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stringlengths 4
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stringclasses 17
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int64 0
59.7M
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timestamp[ns, tz=UTC] | card
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AnonymousSub/SR_bert-base-uncased | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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} | 3 | null | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
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/1421008625095647234/Vfg52xtV_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">Buddha</div>
<div style="text-align: center; font-size: 14px;">@thebuddha_3</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 Buddha.
| Data | Buddha |
| --- | --- |
| Tweets downloaded | 3200 |
| Retweets | 138 |
| Short tweets | 695 |
| Tweets kept | 2367 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/14lqj1g8/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 @thebuddha_3's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3rpocant) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3rpocant/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/thebuddha_3')
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)
|
AnonymousSub/SR_cline | [
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"roberta",
"feature-extraction",
"transformers"
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} | 6 | null | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
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/1396839225249734657/GG6ve7Qv_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/1542608466077855744/a0q2rR-P_400x400.png')">
</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/1529675700772302848/uXtYNx_v_400x400.jpg')">
</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">h b & very tall bart & ppigg</div>
<div style="text-align: center; font-size: 14px;">@h3xenbrenner2-s4m31p4n-tallbart</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 h b & very tall bart & ppigg.
| Data | h b | very tall bart | ppigg |
| --- | --- | --- | --- |
| Tweets downloaded | 1230 | 3194 | 3008 |
| Retweets | 75 | 381 | 957 |
| Short tweets | 155 | 569 | 643 |
| Tweets kept | 1000 | 2244 | 1408 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/34qe4a18/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 @h3xenbrenner2-s4m31p4n-tallbart's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/kg3j88xz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/kg3j88xz/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/h3xenbrenner2-s4m31p4n-tallbart')
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)
|
AnonymousSub/SR_declutr | [
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"roberta",
"feature-extraction",
"transformers"
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} | 6 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/finessafudges-h3xenbrenner2-tallbart/1667781477683/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/1396839225249734657/GG6ve7Qv_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/1542608466077855744/a0q2rR-P_400x400.png')">
</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/1577220932648816642/T4NDjEbG_400x400.jpg')">
</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">h b & very tall bart & Finessa Fudges</div>
<div style="text-align: center; font-size: 14px;">@finessafudges-h3xenbrenner2-tallbart</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 h b & very tall bart & Finessa Fudges.
| Data | h b | very tall bart | Finessa Fudges |
| --- | --- | --- | --- |
| Tweets downloaded | 1230 | 3194 | 3079 |
| Retweets | 75 | 381 | 308 |
| Short tweets | 155 | 569 | 814 |
| Tweets kept | 1000 | 2244 | 1957 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/5vdgcc4y/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 @finessafudges-h3xenbrenner2-tallbart's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3cqh8hdr) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3cqh8hdr/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/finessafudges-h3xenbrenner2-tallbart')
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)
|
AnonymousSub/SR_rule_based_hier_quadruplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
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} | 1 | null | ---
license: mit
---
### EttBlackTeapot on Stable Diffusion
This is the `<my-teapot>` 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`:
|
AnonymousSub/SR_rule_based_only_classfn_twostage_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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}
} | 2 | null | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- wild_receipt
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: OCR-LayoutLMv3-Invoice
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wild_receipt
type: wild_receipt
config: WildReceipt
split: train
args: WildReceipt
metrics:
- name: Precision
type: precision
value: 0.8765398302764851
- name: Recall
type: recall
value: 0.8812439796339617
- name: F1
type: f1
value: 0.8788856103753516
- name: Accuracy
type: accuracy
value: 0.92678512668641
---
<!-- 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. -->
# OCR-LayoutLMv3-Invoice
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the wild_receipt dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3159
- Precision: 0.8765
- Recall: 0.8812
- F1: 0.8789
- Accuracy: 0.9268
## Model description
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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 6000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.16 | 100 | 1.5032 | 0.4934 | 0.1444 | 0.2234 | 0.6064 |
| No log | 0.32 | 200 | 1.0282 | 0.5884 | 0.4420 | 0.5048 | 0.7385 |
| No log | 0.47 | 300 | 0.7856 | 0.7448 | 0.6205 | 0.6770 | 0.8133 |
| No log | 0.63 | 400 | 0.6464 | 0.7736 | 0.6689 | 0.7174 | 0.8399 |
| 1.1733 | 0.79 | 500 | 0.5672 | 0.7609 | 0.7303 | 0.7453 | 0.8557 |
| 1.1733 | 0.95 | 600 | 0.5055 | 0.7658 | 0.7652 | 0.7655 | 0.8677 |
| 1.1733 | 1.1 | 700 | 0.4735 | 0.7946 | 0.7848 | 0.7897 | 0.8784 |
| 1.1733 | 1.26 | 800 | 0.4414 | 0.7962 | 0.7946 | 0.7954 | 0.8818 |
| 1.1733 | 1.42 | 900 | 0.4094 | 0.8176 | 0.8064 | 0.8120 | 0.8894 |
| 0.5047 | 1.58 | 1000 | 0.3971 | 0.8219 | 0.8248 | 0.8234 | 0.8961 |
| 0.5047 | 1.74 | 1100 | 0.4082 | 0.7993 | 0.8362 | 0.8174 | 0.8927 |
| 0.5047 | 1.89 | 1200 | 0.3797 | 0.8240 | 0.8317 | 0.8278 | 0.8962 |
| 0.5047 | 2.05 | 1300 | 0.3597 | 0.8326 | 0.8331 | 0.8329 | 0.9020 |
| 0.5047 | 2.21 | 1400 | 0.3544 | 0.8462 | 0.8283 | 0.8371 | 0.9020 |
| 0.368 | 2.37 | 1500 | 0.3374 | 0.8428 | 0.8435 | 0.8432 | 0.9056 |
| 0.368 | 2.52 | 1600 | 0.3364 | 0.8406 | 0.8522 | 0.8464 | 0.9089 |
| 0.368 | 2.68 | 1700 | 0.3404 | 0.8467 | 0.8536 | 0.8501 | 0.9107 |
| 0.368 | 2.84 | 1800 | 0.3319 | 0.8405 | 0.8501 | 0.8453 | 0.9090 |
| 0.368 | 3.0 | 1900 | 0.3324 | 0.8584 | 0.8492 | 0.8538 | 0.9117 |
| 0.2949 | 3.15 | 2000 | 0.3204 | 0.8691 | 0.8404 | 0.8545 | 0.9119 |
| 0.2949 | 3.31 | 2100 | 0.3107 | 0.8599 | 0.8547 | 0.8573 | 0.9162 |
| 0.2949 | 3.47 | 2200 | 0.3169 | 0.8680 | 0.8489 | 0.8584 | 0.9146 |
| 0.2949 | 3.63 | 2300 | 0.3190 | 0.8683 | 0.8519 | 0.8600 | 0.9152 |
| 0.2949 | 3.79 | 2400 | 0.2975 | 0.8631 | 0.8617 | 0.8624 | 0.9182 |
| 0.2438 | 3.94 | 2500 | 0.3040 | 0.8566 | 0.8640 | 0.8603 | 0.9171 |
| 0.2438 | 4.1 | 2600 | 0.3045 | 0.8585 | 0.8642 | 0.8613 | 0.9181 |
| 0.2438 | 4.26 | 2700 | 0.3139 | 0.8498 | 0.8748 | 0.8621 | 0.9160 |
| 0.2438 | 4.42 | 2800 | 0.2985 | 0.8642 | 0.8672 | 0.8657 | 0.9214 |
| 0.2438 | 4.57 | 2900 | 0.3047 | 0.8688 | 0.8694 | 0.8691 | 0.9214 |
| 0.2028 | 4.73 | 3000 | 0.2986 | 0.8686 | 0.8695 | 0.8691 | 0.9207 |
| 0.2028 | 4.89 | 3100 | 0.3135 | 0.8628 | 0.8755 | 0.8691 | 0.9197 |
| 0.2028 | 5.05 | 3200 | 0.2927 | 0.8656 | 0.8755 | 0.8705 | 0.9217 |
| 0.2028 | 5.21 | 3300 | 0.2992 | 0.8724 | 0.8697 | 0.8711 | 0.9228 |
| 0.2028 | 5.36 | 3400 | 0.2975 | 0.8831 | 0.8639 | 0.8734 | 0.9244 |
| 0.1814 | 5.52 | 3500 | 0.2897 | 0.8736 | 0.8788 | 0.8762 | 0.9250 |
| 0.1814 | 5.68 | 3600 | 0.3118 | 0.8674 | 0.8751 | 0.8712 | 0.9216 |
| 0.1814 | 5.84 | 3700 | 0.2974 | 0.8735 | 0.8779 | 0.8757 | 0.9237 |
| 0.1814 | 5.99 | 3800 | 0.2957 | 0.8696 | 0.8815 | 0.8755 | 0.9240 |
| 0.1814 | 6.15 | 3900 | 0.3120 | 0.8698 | 0.8817 | 0.8757 | 0.9250 |
| 0.1602 | 6.31 | 4000 | 0.3080 | 0.8715 | 0.8800 | 0.8757 | 0.9238 |
| 0.1602 | 6.47 | 4100 | 0.3031 | 0.8767 | 0.8788 | 0.8777 | 0.9261 |
| 0.1602 | 6.62 | 4200 | 0.3146 | 0.8699 | 0.8784 | 0.8741 | 0.9227 |
| 0.1602 | 6.78 | 4300 | 0.3085 | 0.8717 | 0.8788 | 0.8752 | 0.9248 |
| 0.1602 | 6.94 | 4400 | 0.3023 | 0.8749 | 0.8756 | 0.8752 | 0.9250 |
| 0.1383 | 7.1 | 4500 | 0.3025 | 0.8860 | 0.8735 | 0.8797 | 0.9252 |
| 0.1383 | 7.26 | 4600 | 0.3026 | 0.8775 | 0.8810 | 0.8792 | 0.9272 |
| 0.1383 | 7.41 | 4700 | 0.3146 | 0.8715 | 0.8832 | 0.8773 | 0.9251 |
| 0.1383 | 7.57 | 4800 | 0.3113 | 0.8769 | 0.8803 | 0.8786 | 0.9275 |
| 0.1383 | 7.73 | 4900 | 0.3073 | 0.8797 | 0.8786 | 0.8792 | 0.9261 |
| 0.1306 | 7.89 | 5000 | 0.3163 | 0.8714 | 0.8828 | 0.8770 | 0.9248 |
| 0.1306 | 8.04 | 5100 | 0.3163 | 0.8753 | 0.8810 | 0.8781 | 0.9250 |
| 0.1306 | 8.2 | 5200 | 0.3132 | 0.8743 | 0.8804 | 0.8773 | 0.9257 |
| 0.1306 | 8.36 | 5300 | 0.3119 | 0.8735 | 0.8837 | 0.8786 | 0.9264 |
| 0.1306 | 8.52 | 5400 | 0.3145 | 0.8826 | 0.8779 | 0.8802 | 0.9272 |
| 0.1174 | 8.68 | 5500 | 0.3166 | 0.8776 | 0.8811 | 0.8794 | 0.9261 |
| 0.1174 | 8.83 | 5600 | 0.3146 | 0.8776 | 0.8814 | 0.8795 | 0.9260 |
| 0.1174 | 8.99 | 5700 | 0.3135 | 0.8763 | 0.8826 | 0.8795 | 0.9271 |
| 0.1174 | 9.15 | 5800 | 0.3154 | 0.8794 | 0.8818 | 0.8806 | 0.9275 |
| 0.1174 | 9.31 | 5900 | 0.3152 | 0.8788 | 0.8817 | 0.8802 | 0.9274 |
| 0.11 | 9.46 | 6000 | 0.3159 | 0.8765 | 0.8812 | 0.8789 | 0.9268 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AnonymousSub/SR_rule_based_roberta_bert_triplet_epochs_1_shard_1 | [
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} | 2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: t5-small-finetuned-en-to-regex
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-regex
This model is a fine-tuned version of [rymaju/t5-small-finetuned-en-to-regex](https://huggingface.co/rymaju/t5-small-finetuned-en-to-regex) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0032
- Bleu: 12.1984
- Gen Len: 16.7502
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 0.0092 | 1.0 | 6188 | 0.0043 | 12.1984 | 16.7522 |
| 0.0069 | 2.0 | 12376 | 0.0040 | 12.2039 | 16.7502 |
| 0.0056 | 3.0 | 18564 | 0.0034 | 12.2091 | 16.7483 |
| 0.0048 | 4.0 | 24752 | 0.0035 | 12.2103 | 16.7502 |
| 0.0049 | 5.0 | 30940 | 0.0035 | 12.1984 | 16.7502 |
| 0.0046 | 6.0 | 37128 | 0.0033 | 12.1984 | 16.7502 |
| 0.0046 | 7.0 | 43316 | 0.0035 | 12.1984 | 16.7502 |
| 0.0046 | 8.0 | 49504 | 0.0032 | 12.1984 | 16.7502 |
| 0.0042 | 9.0 | 55692 | 0.0032 | 12.1984 | 16.7502 |
| 0.0043 | 10.0 | 61880 | 0.0032 | 12.1984 | 16.7502 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AnonymousSub/SR_rule_based_roberta_hier_triplet_epochs_1_shard_10 | [
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} | 6 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
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-fr
This model is a fine-tuned version of [tkubotake/xlm-roberta-base-finetuned-panx-de](https://huggingface.co/tkubotake/xlm-roberta-base-finetuned-panx-de) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1829
- F1: 0.8671
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.158 | 1.0 | 715 | 0.1689 | 0.8471 |
| 0.099 | 2.0 | 1430 | 0.1781 | 0.8576 |
| 0.0599 | 3.0 | 2145 | 0.1829 | 0.8671 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
AnonymousSub/SR_rule_based_roberta_only_classfn_twostage_epochs_1_shard_1 | [
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} | 8 | null | Access to model sreddy1/t5-end2end-questions-generation is restricted and you are not in the authorized list. Visit https://huggingface.co/sreddy1/t5-end2end-questions-generation to ask for access. |
AnonymousSub/SR_rule_based_roberta_twostagetriplet_epochs_1_shard_1 | [
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} | 8 | null | ---
language: ja
license: cc-by-nc-sa-4.0
tags:
- roberta
- medical
inference: false
---
# alabnii/jmedroberta-base-manbyo-wordpiece
## Model description
This is a Japanese RoBERTa base model pre-trained on academic articles in medical sciences collected by Japan Science and Technology Agency (JST).
This model is released under the [Creative Commons 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/deed) (CC BY-NC-SA 4.0).
#### Reference
Ja:
```
@InProceedings{sugimoto_nlp2023_jmedroberta,
author = "杉本海人 and 壹岐太一 and 知田悠生 and 金沢輝一 and 相澤彰子",
title = "J{M}ed{R}o{BERT}a: 日本語の医学論文にもとづいた事前学習済み言語モデルの構築と評価",
booktitle = "言語処理学会第29回年次大会",
year = "2023",
url = "https://www.anlp.jp/proceedings/annual_meeting/2023/pdf_dir/P3-1.pdf"
}
```
En:
```
@InProceedings{sugimoto_nlp2023_jmedroberta,
author = "Sugimoto, Kaito and Iki, Taichi and Chida, Yuki and Kanazawa, Teruhito and Aizawa, Akiko",
title = "J{M}ed{R}o{BERT}a: a Japanese Pre-trained Language Model on Academic Articles in Medical Sciences (in Japanese)",
booktitle = "Proceedings of the 29th Annual Meeting of the Association for Natural Language Processing",
year = "2023",
url = "https://www.anlp.jp/proceedings/annual_meeting/2023/pdf_dir/P3-1.pdf"
}
```
## Datasets used for pre-training
- abstracts (train: 1.6GB (10M sentences), validation: 0.2GB (1.3M sentences))
- abstracts & body texts (train: 0.2GB (1.4M sentences))
## How to use
**Before using the model, make sure that [Manbyo Dictionary](https://sociocom.naist.jp/manbyou-dic/) has been downloaded under `/usr/local/lib/mecab/dic/userdic`.**
```bash
# download Manbyo-Dictionary
mkdir -p /usr/local/lib/mecab/dic/userdic
wget https://sociocom.jp/~data/2018-manbyo/data/MANBYO_201907_Dic-utf8.dic
mv MANBYO_201907_Dic-utf8.dic /usr/local/lib/mecab/dic/userdic
```
---
**Note: If you don't have root privileges and find it difficult to download the Manbyo Dictionary to `/usr/local/lib/mecab/dic/userdic`, you can still load our model by overriding tokenizer settings as follows:**
```bash
# download Manbyo-Dictionary wherever you like
wget https://sociocom.jp/~data/2018-manbyo/data/MANBYO_201907_Dic-utf8.dic
mv MANBYO_201907_Dic-utf8.dic /anywhere/you/like
```
```python
from transformers import AutoModelForMaskedLM, AutoTokenizer
model = AutoModelForMaskedLM.from_pretrained("alabnii/jmedroberta-base-manbyo-wordpiece")
tokenizer = AutoTokenizer.from_pretrained("alabnii/jmedroberta-base-manbyo-wordpiece", **{
"mecab_kwargs": {
"mecab_option": "-u /anywhere/you/like/MANBYO_201907_Dic-utf8.dic"
}
})
```
---
**Input text must be converted to full-width characters（全角）in advance.**
You can use this model for masked language modeling as follows:
```python
from transformers import AutoModelForMaskedLM, AutoTokenizer
model = AutoModelForMaskedLM.from_pretrained("alabnii/jmedroberta-base-manbyo-wordpiece")
model.eval()
tokenizer = AutoTokenizer.from_pretrained("alabnii/jmedroberta-base-manbyo-wordpiece")
texts = ['この患者は[MASK]と診断された。']
inputs = tokenizer.batch_encode_plus(texts, return_tensors='pt')
outputs = model(**inputs)
tokenizer.convert_ids_to_tokens(outputs.logits[0][1:-1].argmax(axis=-1))
# ['この', '患者', 'は', 'ＡＬＳ', 'と', '診断', 'さ', 'れ', 'た', '。']
```
Alternatively, you can employ [Fill-mask pipeline](https://huggingface.co/tasks/fill-mask).
```python
from transformers import pipeline
fill = pipeline("fill-mask", model="alabnii/jmedroberta-base-manbyo-wordpiece", top_k=10)
fill("この患者は[MASK]と診断された。")
#[{'score': 0.020739275962114334,
# 'token': 11474,
# 'token_str': 'ＡＬＳ',
# 'sequence': 'この 患者 は ＡＬＳ と 診断 さ れ た 。'},
# {'score': 0.0193060003221035,
# 'token': 10777,
# 'token_str': '統合失調症',
# 'sequence': 'この 患者 は 統合失調症 と 診断 さ れ た 。'},
# {'score': 0.014001614414155483,
# 'token': 27318,
# 'token_str': 'Ｆａｂｒｙ病',
# 'sequence': 'この 患者 は Ｆａｂｒｙ病 と 診断 さ れ た 。'},
# ...
```
You can fine-tune this model on downstream tasks.
**See also sample Colab notebooks:** https://colab.research.google.com/drive/1yqUaqLf0Lf_imRT9TXPXEt1dowfK_2CS?usp=sharing
## Tokenization
Mecab (w/ IPAdic & [Manbyo Dictionary](https://sociocom.naist.jp/manbyou-dic/)) was used for pre-training. Each word is tokenized into tokens by [WordPiece](https://huggingface.co/course/chapter6/6).
## Vocabulary
The vocabulary consists of 30000 tokens including words (IPAdic & [Manbyo Dictionary](https://sociocom.naist.jp/manbyou-dic/)) and subwords induced by [WordPiece](https://huggingface.co/course/chapter6/6).
## Training procedure
The following hyperparameters were used during pre-training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 256
- total_eval_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20000
- training_steps: 2000000
- mixed_precision_training: Native AMP
## Note: Why do we call our model RoBERTa, not BERT?
As the config file suggests, our model is based on HuggingFace's `BertForMaskedLM` class. However, we consider our model as **RoBERTa** for the following reasons:
- We kept training only with max sequence length (= 512) tokens.
- We removed the next sentence prediction (NSP) training objective.
- We introduced dynamic masking (changing the masking pattern in each training iteration).
## Acknowledgements
This work was supported by Japan Japan Science and Technology Agency (JST) AIP Trilateral AI Research (Grant Number: JPMJCR20G9), and Joint Usage/Research Center for Interdisciplinary Large-scale Information Infrastructures (JHPCN) (Project ID: jh221004), in Japan.
In this research work, we used the "[mdx: a platform for the data-driven future](https://mdx.jp/)". |
AnonymousSub/SR_rule_based_roberta_twostagetriplet_hier_epochs_1_shard_1 | [
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} | 4 | null | ---
license: apache-2.0
# inference: false
# inference:
# parameters:
tags:
- classification
- zero-shot
---
# Erlangshen-UniMC-DeBERTa-v2-1.4B-Chinese
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/unimc/)
- Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/)
- API: [Fengshen-OpenAPI](https://fengshenbang-lm.com/open-api)
## 简介 Brief Introduction
UniMC 核心思想是将自然语言理解任务转化为 multiple choice 任务，并且使用多个 NLU 任务来进行预训练。我们在英文数据集实验结果表明仅含有 2.35 亿参数的 [ALBERT模型](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-Albert-235M-English)的zero-shot性能可以超越众多千亿的模型。并在中文测评基准 FewCLUE 和 ZeroCLUE 两个榜单中，13亿的[二郎神](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-MegatronBERT-1.3B-Chinese)获得了第一的成绩。
The core idea of UniMC is to convert natural language understanding tasks into multiple choice tasks and use multiple NLU tasks for pre-training. Our experimental results on the English dataset show that the zero-shot performance of a [ALBERT](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-Albert-235M-English) model with only 235 million parameters can surpass that of many hundreds of billions of models. And in the Chinese evaluation benchmarks FewCLUE and ZeroCLUE two lists, 1.3 billion [Erlangshen](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-MegatronBERT-1.3B-Chinese) won the first result.
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 通用 General | 自然语言理解 NLU | 二郎神 Erlangshen | DeBERTa-v2 | 1.4B | Chinese |
## 模型信息 Model Information
我们为零样本学习者提出了一种与输入无关的新范式，从某种意义上说，它与任何格式兼容并适用于一系列语言任务，例如文本分类、常识推理、共指解析、情感分析。我们的方法将零样本学习转化为多项选择任务，避免常用的大型生成模型（如 FLAN）中的问题。它不仅增加了模型的泛化能力，而且显着减少了对参数的需求。我们证明了这种方法可以在通用语言基准上取得最先进的性能，并在自然语言推理和文本分类等任务上产生令人满意的结果。更多详细信息可以参考我们的[论文](https://arxiv.org/abs/2210.08590)或者[GitHub](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/unimc/)
We propose an new paradigm for zero-shot learners that is input-agnostic, in the sense that it is compatible with any format and applicable to a list of language tasks, such as text classification, commonsense reasoning, coreference resolution, sentiment analysis.
Our approach converts zero-shot learning into multiple choice tasks,
avoiding problems in commonly used large generative models such as FLAN. It not only adds generalization ability to the models, but also reduces the needs of parameters significantly. We demonstrate that this approach leads to state-of-the-art performance on common language benchmarks, and produces satisfactory results on tasks such as natural language inference and text classification. For more details, please refer to our [paper](https://arxiv.org/abs/2210.08590) or [github](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/unimc/)
### 下游效果 Performance
我们使用全量数据测评我们的模型性能，并与 RoBERTa 进行对比
We use full data to evaluate our model performance and compare it with RoBERTa
| Model | afqmc | tnews | iflytek | ocnli |
|------------|------------|----------|-----------|----------|
| RoBERTa-110M | 74.06 | 57.5 | 60.36 | 74.3 |
| RoBERTa-330M | 74.88 | 58.79 | 61.52 | 77.77 |
| [Erlangshen-UniMC-DeBERTa-v2-110M-Chinese](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-DeBERTa-v2-110M-Chinese/) | 74.49 | 57.28 | 61.42 | 76.98 |
| [Erlangshen-UniMC-DeBERTa-v2-330M-Chinese](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-DeBERTa-v2-330M-Chinese/) | 76.16 | 58.61 | - |81.86 |
| [Erlangshen-UniMC-DeBERTa-v2-1.4B-Chinese](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-DeBERTa-v2-1.4B-Chinese/) | 76.96 | 60.67 | 63.24 | 83.86 |
## 使用 Usage
```shell
git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git
cd Fengshenbang-LM
pip install --editable .
```
```python3
import argparse
from fengshen.pipelines.multiplechoice import UniMCPipelines
total_parser = argparse.ArgumentParser("TASK NAME")
total_parser = UniMCPipelines.piplines_args(total_parser)
args = total_parser.parse_args()
pretrained_model_path = 'IDEA-CCNL/Erlangshen-UniMC-DeBERTa-v2-1.4B-Chinese'
args.learning_rate=2e-5
args.max_length=512
args.max_epochs=3
args.batchsize=8
args.default_root_dir='./'
model = UniMCPipelines(args, pretrained_model_path)
train_data = []
dev_data = []
test_data = [
{"texta": "放弃了途观L和荣威RX5，果断入手这部车，外观霸气又好开",
"textb": "",
"question": "下面新闻属于哪一个类别？",
"choice": [
"房产",
"汽车",
"教育",
"科技"
],
"answer": "汽车",
"label": 1,
"id": 7759}
]
if args.train:
model.train(train_data, dev_data)
result = model.predict(test_data)
for line in result[:20]:
print(line)
```
## 引用 Citation
如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2210.08590)：
If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2210.08590):
```text
@article{unimc,
author = {Ping Yang and
Junjie Wang and
Ruyi Gan and
Xinyu Zhu and
Lin Zhang and
Ziwei Wu and
Xinyu Gao and
Jiaxing Zhang and
Tetsuya Sakai},
title = {Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective},
journal = {CoRR},
volume = {abs/2210.08590},
year = {2022}
}
```
也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
``` |
AnonymousSub/SR_rule_based_roberta_twostagetriplet_hier_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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} | 7 | 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: 0.9285
- name: F1
type: f1
value: 0.928851862350588
---
<!-- 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-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: 0.2178
- Accuracy: 0.9285
- F1: 0.9289
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8227 | 1.0 | 250 | 0.3212 | 0.8985 | 0.8932 |
| 0.2463 | 2.0 | 500 | 0.2178 | 0.9285 | 0.9289 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.10.0
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AnonymousSub/bert_triplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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} | 2 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 40 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": 40,
"warmup_steps": 4,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, '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})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
AnonymousSub/declutr-model_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
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} | 2 | null | ---
language: ja
license: cc-by-sa-4.0
---
# BERT Base Japanese for Irony
This is a BERT Base model for sentiment analysis in Japanese additionally finetuned for automatic irony detection.
The model was based on [bert-base-japanese-sentiment](https://huggingface.co/daigo/bert-base-japanese-sentiment), and later finetuned on a dataset containing ironic and sarcastic tweets.
## Licenses
The finetuned model with all attached files is licensed under [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/), or Creative Commons Attribution-ShareAlike 4.0 International License.
<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a>
## Citations
Please, cite this model using the following citation.
```
@inproceedings{dan2022bert-base-irony02,
title={北見工業大学 テキスト情報処理研究室 ELECTRA Base 皮肉検出モデル (daigo ver.)},
author={団 俊輔 and プタシンスキ ミハウ and ジェプカ ラファウ and 桝井 文人},
publisher={HuggingFace},
year={2022},
url = "https://huggingface.co/kit-nlp/bert-base-japanese-sentiment-irony"
}
```
|
AnonymousSub/declutr-model_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
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"RobertaForSequenceClassification"
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} | 26 | 2022-11-07T06:37:51Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: Brain_Tumor_Detector_swin
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9981308411214953
- name: F1
type: f1
value: 0.9985111662531018
- name: Recall
type: recall
value: 0.9990069513406157
- name: Precision
type: precision
value: 0.998015873015873
---
<!-- 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. -->
# Brain_Tumor_Detector_swin
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0054
- Accuracy: 0.9981
- F1: 0.9985
- Recall: 0.9990
- Precision: 0.9980
## 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: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.079 | 1.0 | 113 | 0.0283 | 0.9882 | 0.9906 | 0.9930 | 0.9881 |
| 0.0575 | 2.0 | 226 | 0.0121 | 0.9956 | 0.9965 | 0.9950 | 0.9980 |
| 0.0312 | 3.0 | 339 | 0.0054 | 0.9981 | 0.9985 | 0.9990 | 0.9980 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AnonymousSub/declutr-s10-SR | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
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} | 36 | null | ---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: roberta-base-squad-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-squad-finetuned-squad
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.7060
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 17 | 5.9055 |
| No log | 2.0 | 34 | 6.2285 |
| No log | 3.0 | 51 | 6.8639 |
| No log | 4.0 | 68 | 6.3238 |
| No log | 5.0 | 85 | 7.0916 |
| No log | 6.0 | 102 | 6.7791 |
| No log | 7.0 | 119 | 6.8093 |
| No log | 8.0 | 136 | 6.7029 |
| No log | 9.0 | 153 | 6.7142 |
| No log | 10.0 | 170 | 6.7060 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1
- Tokenizers 0.12.1
|
AnonymousSub/roberta-base_squad2.0 | [
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"question-answering",
"transformers",
"autotrain_compatible"
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} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- sentiment140
metrics:
- accuracy
model-index:
- name: Sentiment140_DistilBERT_5E
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: sentiment140
type: sentiment140
config: sentiment140
split: train
args: sentiment140
metrics:
- name: Accuracy
type: accuracy
value: 0.8333333333333334
---
<!-- 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. -->
# Sentiment140_DistilBERT_5E
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the sentiment140 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4897
- Accuracy: 0.8333
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6784 | 0.08 | 50 | 0.6516 | 0.6933 |
| 0.6301 | 0.16 | 100 | 0.5384 | 0.7533 |
| 0.5438 | 0.24 | 150 | 0.4559 | 0.8 |
| 0.4625 | 0.32 | 200 | 0.4287 | 0.8133 |
| 0.4528 | 0.4 | 250 | 0.4056 | 0.8267 |
| 0.4609 | 0.48 | 300 | 0.3883 | 0.8333 |
| 0.4705 | 0.56 | 350 | 0.3886 | 0.8067 |
| 0.4539 | 0.64 | 400 | 0.3967 | 0.82 |
| 0.4483 | 0.72 | 450 | 0.3758 | 0.82 |
| 0.4699 | 0.8 | 500 | 0.4003 | 0.8133 |
| 0.467 | 0.88 | 550 | 0.4021 | 0.8267 |
| 0.454 | 0.96 | 600 | 0.3735 | 0.8333 |
| 0.4227 | 1.04 | 650 | 0.3840 | 0.8267 |
| 0.3584 | 1.12 | 700 | 0.3775 | 0.8333 |
| 0.3618 | 1.2 | 750 | 0.4026 | 0.8267 |
| 0.3634 | 1.28 | 800 | 0.3891 | 0.8133 |
| 0.3751 | 1.36 | 850 | 0.3895 | 0.8267 |
| 0.3484 | 1.44 | 900 | 0.3919 | 0.8267 |
| 0.3764 | 1.52 | 950 | 0.3770 | 0.84 |
| 0.3488 | 1.6 | 1000 | 0.4028 | 0.82 |
| 0.3665 | 1.68 | 1050 | 0.3779 | 0.8333 |
| 0.3925 | 1.76 | 1100 | 0.3726 | 0.84 |
| 0.3624 | 1.84 | 1150 | 0.3655 | 0.84 |
| 0.3876 | 1.92 | 1200 | 0.3648 | 0.8133 |
| 0.3935 | 2.0 | 1250 | 0.3633 | 0.8467 |
| 0.2944 | 2.08 | 1300 | 0.3808 | 0.8333 |
| 0.2957 | 2.16 | 1350 | 0.3836 | 0.8333 |
| 0.266 | 2.24 | 1400 | 0.3940 | 0.8267 |
| 0.2747 | 2.32 | 1450 | 0.3952 | 0.84 |
| 0.314 | 2.4 | 1500 | 0.4060 | 0.8133 |
| 0.3419 | 2.48 | 1550 | 0.4025 | 0.8133 |
| 0.2782 | 2.56 | 1600 | 0.4218 | 0.82 |
| 0.3218 | 2.64 | 1650 | 0.4039 | 0.8333 |
| 0.2863 | 2.72 | 1700 | 0.4130 | 0.8267 |
| 0.3336 | 2.8 | 1750 | 0.4026 | 0.8133 |
| 0.3224 | 2.88 | 1800 | 0.3910 | 0.8267 |
| 0.2709 | 2.96 | 1850 | 0.3979 | 0.84 |
| 0.2701 | 3.04 | 1900 | 0.4127 | 0.8333 |
| 0.2782 | 3.12 | 1950 | 0.4335 | 0.82 |
| 0.2425 | 3.2 | 2000 | 0.4229 | 0.8333 |
| 0.2457 | 3.28 | 2050 | 0.4168 | 0.8333 |
| 0.217 | 3.36 | 2100 | 0.4264 | 0.8267 |
| 0.2522 | 3.44 | 2150 | 0.4250 | 0.8333 |
| 0.2402 | 3.52 | 2200 | 0.4371 | 0.8333 |
| 0.2465 | 3.6 | 2250 | 0.4429 | 0.8333 |
| 0.2427 | 3.68 | 2300 | 0.4435 | 0.8333 |
| 0.2408 | 3.76 | 2350 | 0.4500 | 0.84 |
| 0.1976 | 3.84 | 2400 | 0.4536 | 0.8333 |
| 0.23 | 3.92 | 2450 | 0.4645 | 0.8333 |
| 0.2449 | 4.0 | 2500 | 0.4557 | 0.8467 |
| 0.1933 | 4.08 | 2550 | 0.4672 | 0.84 |
| 0.213 | 4.16 | 2600 | 0.4717 | 0.84 |
| 0.1772 | 4.24 | 2650 | 0.4843 | 0.8267 |
| 0.1917 | 4.32 | 2700 | 0.4690 | 0.8467 |
| 0.2094 | 4.4 | 2750 | 0.4728 | 0.8467 |
| 0.1903 | 4.48 | 2800 | 0.4755 | 0.8467 |
| 0.2541 | 4.56 | 2850 | 0.4791 | 0.84 |
| 0.1805 | 4.64 | 2900 | 0.4877 | 0.84 |
| 0.2183 | 4.72 | 2950 | 0.4940 | 0.8267 |
| 0.2257 | 4.8 | 3000 | 0.4905 | 0.8333 |
| 0.2496 | 4.88 | 3050 | 0.4883 | 0.84 |
| 0.1846 | 4.96 | 3100 | 0.4897 | 0.8333 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AnonymousSub/rule_based_bert_hier_diff_equal_wts_epochs_1_shard_10 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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} | 6 | null | Access to model ardent-figment/gated-model is restricted and you are not in the authorized list. Visit https://huggingface.co/ardent-figment/gated-model to ask for access. |
AnonymousSub/rule_based_bert_mean_diff_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
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} | 3 | null | ---
language: ja
license: cc-by-sa-4.0
---
# bert-base-irony
This is a BERT Base model for the Japanese language finetuned for automatic irony detection.
The model was based on [BERT base Japanese](https://huggingface.co/hiroshi-matsuda-rit/bert-base-japanese-basic-char-v2), and later finetuned on a dataset containing ironic and sarcastic tweets.
## Licenses
The finetuned model with all attached files is licensed under [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/), or Creative Commons Attribution-ShareAlike 4.0 International License.
<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a>
## Citations
Please, cite this model using the following citation.
```
@inproceedings{dan2022bert-base-irony,
title={北見工業大学 テキスト情報処理研究室 BERT Base 皮肉検出モデル (RIT ver.)},
author={団 俊輔 and プタシンスキ ミハウ and ジェプカ ラファウ and 桝井 文人},
publisher={HuggingFace},
year={2022},
url = "https://huggingface.co/kit-nlp/bert-base-japanese-basic-char-v2-irony"
}
```
|
AnonymousSub/rule_based_hier_triplet_epochs_1_shard_1_wikiqa_copy | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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} | 1 | null | Access to model AustinZuo/zeo-bert is restricted and you are not in the authorized list. Visit https://huggingface.co/AustinZuo/zeo-bert to ask for access. |
AnonymousSub/rule_based_only_classfn_twostage_epochs_1_shard_1_wikiqa | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
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}
} | 27 | null | ---
license: mit
tags:
- audio
- music
- generation
- tensorflow
---
# Musika Model: musika_hyperpop
## Model provided by: freepina
Pretrained musika_hyperpop model for the [Musika system](https://github.com/marcoppasini/musika) for fast infinite waveform music generation.
Introduced in [this paper](https://arxiv.org/abs/2208.08706).
## How to use
You can generate music from this pretrained musika_hyperpop model using the notebook available [here](https://colab.research.google.com/drive/1HJWliBXPi-Xlx3gY8cjFI5-xaZgrTD7r).
### Model description
This pretrained GAN system consists of a ResNet-style generator and discriminator. During training, stability is controlled by adapting the strength of gradient penalty regularization on-the-fly. The gradient penalty weighting term is contained in *switch.npy*. The generator is conditioned on a latent coordinate system to produce samples of arbitrary length. The latent representations produced by the generator are then passed to a decoder which converts them into waveform audio.
The generator has a context window of about 12 seconds of audio.
|
AnonymousSub/rule_based_roberta_bert_quadruplet_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
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"RobertaForQuestionAnswering"
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} | 2 | null | ---
license: mit
tags:
- flair
- token-classification
- sequence-tagger-model
language: de
widget:
- text: "ab dryn matten, gelegen ze Niderlentz, hinden in langen matten eychen, waren ze etlichen zitten Jennis Huͤbers von Niderlentz, und hat sie gekoͧft von Walther Renold"
---
# Königsfelden NER
A model for historical German developed by Ismail Prada Ziegler as part of a research project at the University of Bern, Digital Humanities.
## Performance
| | PER | ORG | LOC | Micro-Avg |
| :---: | :---: | :---: | :---: | :---: |
| Precision | 88.76% | 69.01% | 81.96% | 84.19% |
| Recall | 88.99% | 62.97% | 85.10% | 84.02% |
| F1-Score | 88.88% | 65.85% | 83.50% | 84.10% |
## Data Set
[Königsfelden Charters](https://zenodo.org/record/5179361), 14th-17th Century, Early New High German, 623k tokens training, dev, test data.
## Notice
This model was a prototype with no extensive experimenting, better results can be likely be achieved on this dataset.
The documents in the dataset are published [here](https://www.koenigsfelden.uzh.ch).
|
AnonymousSub/rule_based_roberta_bert_triplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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} | 2 | null | ---
language: tr
tag: text-classification
widget:
- text: "Oldukça kullanışlı bir ürün."
---
This repository contains two models that has been finetuned on twitter-XMLRoBERTa https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base.
3_Label model can classify text as positive, neutral and negative.
2_Label_Twitter is finetuned with tweets and can predict tweets as positive and negative. |
AnonymousSub/rule_based_roberta_only_classfn_twostage_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
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} | 2 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: franfram/testpyramidsrnd
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AnonymousSub/rule_based_roberta_twostage_quadruplet_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
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}
} | 24 | null | ---
language:
- lt
license: apache-2.0
tags:
- lt-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small LT - Lithuanian Whisper
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: lt
split: train+validation
args: lt
metrics:
- name: Wer
type: wer
value: 32.65614439629468
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small LT - Lithuanian Whisper
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3871
- Wer: 32.6561
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2419 | 1.8 | 1000 | 0.3749 | 38.7707 |
| 0.0425 | 3.6 | 2000 | 0.3591 | 34.2345 |
| 0.0062 | 5.4 | 3000 | 0.3779 | 32.7555 |
| 0.0034 | 7.19 | 4000 | 0.3871 | 32.6561 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
AnonymousSub/unsup-consert-base_squad2.0 | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
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} | 2 | null | ---
language: en
---
This model is the fine-tuned model of "dbmdz/bert-base-turkish-cased" (https://huggingface.co/dbmdz/bert-base-turkish-128k-uncased) based on TC32 DATASET. |
AntonClaesson/movie-plot-generator | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
],
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}
} | 9 | null | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-amazon-en-es
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0294
- Rouge1: 16.5993
- Rouge2: 8.0138
- Rougel: 16.1315
- Rougelsum: 16.2931
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 6.5928 | 1.0 | 1209 | 3.3005 | 14.7775 | 6.4604 | 14.2574 | 14.3422 |
| 3.9024 | 2.0 | 2418 | 3.1399 | 16.8632 | 8.6474 | 16.065 | 16.2114 |
| 3.5806 | 3.0 | 3627 | 3.0869 | 18.2422 | 9.2647 | 17.6227 | 17.7649 |
| 3.4201 | 4.0 | 4836 | 3.0590 | 17.7826 | 8.9742 | 16.9951 | 17.1804 |
| 3.3202 | 5.0 | 6045 | 3.0598 | 17.7808 | 8.6038 | 17.2243 | 17.4125 |
| 3.2436 | 6.0 | 7254 | 3.0409 | 16.8469 | 8.2339 | 16.3935 | 16.5818 |
| 3.2079 | 7.0 | 8463 | 3.0332 | 16.8148 | 8.2115 | 16.3166 | 16.4832 |
| 3.1801 | 8.0 | 9672 | 3.0294 | 16.5993 | 8.0138 | 16.1315 | 16.2931 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Antony/mint_model | []
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} | 0 | null | ---
tags:
- flair
- token-classification
- sequence-tagger-model
---
### Demo: How to use in Flair
Requires:
- **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("GuiGel/beto-finetune-meddocan")
# make example sentence
sentence = Sentence("On September 1st George won 1 dollar while watching Game of Thrones.")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
``` |
Anubhav23/IndianlegalBert | []
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}
} | 0 | null | ---
language: ja
thumbnail: https://github.com/studio-ousia/luke/raw/master/resources/luke_logo.png
tags:
- luke
- named entity recognition
- entity typing
- relation classification
- question answering
license: apache-2.0
---
## luke-japanese-large
**luke-japanese** is the Japanese version of **LUKE** (**L**anguage
**U**nderstanding with **K**nowledge-based **E**mbeddings), a pre-trained
_knowledge-enhanced_ contextualized representation of words and entities. LUKE
treats words and entities in a given text as independent tokens, and outputs
contextualized representations of them. Please refer to our
[GitHub repository](https://github.com/studio-ousia/luke) for more details and
updates.
This model contains Wikipedia entity embeddings which are not used in general
NLP tasks. Please use the
[lite version](https://huggingface.co/studio-ousia/luke-japanese-large-lite/)
for tasks that do not use Wikipedia entities as inputs.
**luke-japanese**は、単語とエンティティの知識拡張型訓練済み Transformer モデル**LUKE**の日本語版です。LUKE は単語とエンティティを独立したトークンとして扱い、これらの文脈を考慮した表現を出力します。詳細については、[GitHub リポジトリ](https://github.com/studio-ousia/luke)を参照してください。
このモデルは、通常の NLP タスクでは使われない Wikipedia エンティティのエンベディングを含んでいます。単語の入力のみを使うタスクには、[lite version](https://huggingface.co/studio-ousia/luke-japanese-large-lite/)を使用してください。
### Experimental results on JGLUE
The experimental results evaluated on the dev set of
[JGLUE](https://github.com/yahoojapan/JGLUE) is shown as follows:
| Model | MARC-ja | JSTS | JNLI | JCommonsenseQA |
| ----------------------------- | --------- | ------------------- | --------- | -------------- |
| | acc | Pearson/Spearman | acc | acc |
| **LUKE Japanese large** | **0.965** | **0.932**/**0.902** | **0.927** | 0.893 |
| _Baselines:_ | |
| Tohoku BERT large | 0.955 | 0.913/0.872 | 0.900 | 0.816 |
| Waseda RoBERTa large (seq128) | 0.954 | 0.930/0.896 | 0.924 | **0.907** |
| Waseda RoBERTa large (seq512) | 0.961 | 0.926/0.892 | 0.926 | 0.891 |
| XLM RoBERTa large | 0.964 | 0.918/0.884 | 0.919 | 0.840 |
The baseline scores are obtained from
[here](https://github.com/yahoojapan/JGLUE/blob/a6832af23895d6faec8ecf39ec925f1a91601d62/README.md).
### Citation
```latex
@inproceedings{yamada2020luke,
title={LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention},
author={Ikuya Yamada and Akari Asai and Hiroyuki Shindo and Hideaki Takeda and Yuji Matsumoto},
booktitle={EMNLP},
year={2020}
}
```
|
Anubhav23/indianlegal | []
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} | 0 | null | ---
language: ja
thumbnail: https://github.com/studio-ousia/luke/raw/master/resources/luke_logo.png
tags:
- luke
- named entity recognition
- entity typing
- relation classification
- question answering
license: apache-2.0
---
## luke-japanese-large-lite
**luke-japanese** is the Japanese version of **LUKE** (**L**anguage
**U**nderstanding with **K**nowledge-based **E**mbeddings), a pre-trained
_knowledge-enhanced_ contextualized representation of words and entities. LUKE
treats words and entities in a given text as independent tokens, and outputs
contextualized representations of them. Please refer to our
[GitHub repository](https://github.com/studio-ousia/luke) for more details and
updates.
This model is a lightweight version which does not contain Wikipedia entity
embeddings. Please use the
[full version](https://huggingface.co/studio-ousia/luke-japanese-large/) for
tasks that use Wikipedia entities as inputs.
**luke-japanese**は、単語とエンティティの知識拡張型訓練済み Transformer モデル**LUKE**の日本語版です。LUKE は単語とエンティティを独立したトークンとして扱い、これらの文脈を考慮した表現を出力します。詳細については、[GitHub リポジトリ](https://github.com/studio-ousia/luke)を参照してください。
このモデルは、Wikipedia エンティティのエンベディングを含まない軽量版のモデルです。Wikipedia エンティティを入力として使うタスクには、[full version](https://huggingface.co/studio-ousia/luke-japanese-large/)を使用してください。
### Experimental results on JGLUE
The experimental results evaluated on the dev set of
[JGLUE](https://github.com/yahoojapan/JGLUE) is shown as follows:
| Model | MARC-ja | JSTS | JNLI | JCommonsenseQA |
| ----------------------------- | --------- | ------------------- | --------- | -------------- |
| | acc | Pearson/Spearman | acc | acc |
| **LUKE Japanese large** | **0.965** | **0.932**/**0.902** | **0.927** | 0.893 |
| _Baselines:_ | |
| Tohoku BERT large | 0.955 | 0.913/0.872 | 0.900 | 0.816 |
| Waseda RoBERTa large (seq128) | 0.954 | 0.930/0.896 | 0.924 | **0.907** |
| Waseda RoBERTa large (seq512) | 0.961 | 0.926/0.892 | 0.926 | 0.891 |
| XLM RoBERTa large | 0.964 | 0.918/0.884 | 0.919 | 0.840 |
The baseline scores are obtained from
[here](https://github.com/yahoojapan/JGLUE/blob/a6832af23895d6faec8ecf39ec925f1a91601d62/README.md).
### Citation
```latex
@inproceedings{yamada2020luke,
title={LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention},
author={Ikuya Yamada and Akari Asai and Hiroyuki Shindo and Hideaki Takeda and Yuji Matsumoto},
booktitle={EMNLP},
year={2020}
}
```
|
Apisate/DialoGPT-small-jordan | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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}
} | 12 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: wav2vec2-xlsr-53-espeak-cv-ft-evn-ntsema-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: audiofolder
type: audiofolder
config: default
split: train
args: default
metrics:
- name: Wer
type: wer
value: 0.9833333333333333
---
<!-- 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-evn-ntsema-colab
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 audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9676
- Wer: 0.9833
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.9241 | 6.15 | 400 | 1.4873 | 0.9933 |
| 0.6931 | 12.3 | 800 | 1.4871 | 0.9867 |
| 0.3226 | 18.46 | 1200 | 1.7452 | 0.9867 |
| 0.1762 | 24.61 | 1600 | 1.9676 | 0.9833 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.14.0.dev20221107+cu116
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Aplinxy9plin/toxic-detection-rus | []
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} | 0 | null | ---
tags:
- conversational
---
#lucy DialoGPT Model |
Apoorva/k2t-test | [
"pytorch",
"t5",
"text2text-generation",
"en",
"transformers",
"keytotext",
"k2t",
"Keywords to Sentences",
"autotrain_compatible"
]
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"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: "
}
}
} | 7 | null | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- text-to-image
---
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
### Arcane based Artwork Diffusion Model
I present you fine tuned model of stable-diffusion-v1-5, which heavily based of
work of great artworks from Arcane.
Use the tokens **_arcane style_** in your prompts for the effect.
Model was trained using the diffusers library, which based on Dreambooth implementation.
Training steps included:
- prior preservation loss
- train-text-encoder fine tuning
### 🧨 Diffusers
This model can be used just like any other Stable Diffusion model. For more information,
please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX]().
```python
#!pip install diffusers transformers scipy torch
from diffusers import StableDiffusionPipeline
import torch
model_id = "s3nh/artwork-arcane-stable-diffusion"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "Rain forest, arcane style"
image = pipe(prompt).images[0]
image.save("./example_output.png")
```
# Gallery
## Rain forest, arcane style
## Car traffic, arcane style
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
|
ArBert/albert-base-v2-finetuned-ner-kmeans | [
"pytorch",
"tensorboard",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
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"AlbertForTokenClassification"
],
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} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Rundstedtz/distilbert-base-uncased-letters-from-jenny
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. -->
# Rundstedtz/distilbert-base-uncased-letters-from-jenny
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: 3.5319
- Validation Loss: 2.9614
- Epoch: 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -988, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.5319 | 2.9614 | 0 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.2
|
ArBert/bert-base-uncased-finetuned-ner-agglo | []
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} | 0 | null | ---
language:
- de
license: bigscience-bloom-rail-1.0
library_name: transformers
tags:
- ggml
- bloom
datasets:
- oscar
pipeline_tag: text-generation
---
# BLOOM-CLP German (6.4B parameters)
This is a monolingual German language model trained using the [CLP-Transfer](https://arxiv.org/abs/2301.09626) method based on [BLOOM-7b1](https://huggingface.co/bigscience/bloom-7b1).
You can try out the model at [European Language Grid](https://live.european-language-grid.eu/catalogue/tool-service/20825/try%20out/).
### How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='malteos/bloom-6b4-clp-german')
>>> set_seed(42)
>>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=3)
[{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."},
{'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"},
{'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"},]
```
## Training dataset
- ca. 50B German tokens
- Web-crawled content from the German subset [OSCAR v22.01](https://oscar-corpus.com/post/oscar-v22-01/) (excluding content tagged as header, footer, noisy, or adult)
- Web-crawled content from the [GC4 Corpus](https://german-nlp-group.github.io/projects/gc4-corpus.html) (including only the head and middle parts)
- Both Web-crawled datasets are deduplicated with [Google's suffix array implementation](https://github.com/google-research/deduplicate-text-datasets)
- German court decisions from [Open Legal Data](http://openlegaldata.io/)
## Code
- [BigScience's Megatron-Deepspeed fork](https://github.com/bigscience-workshop/Megatron-DeepSpeed)
## Hardware
- 32xA100-40GB GPUs
- 12.5 days
- [Tensorboard logs](https://huggingface.co/malteos/bloom-6b4-clp-german-logs/tensorboard)
## Evaluation
Validation PPL compared to from-scratch training (the lower the better):
<img alt="Tokens vs PPL" src="https://github.com/malteos/clp-transfer/raw/main/german-6b-ppl.png">
Additional evaluations can be found in [our paper](https://arxiv.org/abs/2301.09626).
## How to cite
If you are using our code or models, please cite [our paper](https://arxiv.org/abs/2301.09626):
```bibtex
@misc{Ostendorff2023clp,
doi = {10.48550/ARXIV.2301.09626},
author = {Ostendorff, Malte and Rehm, Georg},
title = {Efficient Language Model Training through Cross-Lingual and Progressive Transfer Learning},
publisher = {arXiv},
year = {2023}
}
```
## License
[BigScience BLOOM RAIL 1.0](https://bigscience.huggingface.co/blog/the-bigscience-rail-license)
|
AragornII/DialoGPT-small-harrypotter | []
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} | 0 | null | ---
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-chinese-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-chinese-finetuned-ner
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0063
- F1: 1.0
## 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: 5e-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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.0712 | 1.0 | 3 | 1.6814 | 0.0472 |
| 1.545 | 2.0 | 6 | 1.1195 | 0.4993 |
| 1.1234 | 3.0 | 9 | 0.7210 | 0.7259 |
| 0.6518 | 4.0 | 12 | 0.4457 | 0.8595 |
| 0.497 | 5.0 | 15 | 0.2754 | 0.9050 |
| 0.2761 | 6.0 | 18 | 0.1742 | 0.9509 |
| 0.2281 | 7.0 | 21 | 0.1053 | 0.9903 |
| 0.1189 | 8.0 | 24 | 0.0642 | 0.9976 |
| 0.1002 | 9.0 | 27 | 0.0416 | 1.0 |
| 0.053 | 10.0 | 30 | 0.0280 | 1.0 |
| 0.0525 | 11.0 | 33 | 0.0206 | 1.0 |
| 0.0412 | 12.0 | 36 | 0.0156 | 1.0 |
| 0.0284 | 13.0 | 39 | 0.0123 | 1.0 |
| 0.0191 | 14.0 | 42 | 0.0101 | 1.0 |
| 0.0227 | 15.0 | 45 | 0.0087 | 1.0 |
| 0.0167 | 16.0 | 48 | 0.0077 | 1.0 |
| 0.0161 | 17.0 | 51 | 0.0071 | 1.0 |
| 0.015 | 18.0 | 54 | 0.0066 | 1.0 |
| 0.0167 | 19.0 | 57 | 0.0064 | 1.0 |
| 0.0121 | 20.0 | 60 | 0.0063 | 1.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.12.0+cu102
- Datasets 1.18.4
- Tokenizers 0.12.1
|
ArcQ/gpt-experiments | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: wav2vec2-xlsr-53-espeak-cv-ft-mhr-ntsema-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: audiofolder
type: audiofolder
config: default
split: train
args: default
metrics:
- name: Wer
type: wer
value: 0.8127090301003345
---
<!-- 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-mhr-ntsema-colab
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 audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7728
- Wer: 0.8127
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.8463 | 5.79 | 400 | 1.0428 | 0.9331 |
| 1.4576 | 11.59 | 800 | 0.6796 | 0.8495 |
| 0.8054 | 17.39 | 1200 | 0.7131 | 0.8227 |
| 0.4946 | 23.19 | 1600 | 0.7202 | 0.8194 |
| 0.3157 | 28.98 | 2000 | 0.7728 | 0.8127 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.14.0.dev20221107+cu116
- Datasets 2.6.1
- Tokenizers 0.13.2
|
AryanLala/autonlp-Scientific_Title_Generator-34558227 | [
"pytorch",
"pegasus",
"text2text-generation",
"en",
"dataset:AryanLala/autonlp-data-Scientific_Title_Generator",
"transformers",
"autonlp",
"co2_eq_emissions",
"autotrain_compatible",
"has_space"
]
| text2text-generation | {
"architectures": [
"PegasusForConditionalGeneration"
],
"model_type": "pegasus",
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}
} | 103 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERTModified-finetuned-wikitext-test
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. -->
# BERTModified-finetuned-wikitext-test
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 18.8994
- Precision: 0.25
- Recall: 0.25
- F1: 0.25
- Accuracy: 0.25
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 19.9877 | 1.0 | 250 | 19.8070 | 0.0385 | 0.0385 | 0.0385 | 0.0385 |
| 15.4776 | 2.0 | 500 | 20.2930 | 0.0577 | 0.0577 | 0.0577 | 0.0577 |
| 13.1238 | 3.0 | 750 | 20.1112 | 0.0769 | 0.0769 | 0.0769 | 0.0769 |
| 11.1387 | 4.0 | 1000 | 19.9105 | 0.0897 | 0.0897 | 0.0897 | 0.0897 |
| 9.5317 | 5.0 | 1250 | 19.9108 | 0.1282 | 0.1282 | 0.1282 | 0.1282 |
| 8.037 | 6.0 | 1500 | 19.6093 | 0.1410 | 0.1410 | 0.1410 | 0.1410 |
| 6.7498 | 7.0 | 1750 | 19.1636 | 0.1474 | 0.1474 | 0.1474 | 0.1474 |
| 5.6472 | 8.0 | 2000 | 19.6709 | 0.1538 | 0.1538 | 0.1538 | 0.1538 |
| 4.6665 | 9.0 | 2250 | 19.2537 | 0.1667 | 0.1667 | 0.1667 | 0.1667 |
| 3.9107 | 10.0 | 2500 | 19.1982 | 0.1474 | 0.1474 | 0.1474 | 0.1474 |
| 3.1874 | 11.0 | 2750 | 18.9938 | 0.1731 | 0.1731 | 0.1731 | 0.1731 |
| 2.5846 | 12.0 | 3000 | 18.7462 | 0.2115 | 0.2115 | 0.2115 | 0.2115 |
| 2.1464 | 13.0 | 3250 | 19.0017 | 0.1667 | 0.1667 | 0.1667 | 0.1667 |
| 1.7521 | 14.0 | 3500 | 18.4513 | 0.1859 | 0.1859 | 0.1859 | 0.1859 |
| 1.4561 | 15.0 | 3750 | 18.7532 | 0.2051 | 0.2051 | 0.2051 | 0.2051 |
| 1.2254 | 16.0 | 4000 | 18.3970 | 0.2179 | 0.2179 | 0.2179 | 0.2179 |
| 1.0416 | 17.0 | 4250 | 18.9764 | 0.1859 | 0.1859 | 0.1859 | 0.1859 |
| 0.8923 | 18.0 | 4500 | 18.3271 | 0.2244 | 0.2244 | 0.2244 | 0.2244 |
| 0.7803 | 19.0 | 4750 | 18.5893 | 0.2436 | 0.2436 | 0.2436 | 0.2436 |
| 0.6839 | 20.0 | 5000 | 18.3505 | 0.2051 | 0.2051 | 0.2051 | 0.2051 |
| 0.6175 | 21.0 | 5250 | 18.6798 | 0.2051 | 0.2051 | 0.2051 | 0.2051 |
| 0.5491 | 22.0 | 5500 | 18.7426 | 0.2115 | 0.2115 | 0.2115 | 0.2115 |
| 0.4952 | 23.0 | 5750 | 18.3955 | 0.2179 | 0.2179 | 0.2179 | 0.2179 |
| 0.4441 | 24.0 | 6000 | 18.5502 | 0.2564 | 0.2564 | 0.2564 | 0.2564 |
| 0.4047 | 25.0 | 6250 | 18.9599 | 0.2244 | 0.2244 | 0.2244 | 0.2244 |
| 0.3768 | 26.0 | 6500 | 18.8141 | 0.2308 | 0.2308 | 0.2308 | 0.2308 |
| 0.3435 | 27.0 | 6750 | 18.9732 | 0.2436 | 0.2436 | 0.2436 | 0.2436 |
| 0.3164 | 28.0 | 7000 | 18.9216 | 0.2372 | 0.2372 | 0.2372 | 0.2372 |
| 0.2954 | 29.0 | 7250 | 18.6152 | 0.1987 | 0.1987 | 0.1987 | 0.1987 |
| 0.2736 | 30.0 | 7500 | 18.6001 | 0.25 | 0.25 | 0.25 | 0.25 |
| 0.2491 | 31.0 | 7750 | 19.1374 | 0.2436 | 0.2436 | 0.2436 | 0.2436 |
| 0.2359 | 32.0 | 8000 | 18.8624 | 0.25 | 0.25 | 0.25 | 0.25 |
| 0.2222 | 33.0 | 8250 | 18.3201 | 0.2308 | 0.2308 | 0.2308 | 0.2308 |
| 0.212 | 34.0 | 8500 | 18.7708 | 0.2179 | 0.2179 | 0.2179 | 0.2179 |
| 0.1864 | 35.0 | 8750 | 18.8994 | 0.2372 | 0.2372 | 0.2372 | 0.2372 |
| 0.1771 | 36.0 | 9000 | 18.3130 | 0.2308 | 0.2308 | 0.2308 | 0.2308 |
| 0.1703 | 37.0 | 9250 | 18.6183 | 0.2436 | 0.2436 | 0.2436 | 0.2436 |
| 0.1554 | 38.0 | 9500 | 18.8593 | 0.2372 | 0.2372 | 0.2372 | 0.2372 |
| 0.1469 | 39.0 | 9750 | 18.8936 | 0.2628 | 0.2628 | 0.2628 | 0.2628 |
| 0.1407 | 40.0 | 10000 | 18.9002 | 0.2372 | 0.2372 | 0.2372 | 0.2372 |
| 0.1328 | 41.0 | 10250 | 19.1827 | 0.2564 | 0.2564 | 0.2564 | 0.2564 |
| 0.1297 | 42.0 | 10500 | 18.5465 | 0.25 | 0.25 | 0.25 | 0.25 |
| 0.1226 | 43.0 | 10750 | 18.9125 | 0.2308 | 0.2308 | 0.2308 | 0.2308 |
| 0.1218 | 44.0 | 11000 | 19.0831 | 0.2308 | 0.2308 | 0.2308 | 0.2308 |
| 0.1136 | 45.0 | 11250 | 18.7969 | 0.2372 | 0.2372 | 0.2372 | 0.2372 |
| 0.1075 | 46.0 | 11500 | 18.7629 | 0.25 | 0.25 | 0.25 | 0.25 |
| 0.1044 | 47.0 | 11750 | 18.9700 | 0.2115 | 0.2115 | 0.2115 | 0.2115 |
| 0.1042 | 48.0 | 12000 | 18.7211 | 0.2628 | 0.2628 | 0.2628 | 0.2628 |
| 0.1008 | 49.0 | 12250 | 18.9104 | 0.2244 | 0.2244 | 0.2244 | 0.2244 |
| 0.1014 | 50.0 | 12500 | 18.7892 | 0.25 | 0.25 | 0.25 | 0.25 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Ashim/dga-transformer | []
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} | 0 | null | ---
license: other
tags:
- generated_from_trainer
datasets:
- AlekseyKorshuk/amazon-reviews-input-output
metrics:
- accuracy
model-index:
- name: amazon-reviews-input-output-6.7b
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: AlekseyKorshuk/amazon-reviews-input-output
type: AlekseyKorshuk/amazon-reviews-input-output
metrics:
- name: Accuracy
type: accuracy
value: 0.03882113821138211
---
<!-- 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. -->
# amazon-reviews-input-output-6.7b
This model is a fine-tuned version of [facebook/opt-6.7b](https://huggingface.co/facebook/opt-6.7b) on the AlekseyKorshuk/amazon-reviews-input-output dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8574
- Accuracy: 0.0388
- Samples: 100
- Perplexity: 17.4166
- Table: <wandb.data_types.Table object at 0x7fd30eb4e940>
## 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
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.9912 | 0.06 | 1 | 2.7441 | 0.0404 |
| 2.9329 | 0.12 | 2 | 2.7441 | 0.0404 |
| 2.9138 | 0.19 | 3 | 2.8262 | 0.0389 |
| 2.9395 | 0.25 | 4 | 2.8262 | 0.0389 |
| 2.9109 | 0.31 | 5 | 2.7949 | 0.0399 |
| 2.8394 | 0.38 | 6 | 2.7461 | 0.0403 |
| 2.9365 | 0.44 | 7 | 2.7207 | 0.0399 |
| 2.7588 | 0.5 | 8 | 2.7070 | 0.0403 |
| 2.9751 | 0.56 | 9 | 2.6816 | 0.0407 |
| 2.844 | 0.62 | 10 | 2.6738 | 0.0404 |
| 2.731 | 0.69 | 11 | 2.6680 | 0.0406 |
| 2.7434 | 0.75 | 12 | 2.6699 | 0.0404 |
| 2.9043 | 0.81 | 13 | 2.6855 | 0.0400 |
| 2.8564 | 0.88 | 14 | 2.6855 | 0.0400 |
| 2.8716 | 0.94 | 15 | 2.6855 | 0.0400 |
| 2.896 | 1.0 | 16 | 2.6953 | 0.0398 |
| 1.9858 | 1.06 | 17 | 2.7070 | 0.0400 |
| 2.0563 | 1.12 | 18 | 2.7285 | 0.0400 |
| 2.04 | 1.19 | 19 | 2.7676 | 0.0398 |
| 1.9885 | 1.25 | 20 | 2.7910 | 0.0396 |
| 2.09 | 1.31 | 21 | 2.7969 | 0.0393 |
| 2.059 | 1.38 | 22 | 2.8105 | 0.0395 |
| 2.0498 | 1.44 | 23 | 2.7930 | 0.0398 |
| 1.9568 | 1.5 | 24 | 2.7910 | 0.0401 |
| 2.1418 | 1.56 | 25 | 2.7930 | 0.0398 |
| 1.975 | 1.62 | 26 | 2.7930 | 0.0397 |
| 1.996 | 1.69 | 27 | 2.7949 | 0.0393 |
| 1.9617 | 1.75 | 28 | 2.8047 | 0.0392 |
| 2.2062 | 1.81 | 29 | 2.8145 | 0.0388 |
| 1.9929 | 1.88 | 30 | 2.8145 | 0.0386 |
| 1.9235 | 1.94 | 31 | 2.8281 | 0.0390 |
| 1.9127 | 2.0 | 32 | 2.8574 | 0.0388 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Ashkanmh/bert-base-parsbert-uncased-finetuned | [
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| fill-mask | {
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} | 3 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_alien
type: atari_alien
metrics:
- type: mean_reward
value: 4380.00 +/- 0.00
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_alien** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Ashok/my-new-tokenizer | []
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} | 0 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_phoenix
type: atari_phoenix
metrics:
- type: mean_reward
value: nan +/- nan
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_phoenix** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
AshtonBenson/DialoGPT-small-quentin-coldwater | []
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} | 0 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_pitfall
type: atari_pitfall
metrics:
- type: mean_reward
value: nan +/- nan
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_pitfall** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Aspect11/DialoGPT-Medium-LiSBot | [
"pytorch",
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"text-generation",
"transformers",
"conversational"
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} | 7 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_pong
type: atari_pong
metrics:
- type: mean_reward
value: 21.00 +/- 0.00
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_pong** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Asuramaru/DialoGPT-small-rintohsaka | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 7 | null | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/understaters/ddpm-butterflies-128/tensorboard?#scalars)
|
At3ee/wav2vec2-base-timit-demo-colab | []
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} | 0 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_privateye
type: atari_privateye
metrics:
- type: mean_reward
value: 100.00 +/- 0.00
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_privateye** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Atampy26/GPT-Glacier | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
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} | 5 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_qbert
type: atari_qbert
metrics:
- type: mean_reward
value: nan +/- nan
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_qbert** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Atarax/rick | []
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} | 0 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_riverraid
type: atari_riverraid
metrics:
- type: mean_reward
value: 15935.00 +/- 755.00
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_riverraid** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Atchuth/MBOT | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: erikdavidsson42/distilbert-base-uncased-finetuned-medium
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. -->
# erikdavidsson42/distilbert-base-uncased-finetuned-medium
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: 2.9469
- Validation Loss: 2.7043
- Epoch: 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7567, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.9469 | 2.7043 | 0 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.5.0
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Augustab/distilbert-base-uncased-finetuned-cola | []
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} | 0 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_timepilot
type: atari_timepilot
metrics:
- type: mean_reward
value: nan +/- nan
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_timepilot** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Augustvember/WOKKAWOKKA | [
"pytorch",
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} | 12 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_tutankham
type: atari_tutankham
metrics:
- type: mean_reward
value: nan +/- nan
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_tutankham** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Augustvember/WokkaBot | []
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} | 0 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_upndown
type: atari_upndown
metrics:
- type: mean_reward
value: nan +/- nan
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_upndown** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Augustvember/WokkaBot2 | []
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} | 0 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_venture
type: atari_venture
metrics:
- type: mean_reward
value: 1650.00 +/- 250.00
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_venture** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Augustvember/WokkaBot6 | []
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} | 0 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_yarsrevenge
type: atari_yarsrevenge
metrics:
- type: mean_reward
value: nan +/- nan
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_yarsrevenge** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Augustvember/WokkaBot7 | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-A3
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. -->
# finetuning-sentiment-model-A3
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3212
- Accuracy: 0.8760
- F1: 0.3516
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Aurora/community.afpglobal | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
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: 1.6340
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4965 | 1.0 | 554 | 1.5562 |
| 1.2141 | 2.0 | 1108 | 1.5012 |
| 0.7883 | 3.0 | 1662 | 1.6340 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
AvatarXD/DialoGPT-medium-Blitzo | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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} | 14 | null | ---
license: creativeml-openrail-m
---
**_WeeBoo Diffusion_** is a model made for **creating characters and backgrounds**
**in model 1**
you can do things in **anime, cartoon, manga, novel**
in 2 you will be able to do in **_addition to the characters, varied things like backgrounds and more complex art styles, try_**
|
Aviora/phobert-ner | []
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} | 0 | null | ---
license: other
tags:
- generated_from_trainer
datasets:
- AlekseyKorshuk/amazon-reviews-input-output
metrics:
- accuracy
model-index:
- name: amazon-reviews-input-output-6.7b-best
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: AlekseyKorshuk/amazon-reviews-input-output
type: AlekseyKorshuk/amazon-reviews-input-output
metrics:
- name: Accuracy
type: accuracy
value: 0.040325203252032524
---
<!-- 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. -->
# amazon-reviews-input-output-6.7b-best
This model is a fine-tuned version of [facebook/opt-6.7b](https://huggingface.co/facebook/opt-6.7b) on the AlekseyKorshuk/amazon-reviews-input-output dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6953
- Accuracy: 0.0403
- Samples: 100
- Perplexity: 14.8101
- Table: <wandb.data_types.Table object at 0x7fc684448b50>
## 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
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.9912 | 0.06 | 1 | 2.7441 | 0.0404 |
| 2.9329 | 0.12 | 2 | 2.7441 | 0.0404 |
| 2.9138 | 0.19 | 3 | 2.8262 | 0.0389 |
| 2.9395 | 0.25 | 4 | 2.8262 | 0.0389 |
| 2.9109 | 0.31 | 5 | 2.7949 | 0.0399 |
| 2.8391 | 0.38 | 6 | 2.7461 | 0.0403 |
| 2.9368 | 0.44 | 7 | 2.7207 | 0.0398 |
| 2.7583 | 0.5 | 8 | 2.7070 | 0.0403 |
| 2.9756 | 0.56 | 9 | 2.6836 | 0.0408 |
| 2.8442 | 0.62 | 10 | 2.6738 | 0.0403 |
| 2.7312 | 0.69 | 11 | 2.6680 | 0.0405 |
| 2.7439 | 0.75 | 12 | 2.6699 | 0.0404 |
| 2.9075 | 0.81 | 13 | 2.6797 | 0.0403 |
| 2.8518 | 0.88 | 14 | 2.6797 | 0.0403 |
| 2.8579 | 0.94 | 15 | 2.6777 | 0.0404 |
| 2.8916 | 1.0 | 16 | 2.6953 | 0.0403 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Axcel/DialoGPT-small-rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 14 | null | ---
license: mit
---
### gibasachan on Stable Diffusion
This is the `gibasachan` 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`:
|
Axon/resnet50-v1 | [
"dataset:ImageNet",
"arxiv:1512.03385",
"Axon",
"Elixir",
"license:apache-2.0"
]
| null | {
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}
} | 0 | null | ---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: roberta-finetuned-country
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-finetuned-country
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Ayah/GPT2-DBpedia | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
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} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: wav2vec2-xlsr-53-espeak-cv-ft-bak2-ntsema-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: audiofolder
type: audiofolder
config: default
split: train
args: default
metrics:
- name: Wer
type: wer
value: 1.0778097982708934
---
<!-- 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-bak2-ntsema-colab
This model is a fine-tuned version of [ntsema/wav2vec2-xlsr-53-espeak-cv-ft-tat-ntsema-colab](https://huggingface.co/ntsema/wav2vec2-xlsr-53-espeak-cv-ft-tat-ntsema-colab) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: inf
- Wer: 1.0778
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1586 | 8.33 | 400 | inf | 1.0029 |
| 0.3107 | 16.66 | 800 | inf | 1.0202 |
| 0.1534 | 24.99 | 1200 | inf | 1.0778 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Ayham/albert_bert_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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}
} | 12 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: distilbert-imdb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.92916
---
<!-- 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-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:
- Loss: 0.1827
- Accuracy: 0.9292
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2182 | 1.0 | 1563 | 0.1827 | 0.9292 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Ayham/albert_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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} | 7 | 2022-11-07T23:20:26Z | ---
license: creativeml-openrail-m
---
To use draw emphasis from the training model include the word `m_yukoring` in your prompt.
Yukoring is an artists that does a lot of anime watercolor style art.
License This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies:
You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here |
Ayham/bert_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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],
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} | 6 | 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
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8598545123891794
---
<!-- 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: 0.1398
- F1: 0.8599
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2567 | 1.0 | 525 | 0.1771 | 0.8164 |
| 0.1305 | 2.0 | 1050 | 0.1406 | 0.8513 |
| 0.0837 | 3.0 | 1575 | 0.1398 | 0.8599 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Ayham/bertgpt2_cnn | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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} | 4 | null | ---
license: "mit"
---
This model takes text (up to a few sentences) and predicts whether the text contains resilience messaging. Resilience messaging is a text message that is about being able to a) "adapt to change” and b) “bounce back after illness or hardship". The predictive model is a fine-tuned RoBERTa NLP model. To see example use cases, please visit https://huggingface.co/spaces/paragon-analytics/ResText.
Example classification:
```python
import torch
import tensorflow as tf
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("paragon-analytics/bert_resil")
model = AutoModelForSequenceClassification.from_pretrained("paragon-analytics/bert_resil")
encoded_input = tokenizer("We will survive this.", return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
scores = tf.nn.softmax(scores)
``` |
Ayham/distilbert_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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} | 8 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 234.84 +/- 22.67
name: mean_reward
verified: false
---
# **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
...
```
|
Ayham/roberta_distilgpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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"EncoderDecoderModel"
],
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}
}
} | 4 | null | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- google/fleurs
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/wanchichen_fleurs_asr_conformer_hier_lid_utt`
This model was trained by William Chen using the fleurs recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd egs2/fleurs/asr1
./run.sh
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Sat Oct 22 17:36:51 CDT 2022`
- python version: `3.8.13 (default, Mar 28 2022, 11:38:47) [GCC 7.5.0]`
- espnet version: `espnet 202207`
- pytorch version: `pytorch 1.12.1+cu116`
- Git hash: `14fcb2d42b2609f766ffaa7a79e9c921cd8398d9`
- Commit date: `Tue Sep 27 20:02:22 2022 +0000`
## asr_train_asr_conformer_lid_utt_scctc_raw_all_bpe6500_train_data_path_and_name_and_typedumprawtrain_all_splid,lid,text_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_lm_lm_train_lm_all_bpe6500_valid.loss.ave_asr_model_valid.acc.ave/dev_all|31622|610500|72.9|24.4|2.7|3.1|30.2|95.5|
|decode_lm_lm_train_lm_all_bpe6500_valid.loss.ave_asr_model_valid.acc.ave/test_all|77809|1592160|72.2|25.0|2.9|3.6|31.5|96.6|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_lm_lm_train_lm_all_bpe6500_valid.loss.ave_asr_model_valid.acc.ave/dev_all|31622|3988181|92.6|4.7|2.6|2.2|9.6|95.5|
|decode_lm_lm_train_lm_all_bpe6500_valid.loss.ave_asr_model_valid.acc.ave/test_all|77809|10235271|92.5|4.7|2.8|2.6|10.1|96.7|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_lm_lm_train_lm_all_bpe6500_valid.loss.ave_asr_model_valid.acc.ave/dev_all|31622|3547834|91.4|5.8|2.8|2.5|11.0|95.4|
|decode_lm_lm_train_lm_all_bpe6500_valid.loss.ave_asr_model_valid.acc.ave/test_all|77809|9622352|91.6|5.6|2.8|2.8|11.2|96.6|
|
Ayham/roberta_gpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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}
} | 31 | null | ---
language: mt
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- maltese
- xlrs-53-maltese
- masri-project
- malta
- university-of-malta
license: cc-by-nc-sa-4.0
widget: null
model-index:
- name: wav2vec2-large-xlsr-53-maltese-64h
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Mozilla Common Voice 11.0 (Test)
type: mozilla-foundation/common_voice_11_0
split: test
args:
language: mt
metrics:
- name: WER
type: wer
value: 1.57
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Mozilla Common Voice 11.0 (Dev)
type: mozilla-foundation/common_voice_11_0
split: validation
args:
language: mt
metrics:
- name: WER
type: wer
value: 1.4
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: MASRI-TEST Corpus
type: MLRS/masri_test
split: test
args:
language: mt
metrics:
- name: WER
type: wer
value: 27.27
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: MASRI-DEV Corpus
type: MLRS/masri_dev
split: validation
args:
language: mt
metrics:
- name: WER
type: wer
value: 24.71
---
# wav2vec2-large-xlsr-53-maltese-64h
The "wav2vec2-large-xlsr-53-maltese-64h" is an acoustic model suitable for Automatic Speech Recognition in Maltese. It is the result of fine-tuning the model "facebook/wav2vec2-large-xlsr-53" with around 64 hours of Maltese data developed by the MASRI Project at the University of Malta between 2019 and 2021. Most of the data is available at the the MASRI Project homepage https://www.um.edu.mt/projects/masri/.
The specific list of corpora used to fine-tune the model is:
- MASRI-HEADSET v2 (6h39m)
- MASRI-Farfield (9h37m)
- MASRI-Booths (2h27m)
- MASRI-MEP (1h17m)
- MASRI-COMVO (7h29m)
- MASRI-TUBE (13h17m)
- MASRI-MERLIN (25h18m) *Not available at the MASRI Project homepage
The fine-tuning process was perform during November (2022) in the servers of the Language and Voice Lab (https://lvl.ru.is/) at Reykjavík University (Iceland) by Carlos Daniel Hernández Mena.
# Evaluation
```python
import torch
from transformers import Wav2Vec2Processor
from transformers import Wav2Vec2ForCTC
#Load the processor and model.
MODEL_NAME="carlosdanielhernandezmena/wav2vec2-large-xlsr-53-maltese-64h"
processor = Wav2Vec2Processor.from_pretrained(MODEL_NAME)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_NAME)
#Load the dataset
from datasets import load_dataset, load_metric, Audio
ds=load_dataset("common_voice", "mt", split="test")
#Normalize the transcriptions
import re
chars_to_ignore_regex = '[\\,\\?\\.\\!\\\;\\:\\"\\“\\%\\‘\\”\\�\\)\\(\\*)]'
def remove_special_characters(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
return batch
ds = ds.map(remove_special_characters)
#Downsample to 16kHz
ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
#Process the dataset
def prepare_dataset(batch):
audio = batch["audio"]
#Batched output is "un-batched" to ensure mapping is correct
batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0]
with processor.as_target_processor():
batch["labels"] = processor(batch["sentence"]).input_ids
return batch
ds = ds.map(prepare_dataset, remove_columns=ds.column_names,num_proc=1)
#Define the evaluation metric
import numpy as np
wer_metric = load_metric("wer")
def compute_metrics(pred):
pred_logits = pred.predictions
pred_ids = np.argmax(pred_logits, axis=-1)
pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id
pred_str = processor.batch_decode(pred_ids)
#We do not want to group tokens when computing the metrics
label_str = processor.batch_decode(pred.label_ids, group_tokens=False)
wer = wer_metric.compute(predictions=pred_str, references=label_str)
return {"wer": wer}
#Do the evaluation (with batch_size=1)
model = model.to(torch.device("cuda"))
def map_to_result(batch):
with torch.no_grad():
input_values = torch.tensor(batch["input_values"], device="cuda").unsqueeze(0)
logits = model(input_values).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_str"] = processor.batch_decode(pred_ids)[0]
batch["sentence"] = processor.decode(batch["labels"], group_tokens=False)
return batch
results = ds.map(map_to_result,remove_columns=ds.column_names)
#Compute the overall WER now.
print("Test WER: {:.3f}".format(wer_metric.compute(predictions=results["pred_str"], references=results["sentence"])))
```
**Test Result**: 0.011
# BibTeX entry and citation info
*When publishing results based on these models please refer to:*
```bibtex
@misc{mena2022xlrs53maltese,
title={Acoustic Model in Maltese: wav2vec2-large-xlsr-53-maltese-64h.},
author={Hernandez Mena, Carlos Daniel},
year={2022},
url={https://huggingface.co/carlosdanielhernandezmena/wav2vec2-large-xlsr-53-maltese-64h},
}
```
# Acknowledgements
The MASRI Project is funded by the University of Malta Research Fund Awards. We want to thank to Merlin Publishers (Malta) for provinding the audiobooks used to create the MASRI-MERLIN Corpus.
Special thanks to Jón Guðnason, head of the Language and Voice Lab for providing computational power to make this model possible. We also want to thank to the "Language Technology Programme for Icelandic 2019-2023" which is managed and coordinated by Almannarómur, and it is funded by the Icelandic Ministry of Education, Science and Culture. |
Ayham/roberta_roberta_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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}
} | 3 | 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
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 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Ayham/xlnet_distilgpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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"EncoderDecoderModel"
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}
} | 13 | null | ---
license: apache-2.0
tags:
- Scene Text Removal
- Image to Image
library_name: pytorch
---
### GaRNet
This is text-removal model that introduced in the paper below and first released at [this page](https://github.com/naver/garnet). \
[The Surprisingly Straightforward Scene Text Removal Method With Gated Attention and Region of Interest Generation: A Comprehensive Prominent Model Analysis](https://arxiv.org/abs/2210.07489). \
Hyeonsu Lee, Chankyu Choi \
Naver Corp. \
In ECCV 2022.
### Model description
GaRNet is a generator that create non-text image with given image and coresponding text box mask. It consists of convolution encoder and decoder. The encoder consists of residual block with attention module called Gated Attention.
Gated Attention module has two Spatial attention branch. Each attention branch finds text stroke or its surrounding regions. The module adjusts the weight of these two domains by trainable parameters.
The model was trained in PatchGAN manner with Region-of-Interest Generation. \
The discriminator is consists of convolution encoder. Given an image, it determines whether each patch, which indicates text-box regions, is real or fake.
All loss functions treat non-textbox regions as 'don't care'.
### Intended uses & limitations
This model can be used for areas that require the process of erasing text from an image, such as concealment private information, text editing.\
You can use the raw model or pre-trained model.\
Note that pre-trained model was trained in both Synthetic and SCUT_EnsText dataset. And the SCUT-EnsText dataset can only be used for non-commercial research purposes.
### How to use
You can use inference code in [this page](https://github.com/naver/garnet).
### BibTeX entry and citation info
```
@inproceedings{lee2022surprisingly,
title={The Surprisingly Straightforward Scene Text Removal Method with Gated Attention and Region of Interest Generation: A Comprehensive Prominent Model Analysis},
author={Lee, Hyeonsu and Choi, Chankyu},
booktitle={European Conference on Computer Vision},
pages={457--472},
year={2022},
organization={Springer}
}
``` |
Ayham/xlnet_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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} | 13 | 2022-11-08T02:14:30Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
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: 1.6727
## 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: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5227 | 1.0 | 1107 | 2.0485 |
| 1.7555 | 2.0 | 2214 | 1.7443 |
| 1.4567 | 3.0 | 3321 | 1.6511 |
| 1.2107 | 4.0 | 4428 | 1.6496 |
| 1.083 | 5.0 | 5535 | 1.6727 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Ayham/xlnet_gpt_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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} | 11 | null | data: https://github.com/BigSalmon2/InformalToFormalDataset
Text Generation Informal Formal
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln90Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln90Paraphrase")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInformalConciseWordy
```
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
input_ids = tokenizer.encode(prompt, return_tensors='pt')
outputs = model.generate(input_ids=input_ids,
max_length=10 + len(prompt),
temperature=1.0,
top_k=50,
top_p=0.95,
do_sample=True,
num_return_sequences=5,
early_stopping=True)
for i in range(5):
print(tokenizer.decode(outputs[i]))
```
Most likely outputs (Disclaimer: I highly recommend using this over just generating):
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
text = tokenizer.encode(prompt)
myinput, past_key_values = torch.tensor([text]), None
myinput = myinput
myinput= myinput.to(device)
logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
logits = logits[0,-1]
probabilities = torch.nn.functional.softmax(logits)
best_logits, best_indices = logits.topk(250)
best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
text.append(best_indices[0].item())
best_probabilities = probabilities[best_indices].tolist()
words = []
print(best_words)
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- nebraska
- unicamerical legislature
- different from federal house and senate
text: featuring a unicameral legislature, nebraska's political system stands in stark contrast to the federal model, comprised of a house and senate.
***
- penny has practically no value
- should be taken out of circulation
- just as other coins have been in us history
- lost use
- value not enough
- to make environmental consequences worthy
text: all but valueless, the penny should be retired. as with other coins in american history, it has become defunct. too minute to warrant the environmental consequences of its production, it has outlived its usefulness.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
```
```
original: had trouble deciding.
translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation.
***
original:
```
```
input: not loyal
1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ).
***
input:
```
```
first: ( was complicit in / was involved in ).
antonym: ( was blameless / was not an accomplice to / had no hand in / was uninvolved in ).
***
first: ( have no qualms about / see no issue with ).
antonym: ( are deeply troubled by / harbor grave reservations about / have a visceral aversion to / take ( umbrage at / exception to ) / are wary of ).
***
first: ( do not see eye to eye / disagree often ).
antonym: ( are in sync / are united / have excellent rapport / are like-minded / are in step / are of one mind / are in lockstep / operate in perfect harmony / march in lockstep ).
***
first:
```
```
stiff with competition, law school {A} is the launching pad for countless careers, {B} is a crowded field, {C} ranks among the most sought-after professional degrees, {D} is a professional proving ground.
***
languishing in viewership, saturday night live {A} is due for a creative renaissance, {B} is no longer a ratings juggernaut, {C} has been eclipsed by its imitators, {C} can still find its mojo.
***
dubbed the "manhattan of the south," atlanta {A} is a bustling metropolis, {B} is known for its vibrant downtown, {C} is a city of rich history, {D} is the pride of georgia.
***
embattled by scandal, harvard {A} is feeling the heat, {B} cannot escape the media glare, {C} is facing its most intense scrutiny yet, {D} is in the spotlight for all the wrong reasons.
```
Infill / Infilling / Masking / Phrase Masking (Works pretty decently actually, especially when you use logprobs code from above):
```
his contention [blank] by the evidence [sep] was refuted [answer]
***
few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep] synonymous with [answer]
***
when rick won the lottery, all of his distant relatives [blank] his winnings [sep] clamored for [answer]
***
the library’s quiet atmosphere encourages visitors to [blank] in their work [sep] immerse themselves [answer]
***
the joy of sport is that no two games are alike. for every exhilarating experience, however, there is an interminable one. the national pastime, unfortunately, has a penchant for the latter. what begins as a summer evening at the ballpark can quickly devolve into a game of tedium. the primary culprit is the [blank] of play. from batters readjusting their gloves to fielders spitting on their mitts, the action is [blank] unnecessary interruptions. the sport's future is [blank] if these tendencies are not addressed [sep] plodding pace [answer] riddled with [answer] bleak [answer]
***
microsoft word's [blank] pricing [blank] competition [sep] unconscionable [answer] invites [answer]
***
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
Backwards
```
Essay Intro (National Parks):
text: tourists are at ease in the national parks, ( swept up in the beauty of their natural splendor ).
***
Essay Intro (D.C. Statehood):
washington, d.c. is a city of outsize significance, ( ground zero for the nation's political life / center stage for the nation's political machinations ).
```
```
topic: the Golden State Warriors.
characterization 1: the reigning kings of the NBA.
characterization 2: possessed of a remarkable cohesion.
characterization 3: helmed by superstar Stephen Curry.
characterization 4: perched atop the league’s hierarchy.
characterization 5: boasting a litany of hall-of-famers.
***
topic: emojis.
characterization 1: shorthand for a digital generation.
characterization 2: more versatile than words.
characterization 3: the latest frontier in language.
characterization 4: a form of self-expression.
characterization 5: quintessentially millennial.
characterization 6: reflective of a tech-centric world.
***
topic:
```
```
regular: illinois went against the census' population-loss prediction by getting more residents.
VBG: defying the census' prediction of population loss, illinois experienced growth.
***
regular: microsoft word’s high pricing increases the likelihood of competition.
VBG: extortionately priced, microsoft word is inviting competition.
***
regular:
```
```
source: badminton should be more popular in the US.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) games played with racquets are popular, (2) just look at tennis and ping pong, (3) but badminton underappreciated, (4) fun, fast-paced, competitive, (5) needs to be marketed more
text: the sporting arena is dominated by games that are played with racquets. tennis and ping pong, in particular, are immensely popular. somewhat curiously, however, badminton is absent from this pantheon. exciting, fast-paced, and competitive, it is an underappreciated pastime. all that it lacks is more effective marketing.
***
source: movies in theaters should be free.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) movies provide vital life lessons, (2) many venues charge admission, (3) those without much money
text: the lessons that movies impart are far from trivial. the vast catalogue of cinematic classics is replete with inspiring sagas of friendship, bravery, and tenacity. it is regrettable, then, that admission to theaters is not free. in their current form, the doors of this most vital of institutions are closed to those who lack the means to pay.
***
source:
```
```
in the private sector, { transparency } is vital to the business’s credibility. the { disclosure of information } can be the difference between success and failure.
***
the labor market is changing, with { remote work } now the norm. this { flexible employment } allows the individual to design their own schedule.
***
the { cubicle } is the locus of countless grievances. many complain that the { enclosed workspace } restricts their freedom of movement.
***
```
```
it would be natural to assume that americans, as a people whose ancestors { immigrated to this country }, would be sympathetic to those seeking to do likewise.
question: what does “do likewise” mean in the above context?
(a) make the same journey
(b) share in the promise of the american dream
(c) start anew in the land of opportunity
(d) make landfall on the united states
***
in the private sector, { transparency } is vital to the business’s credibility. this orientation can be the difference between success and failure.
question: what does “this orientation” mean in the above context?
(a) visible business practices
(b) candor with the public
(c) open, honest communication
(d) culture of accountability
```
```
example: suppose you are a teacher. further suppose you want to tell an accurate telling of history. then suppose a parent takes offense. they do so in the name of name of their kid. this happens a lot.
text: educators' responsibility to remain true to the historical record often clashes with the parent's desire to shelter their child from uncomfortable realities.
***
example: suppose you are a student at college. now suppose you have to buy textbooks. that is going to be worth hundreds of dollars. given how much you already spend on tuition, that is going to hard cost to bear.
text: the exorbitant cost of textbooks, which often reaches hundreds of dollars, imposes a sizable financial burden on the already-strapped college student.
```
```
<Prefix> the atlanta hawks may attribute <Prefix> <Suffix> trae young <Suffix> <Middle> their robust season to <Middle>
***
<Prefix> the nobel prize in literature <Prefix> <Suffix> honor <Suffix> <Middle> is a singularly prestigious <Middle>
```
```
accustomed to having its name uttered ______, harvard university is weathering a rare spell of reputational tumult
(a) in reverential tones
(b) with great affection
(c) in adulatory fashion
(d) in glowing terms
```
```
clarify: international ( {working together} / cooperation ) is called for when ( {issue go beyond lots of borders} / an issue transcends borders / a given matter has transnational implications ).
```
```
description: when someone thinks that their view is the only right one.
synonyms: intolerant, opinionated, narrow-minded, insular, self-righteous.
***
description: when you put something off.
synonyms: shelve, defer, table, postpone.
```
```
organic sentence: crowdfunding is about winner of best ideas and it can test an entrepreneur’s idea.
rewrite phrases: meritocratic, viability, vision
rewritten with phrases: the meritocratic nature of crowdfunding empowers entrepreneurs to test their vision's viability.
```
*Note* Of all the masking techniques, this one works the best.
```
<Prefix> the atlanta hawks may attribute <Prefix> <Suffix> trae young <Suffix> <Middle> their robust season to <Middle>
***
<Prefix> the nobel prize in literature <Prefix> <Suffix> honor <Suffix> <Middle> is a singularly prestigious <Middle>
```
```
essence: when someone's views are keeping within reasonable.
refine: the senator's voting record is ( moderate / centrist / pragmatic / balanced / fair-minded / even-handed ).
***
essence: when things are worked through in a petty way.
refine: the propensity of the u.s. congress to settle every dispute by way of ( mudslinging / bickering / demagoguery / name-calling / finger-pointing / vilification ) is appalling.
```
```
description: when someone thinks that their view is the only right one.
synonyms: intolerant, opinionated, narrow-minded, insular, self-righteous.
***
description: when you put something off.
synonyms: shelve, defer, table, postpone.
```
```
organic sentence: crowdfunding is about winner of best ideas and it can test an entrepreneur’s idea.
rewrite phrases: meritocratic, viability, vision
rewritten with phrases: the meritocratic nature of crowdfunding empowers entrepreneurs to test their vision's viability.
```
```
music before bedtime [makes for being able to relax] -> is a recipe for relaxation.
```
```
[people wanting entertainment love traveling new york city] -> travelers flock to new york city in droves, drawn to its iconic entertainment scene. [cannot blame them] -> one cannot fault them [broadway so fun] -> when it is home to such thrilling fare as Broadway.
```
```
in their ( ‖ when you are rushing because you want to get there on time ‖ / haste to arrive punctually / mad dash to be timely ), morning commuters are too rushed to whip up their own meal.
***
politicians prefer to author vague plans rather than ( ‖ when you can make a plan without many unknowns ‖ / actionable policies / concrete solutions ).
```
```
Q: What is whistleblower protection?
A: Whistleblower protection is a form of legal immunity granted to employees who expose the unethical practices of their employer.
Q: Why are whistleblower protections important?
A: Absent whistleblower protections, employees would be deterred from exposing their employer’s wrongdoing for fear of retribution.
Q: Why would an employer engage in retribution?
A: An employer who has acted unethically stands to suffer severe financial and reputational damage were their transgressions to become public. To safeguard themselves from these consequences, they might seek to dissuade employees from exposing their wrongdoing.
```
```
original: the meritocratic nature of crowdfunding [MASK] into their vision's viability.
infill: the meritocratic nature of crowdfunding [gives investors idea of how successful] -> ( offers entrepreneurs a window ) into their vision's viability.
```
```
Leadership | Lecture 17: Worker Morale
What Workers Look for in Companies:
• Benefits
o Tuition reimbursement
o Paid parental leave
o 401K matching
o Profit sharing
o Pension plans
o Free meals
• Social responsibility
o Environmental stewardship
o Charitable contributions
o Diversity
• Work-life balance
o Telecommuting
o Paid holidays and vacation
o Casual dress
• Growth opportunities
• Job security
• Competitive compensation
• Recognition
o Open-door policies
o Whistleblower protection
o Employee-of-the-month awards
o Positive performance reviews
o Bonuses
```
```
description: business
keywords: for-profit, fiduciary duty, monopolistic, bottom line, return on investment, short-term thinking, capital-intensive, self-interested, risk-taking, fiduciary duty, merger, speculation, profiteering, oversight, capitalism, diversification
```
```
3. In this task, you are given a company name and you need to find its industry.
McDonalds -- Restaurant
Facebook -- Social Network
IKEA -- Furniture
American Express -- Credit Services
Nokia -- Telecom
Nintendo -- Entertainment
4. In this task, you are given a Month and you need to convert it to its corresponding season
April -- Spring
December -- Winter
July -- Summer
October -- Fall
February -- Winter
5. In this task, you are given a sentence with a missing word and you need to predict the correct word.
Managers should set an _____ for their employees. -- example
Some people spend more than four _____ in the gym. -- hours
The police were on the _____ of arresting the suspect. -- verge
They were looking for _____ on how to solve the problem. -- guidance
What is the _____ of the coffee? -- price
6. In this task, you are given a paragraph and you need to reorder it to make it logical.
It was first proposed in 1987. The total length of the bridge is 1,828 meters. The idea of a bridge connects Hong Kong to Macau. -- The idea of bridge connecting Hong Kong and Macau was first proposed in 1987. The total length of the bridge is 1,828 meters.
It is a movie about a brave and noble policeman. The film was produced by Americans. They were Kevin Lima and Chris Buck. They are directors. The movie is called Tarzan. -- Produced by Americans Kevin Lima and Chris Buck, Tarzan is a movie about a brave and noble policeman.
It was first discovered in the mountains of India. The active ingredients in this plant can stimulate hair growth. The plant is called "Hair Plus." -- First discovered in the mountains of India, Hair Plus is a plant whose active ingredients can stimulate hair growth.
```
```
trivia: What is the population of South Korea?
response: 51 million.
***
trivia: What is the minimum voting age in the US?
response: 18.
***
trivia: What are the first ten amendments of the US constitution called?
response: Bill of Rights.
``` |
Ayham/xlnet_roberta_new_summarization_cnn_dailymail | []
| null | {
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"conversational": {
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},
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},
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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: 440.00 +/- 88.54
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
|
Ayjayo/DialoGPT-medium-AyjayoAI | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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"length_penalty": null,
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
} | 12 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: chinese-macbert-base-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chinese-macbert-base-finetuned
This model is a fine-tuned version of [hfl/chinese-macbert-base](https://huggingface.co/hfl/chinese-macbert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2790
- Accuracy: 0.9613
- F1: 0.9548
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.9986 | 1.0 | 3 | 1.5298 | 0.8177 | 0.7357 |
| 1.43 | 2.0 | 6 | 1.2166 | 0.8177 | 0.7357 |
| 1.2494 | 3.0 | 9 | 1.0037 | 0.8177 | 0.7357 |
| 0.9698 | 4.0 | 12 | 0.8538 | 0.8177 | 0.7357 |
| 0.8999 | 5.0 | 15 | 0.7562 | 0.8453 | 0.7811 |
| 0.8945 | 6.0 | 18 | 0.6813 | 0.8619 | 0.7980 |
| 0.7059 | 7.0 | 21 | 0.6101 | 0.8619 | 0.7980 |
| 0.6066 | 8.0 | 24 | 0.5422 | 0.8619 | 0.7983 |
| 0.5938 | 9.0 | 27 | 0.4891 | 0.8619 | 0.7996 |
| 0.5995 | 10.0 | 30 | 0.4493 | 0.8674 | 0.8124 |
| 0.493 | 11.0 | 33 | 0.4113 | 0.8840 | 0.8470 |
| 0.5175 | 12.0 | 36 | 0.3798 | 0.9116 | 0.8893 |
| 0.4615 | 13.0 | 39 | 0.3536 | 0.9392 | 0.9218 |
| 0.4339 | 14.0 | 42 | 0.3311 | 0.9337 | 0.9094 |
| 0.3926 | 15.0 | 45 | 0.3147 | 0.9448 | 0.9317 |
| 0.3507 | 16.0 | 48 | 0.3012 | 0.9503 | 0.9384 |
| 0.3634 | 17.0 | 51 | 0.2926 | 0.9558 | 0.9471 |
| 0.2825 | 18.0 | 54 | 0.2857 | 0.9613 | 0.9548 |
| 0.308 | 19.0 | 57 | 0.2808 | 0.9613 | 0.9548 |
| 0.3323 | 20.0 | 60 | 0.2790 | 0.9613 | 0.9548 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.12.0+cu102
- Datasets 1.18.4
- Tokenizers 0.12.1
|
Aymene/opus-mt-en-ro-finetuned-en-to-ro | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: bigmorning_whisper
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. -->
# bigmorning_whisper
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.25.0.dev0
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Ayou/chinese_mobile_bert | [
"pytorch",
"mobilebert",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
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} | 16 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- audiofolder
model-index:
- name: wav2vec2-xlsr-53-espeak-cv-ft-xas-ntsema-colab
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-xas-ntsema-colab
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 audiofolder 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Ayran/DialoGPT-medium-harry-potter-1-through-4-plus-6 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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} | 12 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
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: 1.5282
## 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: 30
- eval_batch_size: 30
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 295 | 2.3734 |
| 3.0199 | 2.0 | 590 | 1.7638 |
| 3.0199 | 3.0 | 885 | 1.5867 |
| 1.582 | 4.0 | 1180 | 1.5535 |
| 1.582 | 5.0 | 1475 | 1.5281 |
| 1.2859 | 6.0 | 1770 | 1.5282 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Ayran/DialoGPT-small-gandalf | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 11 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-BERTmodel-A3
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. -->
# finetuning-sentiment-BERTmodel-A3
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: 0.3307
- Accuracy: 0.8656
- F1: 0.3576
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
AyushPJ/ai-club-inductions-21-nlp-ALBERT | [
"pytorch",
"albert",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"AlbertForQuestionAnswering"
],
"model_type": "albert",
"task_specific_params": {
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} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-BERTmodel-A3-allcontents
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. -->
# finetuning-sentiment-BERTmodel-A3-allcontents
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: 0.2951
- Accuracy: 0.8814
- F1: 0.4138
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Bagus/SER-LSSED | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-Label-studio-707-invoices
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-Label-studio-707-invoices
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: 12
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Barbarameerr/Barbara | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.88
- name: F1
type: f1
value: 0.880794701986755
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
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:
- Loss: 0.2973
- Accuracy: 0.88
- F1: 0.8808
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Barleysack/klue-roberta-LSTM | [
"pytorch",
"roberta",
"transformers"
]
| null | {
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} | 6 | null | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/alexcaillet/ddpm-butterflies-128/tensorboard?#scalars)
|
Beelow/wav2vec2-ukrainian-model-large | []
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} | 0 | 2022-11-08T12:44:21Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: whisper-medium-amksim
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-medium-amksim
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9089
- Wer: 40.3433
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 4.6349 | 0.83 | 5 | 3.7729 | 73.3906 |
| 3.2338 | 1.67 | 10 | 1.4978 | 69.0987 |
| 1.1335 | 2.5 | 15 | 1.1606 | 97.4249 |
| 0.6838 | 3.33 | 20 | 1.0211 | 66.0944 |
| 0.4383 | 4.17 | 25 | 0.9845 | 65.2361 |
| 0.2514 | 5.0 | 30 | 0.9885 | 61.3734 |
| 0.2053 | 5.83 | 35 | 0.9796 | 76.3948 |
| 0.1353 | 6.67 | 40 | 0.9758 | 49.3562 |
| 0.1142 | 7.5 | 45 | 0.9109 | 60.9442 |
| 0.0889 | 8.33 | 50 | 0.9045 | 41.2017 |
| 0.0854 | 9.17 | 55 | 0.9085 | 42.4893 |
| 0.069 | 10.0 | 60 | 0.9089 | 40.3433 |
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.12.1
|
BigSalmon/FormalBerta2 | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 16 | 2022-11-08T14:02:07Z | ---
license: cc-by-4.0
---
## UoM&MMU at TSAR-2022 Shared Task - Prompt Learning for Lexical Simplification: prompt-ls-es-1
We present **PromptLS**, a method for fine-tuning large pre-trained masked language models to perform the task of Lexical Simplification.
This model is part of a series of models presented at the [TSAR-2022 Shared Task](https://taln.upf.edu/pages/tsar2022-st/)
by the University of Manchester and Manchester Metropolitan University (UoM&MMU) Team in English, Spanish and Portuguese.
You can find more details about the project in our [GitHub](https://github.com/lmvasque/ls-prompt-tsar2022).
## Models
Our models were fine-tuned using prompt-learning for **Lexical Simplification**. These are the available models you can use (current model page in bold):
| Model Name | Run # | Language | Setting |
|----------------------------------------------------------------------|-------|:-----------:|---------------|
| [prompt-ls-en-1](https://huggingface.co/lmvasque/prompt-ls-en-1) | 1 | English | fine-tune |
| [prompt-ls-en-2](https://huggingface.co/lmvasque/prompt-ls-en-2) | 2 | English | fine-tune |
| [roberta-large](https://huggingface.co/roberta-large) | 3 | English | zero-shot |
| **[prompt-ls-es-1](https://huggingface.co/lmvasque/prompt-ls-es-1)** | **1** | **Spanish** | **fine-tune** |
| [prompt-ls-es-2](https://huggingface.co/lmvasque/prompt-ls-es-2) | 2 | Spanish | fine-tune |
| [prompt-ls-es-3](https://huggingface.co/lmvasque/prompt-ls-es-3) | 3 | Spanish | fine-tune |
| [prompt-ls-pt-1](https://huggingface.co/lmvasque/prompt-ls-pt-1) | 1 | Portuguese | fine-tune |
| [prompt-ls-pt-2](https://huggingface.co/lmvasque/prompt-ls-pt-2) | 2 | Portuguese | fine-tune |
| [prompt-ls-pt-3](https://huggingface.co/lmvasque/prompt-ls-pt-3) | 3 | Portuguese | fine-tune |
For the zero-shot setting, we used the original models with no further training. Links to these models are also updated in the table above.
## Results
We include the [official results](https://github.com/LaSTUS-TALN-UPF/TSAR-2022-Shared-Task/tree/main/results/official) from the competition test set as a reference. However, we encourage the users to also check our results in the development set, which show an increased performance for Spanish and Portuguese.
You can find more details in our [paper](https://drive.google.com/file/d/1x5dRxgcSGAaCCrjsgpCHnYek9G-TmZff/view?usp=share_link).
| Language | # | Model | Setting | Prompt1 | Prompt2 | w | k | Acc@1 | A@3 | M@3 | P@3 |
|------------|---|-------|--------------|---------|---------|---|---|-------|-----|-----|-------------|
| English | 1 | RoBERTa-L | fine-tune | simple | word | 5 | 5 | **0.6353** | **0.5308** | **0.4244** | **0.8739** |
| English | 2 | mBERT | multilingual | easier | word | 10 | 10 | 0.4959 | 0.4235 | 0.3273 | 0.7560 |
| English | 3 | RoBERTa-L | zero-shot | easier | word | 5 | - | 0.2654 | 0.268 | 0.1820 | 0.4906 |
| Spanish | 1 | BERTIN | fine-tune | sinónimo | fácil | - | 3 | 0.3451 | **0.2907** | **0.2238** | **0.5543** |
| Spanish | 2 | BERTIN | fine-tune | palabra | simple | - | 10 | 0.3614 | **0.2907**| 0.2225 | 0.538 |
| Spanish | 3 | BERTIN | fine-tune | sinónimo | fácil | 10 | 10 | **0.3668** | 0.269 | 0.2128 | 0.5326 |
| Portuguese | 1 | BR_BERTo | fine-tune | palavra | simples | - | 8 | **0.1711** | 0.1096 | 0.1011 | 0.2486 |
| Portuguese | 2 | BR_BERTo | fine-tune | sinônimo | fácil | - | 10 | 0.1363 | 0.0962 | 0.0944 | 0.2379 |
| Portuguese | 3 | BR_BERTo | fine-tune | sinônimo | simples | 5 | 10 | 0.1577 | **0.1283**| **0.1071**| **0.2834**|
## Citation
If you use our results and scripts in your research, please cite our work:
"[UoM&MMU at TSAR-2022 Shared Task: Prompt Learning for Lexical Simplification](https://drive.google.com/file/d/1x5dRxgcSGAaCCrjsgpCHnYek9G-TmZff/view?usp=share_link)".
```
@inproceedings{vasquez-rodriguez-etal-2022-prompt-ls,
title = "UoM\&MMU at TSAR-2022 Shared Task: Prompt Learning for Lexical Simplification",
author = "V{\'a}squez-Rodr{\'\i}guez, Laura and
Nguyen, Nhung T. H. and
Shardlow, Matthew and
Ananiadou, Sophia",
booktitle = "Shared Task on Text Simplification, Accessibility, and Readability (TSAR-2022), EMNLP 2022",
month = dec,
year = "2022",
}
```
|
BigSalmon/FormalBerta3 | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
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},
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},
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},
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},
"translation_en_to_ro": {
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"max_length": null,
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"prefix": null
}
}
} | 4 | null | ---
license: cc-by-4.0
---
## UoM&MMU at TSAR-2022 Shared Task - Prompt Learning for Lexical Simplification: prompt-ls-es-2
We present **PromptLS**, a method for fine-tuning large pre-trained masked language models to perform the task of Lexical Simplification.
This model is part of a series of models presented at the [TSAR-2022 Shared Task](https://taln.upf.edu/pages/tsar2022-st/)
by the University of Manchester and Manchester Metropolitan University (UoM&MMU) Team in English, Spanish and Portuguese.
You can find more details about the project in our [GitHub](https://github.com/lmvasque/ls-prompt-tsar2022).
## Models
Our models were fine-tuned using prompt-learning for **Lexical Simplification**. These are the available models you can use (current model page in bold):
| Model Name | Run # | Language | Setting |
|----------------------------------------------------------------------|----|:-----------:|-----------|
| [prompt-ls-en-1](https://huggingface.co/lmvasque/prompt-ls-en-1) | 1 | English | fine-tune |
| [prompt-ls-en-2](https://huggingface.co/lmvasque/prompt-ls-en-2) | 2 | English | fine-tune |
| [roberta-large](https://huggingface.co/roberta-large) | 3 | English | zero-shot |
| [prompt-ls-es-1](https://huggingface.co/lmvasque/prompt-ls-es-1) | 1 | Spanish | fine-tune |
| **[prompt-ls-es-2](https://huggingface.co/lmvasque/prompt-ls-es-2)** | **2** | **Spanish** | **fine-tune** |
| [prompt-ls-es-3](https://huggingface.co/lmvasque/prompt-ls-es-3) | 3 | Spanish | fine-tune |
| [prompt-ls-pt-1](https://huggingface.co/lmvasque/prompt-ls-pt-1) | 1 | Portuguese | fine-tune |
| [prompt-ls-pt-2](https://huggingface.co/lmvasque/prompt-ls-pt-2) | 2 | Portuguese | fine-tune |
| [prompt-ls-pt-3](https://huggingface.co/lmvasque/prompt-ls-pt-3) | 3 | Portuguese | fine-tune |
For the zero-shot setting, we used the original models with no further training. Links to these models are also updated in the table above.
## Results
We include the [official results](https://github.com/LaSTUS-TALN-UPF/TSAR-2022-Shared-Task/tree/main/results/official) from the competition test set as a reference. However, we encourage the users to also check our results in the development set, which show an increased performance for Spanish and Portuguese.
You can find more details in our [paper](https://drive.google.com/file/d/1x5dRxgcSGAaCCrjsgpCHnYek9G-TmZff/view?usp=share_link).
| Language | # | Model | Setting | Prompt1 | Prompt2 | w | k | Acc@1 | A@3 | M@3 | P@3 |
|------------|---|-------|--------------|---------|---------|---|---|-------|-----|-----|-------------|
| English | 1 | RoBERTa-L | fine-tune | simple | word | 5 | 5 | **0.6353** | **0.5308** | **0.4244** | **0.8739** |
| English | 2 | mBERT | multilingual | easier | word | 10 | 10 | 0.4959 | 0.4235 | 0.3273 | 0.7560 |
| English | 3 | RoBERTa-L | zero-shot | easier | word | 5 | - | 0.2654 | 0.268 | 0.1820 | 0.4906 |
| Spanish | 1 | BERTIN | fine-tune | sinónimo | fácil | - | 3 | 0.3451 | **0.2907** | **0.2238** | **0.5543** |
| Spanish | 2 | BERTIN | fine-tune | palabra | simple | - | 10 | 0.3614 | **0.2907**| 0.2225 | 0.538 |
| Spanish | 3 | BERTIN | fine-tune | sinónimo | fácil | 10 | 10 | **0.3668** | 0.269 | 0.2128 | 0.5326 |
| Portuguese | 1 | BR_BERTo | fine-tune | palavra | simples | - | 8 | **0.1711** | 0.1096 | 0.1011 | 0.2486 |
| Portuguese | 2 | BR_BERTo | fine-tune | sinônimo | fácil | - | 10 | 0.1363 | 0.0962 | 0.0944 | 0.2379 |
| Portuguese | 3 | BR_BERTo | fine-tune | sinônimo | simples | 5 | 10 | 0.1577 | **0.1283**| **0.1071**| **0.2834**|
## Citation
If you use our results and scripts in your research, please cite our work:
"[UoM&MMU at TSAR-2022 Shared Task: Prompt Learning for Lexical Simplification](https://drive.google.com/file/d/1x5dRxgcSGAaCCrjsgpCHnYek9G-TmZff/view?usp=share_link)".
```
@inproceedings{vasquez-rodriguez-etal-2022-prompt-ls,
title = "UoM\&MMU at TSAR-2022 Shared Task: Prompt Learning for Lexical Simplification",
author = "V{\'a}squez-Rodr{\'\i}guez, Laura and
Nguyen, Nhung T. H. and
Shardlow, Matthew and
Ananiadou, Sophia",
booktitle = "Shared Task on Text Simplification, Accessibility, and Readability (TSAR-2022), EMNLP 2022",
month = dec,
year = "2022",
}
```
|
BigSalmon/FormalRobertaaa | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
"translation_en_to_ro": {
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"max_length": null,
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"prefix": null
}
}
} | 12 | null | ---
license: cc-by-4.0
---
## UoM&MMU at TSAR-2022 Shared Task - Prompt Learning for Lexical Simplification: prompt-ls-pt-1
We present **PromptLS**, a method for fine-tuning large pre-trained masked language models to perform the task of Lexical Simplification.
This model is part of a series of models presented at the [TSAR-2022 Shared Task](https://taln.upf.edu/pages/tsar2022-st/)
by the University of Manchester and Manchester Metropolitan University (UoM&MMU) Team in English, Spanish and Portuguese.
You can find more details about the project in our [GitHub](https://github.com/lmvasque/ls-prompt-tsar2022).
## Models
Our models were fine-tuned using prompt-learning for **Lexical Simplification**. These are the available models you can use (current model page in bold):
| Model Name | Run # | Language | Setting |
|--------------------------------------------------------------------|----|:--------------:|-----------|
| [prompt-ls-en-1](https://huggingface.co/lmvasque/prompt-ls-en-1) | 1 | English | fine-tune |
| [prompt-ls-en-2](https://huggingface.co/lmvasque/prompt-ls-en-2) | 2 | English | fine-tune |
| [roberta-large](https://huggingface.co/roberta-large) | 3 | English | zero-shot |
| [prompt-ls-es-1](https://huggingface.co/lmvasque/prompt-ls-es-1) | 1 | Spanish | fine-tune |
| [prompt-ls-es-2](https://huggingface.co/lmvasque/prompt-ls-es-2) | 2 | Spanish | fine-tune |
| [prompt-ls-es-3](https://huggingface.co/lmvasque/prompt-ls-es-3) | 3 | Spanish | fine-tune |
| **[prompt-ls-pt-1](https://huggingface.co/lmvasque/prompt-ls-pt-1)** | **1** | **Portuguese** | **fine-tune** |
| [prompt-ls-pt-2](https://huggingface.co/lmvasque/prompt-ls-pt-2) | 2 | Portuguese | fine-tune |
| [prompt-ls-pt-3](https://huggingface.co/lmvasque/prompt-ls-pt-3) | 3 | Portuguese | fine-tune |
For the zero-shot setting, we used the original models with no further training. Links to these models are also updated in the table above.
## Results
We include the [official results](https://github.com/LaSTUS-TALN-UPF/TSAR-2022-Shared-Task/tree/main/results/official) from the competition test set as a reference. However, we encourage the users to also check our results in the development set, which show an increased performance for Spanish and Portuguese.
You can find more details in our [paper](https://drive.google.com/file/d/1x5dRxgcSGAaCCrjsgpCHnYek9G-TmZff/view?usp=share_link).
| Language | # | Model | Setting | Prompt1 | Prompt2 | w | k | Acc@1 | A@3 | M@3 | P@3 |
|------------|---|-------|--------------|---------|---------|---|---|-------|-----|-----|-------------|
| English | 1 | RoBERTa-L | fine-tune | simple | word | 5 | 5 | **0.6353** | **0.5308** | **0.4244** | **0.8739** |
| English | 2 | mBERT | multilingual | easier | word | 10 | 10 | 0.4959 | 0.4235 | 0.3273 | 0.7560 |
| English | 3 | RoBERTa-L | zero-shot | easier | word | 5 | - | 0.2654 | 0.268 | 0.1820 | 0.4906 |
| Spanish | 1 | BERTIN | fine-tune | sinónimo | fácil | - | 3 | 0.3451 | **0.2907** | **0.2238** | **0.5543** |
| Spanish | 2 | BERTIN | fine-tune | palabra | simple | - | 10 | 0.3614 | **0.2907**| 0.2225 | 0.538 |
| Spanish | 3 | BERTIN | fine-tune | sinónimo | fácil | 10 | 10 | **0.3668** | 0.269 | 0.2128 | 0.5326 |
| Portuguese | 1 | BR_BERTo | fine-tune | palavra | simples | - | 8 | **0.1711** | 0.1096 | 0.1011 | 0.2486 |
| Portuguese | 2 | BR_BERTo | fine-tune | sinônimo | fácil | - | 10 | 0.1363 | 0.0962 | 0.0944 | 0.2379 |
| Portuguese | 3 | BR_BERTo | fine-tune | sinônimo | simples | 5 | 10 | 0.1577 | **0.1283**| **0.1071**| **0.2834**|
## Citation
If you use our results and scripts in your research, please cite our work:
"[UoM&MMU at TSAR-2022 Shared Task: Prompt Learning for Lexical Simplification](https://drive.google.com/file/d/1x5dRxgcSGAaCCrjsgpCHnYek9G-TmZff/view?usp=share_link)".
```
@inproceedings{vasquez-rodriguez-etal-2022-prompt-ls,
title = "UoM\&MMU at TSAR-2022 Shared Task: Prompt Learning for Lexical Simplification",
author = "V{\'a}squez-Rodr{\'\i}guez, Laura and
Nguyen, Nhung T. H. and
Shardlow, Matthew and
Ananiadou, Sophia",
booktitle = "Shared Task on Text Simplification, Accessibility, and Readability (TSAR-2022), EMNLP 2022",
month = dec,
year = "2022",
}
```
|
BigSalmon/GPT2HardArticleEasyArticle | [
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"length_penalty": null,
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"min_length": null,
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
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}
}
} | 7 | null | ---
license: cc-by-4.0
---
## UoM&MMU at TSAR-2022 Shared Task - Prompt Learning for Lexical Simplification: prompt-ls-pt-3
We present **PromptLS**, a method for fine-tuning large pre-trained masked language models to perform the task of Lexical Simplification.
This model is part of a series of models presented at the [TSAR-2022 Shared Task](https://taln.upf.edu/pages/tsar2022-st/)
by the University of Manchester and Manchester Metropolitan University (UoM&MMU) Team in English, Spanish and Portuguese.
You can find more details about the project in our [GitHub](https://github.com/lmvasque/ls-prompt-tsar2022).
## Models
Our models were fine-tuned using prompt-learning for **Lexical Simplification**. These are the available models you can use (current model page in bold):
| Model Name | Run # | Language | Setting |
|----------------------------------------------------------------------|-------|:--------------:|---------------|
| [prompt-ls-en-1](https://huggingface.co/lmvasque/prompt-ls-en-1) | 1 | English | fine-tune |
| [prompt-ls-en-2](https://huggingface.co/lmvasque/prompt-ls-en-2) | 2 | English | fine-tune |
| [roberta-large](https://huggingface.co/roberta-large) | 3 | English | zero-shot |
| [prompt-ls-es-1](https://huggingface.co/lmvasque/prompt-ls-es-1) | 1 | Spanish | fine-tune |
| [prompt-ls-es-2](https://huggingface.co/lmvasque/prompt-ls-es-2) | 2 | Spanish | fine-tune |
| [prompt-ls-es-3](https://huggingface.co/lmvasque/prompt-ls-es-3) | 3 | Spanish | fine-tune |
| [prompt-ls-pt-1](https://huggingface.co/lmvasque/prompt-ls-pt-1) | 1 | Portuguese | fine-tune |
| [prompt-ls-pt-2](https://huggingface.co/lmvasque/prompt-ls-pt-2) | 2 | Portuguese | fine-tune |
| **[prompt-ls-pt-3](https://huggingface.co/lmvasque/prompt-ls-pt-3)** | **3** | **Portuguese** | **fine-tune** |
For the zero-shot setting, we used the original models with no further training. Links to these models are also updated in the table above.
## Results
We include the [official results](https://github.com/LaSTUS-TALN-UPF/TSAR-2022-Shared-Task/tree/main/results/official) from the competition test set as a reference. However, we encourage the users to also check our results in the development set, which show an increased performance for Spanish and Portuguese.
You can find more details in our [paper](https://drive.google.com/file/d/1x5dRxgcSGAaCCrjsgpCHnYek9G-TmZff/view?usp=share_link).
| Language | # | Model | Setting | Prompt1 | Prompt2 | w | k | Acc@1 | A@3 | M@3 | P@3 |
|------------|---|-------|--------------|---------|---------|---|---|-------|-----|-----|-------------|
| English | 1 | RoBERTa-L | fine-tune | simple | word | 5 | 5 | **0.6353** | **0.5308** | **0.4244** | **0.8739** |
| English | 2 | mBERT | multilingual | easier | word | 10 | 10 | 0.4959 | 0.4235 | 0.3273 | 0.7560 |
| English | 3 | RoBERTa-L | zero-shot | easier | word | 5 | - | 0.2654 | 0.268 | 0.1820 | 0.4906 |
| Spanish | 1 | BERTIN | fine-tune | sinónimo | fácil | - | 3 | 0.3451 | **0.2907** | **0.2238** | **0.5543** |
| Spanish | 2 | BERTIN | fine-tune | palabra | simple | - | 10 | 0.3614 | **0.2907**| 0.2225 | 0.538 |
| Spanish | 3 | BERTIN | fine-tune | sinónimo | fácil | 10 | 10 | **0.3668** | 0.269 | 0.2128 | 0.5326 |
| Portuguese | 1 | BR_BERTo | fine-tune | palavra | simples | - | 8 | **0.1711** | 0.1096 | 0.1011 | 0.2486 |
| Portuguese | 2 | BR_BERTo | fine-tune | sinônimo | fácil | - | 10 | 0.1363 | 0.0962 | 0.0944 | 0.2379 |
| Portuguese | 3 | BR_BERTo | fine-tune | sinônimo | simples | 5 | 10 | 0.1577 | **0.1283**| **0.1071**| **0.2834**|
## Citation
If you use our results and scripts in your research, please cite our work:
"[UoM&MMU at TSAR-2022 Shared Task: Prompt Learning for Lexical Simplification](https://drive.google.com/file/d/1x5dRxgcSGAaCCrjsgpCHnYek9G-TmZff/view?usp=share_link)".
```
@inproceedings{vasquez-rodriguez-etal-2022-prompt-ls,
title = "UoM\&MMU at TSAR-2022 Shared Task: Prompt Learning for Lexical Simplification",
author = "V{\'a}squez-Rodr{\'\i}guez, Laura and
Nguyen, Nhung T. H. and
Shardlow, Matthew and
Ananiadou, Sophia",
booktitle = "Shared Task on Text Simplification, Accessibility, and Readability (TSAR-2022), EMNLP 2022",
month = dec,
year = "2022",
}
```
|
BigSalmon/GPTNeo350MInformalToFormalLincoln2 | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
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},
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}
}
} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: whisper_0010
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. -->
# whisper_0010
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6371
- Train Accuracy: 0.0302
- Validation Loss: 0.7409
- Validation Accuracy: 0.0302
- Epoch: 9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 5.0856 | 0.0116 | 4.4440 | 0.0123 | 0 |
| 4.3149 | 0.0131 | 4.0521 | 0.0142 | 1 |
| 3.9260 | 0.0146 | 3.7264 | 0.0153 | 2 |
| 3.5418 | 0.0160 | 3.3026 | 0.0174 | 3 |
| 2.7510 | 0.0198 | 2.0157 | 0.0241 | 4 |
| 1.6782 | 0.0250 | 1.3567 | 0.0273 | 5 |
| 1.1705 | 0.0274 | 1.0678 | 0.0286 | 6 |
| 0.9126 | 0.0287 | 0.9152 | 0.0294 | 7 |
| 0.7514 | 0.0296 | 0.8057 | 0.0299 | 8 |
| 0.6371 | 0.0302 | 0.7409 | 0.0302 | 9 |
### Framework versions
- Transformers 4.25.0.dev0
- TensorFlow 2.9.2
- Tokenizers 0.13.2
|
BigSalmon/InfillFormalLincoln | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
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}
} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: whisper_0015
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. -->
# whisper_0015
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3281
- Train Accuracy: 0.0322
- Validation Loss: 0.5841
- Validation Accuracy: 0.0311
- Epoch: 14
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 5.0856 | 0.0116 | 4.4440 | 0.0123 | 0 |
| 4.3149 | 0.0131 | 4.0521 | 0.0142 | 1 |
| 3.9260 | 0.0146 | 3.7264 | 0.0153 | 2 |
| 3.5418 | 0.0160 | 3.3026 | 0.0174 | 3 |
| 2.7510 | 0.0198 | 2.0157 | 0.0241 | 4 |
| 1.6782 | 0.0250 | 1.3567 | 0.0273 | 5 |
| 1.1705 | 0.0274 | 1.0678 | 0.0286 | 6 |
| 0.9126 | 0.0287 | 0.9152 | 0.0294 | 7 |
| 0.7514 | 0.0296 | 0.8057 | 0.0299 | 8 |
| 0.6371 | 0.0302 | 0.7409 | 0.0302 | 9 |
| 0.5498 | 0.0307 | 0.6854 | 0.0306 | 10 |
| 0.4804 | 0.0312 | 0.6518 | 0.0307 | 11 |
| 0.4214 | 0.0316 | 0.6200 | 0.0310 | 12 |
| 0.3713 | 0.0319 | 0.5947 | 0.0311 | 13 |
| 0.3281 | 0.0322 | 0.5841 | 0.0311 | 14 |
### Framework versions
- Transformers 4.25.0.dev0
- TensorFlow 2.9.2
- Tokenizers 0.13.2
|
BigSalmon/InformalToFormalLincoln21 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
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}
} | 8 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
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-fr
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: 0.1907
- F1: 0.8682
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2901 | 1.0 | 715 | 0.1864 | 0.8211 |
| 0.1576 | 2.0 | 1430 | 0.1667 | 0.8441 |
| 0.1038 | 3.0 | 2145 | 0.1710 | 0.8452 |
| 0.0701 | 4.0 | 2860 | 0.1787 | 0.8636 |
| 0.0449 | 5.0 | 3575 | 0.1907 | 0.8682 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
BigSalmon/T52 | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
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"prefix": "translate English to German: "
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"prefix": "translate English to French: "
},
"translation_en_to_ro": {
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"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
model-index:
- name: t5-small-finetuned-xsum
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-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
BigSalmon/T5Salmon2 | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
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"T5ForConditionalGeneration"
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},
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"num_beams": 4,
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}
}
} | 13 | 2022-11-08T16:38:49Z | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: imagefolder
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `imagefolder` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/aorhan/ddpm-butterflies-128/tensorboard?#scalars)
|
BigSalmon/TS3 | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible",
"has_space"
]
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}
} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- gsm8k
model-index:
- name: flan-t5-base-finetuned-gsm8k
results: []
widget:
- text: "Please, answer the following question reasoning step-by-step:
Manu bought 4 apples and lost one in the market. How many apples does Manu have?"
---
<!-- 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. -->
# flan-t5-base-finetuned-gsm8k
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the gsm8k dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3652
- Rouge2 Precision: 0.3914
- Rouge2 Recall: 0.0816
- Rouge2 Fmeasure: 0.1308
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.425 | 1.0 | 1869 | 0.3942 | 0.3707 | 0.0774 | 0.1238 |
| 0.3849 | 2.0 | 3738 | 0.3769 | 0.3809 | 0.0795 | 0.1272 |
| 0.3663 | 3.0 | 5607 | 0.3698 | 0.3808 | 0.0805 | 0.1285 |
| 0.3553 | 4.0 | 7476 | 0.3659 | 0.3863 | 0.0805 | 0.129 |
| 0.3421 | 5.0 | 9345 | 0.3652 | 0.3914 | 0.0816 | 0.1308 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
BigSalmon/prepositions | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
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}
} | 7 | 2022-11-08T16:55:26Z | ---
license: gpl-3.0
language:
- en
tags:
- wikipedia
- wikidata
widget:
- text: "Douglas Adams\n
1952 births\n
2001 deaths\n
20th-century atheists\n
21st-century atheists\n
20th-century English novelists\n
21st-century English novelists\n
20th-century English screenwriters\n
Alumni of St John's College, Cambridge\n
Apple Inc. people\n
Audiobook narrators\n
BBC radio producers\n
British atheism activists\n
British child writers\n
Burials at Highgate Cemetery\n
English atheists\n
English comedy writers\n
English essayists\n
English humanists\n
English humorists\n
English radio writers\n
English science fiction writers\n
English social commentators\n
English television writers\n
Infocom\n
Inkpot Award winners\n
Interactive fiction writers\n
British male television writers\n
Monty Python\n
Non-fiction environmental writers\n
People educated at Brentwood School, Essex\n
People from Cambridge\n
Usenet people\n
Weird fiction writers\n
Douglas Adams"
example_title: "Douglas Adams"
- text: "Unincorporated communities in Minnesota\n
Unincorporated communities in St. Louis County, Minnesota\n
St. Louis County, Minnesota geography stubs\n
Sturgeon, Minnesota"
example_title: "Sturgeon, Minnesota"
- text: "Araneus\n
Spiders described in 1884\n
Araneidae stubs\n
Araneus pratensis"
example_title: "Araneus pratensis"
- text: "Mohammedan SC (Dhaka) seasons\n
Bangladeshi football club records and statistics\n
2019 in Bangladeshi football\n
2020 in Bangladeshi football\n
2019–20 Mohammedan SC (Dhaka) season"
example_title: "2019–20 Mohammedan SC (Dhaka) season"
- text: "Waterfalls of Karnataka\n
Tourist attractions in Dakshina Kannada district\n
Geography of Dakshina Kannada district\n
Bandaje Falls
"
example_title: "Bandaje Falls"
---
Psychiq is a model that predicts the instance or subclass of a wikipedia article. The model accepts as input 1) the list of all categories the article is in separated by newlines followed by 2) the title of the article . It makes a guess at the top 1000 most common types or returns unknown. Take a look at the examples to see what the format should look like. |
Bilz/DialoGPT-small-harrypotter | []
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} | 0 | 2022-11-08T17:13:14Z | ---
tags:
- pyannote
- pyannote-audio
- pyannote-audio-model
- audio
- voice
- speech
- speaker
- speaker-segmentation
- voice-activity-detection
- overlapped-speech-detection
- resegmentation
datasets:
- ami
- dihard
- voxconverse
license: mit
inference: false
---
# 🎹 Speaker segmentation
Model from *[End-to-end speaker segmentation for overlap-aware resegmentation](http://arxiv.org/abs/2104.04045)*,
by Hervé Bredin and Antoine Laurent.
[Online demo](https://huggingface.co/spaces/pyannote/pretrained-pipelines) is available as a Hugging Face Space.
## Support
For commercial enquiries and scientific consulting, please contact [me](mailto:[email protected]).
For [technical questions](https://github.com/pyannote/pyannote-audio/discussions) and [bug reports](https://github.com/pyannote/pyannote-audio/issues), please check [pyannote.audio](https://github.com/pyannote/pyannote-audio) Github repository.
## Usage
Relies on pyannote.audio 2.0 currently in development: see [installation instructions](https://github.com/pyannote/pyannote-audio/tree/develop#installation).
### Voice activity detection
```python
from pyannote.audio.pipelines import VoiceActivityDetection
pipeline = VoiceActivityDetection(segmentation="pyannote/segmentation")
HYPER_PARAMETERS = {
# onset/offset activation thresholds
"onset": 0.5, "offset": 0.5,
# remove speech regions shorter than that many seconds.
"min_duration_on": 0.0,
# fill non-speech regions shorter than that many seconds.
"min_duration_off": 0.0
}
pipeline.instantiate(HYPER_PARAMETERS)
vad = pipeline("audio.wav")
# `vad` is a pyannote.core.Annotation instance containing speech regions
```
### Overlapped speech detection
```python
from pyannote.audio.pipelines import OverlappedSpeechDetection
pipeline = OverlappedSpeechDetection(segmentation="pyannote/segmentation")
pipeline.instantiate(HYPER_PARAMETERS)
osd = pipeline("audio.wav")
# `osd` is a pyannote.core.Annotation instance containing overlapped speech regions
```
### Resegmentation
```python
from pyannote.audio.pipelines import Resegmentation
pipeline = Resegmentation(segmentation="pyannote/segmentation",
diarization="baseline")
pipeline.instantiate(HYPER_PARAMETERS)
resegmented_baseline = pipeline({"audio": "audio.wav", "baseline": baseline})
# where `baseline` should be provided as a pyannote.core.Annotation instance
```
### Raw scores
```python
from pyannote.audio import Inference
inference = Inference("pyannote/segmentation")
segmentation = inference("audio.wav")
# `segmentation` is a pyannote.core.SlidingWindowFeature
# instance containing raw segmentation scores like the
# one pictured above (output)
```
## Reproducible research
In order to reproduce the results of the paper ["End-to-end speaker segmentation for overlap-aware resegmentation
"](https://arxiv.org/abs/2104.04045), use `pyannote/segmentation@Interspeech2021` with the following hyper-parameters:
| Voice activity detection | `onset` | `offset` | `min_duration_on` | `min_duration_off` |
| ------------------------ | ------- | -------- | ----------------- | ------------------ |
| AMI Mix-Headset | 0.684 | 0.577 | 0.181 | 0.037 |
| DIHARD3 | 0.767 | 0.377 | 0.136 | 0.067 |
| VoxConverse | 0.767 | 0.713 | 0.182 | 0.501 |
| Overlapped speech detection | `onset` | `offset` | `min_duration_on` | `min_duration_off` |
| --------------------------- | ------- | -------- | ----------------- | ------------------ |
| AMI Mix-Headset | 0.448 | 0.362 | 0.116 | 0.187 |
| DIHARD3 | 0.430 | 0.320 | 0.091 | 0.144 |
| VoxConverse | 0.587 | 0.426 | 0.337 | 0.112 |
| Resegmentation of VBx | `onset` | `offset` | `min_duration_on` | `min_duration_off` |
| --------------------- | ------- | -------- | ----------------- | ------------------ |
| AMI Mix-Headset | 0.542 | 0.527 | 0.044 | 0.705 |
| DIHARD3 | 0.592 | 0.489 | 0.163 | 0.182 |
| VoxConverse | 0.537 | 0.724 | 0.410 | 0.563 |
Expected outputs (and VBx baseline) are also provided in the `/reproducible_research` sub-directories.
## Citation
```bibtex
@inproceedings{Bredin2021,
Title = {{End-to-end speaker segmentation for overlap-aware resegmentation}},
Author = {{Bredin}, Herv{\'e} and {Laurent}, Antoine},
Booktitle = {Proc. Interspeech 2021},
Address = {Brno, Czech Republic},
Month = {August},
Year = {2021},
```
```bibtex
@inproceedings{Bredin2020,
Title = {{pyannote.audio: neural building blocks for speaker diarization}},
Author = {{Bredin}, Herv{\'e} and {Yin}, Ruiqing and {Coria}, Juan Manuel and {Gelly}, Gregory and {Korshunov}, Pavel and {Lavechin}, Marvin and {Fustes}, Diego and {Titeux}, Hadrien and {Bouaziz}, Wassim and {Gill}, Marie-Philippe},
Booktitle = {ICASSP 2020, IEEE International Conference on Acoustics, Speech, and Signal Processing},
Address = {Barcelona, Spain},
Month = {May},
Year = {2020},
}
```
|
BinksSachary/DialoGPT-small-shaxx | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
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}
} | 12 | 2022-11-08T18:23:44Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: hcho22/opus-mt-ko-en-finetuned-kr-to-en
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. -->
# hcho22/opus-mt-ko-en-finetuned-kr-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ko-en](https://huggingface.co/Helsinki-NLP/opus-mt-ko-en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.2330
- Validation Loss: 1.2844
- Train Bleu: 30.7578
- Train Gen Len: 13.9104
- Epoch: 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Bleu | Train Gen Len | Epoch |
|:----------:|:---------------:|:----------:|:-------------:|:-----:|
| 1.2330 | 1.2844 | 30.7578 | 13.9104 | 0 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.2
|
BinksSachary/ShaxxBot2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
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"task_specific_params": {
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}
}
} | 12 | 2022-11-08T18:37:26Z |
---
language:
- multilingual
- en
- fo
- is
- nn
- nb
- no
- da
- sv
license: cc-by-4.0
tags:
- norwegian
- bert
pipeline_tag: fill-mask
widget:
- text: På biblioteket kan du <mask> en bok.
- text: Dette er et <mask> eksempel.
- text: Av og til kan en språkmodell gi et <mask> resultat.
- text: Som ansat får du <mask> for at bidrage til borgernes adgang til dansk kulturarv, til forskning og til samfundets demokratiske udvikling.
---
# Scandinavian XLM-RoBERTa (base-sized model)
This model is currently being created. Do not use yet. |
Blabla/Pipipopo | []
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}
} | 0 | 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: 7.50 +/- 2.72
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="mrahusain/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"])
```
|
Broadus20/DialoGPT-small-joshua | [
"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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},
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},
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},
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"early_stopping": null,
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}
}
} | 12 | null | ---
language:
- en
license: creativeml-openrail-m
thumbnail: "https://s3.amazonaws.com/moonup/production/uploads/1667942059199-6305d083df993a789e61126d.jpeg"
tags:
- stable-diffusion
- text-to-image
---
## Model description
<b>isoCities</b> v1
This model trained based on Stable Diffusion 1.5 model to create isometric cities, venues, etc more precisely.
Trained isometric city model merged with SD 1.5 with Automatic1111's checkpoint merger tool (Couldn't remember exactly the merging ratio and the interpolation method)
Use "<b>an illustration of an isometric city, digital art, highly detailed</b>" in your prompts.
[CKPT download link](https://huggingface.co/Astroboy/isoCities/blob/main/isometric_city%2BSD1.5.ckpt)
## Intended uses & limitations
Do not use for commercial purposes.
## Training procedure
Trained on a WSL2 Ubuntu machine with DreamBooth.
### Training hyperparameters
The following hyperparameters were used during training:
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="an illustration of an isometric city, digital art, highly detailed" \
--class_prompt="isometric city" \
--resolution=512 \
--train_batch_size=1 \
--mixed_precision="fp16" \
--gradient_accumulation_steps=1 --gradient_checkpointing \
--use_8bit_adam \
--learning_rate=5e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--num_class_images=50 \
--max_train_steps=1500
### Training results
Sample images
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) |
Brona/model1 | []
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}
}
} | 0 | null | ---
license: other
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dalio-6.7b-test
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. -->
# dalio-6.7b-test
This model is a fine-tuned version of [facebook/opt-6.7b](https://huggingface.co/facebook/opt-6.7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6641
- Accuracy: 0.0662
## 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 8
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 2.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.5958 | 0.31 | 16 | 2.5371 | 0.0659 |
| 2.3784 | 0.62 | 32 | 2.5039 | 0.0670 |
| 2.3578 | 0.92 | 48 | 2.6074 | 0.0654 |
| 1.3819 | 1.23 | 64 | 2.6680 | 0.0658 |
| 1.1529 | 1.54 | 80 | 2.6738 | 0.0665 |
| 1.2938 | 1.85 | 96 | 2.6641 | 0.0662 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Brykee/BrykeeBot | []
| null | {
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}
}
} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlnet-base-cased-fine-Disaster-Tweets-Part3
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. -->
# xlnet-base-cased-fine-Disaster-Tweets-Part3
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3924
- Accuracy: 0.8468
- F1: 0.8467
## 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: 8e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 203 | 0.4457 | 0.8257 | 0.8253 |
| No log | 2.0 | 406 | 0.3924 | 0.8468 | 0.8467 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Bryson575x/riceboi | []
| null | {
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}
} | 0 | 2022-11-08T21:40:43Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6230
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.9098 | 1.0 | 554 | 1.8512 |
| 1.6186 | 2.0 | 1108 | 1.6220 |
| 1.3034 | 3.0 | 1662 | 1.6230 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
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