reading list" of this model </summary>
* 孟子 "mengzi"
* 论语 "analects"
* 商君书 "shang-jun-shu"
* 礼记 "liji"
* 孙子兵法 "art-of-war"
* 墨子 "mozi"
* 庄子 "zhuangzi"
* 道德经 "dao-de-jing"
* 韩非子 "hanfeizi"
* 史记 "shiji"
* 战国策 "zhan-guo-ce"
* 汉书 "han-shu"
* 后汉书 "hou-han-shu"
* 三国志 "sanguozhi"
* 世说新语 "shi-shuo-xin-yu"
* 颜氏家训 "yan-shi-jia-xun"
* 金瓶梅 "jinpingmei"
* 西游记 "xiyouji"
* 红楼梦 "hongloumeng"
</details>
## How to use
You can use the model directly with a pipeline for text generation:
```python
from transformers import pipeline, GPT2LMHeadModel
model = GPT2LMHeadModel.from_pretrained("binxu/Ziyue-GPT2")
generator = pipeline('text-generation', model=model, tokenizer='bert-base-chinese')
outputs = generator("子墨子曰", max_length=50, num_return_sequences=5, num_beams=10, repetition_penalty=1.5)
[{'generated_text': '子墨子曰 ： 吾 未 得 见 之 时 ， 知 有 失 得 之 时 ， 有 为 之 者 。 氏 ， 圣 王 之 时 ， 万 乘 之 世 ， 圣 人 不 易 之 道 也 。'}]
``` |
dccuchile/distilbert-base-spanish-uncased | [
"pytorch",
"distilbert",
"fill-mask",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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} | 670 | null | Pre-trained evaluator in EMNLP 2022 paper
*[Towards a Unified Multi-Dimensional Evaluator for Text Generation](https://arxiv.org/abs/2210.07197)*
## Introduction
**Multi-dimensional evaluation** is the dominant paradigm for human evaluation in Natural Language Generation (NLG), i.e., evaluating the generated text from multiple explainable dimensions, such as coherence and fluency.
However, automatic evaluation in NLG is still dominated by similarity-based metrics (e.g., ROUGE, BLEU), but they are not sufficient to portray the difference between the advanced generation models.
Therefore, we propose **UniEval** to bridge this gap so that a more comprehensive and fine-grained evaluation of NLG systems can be achieved.
## Pre-trained Evaluator
**unieval-intermediate** is a pre-trained Boolean Answer Generator after performing intermediate multi-task learning. On top of this checkpoint, you can also train a custom evaluator for a specific NLG task.
## Usage
Please refer to [our GitHub repository](https://github.com/maszhongming/UniEval). |
CennetOguz/distilbert-base-uncased-finetuned-recipe-1 | [
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
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} | 7 | null | ---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.24 +/- 0.43
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Michael02/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
CennetOguz/distilbert-base-uncased-finetuned-recipe-accelerate-1 | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
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} | 1 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Michael02/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
CennetOguz/distilbert-base-uncased-finetuned-recipe-accelerate | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
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"max_length": null
},
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},
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"max_length": null
},
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},
"translation_en_to_fr": {
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},
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}
}
} | 7 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: trainer
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. -->
# trainer
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0715
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.1
|
Certified-Zoomer/DialoGPT-small-rick | []
| null | {
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}
} | 0 | null | ---
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: mBART_translator_json_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. -->
# mBART_translator_json_2
This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1203
- Bleu: 77.8658
- Gen Len: 36.1527
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 1.7858 | 1.0 | 1912 | 0.6568 | 55.2937 | 75.6389 |
| 0.994 | 2.0 | 3824 | 0.4015 | 71.3655 | 35.744 |
| 0.7267 | 3.0 | 5736 | 0.2971 | 66.7522 | 34.5473 |
| 0.5916 | 4.0 | 7648 | 0.2437 | 80.0233 | 37.4331 |
| 0.502 | 5.0 | 9560 | 0.2072 | 80.9632 | 36.9833 |
| 0.433 | 6.0 | 11472 | 0.1767 | 69.9384 | 36.6381 |
| 0.3581 | 7.0 | 13384 | 0.1566 | 64.615 | 34.8954 |
| 0.3244 | 8.0 | 15296 | 0.1382 | 77.5563 | 36.1736 |
| 0.2815 | 9.0 | 17208 | 0.1259 | 76.1662 | 36.1548 |
| 0.2555 | 10.0 | 19120 | 0.1203 | 77.8658 | 36.1527 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.1
|
Chaddmckay/Cdm | []
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} | 0 | null | ---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-segments-sidewalk-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. -->
# segformer-b0-finetuned-segments-sidewalk-2
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9042
- Mean Iou: 0.1600
- Mean Accuracy: 0.1997
- Overall Accuracy: 0.7338
- Per Category Iou: [nan, 0.27359520957005035, 0.6563592089876799, 0.0, 0.23344374046535918, 0.0, nan, 0.0, 0.0, 0.0, 0.5539341917024321, nan, nan, nan, nan, 0.0, 0.0, nan, 0.6213519498256361, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.8012808797206368, 0.0, 0.8609473035107046, nan, 0.0, 0.0, 0.0]
- Per Category Accuracy: [nan, 0.38598740280061317, 0.9344800917343116, 0.0, 0.23402267811135147, 0.0, nan, 0.0, 0.0, 0.0, 0.6574569071869553, nan, nan, nan, nan, 0.0, 0.0, nan, 0.889953470705536, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.9339123774958169, 0.0, 0.9562267789312698, nan, 0.0, 0.0, 0.0]
## 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: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- 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 | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 2.8419 | 0.42 | 20 | 3.2243 | 0.1239 | 0.1973 | 0.6992 | [0.0, 0.221283072298205, 0.6482498250140304, 0.0, 0.36607695456244177, 0.013827775204570018, nan, 1.0254201659129828e-05, 0.0, 0.0, 0.5416500682753081, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.5339731316050166, 0.0, 0.0006440571922786744, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7498440701547007, 0.0, 0.7659222854515146, 0.0, 0.0, 0.0, 0.0] | [nan, 0.3346613609105567, 0.8582083544770268, 0.0, 0.5101472837243907, 0.015482685970504024, nan, 1.0366454154356502e-05, 0.0, 0.0, 0.6745826026281508, nan, nan, nan, nan, 0.0, 0.0, nan, 0.8093545247364923, 0.0, 0.0006458279514337381, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.9324806212895075, 0.0, 0.797418357423677, nan, 0.0, 0.0, 0.0] |
| 2.3662 | 0.83 | 40 | 2.5147 | 0.1402 | 0.1798 | 0.6989 | [nan, 0.19549119549985344, 0.6036027201962391, 0.0, 0.0019222772099991463, 0.000300503343099692, nan, 0.0, 0.0, 0.0, 0.47853978429259575, nan, nan, nan, nan, 0.0, 0.0, nan, 0.5820555774612892, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.7898452112422248, 0.0, 0.8521568687502872, nan, 0.0, 0.0, 0.0] | [nan, 0.25107981668136076, 0.9396577375184628, 0.0, 0.0019233683746435017, 0.0003025228242666523, nan, 0.0, 0.0, 0.0, 0.5513810659584686, nan, nan, nan, nan, 0.0, 0.0, nan, 0.8953553793561865, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.9300976130892274, 0.0, 0.9250758451014455, nan, 0.0, 0.0, 0.0] |
| 2.1745 | 1.25 | 60 | 2.0428 | 0.1485 | 0.1882 | 0.7162 | [nan, 0.24240648716131, 0.6262941164542789, 0.0, 0.04440846090507781, 0.0, nan, 0.0, 0.0, 0.0, 0.522913696330921, nan, nan, nan, nan, 0.0, 0.0, nan, 0.6194890050543631, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.7947837731119848, 0.0, 0.8609570537373858, nan, 0.0, 0.0, 0.0] | [nan, 0.3318909301752965, 0.9392945927202885, 0.0, 0.04443587164684973, 0.0, nan, 0.0, 0.0, 0.0, 0.6149676720993105, nan, nan, nan, nan, 0.0, 0.0, nan, 0.8836542113759377, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.9409947331534898, 0.0, 0.9509521157666382, nan, 0.0, 0.0, 0.0] |
| 1.986 | 1.67 | 80 | 1.9042 | 0.1600 | 0.1997 | 0.7338 | [nan, 0.27359520957005035, 0.6563592089876799, 0.0, 0.23344374046535918, 0.0, nan, 0.0, 0.0, 0.0, 0.5539341917024321, nan, nan, nan, nan, 0.0, 0.0, nan, 0.6213519498256361, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.8012808797206368, 0.0, 0.8609473035107046, nan, 0.0, 0.0, 0.0] | [nan, 0.38598740280061317, 0.9344800917343116, 0.0, 0.23402267811135147, 0.0, nan, 0.0, 0.0, 0.0, 0.6574569071869553, nan, nan, nan, nan, 0.0, 0.0, nan, 0.889953470705536, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.9339123774958169, 0.0, 0.9562267789312698, nan, 0.0, 0.0, 0.0] |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.1
|
Chuah/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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} | 9 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-large-960h-lv60-self-intent-classification-ori
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-large-960h-lv60-self-intent-classification-ori
This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1985
- Accuracy: 0.5417
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 45
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2033 | 1.0 | 14 | 2.2126 | 0.0833 |
| 2.2006 | 2.0 | 28 | 2.2026 | 0.0833 |
| 2.1786 | 3.0 | 42 | 2.1758 | 0.3333 |
| 2.1712 | 4.0 | 56 | 2.1436 | 0.3333 |
| 2.1495 | 5.0 | 70 | 2.1120 | 0.3333 |
| 2.1326 | 6.0 | 84 | 2.0909 | 0.3333 |
| 2.1039 | 7.0 | 98 | 2.0966 | 0.3333 |
| 2.0931 | 8.0 | 112 | 2.0355 | 0.3333 |
| 2.1144 | 9.0 | 126 | 2.0082 | 0.3333 |
| 2.0258 | 10.0 | 140 | 1.9901 | 0.375 |
| 2.0028 | 11.0 | 154 | 1.9429 | 0.3958 |
| 1.9737 | 12.0 | 168 | 1.9538 | 0.3958 |
| 1.9023 | 13.0 | 182 | 1.8824 | 0.375 |
| 1.9226 | 14.0 | 196 | 1.8607 | 0.3958 |
| 1.8521 | 15.0 | 210 | 1.8065 | 0.3958 |
| 1.7752 | 16.0 | 224 | 1.8153 | 0.4167 |
| 1.8391 | 17.0 | 238 | 1.7470 | 0.4375 |
| 1.7041 | 18.0 | 252 | 1.7419 | 0.4167 |
| 1.7075 | 19.0 | 266 | 1.6644 | 0.4375 |
| 1.6845 | 20.0 | 280 | 1.6340 | 0.4375 |
| 1.6275 | 21.0 | 294 | 1.6271 | 0.4167 |
| 1.4586 | 22.0 | 308 | 1.5640 | 0.4375 |
| 1.4987 | 23.0 | 322 | 1.5279 | 0.4583 |
| 1.5513 | 24.0 | 336 | 1.4873 | 0.4792 |
| 1.4828 | 25.0 | 350 | 1.4887 | 0.4583 |
| 1.4711 | 26.0 | 364 | 1.4613 | 0.4583 |
| 1.371 | 27.0 | 378 | 1.4062 | 0.4792 |
| 1.3789 | 28.0 | 392 | 1.4038 | 0.4792 |
| 1.3579 | 29.0 | 406 | 1.4031 | 0.4792 |
| 1.2771 | 30.0 | 420 | 1.3637 | 0.5 |
| 1.3417 | 31.0 | 434 | 1.3655 | 0.5 |
| 1.231 | 32.0 | 448 | 1.3698 | 0.5 |
| 1.2367 | 33.0 | 462 | 1.3394 | 0.5 |
| 1.2933 | 34.0 | 476 | 1.3448 | 0.4792 |
| 1.1631 | 35.0 | 490 | 1.2867 | 0.5417 |
| 1.165 | 36.0 | 504 | 1.2624 | 0.5417 |
| 1.2431 | 37.0 | 518 | 1.2252 | 0.5625 |
| 1.1731 | 38.0 | 532 | 1.2082 | 0.5625 |
| 1.1734 | 39.0 | 546 | 1.2062 | 0.5417 |
| 1.1631 | 40.0 | 560 | 1.2034 | 0.5417 |
| 1.0963 | 41.0 | 574 | 1.1973 | 0.5417 |
| 1.2157 | 42.0 | 588 | 1.1988 | 0.5625 |
| 1.1467 | 43.0 | 602 | 1.2018 | 0.5417 |
| 1.1503 | 44.0 | 616 | 1.1986 | 0.5417 |
| 1.0945 | 45.0 | 630 | 1.1985 | 0.5417 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Chungu424/DATA | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- filter_sort
metrics:
- f1
- accuracy
model-index:
- name: favs-filtersort-multilabel-classification-bert-base-cased
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: filter_sort
type: filter_sort
config: default
split: train
args: default
metrics:
- name: F1
type: f1
value: 0.7428571428571428
- name: Accuracy
type: accuracy
value: 0.2
---
<!-- 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. -->
# favs-filtersort-multilabel-classification-bert-base-cased
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the filter_sort dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3066
- F1: 0.7429
- Roc Auc: 0.8142
- Accuracy: 0.2
## 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:
- learning_rate: 1.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.7601 | 1.0 | 12 | 0.6966 | 0.2564 | 0.4518 | 0.0 |
| 0.6757 | 2.0 | 24 | 0.5629 | 0.6667 | 0.7785 | 0.0 |
| 0.5796 | 3.0 | 36 | 0.4652 | 0.6286 | 0.7477 | 0.0 |
| 0.5026 | 4.0 | 48 | 0.4161 | 0.6479 | 0.7605 | 0.0 |
| 0.4282 | 5.0 | 60 | 0.3830 | 0.6849 | 0.7862 | 0.0 |
| 0.4085 | 6.0 | 72 | 0.3658 | 0.7273 | 0.7962 | 0.0 |
| 0.3847 | 7.0 | 84 | 0.3538 | 0.7353 | 0.8052 | 0.0 |
| 0.3829 | 8.0 | 96 | 0.3457 | 0.6761 | 0.7772 | 0.0 |
| 0.3758 | 9.0 | 108 | 0.3409 | 0.6857 | 0.7810 | 0.0 |
| 0.3487 | 10.0 | 120 | 0.3327 | 0.7143 | 0.7976 | 0.0 |
| 0.3421 | 11.0 | 132 | 0.3268 | 0.6866 | 0.7758 | 0.0 |
| 0.3351 | 12.0 | 144 | 0.3183 | 0.7059 | 0.7886 | 0.0 |
| 0.3245 | 13.0 | 156 | 0.3149 | 0.7246 | 0.8014 | 0.0 |
| 0.3191 | 14.0 | 168 | 0.3087 | 0.7246 | 0.8014 | 0.1 |
| 0.3083 | 15.0 | 180 | 0.3066 | 0.7429 | 0.8142 | 0.2 |
| 0.3061 | 16.0 | 192 | 0.3062 | 0.7429 | 0.8142 | 0.2 |
| 0.2935 | 17.0 | 204 | 0.3017 | 0.7429 | 0.8142 | 0.2 |
| 0.2888 | 18.0 | 216 | 0.3009 | 0.7429 | 0.8142 | 0.2 |
| 0.297 | 19.0 | 228 | 0.3022 | 0.7429 | 0.8142 | 0.2 |
| 0.2868 | 20.0 | 240 | 0.3014 | 0.7429 | 0.8142 | 0.2 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Cinnamon/electra-small-japanese-discriminator | [
"pytorch",
"electra",
"pretraining",
"ja",
"transformers",
"license:apache-2.0"
]
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} | 419 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/deepleffen-the_dealersh1p/1665552272191/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1211158441504456704/dCNSnY4k_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1241879678455078914/e2EdZIrr_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">『 』『dan』『 』 & Deep Leffen Bot</div>
<div style="text-align: center; font-size: 14px;">@deepleffen-the_dealersh1p</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from 『 』『dan』『 』 & Deep Leffen Bot.
| Data | 『 』『dan』『 』 | Deep Leffen Bot |
| --- | --- | --- |
| Tweets downloaded | 2673 | 608 |
| Retweets | 1336 | 14 |
| Short tweets | 235 | 27 |
| Tweets kept | 1102 | 567 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2xu780cl/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @deepleffen-the_dealersh1p's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3w2qdw30) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3w2qdw30/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/deepleffen-the_dealersh1p')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
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} | 26 | null | ---
language: zh
datasets:
- DeltaReadingComprehensionDataset
widget:
- text: "中興大學在哪裡?"
context: "國立中興大學（簡稱興大、NCHU），是位於臺中的一所高等教育機構。中興大學以農業科學、農業經濟學、獸醫、生命科學、轉譯醫學、生醫工程、生物科技、綠色科技等研究領域見長 。近年中興大學與臺中榮民總醫院、彰化師範大學、中國醫藥大學等機構合作，聚焦於癌症醫學、免疫醫學及醫學工程三項領域，將實驗室成果逐步應用到臨床上，未來「衛生福利部南投醫院中興院區」將改為「國立中興大學醫學院附設醫院」。興大也與臺中市政府合作，簽訂合作意向書，共同推動數位文化、智慧城市等面相帶動區域發展。"
- text: "我住在哪裡?"
context: "我叫翰承，我住在台中。"
---
# bert-base-chinese for QA
This is the [bert-base-chinese](https://huggingface.co/bert-base-chinese) model, fine-tuned using the DRCD dataset. It's been trained on question-answer pairs for the task of Question Answering.
## Usage
### In Transformers
```python
from transformers import BertTokenizerFast, BertForQuestionAnswering, pipeline
model_name = "NchuNLP/Chinese-Question-Answering"
tokenizer = BertTokenizerFast.from_pretrained(model_name)
model = BertForQuestionAnswering.from_pretrained(model_name)
# a) Get predictions
nlp = pipeline('question-answering', model=model, tokenizer=tokenizer)
QA_input = {
'question': '中興大學在哪裡？',
'context': '國立中興大學（簡稱興大、NCHU），是位於臺中的一所高等教育機構。中興大學以農業科學、農業經濟學、獸醫、生命科學、轉譯醫學、生醫工程、生物科技、綠色科技等研究領域見長 。近年中興大學與臺中榮民總醫院、彰化師範大學、中國醫藥大學等機構合作，聚焦於癌症醫學、免疫醫學及醫學工程三項領域，將實驗室成果逐步應用到臨床上，未來「衛生福利部南投醫院中興院區」將改為「國立中興大學醫學院附設醫院」。興大也與臺中市政府合作，簽訂合作意向書，共同推動數位文化、智慧城市等面相帶動區域發展。'
}
res = nlp(QA_input)
{'score': 1.0, 'start': 21, 'end': 23, 'answer': '臺中'}
# b) Inside the Question answering pipeline
inputs = tokenizer(query, text, return_tensors="pt",padding=True, truncation=True, max_length=512, stride=256)
outputs = model(**inputs)
sequence_ids = inputs.sequence_ids()
# Mask everything apart from the tokens of the context
mask = [i != 1 for i in sequence_ids]
# Unmask the [CLS] token
mask[0] = False
mask = torch.tensor(mask)[None]
start_logits[mask] = -10000
end_logits[mask] = -10000
start_probabilities = torch.nn.functional.softmax(start_logits, dim=-1)[0]
end_probabilities = torch.nn.functional.softmax(end_logits, dim=-1)[0]
scores = start_probabilities[:, None] * end_probabilities[None, :]
max_index = scores.argmax().item()
start_index = max_index // scores.shape[1]
end_index = max_index % scores.shape[1]
inputs_with_offsets = tokenizer(query, text, return_offsets_mapping=True)
offsets = inputs_with_offsets["offset_mapping"]
start_char, _ = offsets[start_index]
_, end_char = offsets[end_index]
answer = text[start_char:end_char]
result = {
"answer": answer,
"start": start_char,
"end": end_char,
"score": scores[start_index, end_index],
}
print(result)
```
## Authors
**Han Cheng Yu:** [email protected]
**Yao-Chung Fan:** [email protected]
## About us
[中興大學自然語言處理實驗室](https://nlpnchu.org/)研究方向圍繞於深度學習技術在文字資料探勘 (Text Mining) 與自然語言處理 (Natural Language Processing) 方面之研究，目前實驗室成員的研究主題著重於機器閱讀理解 (Machine Reading Comprehension) 以及自然語言生成 (Natural Language Generation) 兩面向。
## More Information
<p>For more info about Nchu NLP Lab, visit our <strong><a href="https://demo.nlpnchu.org/">Lab Online Demo</a></strong> repo and <strong><a href="https://github.com/NCHU-NLP-Lab">GitHub</a></strong>. |
CoachCarter/distilbert-base-uncased-finetuned-squad | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-conformer-rel-pos-large-960h-ft-intent-classification-ori
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-conformer-rel-pos-large-960h-ft-intent-classification-ori
This model is a fine-tuned version of [facebook/wav2vec2-conformer-rel-pos-large-960h-ft](https://huggingface.co/facebook/wav2vec2-conformer-rel-pos-large-960h-ft) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2518
- Accuracy: 0.5833
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 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_ratio: 0.1
- num_epochs: 45
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2018 | 1.0 | 28 | 2.1963 | 0.125 |
| 2.1871 | 2.0 | 56 | 2.1715 | 0.3333 |
| 2.1499 | 3.0 | 84 | 2.1349 | 0.3333 |
| 2.1236 | 4.0 | 112 | 2.0749 | 0.3333 |
| 2.0814 | 5.0 | 140 | 2.0232 | 0.3333 |
| 2.0905 | 6.0 | 168 | 1.9028 | 0.375 |
| 1.9167 | 7.0 | 196 | 1.8469 | 0.3958 |
| 1.7048 | 8.0 | 224 | 1.6481 | 0.4583 |
| 1.4723 | 9.0 | 252 | 1.5350 | 0.4583 |
| 1.5265 | 10.0 | 280 | 1.4526 | 0.5 |
| 1.2621 | 11.0 | 308 | 1.4451 | 0.4583 |
| 1.5083 | 12.0 | 336 | 1.3296 | 0.4792 |
| 1.1857 | 13.0 | 364 | 1.2983 | 0.4792 |
| 1.3449 | 14.0 | 392 | 1.3026 | 0.4792 |
| 1.2061 | 15.0 | 420 | 1.3181 | 0.4792 |
| 1.2544 | 16.0 | 448 | 1.2603 | 0.4792 |
| 1.0731 | 17.0 | 476 | 1.2607 | 0.4792 |
| 0.8836 | 18.0 | 504 | 1.2644 | 0.4792 |
| 1.0917 | 19.0 | 532 | 1.2345 | 0.4792 |
| 1.0786 | 20.0 | 560 | 1.2791 | 0.4792 |
| 1.1616 | 21.0 | 588 | 1.2238 | 0.4792 |
| 1.0614 | 22.0 | 616 | 1.2305 | 0.4583 |
| 0.9617 | 23.0 | 644 | 1.2315 | 0.4792 |
| 0.9652 | 24.0 | 672 | 1.2931 | 0.4792 |
| 0.9042 | 25.0 | 700 | 1.1246 | 0.5 |
| 1.0865 | 26.0 | 728 | 1.1490 | 0.4792 |
| 0.9653 | 27.0 | 756 | 1.1713 | 0.5 |
| 0.858 | 28.0 | 784 | 1.1726 | 0.5208 |
| 0.8364 | 29.0 | 812 | 1.2142 | 0.5 |
| 0.6798 | 30.0 | 840 | 1.2163 | 0.5208 |
| 0.9284 | 31.0 | 868 | 1.1398 | 0.4792 |
| 0.7383 | 32.0 | 896 | 1.2418 | 0.5208 |
| 0.651 | 33.0 | 924 | 1.1734 | 0.5 |
| 0.7416 | 34.0 | 952 | 1.2285 | 0.5 |
| 0.6287 | 35.0 | 980 | 1.1467 | 0.5833 |
| 0.6806 | 36.0 | 1008 | 1.1589 | 0.5625 |
| 0.6148 | 37.0 | 1036 | 1.1373 | 0.5833 |
| 0.7174 | 38.0 | 1064 | 1.2118 | 0.5625 |
| 0.6056 | 39.0 | 1092 | 1.2205 | 0.5833 |
| 0.7041 | 40.0 | 1120 | 1.2408 | 0.5833 |
| 0.631 | 41.0 | 1148 | 1.2350 | 0.5833 |
| 0.6028 | 42.0 | 1176 | 1.2787 | 0.5833 |
| 0.5942 | 43.0 | 1204 | 1.2463 | 0.5833 |
| 0.5441 | 44.0 | 1232 | 1.2496 | 0.5833 |
| 0.5042 | 45.0 | 1260 | 1.2518 | 0.5833 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
CoachCarter/distilbert-base-uncased | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-sroie
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. -->
# donut-base-sroie
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder 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: 2
- 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.1
|
CodeDanCode/SP-KyleBot | [
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"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 15 | null | This is a fork of the GPT NeoX 20B tokenizer, edited to split every numerical digit into a separate token. This has the goal of making it easier for the model to learn arithmetic capabilities and to hopefully be more interpretable, and copies the idea from the [PaLM tokenizer](https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html).
This was done, extremely hackily, by just removing every token that contained "\d\d" (eg "2013"). All remaining digit containing tokens are "0" ... "9" and " 0" ... " 9"
This comes at the cost of making modelling normal text harder, since eg dates like 2013 which naturally *should* be a single token are now 2|0|1|3.
This has a reduced vocab size of 48252 (several of the tokens towards the end are special whitespace tokens copied in from GPT-NeoX to make tokenizing code easier - some of these are duplicated in the vocabulary and thus may not actually show up at train time).
It includes a padding token (<|PAD|>) an End-Of-String token (<|EOS|>) and a Beginning-Of-String token (<|BOS|>) |
CodeNinja1126/bert-p-encoder | [
"pytorch"
]
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} | 3 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-it
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1475
- F1: 0.8589
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2761 | 1.0 | 595 | 0.1644 | 0.8164 |
| 0.1333 | 2.0 | 1190 | 0.1473 | 0.8525 |
| 0.0848 | 3.0 | 1785 | 0.1475 | 0.8589 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
CodeNinja1126/test-model | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
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} | 24 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.8346456692913387
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2763
- F1: 0.8346
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5779 | 1.0 | 191 | 0.3701 | 0.7701 |
| 0.2735 | 2.0 | 382 | 0.2908 | 0.8254 |
| 0.1769 | 3.0 | 573 | 0.2763 | 0.8346 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
CoderEFE/DialoGPT-marxbot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational",
"has_space"
]
| conversational | {
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} | 11 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: TESTq-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="ThomasSimonini/TESTq-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Venkatakrishnan-Ramesh/Text_gen | []
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} | 0 | 2022-10-12T07:46:48Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: TESTq-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="ThomasSimonini/TESTq-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
CoffeeAddict93/gpt2-medium-modest-proposal | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
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} | 7 | 2022-10-12T07:57:11Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery-works
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="osanseviero/q-FrozenLake-v1-4x4-noSlippery-works", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
CohleM/bert-nepali-tokenizer | []
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} | 0 | null | ---
tags:
- generated_from_trainer
model-index:
- name: SpanBERT-hatexplain-label-all-tokens-False-3epoch
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. -->
# SpanBERT-hatexplain-label-all-tokens-False-3epoch
This model is a fine-tuned version of [SpanBERT/spanbert-large-cased](https://huggingface.co/SpanBERT/spanbert-large-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1749
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 174 | 0.1810 |
| No log | 2.0 | 348 | 0.1657 |
| 0.1781 | 3.0 | 522 | 0.1749 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.1
|
Connorvr/TeachingGen | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit"
]
| text-generation | {
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} | 4 | null | A 2L, width 736 SoLU model trained on 15B tokens of the Pile. Bugs: the layernorm just before the unembed is an RMS norm, and the width is not a multiple of 64, so d_head=64 and n_heads=11, and n_heads * d_head != d_model :( |
Crasher222/kaggle-comp-test | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:Crasher222/autonlp-data-kaggle-test",
"transformers",
"autonlp",
"co2_eq_emissions"
]
| text-classification | {
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"BertForSequenceClassification"
],
"model_type": "bert",
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}
}
} | 29 | null | Access to model andrecaruso/aaa is restricted and you are not in the authorized list. Visit https://huggingface.co/andrecaruso/aaa to ask for access. |
CurtisASmith/GPT-JRT | []
| null | {
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} | 0 | 2022-10-12T14:29:12Z | # clinical-led-summarizer
HuggingFace Model Weights for the LongFormer Hospital-Course Summarization model trained on Revised References, as described in Findings of EMNLP 2022 Paper "Learning to Revise References for Faithful Summarization"
[Paper Link](https://aclanthology.org/2022.findings-emnlp.296/)
---
language:
- en
tags:
- summarization
license: apache-2.0
datasets:
- MIMIC-III
metrics:
- rouge
- bertscore
---
|
DTAI-KULeuven/mbert-corona-tweets-belgium-topics | [
"pytorch",
"jax",
"bert",
"text-classification",
"multilingual",
"nl",
"fr",
"en",
"arxiv:2104.09947",
"transformers",
"Dutch",
"French",
"English",
"Tweets",
"Topic classification"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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}
} | 167 | 2022-10-12T18:00:21Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- sst2
model-index:
- name: distilled_bert_finetuning
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. -->
# distilled_bert_finetuning
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the sst2 dataset.
Label 0 is Negative
Label 1 is Positive
## 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: 5
### Training results
### Framework versions
- Transformers 4.21.2
- Pytorch 1.11.0+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
|
Danbi/distilgpt2-finetuned-wikitext2 | []
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} | 0 | null | a quick little model I did. Probably not going to update this. prompt is "GoosebumpsCover book_cover" |
Darkrider/covidbert_mednli | [
"transformers"
]
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} | 3 | null | ---
license: apache-2.0
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: 2.1706
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 20 | 2.2325 |
| No log | 2.0 | 40 | 2.1603 |
| No log | 3.0 | 60 | 2.2368 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.1
|
DarshanDeshpande/marathi-distilbert | [
"pytorch",
"tf",
"distilbert",
"fill-mask",
"mr",
"dataset:Oscar Corpus, News, Stories",
"arxiv:1910.01108",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
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} | 14 | 2022-10-12T23:05:57Z | ---
license: unknown
inference: false
tags:
- mlconsole
- tabular-regression
library_name: mlconsole
metrics:
- mae
- loss
datasets:
- pokemon.csv
model-index:
- name: pokemon.csv
results:
- task:
type: tabular-regression
name: tabular-regression
dataset:
type: pokemon.csv
name: pokemon.csv
metrics:
- type: mae
name: Mean absolute error
value: 3.10766339302063
- type: loss
name: Model loss
value: 18.141359329223633
---
# pokemon.csv (#0)
Trained on [ML Console](https://mlconsole.com).
[Load the model on ML Console](https://mlconsole.com/model/hf/halflings/pokemon.csv).
|
Darya/layoutlmv2-finetuned-funsd-test | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: t5-small-finetuned-en-to-it-hrs
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. -->
# t5-small-finetuned-en-to-it-hrs
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1558
- Bleu: 9.8991
- Gen Len: 51.8287
## 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: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| 2.0084 | 1.0 | 1125 | 2.8804 | 4.4102 | 67.6067 |
| 1.7918 | 2.0 | 2250 | 2.7757 | 6.1959 | 58.0313 |
| 1.6944 | 3.0 | 3375 | 2.6845 | 6.9152 | 55.6953 |
| 1.5955 | 4.0 | 4500 | 2.6219 | 7.3056 | 54.8213 |
| 1.5304 | 5.0 | 5625 | 2.5659 | 7.9427 | 53.4173 |
| 1.52 | 6.0 | 6750 | 2.5249 | 8.2049 | 53.678 |
| 1.4934 | 7.0 | 7875 | 2.4853 | 8.6612 | 52.304 |
| 1.4518 | 8.0 | 9000 | 2.4522 | 8.7991 | 52.6467 |
| 1.4393 | 9.0 | 10125 | 2.4353 | 8.8251 | 52.7047 |
| 1.4196 | 10.0 | 11250 | 2.4027 | 9.01 | 52.5387 |
| 1.405 | 11.0 | 12375 | 2.3797 | 9.1513 | 52.0273 |
| 1.3741 | 12.0 | 13500 | 2.3590 | 9.2401 | 52.3373 |
| 1.3693 | 13.0 | 14625 | 2.3378 | 9.3611 | 52.1507 |
| 1.3638 | 14.0 | 15750 | 2.3226 | 9.4213 | 52.2813 |
| 1.3366 | 15.0 | 16875 | 2.3071 | 9.5199 | 52.1507 |
| 1.3294 | 16.0 | 18000 | 2.2943 | 9.5296 | 51.9587 |
| 1.3258 | 17.0 | 19125 | 2.2788 | 9.6231 | 51.5807 |
| 1.3152 | 18.0 | 20250 | 2.2693 | 9.6586 | 51.8933 |
| 1.3023 | 19.0 | 21375 | 2.2543 | 9.6762 | 51.5733 |
| 1.3061 | 20.0 | 22500 | 2.2451 | 9.6926 | 51.6727 |
| 1.3004 | 21.0 | 23625 | 2.2344 | 9.773 | 51.6527 |
| 1.2839 | 22.0 | 24750 | 2.2242 | 9.7973 | 51.8113 |
| 1.2869 | 23.0 | 25875 | 2.2161 | 9.8177 | 51.9073 |
| 1.2819 | 24.0 | 27000 | 2.2115 | 9.8183 | 51.6707 |
| 1.2642 | 25.0 | 28125 | 2.2037 | 9.7645 | 52.0853 |
| 1.2685 | 26.0 | 29250 | 2.1984 | 9.7764 | 51.6927 |
| 1.2609 | 27.0 | 30375 | 2.1934 | 9.7205 | 51.9647 |
| 1.2585 | 28.0 | 31500 | 2.1834 | 9.8116 | 51.7373 |
| 1.2564 | 29.0 | 32625 | 2.1811 | 9.8547 | 51.8553 |
| 1.2563 | 30.0 | 33750 | 2.1766 | 9.8346 | 51.7293 |
| 1.258 | 31.0 | 34875 | 2.1748 | 9.8204 | 51.6747 |
| 1.2391 | 32.0 | 36000 | 2.1708 | 9.8485 | 51.7647 |
| 1.2364 | 33.0 | 37125 | 2.1644 | 9.8503 | 51.6713 |
| 1.2436 | 34.0 | 38250 | 2.1629 | 9.8457 | 51.76 |
| 1.2408 | 35.0 | 39375 | 2.1614 | 9.8899 | 51.6893 |
| 1.2564 | 36.0 | 40500 | 2.1591 | 9.8867 | 51.706 |
| 1.2318 | 37.0 | 41625 | 2.1575 | 9.866 | 51.782 |
| 1.2423 | 38.0 | 42750 | 2.1570 | 9.8756 | 51.8933 |
| 1.2399 | 39.0 | 43875 | 2.1558 | 9.8871 | 51.7967 |
| 1.2339 | 40.0 | 45000 | 2.1558 | 9.8991 | 51.8287 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.11.0
|
Dawit/DialogGPT-small-ironman | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 7 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# contradiction-mini-lds
A model for the identification of contradiction sentences in patents using all-MiniLM-L6-v2
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('nategro/contradiction-mini-lds')
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 1128 with parameters:
```
{'batch_size': 16, '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": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 1128,
"warmup_steps": 113,
"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': 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
The following pre-trained model was used: [`sentence-transformers/all-MiniLM-L6-v2`](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) |
DeadBeast/marathi-roberta-base | []
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} | 0 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# nps-mini
A model for the identification of problem and solution sentences in patents using all-MiniLM-L6-v2
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('nategro/nps-mini')
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 276 with parameters:
```
{'batch_size': 16, '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": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 276,
"warmup_steps": 28,
"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': 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
The following pre-trained model was used: [`sentence-transformers/all-MiniLM-L6-v2`](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) |
Declan/WallStreetJournal_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 3 | 2022-10-13T14:52:28Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 253.01 +/- 24.02
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from huggingface_sb3 import load_from_hub
checkpoint = load_from_hub(
repo_id="tkurtulus/ppo-LunarLander-v3",
filename="ppo-LunarLander-v3.zip",
)
``` |
Declan/test_model | []
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} | 0 | null | ---
widget:
- text: "city ___ '\"Europe\",\"continent_code\":\"EU\",\"country\":\"Portugal\",\"country_code\":\"PT\",\"state\":\"\",\"city\":\"\",\"postal\":\"\",\"time_zone\":\"Europe/<TARGET_CITY>\",\"region\":\"EMEA\",\"ipAddress\":\"94.46.24.35\",\"latitude\":38.7057,\"longitude\":-9.1359}'"
example_title: "City True Positive Example"
- text: "lat ___ '#d52b1e\\\\\\\\\\\\\",\\\\\\\\\\\\\"d\\\\\\\\\\\\\":\\\\\\\\\\\\\"M8.96 0a8.96 8.96 0 1 1 0 17.92A8.96 8.96 0 0 1 8.96 0zm4.926 4.03a25.104 25.104 0 0 0-6.926 8.215 <TARGET_LAT> 38.068 0 0 1-2.966-2.374c-.348.763-.66 1.546-.939 2.346 1.511 1.282 3.532 2.75 4.92 3.577 1.797-4.654 4.601-7.69 7.287-9.811'"
example_title: "Lat False Positive Example"
- text: "lng ___ '7146,\\\\\"lon\\\\\":-79.403752,\\\\\"type\\\\\":metroIcon},{\\\\\"anchText\\\\\":\\\\\"St George\\\\\",\\\\\"lat\\\\\":43.6683188,\\\\\"lon\\\\\":-<TARGET_LNG>,\\\\\"type\\\\\":metroIcon},{\\\\\"anchText\\\\\":\\\\\"Museum\\\\\",\\\\\"lat\\\\\":43.6671771,\\\\\"lon\\\\\":-79.393502,\\\\\"type\\\\\":metroIc'"
example_title: "Lng True Positive Example"
- text: "region ___'><a class=\"SummaryItemImageLink-gFtgmn gGNXIl summary-item__image-link summary-item-tracking__image-link\" href=\"/story/adaku-<TARGET_REGION>-reproductive-justice-organizer-supreme-court-roe-v-wade-interview\" aria-hidden=\"true\" tabindex=\"-1\" data-component-type=\"rec'"
example_title: "Region False Positive Example"
- text: "zip ___ '\",\\\\\"client_country_code\\\\\": \\\\\"US\\\\\",\\\\\"client_continent_code\\\\\": \\\\\"NA\\\\\",\\\\\"client_metro_code\\\\\": \\\\\"753\\\\\",\\\\\"client_postal_code\\\\\": \\\\\"<TARGET_ZIP>\\\\\",\\\\\"client_conn_speed\\\\\": \\\\\"broadband\\\\\",\\\\\"client_gmt_offset\\\\\": \\\\\"-700\\\\\",\\\\\"client_latitude\\\\\": \\\\\"33.440\\\\\",\\\\\"client_longitude\\\\\": '"
example_title: "Zip True Positive Example"
--- |
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}
} | 0 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 71.30 +/- 30.34
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
DeepPavlov/distilrubert-tiny-cased-conversational-v1 | [
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
]
| null | {
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} | 9,141 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
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-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.3049
- eval_accuracy: 0.9252
- eval_runtime: 423.5074
- eval_samples_per_second: 59.031
- eval_steps_per_second: 1.846
- epoch: 1.35
- step: 1054
## 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
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.0
- Tokenizers 0.13.1
|
DemangeJeremy/4-sentiments-with-flaubert | [
"pytorch",
"flaubert",
"text-classification",
"fr",
"transformers",
"sentiments",
"french",
"flaubert-large"
]
| text-classification | {
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"FlaubertForSequenceClassification"
],
"model_type": "flaubert",
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}
}
} | 226 | null | ---
license: mit
---
### Shoe on Stable Diffusion via Dreambooth
#### model by ejcho623
This your the Stable Diffusion model fine-tuned the Shoe concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **sks shoe**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Here are the images used for training this concept:
|
Denilson/gbert-base-germaner | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: camembert-base-cae-ressentis
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. -->
# camembert-base-cae-ressentis
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8112
- Precision: 0.8116
- Recall: 0.8034
- F1: 0.8060
## 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_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 1.2699 | 1.0 | 59 | 1.1005 | 0.2718 | 0.5214 | 0.3573 |
| 1.0852 | 2.0 | 118 | 0.8127 | 0.6403 | 0.7179 | 0.6708 |
| 0.7006 | 3.0 | 177 | 0.6582 | 0.7407 | 0.7436 | 0.7310 |
| 0.4187 | 4.0 | 236 | 0.5833 | 0.8075 | 0.7863 | 0.7817 |
| 0.2017 | 5.0 | 295 | 0.5869 | 0.8537 | 0.8376 | 0.8400 |
| 0.1142 | 6.0 | 354 | 0.6433 | 0.8125 | 0.8034 | 0.8064 |
| 0.0735 | 7.0 | 413 | 0.7700 | 0.8027 | 0.7949 | 0.7959 |
| 0.0572 | 8.0 | 472 | 0.8023 | 0.7915 | 0.7863 | 0.7877 |
| 0.0445 | 9.0 | 531 | 0.8010 | 0.8116 | 0.8034 | 0.8060 |
| 0.033 | 10.0 | 590 | 0.8112 | 0.8116 | 0.8034 | 0.8060 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.0
- Tokenizers 0.13.1
|
Deniskin/essays_small_2000 | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_9_0
model-index:
- name: fine-tune-xls-r-300m-wav2vec2-on-swahili-sagemaker
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. -->
# fine-tune-xls-r-300m-wav2vec2-on-swahili-sagemaker
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_9_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1866
- Wer: 0.2346
## 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: 16
- eval_batch_size: 16
- 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 5.0466 | 0.22 | 400 | 3.2175 | 1.0 |
| 1.8604 | 0.44 | 800 | 0.6654 | 0.6770 |
| 0.5942 | 0.66 | 1200 | 0.4830 | 0.5013 |
| 0.4631 | 0.89 | 1600 | 0.3532 | 0.4274 |
| 0.3949 | 1.11 | 2000 | 0.3612 | 0.4008 |
| 0.3634 | 1.33 | 2400 | 0.3024 | 0.3691 |
| 0.3385 | 1.55 | 2800 | 0.3271 | 0.3633 |
| 0.3225 | 1.77 | 3200 | 0.2795 | 0.3431 |
| 0.3062 | 1.99 | 3600 | 0.2651 | 0.3353 |
| 0.2573 | 2.22 | 4000 | 0.2832 | 0.3241 |
| 0.2608 | 2.44 | 4400 | 0.2921 | 0.3225 |
| 0.2583 | 2.66 | 4800 | 0.3887 | 0.3119 |
| 0.2536 | 2.88 | 5200 | 0.2526 | 0.3156 |
| 0.236 | 3.1 | 5600 | 0.2473 | 0.2988 |
| 0.2202 | 3.32 | 6000 | 0.2537 | 0.3016 |
| 0.2119 | 3.55 | 6400 | 0.2769 | 0.2979 |
| 0.2128 | 3.77 | 6800 | 0.2363 | 0.2940 |
| 0.2124 | 3.99 | 7200 | 0.2198 | 0.2846 |
| 0.1854 | 4.21 | 7600 | 0.2230 | 0.2855 |
| 0.184 | 4.43 | 8000 | 0.2194 | 0.2781 |
| 0.1799 | 4.65 | 8400 | 0.2226 | 0.2752 |
| 0.1792 | 4.88 | 8800 | 0.2242 | 0.2712 |
| 0.1702 | 5.1 | 9200 | 0.2185 | 0.2721 |
| 0.1613 | 5.32 | 9600 | 0.2106 | 0.2696 |
| 0.1611 | 5.54 | 10000 | 0.2132 | 0.2673 |
| 0.1588 | 5.76 | 10400 | 0.1980 | 0.2604 |
| 0.1564 | 5.98 | 10800 | 0.2021 | 0.2617 |
| 0.1386 | 6.2 | 11200 | 0.2017 | 0.2603 |
| 0.1378 | 6.43 | 11600 | 0.1994 | 0.2585 |
| 0.1345 | 6.65 | 12000 | 0.1974 | 0.2559 |
| 0.1407 | 6.87 | 12400 | 0.1946 | 0.2526 |
| 0.1336 | 7.09 | 12800 | 0.1991 | 0.2505 |
| 0.1178 | 7.31 | 13200 | 0.1973 | 0.2504 |
| 0.1174 | 7.53 | 13600 | 0.1977 | 0.2485 |
| 0.1169 | 7.76 | 14000 | 0.1936 | 0.2454 |
| 0.1163 | 7.98 | 14400 | 0.1910 | 0.2441 |
| 0.1074 | 8.2 | 14800 | 0.1990 | 0.2456 |
| 0.1055 | 8.42 | 15200 | 0.1901 | 0.2420 |
| 0.108 | 8.64 | 15600 | 0.1900 | 0.2389 |
| 0.1019 | 8.86 | 16000 | 0.1895 | 0.2385 |
| 0.0998 | 9.09 | 16400 | 0.1919 | 0.2364 |
| 0.0966 | 9.31 | 16800 | 0.1897 | 0.2362 |
| 0.0903 | 9.53 | 17200 | 0.1858 | 0.2347 |
| 0.0887 | 9.75 | 17600 | 0.1883 | 0.2348 |
| 0.0934 | 9.97 | 18000 | 0.1866 | 0.2345 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.13.0
|
Deniskin/gpt3_medium | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
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"GPT2LMHeadModel"
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}
} | 52 | null | ---
license: mit
---
### Shoe2 on Stable Diffusion via Dreambooth
#### model by ejcho623
This your the Stable Diffusion model fine-tuned the Shoe2 concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **a sks shoe**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Here are the images used for training this concept:
|
Denny29/DialoGPT-medium-asunayuuki | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
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} | 9 | 2022-10-13T18:00:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: my_awesome_qa_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_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8522
## 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 | 250 | 2.6732 |
| 2.8375 | 2.0 | 500 | 1.9453 |
| 2.8375 | 3.0 | 750 | 1.8522 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.0
- Tokenizers 0.13.1
|
DeskDown/MarianMix_en-zh-10 | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
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} | 3 | null | ---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
---
# (CleanRL) **PPO** Agent Playing **CartPole-v1**
This is a trained model of a PPO agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo.py).
# Hyperparameters
```python
{'anneal_lr': True,
'batch_size': 512,
'capture_video': True,
'clip_coef': 0.2,
'clip_vloss': True,
'cuda': False,
'ent_coef': 0.01,
'env_id': 'CartPole-v1',
'exp_name': 'ppo',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_repo_id': 'cleanrl/ppo',
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 128,
'norm_adv': True,
'num_envs': 4,
'num_minibatches': 4,
'num_steps': 128,
'save_model': True,
'seed': 1,
'target_kl': None,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': False,
'update_epochs': 4,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
Dev-DGT/food-dbert-multiling | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
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} | 17 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: fassahat/distillbert-base-uncased-finetuned-150k-patent-sentences
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. -->
# fassahat/distillbert-base-uncased-finetuned-150k-patent-sentences
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: 0.2887
- Validation Loss: 0.4392
- Train Accuracy: 0.8414
- 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', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 22500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, '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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.4810 | 0.4276 | 0.8330 | 0 |
| 0.3714 | 0.4163 | 0.8415 | 1 |
| 0.2887 | 0.4392 | 0.8414 | 2 |
### Framework versions
- Transformers 4.23.1
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Devmapall/paraphrase-quora | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 3 | null | ---
license: mit
---
### Kumiko on Stable Diffusion
This is the `Kumiko` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
I guess because they are png you can't see them? Idk, I'll fix it later. It's Kumiko! It looks like Kumiko.
Here is the new concept you will be able to use as a `style`:
|
Dhito/am | []
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}
}
} | 0 | 2022-10-13T19:54:57Z | ---
license: mit
---
### Tails from Sonic on Stable Diffusion via Dreambooth
#### model by Skittleology
This your the Stable Diffusion model fine-tuned the Tails from Sonic concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **tails**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Here are the images used for training this concept:
|
DicoTiar/wisdomfiy | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
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}
}
} | 3 | null | Access to model Digitalwitness/distilgpt2-finetuned-beatles is restricted and you are not in the authorized list. Visit https://huggingface.co/Digitalwitness/distilgpt2-finetuned-beatles to ask for access. |
DiegoAlysson/opus-mt-en-ro-finetuned-en-to-ro | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"dataset:wmt16",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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}
}
} | 1 | null | ---
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
---
# (CleanRL) **PPO** Agent Playing **CartPole-v1**
This is a trained model of a PPO agent playing CartPole-v1.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo.py).
# Hyperparameters
```python
{'anneal_lr': True,
'batch_size': 512,
'capture_video': True,
'clip_coef': 0.2,
'clip_vloss': True,
'cuda': False,
'ent_coef': 0.01,
'env_id': 'CartPole-v1',
'exp_name': 'ppo',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_repo_id': 'vwxyzjn/ppo2',
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 128,
'norm_adv': True,
'num_envs': 4,
'num_minibatches': 4,
'num_steps': 128,
'save_model': True,
'seed': 1,
'target_kl': None,
'torch_deterministic': True,
'total_timesteps': 500000,
'track': False,
'update_epochs': 4,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
Dkwkk/Da | []
| null | {
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}
}
} | 0 | null | ---
license: mit
---
### lucario on Stable Diffusion
This is the `<lucario>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
DongHai/DialoGPT-small-rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
}
} | 9 | null | ---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: randomcomb_mlm_ep5_mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.8615744507729862
---
<!-- 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. -->
# randomcomb_mlm_ep5_mnli
This model is a fine-tuned version of [cuenb](https://huggingface.co/joey234/cuenb) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4416
- Accuracy: 0.8616
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5569 | 0.41 | 5000 | 0.4415 | 0.8273 |
| 0.4598 | 0.81 | 10000 | 0.4234 | 0.8425 |
| 0.3832 | 1.22 | 15000 | 0.4398 | 0.8475 |
| 0.3314 | 1.63 | 20000 | 0.4137 | 0.8494 |
| 0.3158 | 2.04 | 25000 | 0.4484 | 0.8527 |
| 0.2294 | 2.44 | 30000 | 0.4471 | 0.8552 |
| 0.2283 | 2.85 | 35000 | 0.4541 | 0.8557 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.8.0
- Datasets 1.18.3
- Tokenizers 0.12.1
|
Dongjae/mrc2reader | [
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"XLMRobertaForQuestionAnswering"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 3 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: cuenb
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. -->
# cuenb
This model is a version of [roberta-base](https://huggingface.co/roberta-base) with adaptive pre-training using the [negation-focused pre-training strategy](https://github.com/joey234/negation-focused-pretraining) on 1.2M sentences containing negation.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 1.8998 | 2.57 | 200000 | 1.6606 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.8.0
- Datasets 1.15.1
- Tokenizers 0.10.3
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-12 | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
}
} | 29 | null | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
albert-base-v2 | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"text-generation": {
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},
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},
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},
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}
}
} | 4,785,283 | 2022-10-14T02:06:43Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- OpenTable
metrics:
- accuracy
model-index:
- name: roberta-base.CEBaB_confounding.observational.sa.5-class.seed_43
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: OpenTable OPENTABLE
type: OpenTable
args: opentable
metrics:
- name: Accuracy
type: accuracy
value: 0.698744769874477
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base.CEBaB_confounding.observational.sa.5-class.seed_43
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the OpenTable OPENTABLE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8001
- Accuracy: 0.6987
- Macro-f1: 0.6805
- Weighted-macro-f1: 0.6922
## 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: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.5.2
- Tokenizers 0.12.1
|
albert-large-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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}
}
} | 26,792 | 2022-10-14T02:09:34Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- OpenTable
metrics:
- accuracy
model-index:
- name: roberta-base.CEBaB_confounding.observational.sa.5-class.seed_44
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: OpenTable OPENTABLE
type: OpenTable
args: opentable
metrics:
- name: Accuracy
type: accuracy
value: 0.7190675433353257
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base.CEBaB_confounding.observational.sa.5-class.seed_44
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the OpenTable OPENTABLE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7230
- Accuracy: 0.7191
- Macro-f1: 0.7052
- Weighted-macro-f1: 0.7128
## 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: 44
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.5.2
- Tokenizers 0.12.1
|
albert-xlarge-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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} | 341 | 2022-10-14T02:11:21Z | ---
language: en
thumbnail: http://www.huggingtweets.com/pilltoledo/1665713586824/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1507166369716097033/1zugU9LF_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">juliet</div>
<div style="text-align: center; font-size: 14px;">@pilltoledo</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from juliet.
| Data | juliet |
| --- | --- |
| Tweets downloaded | 3196 |
| Retweets | 126 |
| Short tweets | 860 |
| Tweets kept | 2210 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2k71hn5a/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @pilltoledo's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ufuyucr) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ufuyucr/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/pilltoledo')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
albert-xlarge-v2 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
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} | 2,973 | 2022-10-14T02:12:45Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- OpenTable
metrics:
- accuracy
model-index:
- name: roberta-base.CEBaB_confounding.uniform.sa.5-class.seed_42
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: OpenTable OPENTABLE
type: OpenTable
args: opentable
metrics:
- name: Accuracy
type: accuracy
value: 0.726240286909743
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base.CEBaB_confounding.uniform.sa.5-class.seed_42
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the OpenTable OPENTABLE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6956
- Accuracy: 0.7262
- Macro-f1: 0.7053
- Weighted-macro-f1: 0.7201
## 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: 5.0
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.5.2
- Tokenizers 0.12.1
|
albert-xxlarge-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
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"AlbertForMaskedLM"
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}
} | 7,091 | 2022-10-14T02:15:32Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- OpenTable
metrics:
- accuracy
model-index:
- name: roberta-base.CEBaB_confounding.uniform.sa.5-class.seed_43
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: OpenTable OPENTABLE
type: OpenTable
args: opentable
metrics:
- name: Accuracy
type: accuracy
value: 0.735803945008966
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base.CEBaB_confounding.uniform.sa.5-class.seed_43
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the OpenTable OPENTABLE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6596
- Accuracy: 0.7358
- Macro-f1: 0.7204
- Weighted-macro-f1: 0.7325
## 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: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.5.2
- Tokenizers 0.12.1
|
albert-xxlarge-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
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},
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}
} | 42,640 | 2022-10-14T02:18:22Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- OpenTable
metrics:
- accuracy
model-index:
- name: roberta-base.CEBaB_confounding.uniform.sa.5-class.seed_44
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: OpenTable OPENTABLE
type: OpenTable
args: opentable
metrics:
- name: Accuracy
type: accuracy
value: 0.7411835026897788
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base.CEBaB_confounding.uniform.sa.5-class.seed_44
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the OpenTable OPENTABLE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6254
- Accuracy: 0.7412
- Macro-f1: 0.7202
- Weighted-macro-f1: 0.7349
## 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: 44
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.5.2
- Tokenizers 0.12.1
|
bert-base-cased-finetuned-mrpc | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
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}
} | 11,644 | 2022-10-14T02:21:11Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- OpenTable
metrics:
- accuracy
model-index:
- name: roberta-base.CEBaB_confounding.price_food_ambiance_negative.sa.5-class.seed_42
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: OpenTable OPENTABLE
type: OpenTable
args: opentable
metrics:
- name: Accuracy
type: accuracy
value: 0.7352062163777645
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base.CEBaB_confounding.price_food_ambiance_negative.sa.5-class.seed_42
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the OpenTable OPENTABLE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6579
- Accuracy: 0.7352
- Macro-f1: 0.7190
- Weighted-macro-f1: 0.7313
## 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: 5.0
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.5.2
- Tokenizers 0.12.1
|
bert-base-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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} | 8,621,271 | 2022-10-14T02:21:39Z | ---
license: mit
language: en
tags:
- bert
- cloze
- distractor
- generation
datasets:
- cloth
widget:
- text: "I feel [MASK] now. [SEP] happy"
- text: "The old man was waiting for a ride across the [MASK]. [SEP] river"
---
# cdgp-csg-scibert-cloth
## Model description
This model is a Candidate Set Generator in **"CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model", Findings of EMNLP 2022**.
Its input are stem and answer, and output is candidate set of distractors. It is fine-tuned by [**CLOTH**](https://www.cs.cmu.edu/~glai1/data/cloth/) dataset based on [**allenai/scibert_scivocab_uncased**](https://huggingface.co/allenai/scibert_scivocab_uncased) model.
For more details, you can see our **paper** or [**GitHub**](https://github.com/AndyChiangSH/CDGP).
## How to use?
1. Download the model by hugging face transformers.
```python
from transformers import BertTokenizer, BertForMaskedLM, pipeline
tokenizer = BertTokenizer.from_pretrained("AndyChiang/cdgp-csg-scibert-cloth")
csg_model = BertForMaskedLM.from_pretrained("AndyChiang/cdgp-csg-scibert-cloth")
```
2. Create a unmasker.
```python
unmasker = pipeline("fill-mask", tokenizer=tokenizer, model=csg_model, top_k=10)
```
3. Use the unmasker to generate the candidate set of distractors.
```python
sent = "I feel [MASK] now. [SEP] happy"
cs = unmasker(sent)
print(cs)
```
## Dataset
This model is fine-tuned by [CLOTH](https://www.cs.cmu.edu/~glai1/data/cloth/) dataset, which is a collection of nearly 100,000 cloze questions from middle school and high school English exams. The detail of CLOTH dataset is shown below.
| Number of questions | Train | Valid | Test |
| ------------------- | ----- | ----- | ----- |
| Middle school | 22056 | 3273 | 3198 |
| High school | 54794 | 7794 | 8318 |
| Total | 76850 | 11067 | 11516 |
You can also use the [dataset](https://huggingface.co/datasets/AndyChiang/cloth) we have already cleaned.
## Training
We use a special way to fine-tune model, which is called **"Answer-Relating Fine-Tune"**. More detail is in our paper.
### Training hyperparameters
The following hyperparameters were used during training:
- Pre-train language model: [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased)
- Optimizer: adam
- Learning rate: 0.0001
- Max length of input: 64
- Batch size: 64
- Epoch: 1
- Device: NVIDIA® Tesla T4 in Google Colab
## Testing
The evaluations of this model as a Candidate Set Generator in CDGP is as follows:
| P@1 | F1@3 | F1@10 | MRR | NDCG@10 |
| ---- | ---- | ----- | ----- | ------- |
| 8.10 | 9.13 | 12.22 | 19.53 | 28.76 |
## Other models
### Candidate Set Generator
| Models | CLOTH | DGen |
| ----------- | ----------------------------------------------------------------------------------- | -------------------------------------------------------------------------------- |
| **BERT** | [cdgp-csg-bert-cloth](https://huggingface.co/AndyChiang/cdgp-csg-bert-cloth) | [cdgp-csg-bert-dgen](https://huggingface.co/AndyChiang/cdgp-csg-bert-dgen) |
| **SciBERT** | [*cdgp-csg-scibert-cloth*](https://huggingface.co/AndyChiang/cdgp-csg-scibert-cloth) | [cdgp-csg-scibert-dgen](https://huggingface.co/AndyChiang/cdgp-csg-scibert-dgen) |
| **RoBERTa** | [cdgp-csg-roberta-cloth](https://huggingface.co/AndyChiang/cdgp-csg-roberta-cloth) | [cdgp-csg-roberta-dgen](https://huggingface.co/AndyChiang/cdgp-csg-roberta-dgen) |
| **BART** | [cdgp-csg-bart-cloth](https://huggingface.co/AndyChiang/cdgp-csg-bart-cloth) | [cdgp-csg-bart-dgen](https://huggingface.co/AndyChiang/cdgp-csg-bart-dgen) |
### Distractor Selector
**fastText**: [cdgp-ds-fasttext](https://huggingface.co/AndyChiang/cdgp-ds-fasttext)
## Citation
None |
bert-base-german-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
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"BertForMaskedLM"
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}
} | 175,983 | 2022-10-14T02:23:56Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- OpenTable
metrics:
- accuracy
model-index:
- name: roberta-base.CEBaB_confounding.price_food_ambiance_negative.sa.5-class.seed_43
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: OpenTable OPENTABLE
type: OpenTable
args: opentable
metrics:
- name: Accuracy
type: accuracy
value: 0.7519426180514046
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base.CEBaB_confounding.price_food_ambiance_negative.sa.5-class.seed_43
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the OpenTable OPENTABLE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6408
- Accuracy: 0.7519
- Macro-f1: 0.7321
- Weighted-macro-f1: 0.7464
## 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: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.5.2
- Tokenizers 0.12.1
|
bert-base-german-dbmdz-cased | [
"pytorch",
"jax",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
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],
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} | 1,814 | 2022-10-14T02:26:43Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- OpenTable
metrics:
- accuracy
model-index:
- name: roberta-base.CEBaB_confounding.price_food_ambiance_negative.sa.5-class.seed_44
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: OpenTable OPENTABLE
type: OpenTable
args: opentable
metrics:
- name: Accuracy
type: accuracy
value: 0.7471607890017932
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base.CEBaB_confounding.price_food_ambiance_negative.sa.5-class.seed_44
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the OpenTable OPENTABLE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6246
- Accuracy: 0.7472
- Macro-f1: 0.7303
- Weighted-macro-f1: 0.7434
## 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: 44
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.5.2
- Tokenizers 0.12.1
|
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