modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-29 18:27:06
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 526
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
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huggingtweets/g__j
|
huggingtweets
| 2022-06-28T13:36:16Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-28T13:36:09Z |
---
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/959389610978742273/jfOMGQ1B_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">Greg Jackson</div>
<div style="text-align: center; font-size: 14px;">@g__j</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 Greg Jackson.
| Data | Greg Jackson |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 187 |
| Short tweets | 179 |
| Tweets kept | 2884 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2sl53oes/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 @g__j's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/stwh74do) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/stwh74do/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/g__j')
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)
|
choonlee/TEST2ppo-LunarLander-v2
|
choonlee
| 2022-06-28T13:12:23Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-28T13:11:51Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 179.80 +/- 62.46
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
twieland/MIX3_ja-en_helsinki
|
twieland
| 2022-06-28T11:46:58Z | 113 | 0 |
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-22T00:54:09Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: MIX3_ja-en_helsinki
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. -->
# MIX3_ja-en_helsinki
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/opus-mt-ja-en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4832
## 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: 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-------:|:---------------:|
| 2.8699 | 0.01 | 5000 | 2.3465 |
| 2.6168 | 0.02 | 10000 | 2.2205 |
| 2.5083 | 0.03 | 15000 | 2.2382 |
| 2.4359 | 0.04 | 20000 | 2.1670 |
| 2.3821 | 0.06 | 25000 | 2.1122 |
| 2.3358 | 0.07 | 30000 | 2.0902 |
| 2.3045 | 0.08 | 35000 | 2.0461 |
| 2.2782 | 0.09 | 40000 | 2.0290 |
| 2.2481 | 0.1 | 45000 | 1.9910 |
| 2.2267 | 0.11 | 50000 | 2.0059 |
| 2.2056 | 0.12 | 55000 | 1.9858 |
| 2.1903 | 0.13 | 60000 | 1.9725 |
| 2.173 | 0.15 | 65000 | 1.9797 |
| 2.154 | 0.16 | 70000 | 1.9654 |
| 2.1429 | 0.17 | 75000 | 1.9567 |
| 2.1304 | 0.18 | 80000 | 1.9348 |
| 2.1232 | 0.19 | 85000 | 1.9361 |
| 2.116 | 0.2 | 90000 | 1.9277 |
| 2.1016 | 0.21 | 95000 | 1.9193 |
| 2.0984 | 0.22 | 100000 | 1.9064 |
| 2.0797 | 0.24 | 105000 | 1.9177 |
| 2.0767 | 0.25 | 110000 | 1.8975 |
| 2.0642 | 0.26 | 115000 | 1.8782 |
| 2.0595 | 0.27 | 120000 | 1.9012 |
| 2.0533 | 0.28 | 125000 | 1.8977 |
| 2.044 | 0.29 | 130000 | 1.8984 |
| 2.0374 | 0.3 | 135000 | 1.9221 |
| 2.0305 | 0.31 | 140000 | 1.9243 |
| 2.02 | 0.32 | 145000 | 1.8773 |
| 2.0195 | 0.34 | 150000 | 1.8676 |
| 2.0151 | 0.35 | 155000 | 1.8637 |
| 2.0065 | 0.36 | 160000 | 1.8556 |
| 2.0037 | 0.37 | 165000 | 1.8399 |
| 1.9963 | 0.38 | 170000 | 1.8452 |
| 1.9878 | 0.39 | 175000 | 1.8644 |
| 1.9871 | 0.4 | 180000 | 1.8576 |
| 1.9779 | 0.41 | 185000 | 1.8509 |
| 1.9721 | 0.43 | 190000 | 1.8405 |
| 1.9724 | 0.44 | 195000 | 1.8594 |
| 1.9685 | 0.45 | 200000 | 1.8540 |
| 1.9634 | 0.46 | 205000 | 1.8694 |
| 1.9583 | 0.47 | 210000 | 1.8591 |
| 1.9557 | 0.48 | 215000 | 1.8539 |
| 1.9494 | 0.49 | 220000 | 1.8673 |
| 1.9484 | 0.5 | 225000 | 1.8021 |
| 1.9395 | 0.52 | 230000 | 1.8309 |
| 1.9384 | 0.53 | 235000 | 1.7933 |
| 1.937 | 0.54 | 240000 | 1.8199 |
| 1.9315 | 0.55 | 245000 | 1.8065 |
| 1.9276 | 0.56 | 250000 | 1.7857 |
| 1.9248 | 0.57 | 255000 | 1.8207 |
| 1.9195 | 0.58 | 260000 | 1.7898 |
| 1.9187 | 0.59 | 265000 | 1.8097 |
| 1.9138 | 0.6 | 270000 | 1.7909 |
| 1.9094 | 0.62 | 275000 | 1.7995 |
| 1.9098 | 0.63 | 280000 | 1.8165 |
| 1.9038 | 0.64 | 285000 | 1.8132 |
| 1.9034 | 0.65 | 290000 | 1.7951 |
| 1.899 | 0.66 | 295000 | 1.7880 |
| 1.8965 | 0.67 | 300000 | 1.7953 |
| 1.8941 | 0.68 | 305000 | 1.7986 |
| 1.8919 | 0.69 | 310000 | 1.7964 |
| 1.8875 | 0.71 | 315000 | 1.8041 |
| 1.884 | 0.72 | 320000 | 1.7764 |
| 1.8798 | 0.73 | 325000 | 1.8019 |
| 1.8801 | 0.74 | 330000 | 1.7790 |
| 1.8809 | 0.75 | 335000 | 1.7849 |
| 1.8736 | 0.76 | 340000 | 1.7800 |
| 1.8727 | 0.77 | 345000 | 1.7900 |
| 1.8722 | 0.78 | 350000 | 1.7727 |
| 1.8699 | 0.8 | 355000 | 1.7597 |
| 1.8672 | 0.81 | 360000 | 1.7824 |
| 1.8638 | 0.82 | 365000 | 1.7674 |
| 1.8609 | 0.83 | 370000 | 1.7715 |
| 1.8584 | 0.84 | 375000 | 1.7694 |
| 1.8568 | 0.85 | 380000 | 1.7776 |
| 1.8523 | 0.86 | 385000 | 1.7697 |
| 1.8584 | 0.87 | 390000 | 1.7436 |
| 1.8474 | 0.88 | 395000 | 1.7644 |
| 1.8492 | 0.9 | 400000 | 1.7732 |
| 1.8465 | 0.91 | 405000 | 1.7611 |
| 1.846 | 0.92 | 410000 | 1.7717 |
| 1.8431 | 0.93 | 415000 | 1.7514 |
| 1.8402 | 0.94 | 420000 | 1.7353 |
| 1.8398 | 0.95 | 425000 | 1.7720 |
| 1.8314 | 0.96 | 430000 | 1.7728 |
| 1.8322 | 0.97 | 435000 | 1.7491 |
| 1.8284 | 0.99 | 440000 | 1.7561 |
| 1.8301 | 1.0 | 445000 | 1.7499 |
| 1.8182 | 1.01 | 450000 | 1.7514 |
| 1.8111 | 1.02 | 455000 | 1.7596 |
| 1.8116 | 1.03 | 460000 | 1.7455 |
| 1.8098 | 1.04 | 465000 | 1.7495 |
| 1.809 | 1.05 | 470000 | 1.7446 |
| 1.8088 | 1.06 | 475000 | 1.7290 |
| 1.8127 | 1.08 | 480000 | 1.7453 |
| 1.8051 | 1.09 | 485000 | 1.7495 |
| 1.8026 | 1.1 | 490000 | 1.7453 |
| 1.8028 | 1.11 | 495000 | 1.7615 |
| 1.8046 | 1.12 | 500000 | 1.7491 |
| 1.8052 | 1.13 | 505000 | 1.7280 |
| 1.7997 | 1.14 | 510000 | 1.7482 |
| 1.7976 | 1.15 | 515000 | 1.7368 |
| 1.7981 | 1.16 | 520000 | 1.7354 |
| 1.7949 | 1.18 | 525000 | 1.7076 |
| 1.7943 | 1.19 | 530000 | 1.7020 |
| 1.7911 | 1.2 | 535000 | 1.7121 |
| 1.7909 | 1.21 | 540000 | 1.7170 |
| 1.7926 | 1.22 | 545000 | 1.7310 |
| 1.7856 | 1.23 | 550000 | 1.7218 |
| 1.7875 | 1.24 | 555000 | 1.7362 |
| 1.7801 | 1.25 | 560000 | 1.7484 |
| 1.7854 | 1.27 | 565000 | 1.7466 |
| 1.7799 | 1.28 | 570000 | 1.7248 |
| 1.7823 | 1.29 | 575000 | 1.7355 |
| 1.7765 | 1.3 | 580000 | 1.7188 |
| 1.7779 | 1.31 | 585000 | 1.6993 |
| 1.7751 | 1.32 | 590000 | 1.7154 |
| 1.7762 | 1.33 | 595000 | 1.7348 |
| 1.7725 | 1.34 | 600000 | 1.7272 |
| 1.7701 | 1.36 | 605000 | 1.7157 |
| 1.7644 | 1.37 | 610000 | 1.7161 |
| 1.7707 | 1.38 | 615000 | 1.6961 |
| 1.764 | 1.39 | 620000 | 1.6930 |
| 1.7639 | 1.4 | 625000 | 1.6927 |
| 1.7654 | 1.41 | 630000 | 1.6989 |
| 1.7623 | 1.42 | 635000 | 1.6892 |
| 1.7598 | 1.43 | 640000 | 1.6911 |
| 1.7575 | 1.44 | 645000 | 1.7199 |
| 1.7574 | 1.46 | 650000 | 1.6992 |
| 1.7526 | 1.47 | 655000 | 1.6981 |
| 1.7556 | 1.48 | 660000 | 1.6860 |
| 1.7558 | 1.49 | 665000 | 1.7099 |
| 1.7539 | 1.5 | 670000 | 1.6950 |
| 1.7454 | 1.51 | 675000 | 1.6999 |
| 1.748 | 1.52 | 680000 | 1.6871 |
| 1.7476 | 1.53 | 685000 | 1.6884 |
| 1.7493 | 1.55 | 690000 | 1.6984 |
| 1.745 | 1.56 | 695000 | 1.6999 |
| 1.7397 | 1.57 | 700000 | 1.7036 |
| 1.7429 | 1.58 | 705000 | 1.7223 |
| 1.7367 | 1.59 | 710000 | 1.7111 |
| 1.7403 | 1.6 | 715000 | 1.6691 |
| 1.7361 | 1.61 | 720000 | 1.6693 |
| 1.737 | 1.62 | 725000 | 1.6884 |
| 1.7347 | 1.63 | 730000 | 1.6641 |
| 1.7323 | 1.65 | 735000 | 1.6628 |
| 1.7329 | 1.66 | 740000 | 1.6759 |
| 1.7292 | 1.67 | 745000 | 1.6654 |
| 1.7275 | 1.68 | 750000 | 1.6738 |
| 1.7266 | 1.69 | 755000 | 1.6792 |
| 1.7259 | 1.7 | 760000 | 1.6752 |
| 1.7231 | 1.71 | 765000 | 1.6641 |
| 1.7238 | 1.72 | 770000 | 1.6676 |
| 1.7223 | 1.74 | 775000 | 1.6563 |
| 1.722 | 1.75 | 780000 | 1.6541 |
| 1.7195 | 1.76 | 785000 | 1.6560 |
| 1.7171 | 1.77 | 790000 | 1.6786 |
| 1.7187 | 1.78 | 795000 | 1.6434 |
| 1.7186 | 1.79 | 800000 | 1.6538 |
| 1.7115 | 1.8 | 805000 | 1.6535 |
| 1.7119 | 1.81 | 810000 | 1.6738 |
| 1.7106 | 1.83 | 815000 | 1.6597 |
| 1.7088 | 1.84 | 820000 | 1.6486 |
| 1.7079 | 1.85 | 825000 | 1.6576 |
| 1.7062 | 1.86 | 830000 | 1.6676 |
| 1.7084 | 1.87 | 835000 | 1.6449 |
| 1.7059 | 1.88 | 840000 | 1.6515 |
| 1.7057 | 1.89 | 845000 | 1.6609 |
| 1.7021 | 1.9 | 850000 | 1.6482 |
| 1.7005 | 1.91 | 855000 | 1.6653 |
| 1.6988 | 1.93 | 860000 | 1.6801 |
| 1.6964 | 1.94 | 865000 | 1.6830 |
| 1.6954 | 1.95 | 870000 | 1.6589 |
| 1.693 | 1.96 | 875000 | 1.6553 |
| 1.689 | 1.97 | 880000 | 1.6554 |
| 1.69 | 1.98 | 885000 | 1.6424 |
| 1.6893 | 1.99 | 890000 | 1.6628 |
| 1.6772 | 2.0 | 895000 | 1.6709 |
| 1.6703 | 2.02 | 900000 | 1.6627 |
| 1.6726 | 2.03 | 905000 | 1.6612 |
| 1.669 | 2.04 | 910000 | 1.6595 |
| 1.6696 | 2.05 | 915000 | 1.6427 |
| 1.6672 | 2.06 | 920000 | 1.6497 |
| 1.669 | 2.07 | 925000 | 1.6288 |
| 1.6675 | 2.08 | 930000 | 1.6443 |
| 1.6685 | 2.09 | 935000 | 1.6316 |
| 1.6671 | 2.11 | 940000 | 1.6451 |
| 1.6673 | 2.12 | 945000 | 1.6313 |
| 1.6649 | 2.13 | 950000 | 1.6363 |
| 1.6655 | 2.14 | 955000 | 1.6440 |
| 1.6637 | 2.15 | 960000 | 1.6238 |
| 1.6632 | 2.16 | 965000 | 1.6226 |
| 1.6599 | 2.17 | 970000 | 1.6171 |
| 1.6602 | 2.18 | 975000 | 1.6466 |
| 1.658 | 2.19 | 980000 | 1.6341 |
| 1.6571 | 2.21 | 985000 | 1.6500 |
| 1.6572 | 2.22 | 990000 | 1.6225 |
| 1.6572 | 2.23 | 995000 | 1.6296 |
| 1.6552 | 2.24 | 1000000 | 1.6437 |
| 1.6548 | 2.25 | 1005000 | 1.6162 |
| 1.6552 | 2.26 | 1010000 | 1.6223 |
| 1.6544 | 2.27 | 1015000 | 1.6355 |
| 1.6464 | 2.28 | 1020000 | 1.6250 |
| 1.652 | 2.3 | 1025000 | 1.6217 |
| 1.6481 | 2.31 | 1030000 | 1.6079 |
| 1.6466 | 2.32 | 1035000 | 1.6110 |
| 1.6462 | 2.33 | 1040000 | 1.6210 |
| 1.6448 | 2.34 | 1045000 | 1.5993 |
| 1.6461 | 2.35 | 1050000 | 1.6096 |
| 1.6396 | 2.36 | 1055000 | 1.6137 |
| 1.644 | 2.37 | 1060000 | 1.6189 |
| 1.6396 | 2.39 | 1065000 | 1.6211 |
| 1.639 | 2.4 | 1070000 | 1.6149 |
| 1.6358 | 2.41 | 1075000 | 1.6144 |
| 1.6356 | 2.42 | 1080000 | 1.6018 |
| 1.6364 | 2.43 | 1085000 | 1.5999 |
| 1.6352 | 2.44 | 1090000 | 1.6095 |
| 1.634 | 2.45 | 1095000 | 1.6114 |
| 1.6279 | 2.46 | 1100000 | 1.6156 |
| 1.6272 | 2.47 | 1105000 | 1.6124 |
| 1.6319 | 2.49 | 1110000 | 1.6046 |
| 1.6276 | 2.5 | 1115000 | 1.6152 |
| 1.6285 | 2.51 | 1120000 | 1.6129 |
| 1.6242 | 2.52 | 1125000 | 1.5984 |
| 1.6261 | 2.53 | 1130000 | 1.6116 |
| 1.623 | 2.54 | 1135000 | 1.6061 |
| 1.6203 | 2.55 | 1140000 | 1.6182 |
| 1.62 | 2.56 | 1145000 | 1.5887 |
| 1.6177 | 2.58 | 1150000 | 1.5731 |
| 1.6172 | 2.59 | 1155000 | 1.5990 |
| 1.6179 | 2.6 | 1160000 | 1.5965 |
| 1.6206 | 2.61 | 1165000 | 1.6000 |
| 1.6156 | 2.62 | 1170000 | 1.5873 |
| 1.6124 | 2.63 | 1175000 | 1.5899 |
| 1.613 | 2.64 | 1180000 | 1.5910 |
| 1.6134 | 2.65 | 1185000 | 1.6017 |
| 1.609 | 2.67 | 1190000 | 1.5822 |
| 1.6084 | 2.68 | 1195000 | 1.5906 |
| 1.6101 | 2.69 | 1200000 | 1.6218 |
| 1.6077 | 2.7 | 1205000 | 1.6149 |
| 1.6057 | 2.71 | 1210000 | 1.5994 |
| 1.6018 | 2.72 | 1215000 | 1.5839 |
| 1.6049 | 2.73 | 1220000 | 1.5864 |
| 1.6012 | 2.74 | 1225000 | 1.5994 |
| 1.6013 | 2.75 | 1230000 | 1.5821 |
| 1.5957 | 2.77 | 1235000 | 1.5964 |
| 1.5971 | 2.78 | 1240000 | 1.5897 |
| 1.5967 | 2.79 | 1245000 | 1.5774 |
| 1.5927 | 2.8 | 1250000 | 1.5861 |
| 1.5954 | 2.81 | 1255000 | 1.5789 |
| 1.5937 | 2.82 | 1260000 | 1.5739 |
| 1.5895 | 2.83 | 1265000 | 1.5701 |
| 1.5912 | 2.84 | 1270000 | 1.5622 |
| 1.5922 | 2.86 | 1275000 | 1.5730 |
| 1.5883 | 2.87 | 1280000 | 1.5775 |
| 1.5864 | 2.88 | 1285000 | 1.5726 |
| 1.5837 | 2.89 | 1290000 | 1.5679 |
| 1.5824 | 2.9 | 1295000 | 1.5683 |
| 1.5817 | 2.91 | 1300000 | 1.5508 |
| 1.5778 | 2.92 | 1305000 | 1.5620 |
| 1.5822 | 2.93 | 1310000 | 1.5556 |
| 1.5783 | 2.95 | 1315000 | 1.5693 |
| 1.5751 | 2.96 | 1320000 | 1.5781 |
| 1.5716 | 2.97 | 1325000 | 1.5655 |
| 1.5765 | 2.98 | 1330000 | 1.5528 |
| 1.5728 | 2.99 | 1335000 | 1.5748 |
| 1.5672 | 3.0 | 1340000 | 1.5597 |
| 1.5467 | 3.01 | 1345000 | 1.5461 |
| 1.547 | 3.02 | 1350000 | 1.5516 |
| 1.5462 | 3.03 | 1355000 | 1.5519 |
| 1.5464 | 3.05 | 1360000 | 1.5593 |
| 1.5457 | 3.06 | 1365000 | 1.5576 |
| 1.5441 | 3.07 | 1370000 | 1.5653 |
| 1.544 | 3.08 | 1375000 | 1.5662 |
| 1.5467 | 3.09 | 1380000 | 1.5611 |
| 1.5439 | 3.1 | 1385000 | 1.5635 |
| 1.5449 | 3.11 | 1390000 | 1.5467 |
| 1.5417 | 3.12 | 1395000 | 1.5495 |
| 1.5428 | 3.14 | 1400000 | 1.5552 |
| 1.5432 | 3.15 | 1405000 | 1.5347 |
| 1.5401 | 3.16 | 1410000 | 1.5394 |
| 1.5391 | 3.17 | 1415000 | 1.5497 |
| 1.539 | 3.18 | 1420000 | 1.5431 |
| 1.5368 | 3.19 | 1425000 | 1.5479 |
| 1.5365 | 3.2 | 1430000 | 1.5513 |
| 1.5327 | 3.21 | 1435000 | 1.5467 |
| 1.5337 | 3.23 | 1440000 | 1.5477 |
| 1.5317 | 3.24 | 1445000 | 1.5398 |
| 1.5315 | 3.25 | 1450000 | 1.5481 |
| 1.532 | 3.26 | 1455000 | 1.5385 |
| 1.5312 | 3.27 | 1460000 | 1.5520 |
| 1.5328 | 3.28 | 1465000 | 1.5423 |
| 1.5288 | 3.29 | 1470000 | 1.5489 |
| 1.5271 | 3.3 | 1475000 | 1.5395 |
| 1.5273 | 3.31 | 1480000 | 1.5335 |
| 1.5235 | 3.33 | 1485000 | 1.5381 |
| 1.5224 | 3.34 | 1490000 | 1.5289 |
| 1.5206 | 3.35 | 1495000 | 1.5331 |
| 1.5189 | 3.36 | 1500000 | 1.5343 |
| 1.5152 | 3.37 | 1505000 | 1.5246 |
| 1.5225 | 3.38 | 1510000 | 1.5280 |
| 1.5168 | 3.39 | 1515000 | 1.5315 |
| 1.5161 | 3.4 | 1520000 | 1.5284 |
| 1.5111 | 3.42 | 1525000 | 1.5278 |
| 1.5154 | 3.43 | 1530000 | 1.5148 |
| 1.515 | 3.44 | 1535000 | 1.5286 |
| 1.5117 | 3.45 | 1540000 | 1.5291 |
| 1.5099 | 3.46 | 1545000 | 1.5320 |
| 1.5097 | 3.47 | 1550000 | 1.5323 |
| 1.5075 | 3.48 | 1555000 | 1.5157 |
| 1.5059 | 3.49 | 1560000 | 1.5214 |
| 1.5011 | 3.51 | 1565000 | 1.5199 |
| 1.5074 | 3.52 | 1570000 | 1.5114 |
| 1.5033 | 3.53 | 1575000 | 1.5145 |
| 1.5009 | 3.54 | 1580000 | 1.5184 |
| 1.4994 | 3.55 | 1585000 | 1.5125 |
| 1.5041 | 3.56 | 1590000 | 1.5048 |
| 1.5002 | 3.57 | 1595000 | 1.5156 |
| 1.4967 | 3.58 | 1600000 | 1.5176 |
| 1.4923 | 3.59 | 1605000 | 1.5128 |
| 1.495 | 3.61 | 1610000 | 1.5188 |
| 1.4929 | 3.62 | 1615000 | 1.5149 |
| 1.4921 | 3.63 | 1620000 | 1.5097 |
| 1.4916 | 3.64 | 1625000 | 1.5161 |
| 1.4852 | 3.65 | 1630000 | 1.5134 |
| 1.4881 | 3.66 | 1635000 | 1.5101 |
| 1.4873 | 3.67 | 1640000 | 1.5027 |
| 1.4911 | 3.68 | 1645000 | 1.4968 |
| 1.488 | 3.7 | 1650000 | 1.4962 |
| 1.4842 | 3.71 | 1655000 | 1.5030 |
| 1.4829 | 3.72 | 1660000 | 1.5041 |
| 1.4816 | 3.73 | 1665000 | 1.5076 |
| 1.479 | 3.74 | 1670000 | 1.5029 |
| 1.4768 | 3.75 | 1675000 | 1.5053 |
| 1.4769 | 3.76 | 1680000 | 1.5026 |
| 1.4781 | 3.77 | 1685000 | 1.5016 |
| 1.4781 | 3.79 | 1690000 | 1.5034 |
| 1.4777 | 3.8 | 1695000 | 1.4976 |
| 1.4736 | 3.81 | 1700000 | 1.5002 |
| 1.4715 | 3.82 | 1705000 | 1.4995 |
| 1.4716 | 3.83 | 1710000 | 1.4996 |
| 1.4648 | 3.84 | 1715000 | 1.4952 |
| 1.4711 | 3.85 | 1720000 | 1.4934 |
| 1.4682 | 3.86 | 1725000 | 1.4965 |
| 1.4659 | 3.87 | 1730000 | 1.4932 |
| 1.4689 | 3.89 | 1735000 | 1.4920 |
| 1.4656 | 3.9 | 1740000 | 1.4910 |
| 1.4666 | 3.91 | 1745000 | 1.4893 |
| 1.4611 | 3.92 | 1750000 | 1.4888 |
| 1.4623 | 3.93 | 1755000 | 1.4898 |
| 1.4637 | 3.94 | 1760000 | 1.4909 |
| 1.4585 | 3.95 | 1765000 | 1.4858 |
| 1.4586 | 3.96 | 1770000 | 1.4847 |
| 1.4579 | 3.98 | 1775000 | 1.4841 |
| 1.458 | 3.99 | 1780000 | 1.4840 |
| 1.4572 | 4.0 | 1785000 | 1.4832 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
facebook/regnet-y-032
|
facebook
| 2022-06-28T11:39:30Z | 68 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"regnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2003.13678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-18T15:35:16Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycls).
Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The authors design search spaces to perform Neural Architecture Search (NAS). They first start from a high dimensional search space and iteratively reduce the search space by empirically applying constraints based on the best-performing models sampled by the current search space.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=regnet) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
>>> from transformers import AutoFeatureExtractor, RegNetForImageClassification
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("zuppif/regnet-y-040")
>>> model = RegNetForImageClassification.from_pretrained("zuppif/regnet-y-040")
>>> inputs = feature_extractor(image, return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
'tabby, tabby cat'
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/regnet).
|
facebook/regnet-y-016
|
facebook
| 2022-06-28T11:38:42Z | 64 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"regnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2003.13678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-18T15:34:34Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycls).
Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The authors design search spaces to perform Neural Architecture Search (NAS). They first start from a high dimensional search space and iteratively reduce the search space by empirically applying constraints based on the best-performing models sampled by the current search space.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=regnet) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
>>> from transformers import AutoFeatureExtractor, RegNetForImageClassification
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("zuppif/regnet-y-040")
>>> model = RegNetForImageClassification.from_pretrained("zuppif/regnet-y-040")
>>> inputs = feature_extractor(image, return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
'tabby, tabby cat'
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/regnet).
|
AIDA-UPM/BERTuit-base
|
AIDA-UPM
| 2022-06-28T11:13:39Z | 6 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"online social networks",
"twitter",
"spanish",
"fill-mask",
"es",
"arxiv:2204.03465",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-28T09:55:34Z |
---
pipeline_tag: fill-mask
tags:
- online social networks
- twitter
- spanish
language: es
license: apache-2.0
widget:
- text: "Las <mask> causan hipoxia."
example_title: "Mask filling"
---
Model BERTuit as presented in the [BERTuit: Understanding Spanish language in Twitter through a native transformer](https://arxiv.org/abs/2204.03465) article.
Before tokenization replace user tags and urls with "\<usr\>" and "\<url\>" respectively.
Tokenize text with base class RoBERTaTokenizer.
|
fusing/glide-base
|
fusing
| 2022-06-28T10:27:26Z | 0 | 2 | null |
[
"arxiv:2112.10741",
"license:apache-2.0",
"region:us"
] | null | 2022-06-07T12:52:41Z |
---
license: apache-2.0
---
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
**Paper**: [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://arxiv.org/abs/2112.10741)
**Abstract**:
*Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity. We explore diffusion models for the problem of text-conditional image synthesis and compare two different guidance strategies: CLIP guidance and classifier-free guidance. We find that the latter is preferred by human evaluators for both photorealism and caption similarity, and often produces photorealistic samples. Samples from a 3.5 billion parameter text-conditional diffusion model using classifier-free guidance are favored by human evaluators to those from DALL-E, even when the latter uses expensive CLIP reranking. Additionally, we find that our models can be fine-tuned to perform image inpainting, enabling powerful text-driven image editing.*
## Usage
```python
# !pip install diffusers
import torch
from diffusers import DiffusionPipeline
import PIL.Image
model_id = "fusing/glide-base"
# load model and scheduler
pipeline = DiffusionPipeline.from_pretrained(model_id)
# run inference (text-conditioned denoising + upscaling)
img = pipeline("a crayon drawing of a corgi")
# process image to PIL
img = img.squeeze(0)
img = ((img + 1)*127.5).round().clamp(0, 255).to(torch.uint8).cpu().numpy()
image_pil = PIL.Image.fromarray(img)
# save image
image_pil.save("test.png")
```
## Samples
1. 
2. 
3. 
|
rtorrero/my-first-model
|
rtorrero
| 2022-06-28T08:44:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-06-28T07:41:49Z |
This is just me playing around with Hugging Face :-)
|
AdiKompella/q-FrozenLake-v1-4x4-Slippery
|
AdiKompella
| 2022-06-28T05:46:27Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-28T05:46:20Z |
---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-Slippery
results:
- metrics:
- type: mean_reward
value: 0.47 +/- 0.50
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="AdiKompella/q-FrozenLake-v1-4x4-Slippery", 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"])
```
|
egumasa/roberta-base-finetuned-academic
|
egumasa
| 2022-06-28T05:06:29Z | 7 | 4 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"dataset:elsevier-oa-cc-by",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-27T07:06:21Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- elsevier-oa-cc-by
model-index:
- name: roberta-base-finetuned-academic
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-finetuned-academic
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the elsevier-oa-cc-by dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1158
## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.1903 | 0.25 | 1025 | 2.0998 |
| 2.1752 | 0.5 | 2050 | 2.1186 |
| 2.1864 | 0.75 | 3075 | 2.1073 |
| 2.1874 | 1.0 | 4100 | 2.1177 |
| 2.1669 | 1.25 | 5125 | 2.1091 |
| 2.1859 | 1.5 | 6150 | 2.1212 |
| 2.1783 | 1.75 | 7175 | 2.1096 |
| 2.1734 | 2.0 | 8200 | 2.0998 |
| 2.1712 | 2.25 | 9225 | 2.0972 |
| 2.1812 | 2.5 | 10250 | 2.1051 |
| 2.1811 | 2.75 | 11275 | 2.1150 |
| 2.1826 | 3.0 | 12300 | 2.1097 |
| 2.172 | 3.25 | 13325 | 2.1115 |
| 2.1745 | 3.5 | 14350 | 2.1098 |
| 2.1758 | 3.75 | 15375 | 2.1101 |
| 2.1834 | 4.0 | 16400 | 2.1232 |
| 2.1836 | 4.25 | 17425 | 2.1052 |
| 2.1791 | 4.5 | 18450 | 2.1186 |
| 2.172 | 4.75 | 19475 | 2.1039 |
| 2.1797 | 5.0 | 20500 | 2.1015 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
vebie91/dqn-SpaceInvadersNoFrameskip-v4-1.2
|
vebie91
| 2022-06-28T04:33:56Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-28T04:33:19Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 563.00 +/- 159.85
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga vebie91 -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga vebie91
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 6),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 2000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Nabby/PPO-LunarLander-v2
|
Nabby
| 2022-06-28T04:21:46Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-28T04:21:21Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 276.91 +/- 22.39
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
mastak128/unit1
|
mastak128
| 2022-06-28T04:20:00Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-28T04:19:30Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 182.30 +/- 78.62
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
chandrasutrisnotjhong/marian-finetuned-kde4-en-to-fr
|
chandrasutrisnotjhong
| 2022-06-28T04:10:31Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-06-22T02:01:51Z |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-fr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 52.83242564204547
---
<!-- 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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8560
- Bleu: 52.8324
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jackieliu930/bart-large-cnn-samsum
|
jackieliu930
| 2022-06-28T03:46:12Z | 15 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"sagemaker",
"summarization",
"en",
"dataset:samsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- sagemaker
- bart
- summarization
license: apache-2.0
datasets:
- samsum
model-index:
- name: bart-large-cnn-samsum
results:
- task:
name: Abstractive Text Summarization
type: abstractive-text-summarization
dataset:
name: 'SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization'
type: samsum
metrics:
- name: Validation ROGUE-1
type: rogue-1
value: 42.621
- name: Validation ROGUE-2
type: rogue-2
value: 21.9825
- name: Validation ROGUE-L
type: rogue-l
value: 33.034
- name: Test ROGUE-1
type: rogue-1
value: 41.3174
- name: Test ROGUE-2
type: rogue-2
value: 20.8716
- name: Test ROGUE-L
type: rogue-l
value: 32.1337
- task:
type: summarization
name: Summarization
dataset:
name: samsum
type: samsum
config: samsum
split: test
metrics:
- name: ROUGE-1
type: rouge
value: 40.8911
verified: true
- name: ROUGE-2
type: rouge
value: 20.3551
verified: true
- name: ROUGE-L
type: rouge
value: 31.2696
verified: true
- name: ROUGE-LSUM
type: rouge
value: 37.9313
verified: true
- name: loss
type: loss
value: 1.4995627403259277
verified: true
- name: gen_len
type: gen_len
value: 60.2247
verified: true
widget:
- text: "Jeff: Can I train a \U0001F917 Transformers model on Amazon SageMaker? \n\
Philipp: Sure you can use the new Hugging Face Deep Learning Container. \nJeff:\
\ ok.\nJeff: and how can I get started? \nJeff: where can I find documentation?\
\ \nPhilipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face "
---
## `bart-large-cnn-samsum`
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.
For more information look at:
- [🤗 Transformers Documentation: Amazon SageMaker](https://huggingface.co/transformers/sagemaker.html)
- [Example Notebooks](https://github.com/huggingface/notebooks/tree/master/sagemaker)
- [Amazon SageMaker documentation for Hugging Face](https://docs.aws.amazon.com/sagemaker/latest/dg/hugging-face.html)
- [Python SDK SageMaker documentation for Hugging Face](https://sagemaker.readthedocs.io/en/stable/frameworks/huggingface/index.html)
- [Deep Learning Container](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#huggingface-training-containers)
## Hyperparameters
{
"dataset_name": "samsum",
"do_eval": true,
"do_predict": true,
"do_train": true,
"fp16": true,
"learning_rate": 5e-05,
"model_name_or_path": "facebook/bart-large-cnn",
"num_train_epochs": 3,
"output_dir": "/opt/ml/model",
"per_device_eval_batch_size": 4,
"per_device_train_batch_size": 4,
"predict_with_generate": true,
"sagemaker_container_log_level": 20,
"sagemaker_job_name": "huggingface-pytorch-training-2021-09-08-06-40-19-182",
"sagemaker_program": "run_summarization.py",
"sagemaker_region": "us-west-2",
"sagemaker_submit_directory": "s3://sagemaker-us-west-2-847380964353/huggingface-pytorch-training-2021-09-08-06-40-19-182/source/sourcedir.tar.gz",
"seed": 7
}
## Usage
from transformers import pipeline
summarizer = pipeline("summarization", model="philschmid/bart-large-cnn-samsum")
conversation = '''Jeff: Can I train a 🤗 Transformers model on Amazon SageMaker?
Philipp: Sure you can use the new Hugging Face Deep Learning Container.
Jeff: ok.
Jeff: and how can I get started?
Jeff: where can I find documentation?
Philipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face
'''
nlp(conversation)
## Results
| key | value |
| --- | ----- |
| eval_rouge1 | 42.059 |
| eval_rouge2 | 21.5509 |
| eval_rougeL | 32.4083 |
| eval_rougeLsum | 39.0015 |
| test_rouge1 | 40.8656 |
| test_rouge2 | 20.3517 |
| test_rougeL | 31.2268 |
| test_rougeLsum | 37.9301 |
|
jcmc/dqn-SpaceInvadersNoFrameskip-v4
|
jcmc
| 2022-06-28T03:41:05Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-28T03:40:33Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 416.50 +/- 122.17
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga jcmc -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga jcmc
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
lingchensanwen/distilbert-base-uncased-finetuned-squad
|
lingchensanwen
| 2022-06-28T02:57:31Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-27T00:42:11Z |
---
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: 0.0337
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 46 | 0.4284 |
| No log | 2.0 | 92 | 0.0573 |
| No log | 3.0 | 138 | 0.0337 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
KaliYuga/spritesheetdiffusion
|
KaliYuga
| 2022-06-28T00:52:09Z | 0 | 4 | null |
[
"license:cc-by-4.0",
"region:us"
] | null | 2022-06-23T01:10:50Z |
---
license: cc-by-4.0
---
This model is really only supposed to be for my [patreon patrons](https://www.patreon.com/kaliyuga_ai). I ask that, unless you *truly* can't afford to pay $5 to access this model, you not use it without being a patron. Regardless, you must give attribution if you use this model in any product/app/game, etc
|
Abdelmageed95/distilgpt2-finetuned-wikitext2
|
Abdelmageed95
| 2022-06-27T22:58:48Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-27T22:27:02Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
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. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6421
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7602 | 1.0 | 2334 | 3.6669 |
| 3.653 | 2.0 | 4668 | 3.6472 |
| 3.6006 | 3.0 | 7002 | 3.6421 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
matteopilotto/vit-base-patch16-224-in21k-snacks
|
matteopilotto
| 2022-06-27T22:19:35Z | 65 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"dataset:Matthijs/snacks",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-05-14T16:23:18Z |
---
datasets:
- Matthijs/snacks
model-index:
- name: matteopilotto/vit-base-patch16-224-in21k-snacks
results:
- task:
type: image-classification
name: Image Classification
dataset:
name: Matthijs/snacks
type: Matthijs/snacks
config: default
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.8928571428571429
verified: true
- name: Precision Macro
type: precision
value: 0.8990033704680036
verified: true
- name: Precision Micro
type: precision
value: 0.8928571428571429
verified: true
- name: Precision Weighted
type: precision
value: 0.8972398709051788
verified: true
- name: Recall Macro
type: recall
value: 0.8914608843537415
verified: true
- name: Recall Micro
type: recall
value: 0.8928571428571429
verified: true
- name: Recall Weighted
type: recall
value: 0.8928571428571429
verified: true
- name: F1 Macro
type: f1
value: 0.892544821273258
verified: true
- name: F1 Micro
type: f1
value: 0.8928571428571429
verified: true
- name: F1 Weighted
type: f1
value: 0.8924168605019522
verified: true
- name: loss
type: loss
value: 0.479541540145874
verified: true
---
# Vision Transformer fine-tuned on `Matthijs/snacks` dataset
Vision Transformer (ViT) model pre-trained on ImageNet-21k and fine-tuned on [**Matthijs/snacks**](https://huggingface.co/datasets/Matthijs/snacks) for 5 epochs using various data augmentation transformations from `torchvision`.
The model achieves a **94.97%** and **94.43%** accuracy on the validation and test set, respectively.
## Data augmentation pipeline
The code block below shows the various transformations applied during pre-processing to augment the original dataset.
The augmented images where generated on-the-fly with the `set_transform` method.
```python
from transformers import ViTFeatureExtractor
from torchvision.transforms import (
Compose,
Normalize,
Resize,
RandomResizedCrop,
RandomHorizontalFlip,
RandomAdjustSharpness,
ToTensor
)
checkpoint = 'google/vit-base-patch16-224-in21k'
feature_extractor = ViTFeatureExtractor.from_pretrained(checkpoint)
# transformations on the training set
train_aug_transforms = Compose([
RandomResizedCrop(size=feature_extractor.size),
RandomHorizontalFlip(p=0.5),
RandomAdjustSharpness(sharpness_factor=5, p=0.5),
ToTensor(),
Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std),
])
# transformations on the validation/test set
valid_aug_transforms = Compose([
Resize(size=(feature_extractor.size, feature_extractor.size)),
ToTensor(),
Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std),
])
```
|
Dizzykong/charles-dickens
|
Dizzykong
| 2022-06-27T21:13:14Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-27T19:27:02Z |
---
tags:
- generated_from_trainer
model-index:
- name: charles-dickens
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. -->
# charles-dickens
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
SEBIS/code_trans_t5_small_program_synthese_transfer_learning_finetune
|
SEBIS
| 2022-06-27T20:56:39Z | 34 | 5 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"feature-extraction",
"summarization",
"arxiv:2104.02443",
"arxiv:1910.09700",
"arxiv:2105.09680",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:04Z |
---
tags:
- summarization
widget:
- text: "you are given an array of numbers a and a number b , compute the difference of elements in a and b"
---
# CodeTrans model for program synthesis
## Table of Contents
- [Model Details](#model-details)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-impact)
- [Citation Information](#citation-information)
## Model Details
- **Model Description:** This CodeTrans model is based on the `t5-small` model. It has its own SentencePiece vocabulary model. It used transfer-learning pre-training on 7 unsupervised datasets in the software development domain. It is then fine-tuned on the program synthesis task for the lisp inspired DSL code.
- **Developed by:** [Ahmed Elnaggar](https://www.linkedin.com/in/prof-ahmed-elnaggar/),[Wei Ding](https://www.linkedin.com/in/wei-ding-92561270/)
- **Model Type:** Summarization
- **Language(s):** English
- **License:** Unknown
- **Resources for more information:**
- [Research Paper](https://arxiv.org/pdf/2104.02443.pdf)
- [GitHub Repo](https://github.com/agemagician/CodeTrans)
## How to Get Started With the Model
Here is how to use this model to generate lisp inspired DSL code using Transformers SummarizationPipeline:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline
pipeline = SummarizationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_small_program_synthese_transfer_learning_finetune"),
tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_program_synthese_transfer_learning_finetune", skip_special_tokens=True),
device=0
)
tokenized_code = "you are given an array of numbers a and a number b , compute the difference of elements in a and b"
pipeline([tokenized_code])
```
Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/transfer%20learning%20fine-tuning/small_model.ipynb).
## Training data
The supervised training tasks datasets can be downloaded on [Link](https://www.dropbox.com/sh/488bq2of10r4wvw/AACs5CGIQuwtsD7j_Ls_JAORa/finetuning_dataset?dl=0&subfolder_nav_tracking=1)
## Uses
#### Direct Use
The model could be used to generate lisp inspired DSL code given the human language description tasks.
## Risks, Limitations and Biases
As detailed in this model’s [publication](https://arxiv.org/pdf/2104.02443.pdf), this model makes use of the data-set [One Billion Word Language Model Benchmark corpus](https://www.researchgate.net/publication/259239818_One_Billion_Word_Benchmark_for_Measuring_Progress_in_Statistical_Language_Modeling) in order to gather the self-supervised English data samples.
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
As such, it should be noted that language models that are pretrained from text corpus such as the One Billion Word Word Language Model Benchmark corpus have been further explored (e.g by [Ngo, Helen & Araújo et al(2021)](https://www.researchgate.net/publication/355582954_No_News_is_Good_News_A_Critique_of_the_One_Billion_Word_Benchmark) reports that the One Billion Word Word Language Model Benchmark corpus
> “generate text in the linguistic style of news, without any grounding in the real world. In addition to potential harms from models which are inadvertently optimized for generating fake news.”
The aforementioned publication continues to warn that the One Billion Word Word Language Model Benchmark corpus
> contains sentences which contain words commonly found on blocklists. While these sentences may have plausibly been used in expository contexts within the article, the destructive sentence-level preprocessing and shuffling applied to lm1b [One Billion Word Word Language Model Benchmark corpus] removes all long-range structure from the text and makes it infeasible to track the context and intent of individual examples.
[Ngo, Helen & Araújo et al(2021)](https://www.researchgate.net/publication/355582954_No_News_is_Good_News_A_Critique_of_the_One_Billion_Word_Benchmark)
## Training
#### Training Data
The supervised training tasks datasets can be downloaded on [Link](https://www.dropbox.com/sh/488bq2of10r4wvw/AACs5CGIQuwtsD7j_Ls_JAORa/finetuning_dataset?dl=0&subfolder_nav_tracking=1)
The authors provide additionally notes about the vocabulary used, in the [associated paper](https://arxiv.org/pdf/2104.02443.pdf):
> We used the SentencePiece model (Kudo, 2018) to construct the vocabulary for this research, as well as to decode and encode the input/output.
## Training procedure
#### Preprocessing
##### Transfer-learning Pretraining
The model was trained on a single TPU Pod V3-8 for 500,000 steps in total, using sequence length 512 (batch size 4096).
It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture.
The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.
###### Fine-tuning
This model was then fine-tuned on a single TPU Pod V2-8 for 5,000 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing lisp inspired DSL data.
## Evaluation
#### Results
For the code documentation tasks, different models achieves the following results on different programming languages (in BLEU score):
Test results :
| Language / Model | LISP |
| -------------------- | :------------: |
| CodeTrans-ST-Small | 89.43 |
| CodeTrans-ST-Base | 89.65 |
| CodeTrans-TF-Small | 90.30 |
| CodeTrans-TF-Base | 90.24 |
| CodeTrans-TF-Large | 90.21 |
| CodeTrans-MT-Small | 82.88 |
| CodeTrans-MT-Base | 86.99 |
| CodeTrans-MT-Large | 90.27 |
| CodeTrans-MT-TF-Small | **90.31** |
| CodeTrans-MT-TF-Base | 90.30 |
| CodeTrans-MT-TF-Large | 90.17 |
| State of the art | 85.80 |
## Environmental Impact
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). We present the hardware type based on the [associated paper](https://arxiv.org/pdf/2105.09680.pdf).
- **Hardware Type:** Nvidia RTX 8000 GPUs
- **Hours used:** Unknown
- **Cloud Provider:** GCC TPU v2-8 and v3-8.
- **Compute Region:** Unknown
- **Carbon Emitted:** Unknown
## Citation Information
```bibtex
@misc{elnaggar2021codetrans,
title={CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computing},
author={Ahmed Elnaggar and Wei Ding and Llion Jones and Tom Gibbs and Tamas Feher and Christoph Angerer and Silvia Severini and Florian Matthes and Burkhard Rost},
year={2021},
eprint={2104.02443},
archivePrefix={arXiv},
primaryClass={cs.SE}
}
```
|
BeardedJohn/bert-finetuned-ner-ubb-conll
|
BeardedJohn
| 2022-06-27T16:24:46Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-24T12:42:22Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: BeardedJohn/bert-finetuned-ner-ubb-conll
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. -->
# BeardedJohn/bert-finetuned-ner-ubb-conll
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0351
- Validation Loss: 0.0581
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1317, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.2302 | 0.0731 | 0 |
| 0.0556 | 0.0593 | 1 |
| 0.0351 | 0.0581 | 2 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
microsoft/deberta-xlarge-mnli
|
microsoft
| 2022-06-27T15:47:33Z | 504,931 | 16 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"deberta",
"text-classification",
"deberta-v1",
"deberta-mnli",
"en",
"arxiv:2006.03654",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- deberta-v1
- deberta-mnli
tasks: mnli
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
widget:
- text: "[CLS] I love you. [SEP] I like you. [SEP]"
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official repository](https://github.com/microsoft/DeBERTa) for more details and updates.
This the DeBERTa xlarge model(750M) fine-tuned with mnli task.
### Fine-tuning on NLU tasks
We present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.
| Model | SQuAD 1.1 | SQuAD 2.0 | MNLI-m/mm | SST-2 | QNLI | CoLA | RTE | MRPC | QQP |STS-B |
|---------------------------|-----------|-----------|-------------|-------|------|------|--------|-------|-------|------|
| | F1/EM | F1/EM | Acc | Acc | Acc | MCC | Acc |Acc/F1 |Acc/F1 |P/S |
| BERT-Large | 90.9/84.1 | 81.8/79.0 | 86.6/- | 93.2 | 92.3 | 60.6 | 70.4 | 88.0/- | 91.3/- |90.0/- |
| RoBERTa-Large | 94.6/88.9 | 89.4/86.5 | 90.2/- | 96.4 | 93.9 | 68.0 | 86.6 | 90.9/- | 92.2/- |92.4/- |
| XLNet-Large | 95.1/89.7 | 90.6/87.9 | 90.8/- | 97.0 | 94.9 | 69.0 | 85.9 | 90.8/- | 92.3/- |92.5/- |
| [DeBERTa-Large](https://huggingface.co/microsoft/deberta-large)<sup>1</sup> | 95.5/90.1 | 90.7/88.0 | 91.3/91.1| 96.5|95.3| 69.5| 91.0| 92.6/94.6| 92.3/- |92.8/92.5 |
| [DeBERTa-XLarge](https://huggingface.co/microsoft/deberta-xlarge)<sup>1</sup> | -/- | -/- | 91.5/91.2| 97.0 | - | - | 93.1 | 92.1/94.3 | - |92.9/92.7|
| [DeBERTa-V2-XLarge](https://huggingface.co/microsoft/deberta-v2-xlarge)<sup>1</sup>|95.8/90.8| 91.4/88.9|91.7/91.6| **97.5**| 95.8|71.1|**93.9**|92.0/94.2|92.3/89.8|92.9/92.9|
|**[DeBERTa-V2-XXLarge](https://huggingface.co/microsoft/deberta-v2-xxlarge)<sup>1,2</sup>**|**96.1/91.4**|**92.2/89.7**|**91.7/91.9**|97.2|**96.0**|**72.0**| 93.5| **93.1/94.9**|**92.7/90.3** |**93.2/93.1** |
--------
#### Notes.
- <sup>1</sup> Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on [DeBERTa-Large-MNLI](https://huggingface.co/microsoft/deberta-large-mnli), [DeBERTa-XLarge-MNLI](https://huggingface.co/microsoft/deberta-xlarge-mnli), [DeBERTa-V2-XLarge-MNLI](https://huggingface.co/microsoft/deberta-v2-xlarge-mnli), [DeBERTa-V2-XXLarge-MNLI](https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli). The results of SST-2/QQP/QNLI/SQuADv2 will also be slightly improved when start from MNLI fine-tuned models, however, we only report the numbers fine-tuned from pretrained base models for those 4 tasks.
- <sup>2</sup> To try the **XXLarge** model with **[HF transformers](https://huggingface.co/transformers/main_classes/trainer.html)**, you need to specify **--sharded_ddp**
```bash
cd transformers/examples/text-classification/
export TASK_NAME=mrpc
python -m torch.distributed.launch --nproc_per_node=8 run_glue.py --model_name_or_path microsoft/deberta-v2-xxlarge \\
--task_name $TASK_NAME --do_train --do_eval --max_seq_length 128 --per_device_train_batch_size 4 \\
--learning_rate 3e-6 --num_train_epochs 3 --output_dir /tmp/$TASK_NAME/ --overwrite_output_dir --sharded_ddp --fp16
```
### Citation
If you find DeBERTa useful for your work, please cite the following paper:
``` latex
@inproceedings{
he2021deberta,
title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION},
author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=XPZIaotutsD}
}
```
|
jcastanyo/dqn-SpaceInvadersNoFrameskip-v4
|
jcastanyo
| 2022-06-27T15:43:15Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-27T15:42:39Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 644.00 +/- 281.09
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga jcastanyo -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga jcastanyo
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
begmannen/ju
|
begmannen
| 2022-06-27T15:15:07Z | 0 | 0 | null |
[
"license:bsd-3-clause-clear",
"region:us"
] | null | 2022-06-27T15:15:06Z |
---
license: bsd-3-clause-clear
---
|
Adapting/comfort_congratulations_neutral-classifier
|
Adapting
| 2022-06-27T14:24:27Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-12T18:55:41Z |
# Adapting/comfort_congratulations_neutral-classifier
code used to train this model: https://colab.research.google.com/drive/1BHc8UMuT0sRyA_M24Acits5oHwUmjsFm?usp=sharing
dataset: https://huggingface.co/datasets/Adapting/empathetic_dialogues_v2
LABEL_0: neutral
LABEL_1: congratulating
LABEL_2: comforting
|
BukaByaka/opus-mt-ru-en-finetuned-ru-to-en
|
BukaByaka
| 2022-06-27T14:05:53Z | 43 | 0 |
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-26T14:26:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wmt16
metrics:
- bleu
model-index:
- name: opus-mt-ru-en-finetuned-ru-to-en
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wmt16
type: wmt16
args: ru-en
metrics:
- name: Bleu
type: bleu
value: 30.4049
---
<!-- 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. -->
# opus-mt-ru-en-finetuned-ru-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ru-en](https://huggingface.co/Helsinki-NLP/opus-mt-ru-en) on the wmt16 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4092
- Bleu: 30.4049
- Gen Len: 26.3911
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 2.2606 | 1.0 | 94761 | 1.4092 | 30.4049 | 26.3911 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0.post202
- Datasets 2.3.2
- Tokenizers 0.12.1
|
kktoto/tiny_focal_alpah
|
kktoto
| 2022-06-27T13:47:19Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-27T01:31:29Z |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tiny_focal_alpah
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. -->
# tiny_focal_alpah
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0492
- Precision: 0.6951
- Recall: 0.6796
- F1: 0.6873
- Accuracy: 0.9512
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0588 | 1.0 | 5561 | 0.0548 | 0.6801 | 0.6235 | 0.6506 | 0.9453 |
| 0.054 | 2.0 | 11122 | 0.0521 | 0.6850 | 0.6478 | 0.6659 | 0.9476 |
| 0.0525 | 3.0 | 16683 | 0.0509 | 0.6834 | 0.6676 | 0.6754 | 0.9486 |
| 0.0492 | 4.0 | 22244 | 0.0503 | 0.6829 | 0.6754 | 0.6791 | 0.9491 |
| 0.0482 | 5.0 | 27805 | 0.0500 | 0.6917 | 0.6727 | 0.6820 | 0.9501 |
| 0.0471 | 6.0 | 33366 | 0.0491 | 0.7085 | 0.6546 | 0.6805 | 0.9510 |
| 0.0459 | 7.0 | 38927 | 0.0486 | 0.6964 | 0.6746 | 0.6853 | 0.9510 |
| 0.0448 | 8.0 | 44488 | 0.0495 | 0.6922 | 0.6813 | 0.6867 | 0.9509 |
| 0.044 | 9.0 | 50049 | 0.0491 | 0.6961 | 0.6755 | 0.6857 | 0.9511 |
| 0.0433 | 10.0 | 55610 | 0.0492 | 0.6951 | 0.6796 | 0.6873 | 0.9512 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v4
|
gary109
| 2022-06-27T13:34:30Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-26T14:19:49Z |
---
license: apache-2.0
tags:
- automatic-speech-recognition
- gary109/AI_Light_Dance
- generated_from_trainer
model-index:
- name: ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v4
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. -->
# ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v4
This model is a fine-tuned version of [gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v3](https://huggingface.co/gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v3) on the GARY109/AI_LIGHT_DANCE - ONSET-STEPMANIA2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0298
- Wer: 0.6642
## 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: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.9218 | 1.0 | 188 | 1.0718 | 0.6958 |
| 0.9194 | 2.0 | 376 | 1.0354 | 0.6937 |
| 0.9077 | 3.0 | 564 | 1.0365 | 0.6730 |
| 0.8956 | 4.0 | 752 | 1.0497 | 0.6727 |
| 0.877 | 5.0 | 940 | 1.0299 | 0.6694 |
| 0.8736 | 6.0 | 1128 | 1.0298 | 0.6642 |
| 0.8769 | 7.0 | 1316 | 1.0348 | 0.6584 |
| 0.8571 | 8.0 | 1504 | 1.0689 | 0.6602 |
| 0.8573 | 9.0 | 1692 | 1.0559 | 0.6549 |
| 0.8458 | 10.0 | 1880 | 1.0706 | 0.6588 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.3.dev0
- Tokenizers 0.12.1
|
gopalkalpande/t5-small-finetuned-bbc-news-summarization
|
gopalkalpande
| 2022-06-27T13:15:58Z | 5 | 1 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-27T13:12:49Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: gopalkalpande/t5-small-finetuned-bbc-news-summarization
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. -->
# gopalkalpande/t5-small-finetuned-bbc-news-summarization
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7637
- Validation Loss: 0.3528
- Train Rouge1: 19.4783
- Train Rouge2: 13.2994
- Train Rougel: 17.4791
- Train Rougelsum: 17.6204
- Train Gen Len: 19.0
- 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': 4e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.001}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 0.7637 | 0.3528 | 19.4783 | 13.2994 | 17.4791 | 17.6204 | 19.0 | 0 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Davlan/naija-twitter-sentiment-afriberta-large
|
Davlan
| 2022-06-27T11:50:40Z | 69 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"text-classification",
"arxiv:2201.08277",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
Hugging Face's logo
---
language:
- hau
- ibo
- pcm
- yor
- multilingual
---
# naija-twitter-sentiment-afriberta-large
## Model description
**naija-twitter-sentiment-afriberta-large** is the first multilingual twitter **sentiment classification** model for four (4) Nigerian languages (Hausa, Igbo, Nigerian Pidgin, and Yorùbá) based on a fine-tuned castorini/afriberta_large large model.
It achieves the **state-of-the-art performance** for the twitter sentiment classification task trained on the [NaijaSenti corpus](https://github.com/hausanlp/NaijaSenti).
The model has been trained to classify tweets into 3 sentiment classes: negative, neutral and positive
Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of 4 Nigerian language datasets obtained from [NaijaSenti](https://github.com/hausanlp/NaijaSenti) dataset.
## Intended uses & limitations
#### How to use
You can use this model with Transformers for Sentiment Classification.
```python
from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer
import numpy as np
from scipy.special import softmax
MODEL = "Davlan/naija-twitter-sentiment-afriberta-large"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
# PT
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
text = "I like you"
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
scores = softmax(scores)
id2label = {0:"positive", 1:"neutral", 2:"negative"}
ranking = np.argsort(scores)
ranking = ranking[::-1]
for i in range(scores.shape[0]):
l = id2label[ranking[i]]
s = scores[ranking[i]]
print(f"{i+1}) {l} {np.round(float(s), 4)}")
```
#### Limitations and bias
This model is limited by its training dataset and domain i.e Twitter. This may not generalize well for all use cases in different domains.
## Training procedure
This model was trained on a single Nvidia RTX 2080 GPU with recommended hyperparameters from the [original NaijaSenti paper](https://arxiv.org/abs/2201.08277).
## Eval results on Test set (F-score), average over 5 runs.
language|F1-score
-|-
hau |81.2
ibo |80.8
pcm |74.5
yor |80.4
### BibTeX entry and citation info
```
@inproceedings{Muhammad2022NaijaSentiAN,
title={NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis},
author={Shamsuddeen Hassan Muhammad and David Ifeoluwa Adelani and Sebastian Ruder and Ibrahim Said Ahmad and Idris Abdulmumin and Bello Shehu Bello and Monojit Choudhury and Chris C. Emezue and Saheed Salahudeen Abdullahi and Anuoluwapo Aremu and Alipio Jeorge and Pavel B. Brazdil},
year={2022}
}
```
|
abhishek/convnext-tiny-finetuned-dogfood
|
abhishek
| 2022-06-27T11:01:31Z | 59 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"convnext",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"dataset:lewtun/dog_food",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-26T09:36:31Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
- lewtun/dog_food
metrics:
- accuracy
model-index:
- name: convnext-tiny-finetuned-dogfood
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: lewtun/dog_food
type: lewtun/dog_food
args: lewtun--dog_food
metrics:
- name: Accuracy
type: accuracy
value: 0.7253333333333334
- task:
type: image-classification
name: Image Classification
dataset:
name: lewtun/dog_food
type: lewtun/dog_food
config: lewtun--dog_food
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.6866666666666666
verified: true
- name: Precision Macro
type: precision
value: 0.7181484576740136
verified: true
- name: Precision Micro
type: precision
value: 0.6866666666666666
verified: true
- name: Precision Weighted
type: precision
value: 0.7235392474854474
verified: true
- name: Recall Macro
type: recall
value: 0.7006250320552644
verified: true
- name: Recall Micro
type: recall
value: 0.6866666666666666
verified: true
- name: Recall Weighted
type: recall
value: 0.6866666666666666
verified: true
- name: F1 Macro
type: f1
value: 0.6690027379410202
verified: true
- name: F1 Micro
type: f1
value: 0.6866666666666666
verified: true
- name: F1 Weighted
type: f1
value: 0.6647526870157503
verified: true
- name: loss
type: loss
value: 0.9549381732940674
verified: true
- name: matthews_correlation
type: matthews_correlation
value: 0.5737269361889515
verified: true
---
<!-- 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. -->
# convnext-tiny-finetuned-dogfood
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the lewtun/dog_food dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9277
- Accuracy: 0.7253
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0681 | 1.0 | 16 | 0.9125 | 0.7422 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Davlan/distilbert-base-multilingual-cased-masakhaner
|
Davlan
| 2022-06-27T10:57:26Z | 27 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"distilbert",
"token-classification",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:04Z |
Hugging Face's logo
---
language:
- ha
- ig
- rw
- lg
- luo
- pcm
- sw
- wo
- yo
- multilingual
datasets:
- masakhaner
---
# bert-base-multilingual-cased-masakhaner
## Model description
**distilbert-base-multilingual-cased-masakhaner** is the first **Named Entity Recognition** model for 9 African languages (Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) based on a fine-tuned BERT base model. It has been trained to recognize four types of entities: dates & times (DATE), location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *distilbert-base-multilingual-cased* model that was fine-tuned on an aggregation of African language datasets obtained from Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for NER.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("Davlan/distilbert-base-multilingual-cased-masakhaner")
model = AutoModelForTokenClassification.from_pretrained("Davlan/distilbert-base-multilingual-cased-masakhaner")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Emir of Kano turban Zhang wey don spend 18 years for Nigeria"
ner_results = nlp(example)
print(ner_results)
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on 9 African NER datasets (Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset
The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Abbreviation|Description
-|-
O|Outside of a named entity
B-DATE |Beginning of a DATE entity right after another DATE entity
I-DATE |DATE entity
B-PER |Beginning of a person’s name right after another person’s name
I-PER |Person’s name
B-ORG |Beginning of an organisation right after another organisation
I-ORG |Organisation
B-LOC |Beginning of a location right after another location
I-LOC |Location
## Training procedure
This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the [original MasakhaNER paper](https://arxiv.org/abs/2103.11811) which trained & evaluated the model on MasakhaNER corpus.
## Eval results on Test set (F-score)
language|F1-score
-|-
hau |88.88
ibo |84.87
kin |74.19
lug |78.43
luo |73.32
pcm |87.98
swa |86.20
wol |64.67
yor |78.10
### BibTeX entry and citation info
```
@article{adelani21tacl,
title = {Masakha{NER}: Named Entity Recognition for African Languages},
author = {David Ifeoluwa Adelani and Jade Abbott and Graham Neubig and Daniel D'souza and Julia Kreutzer and Constantine Lignos and Chester Palen-Michel and Happy Buzaaba and Shruti Rijhwani and Sebastian Ruder and Stephen Mayhew and Israel Abebe Azime and Shamsuddeen Muhammad and Chris Chinenye Emezue and Joyce Nakatumba-Nabende and Perez Ogayo and Anuoluwapo Aremu and Catherine Gitau and Derguene Mbaye and Jesujoba Alabi and Seid Muhie Yimam and Tajuddeen Gwadabe and Ignatius Ezeani and Rubungo Andre Niyongabo and Jonathan Mukiibi and Verrah Otiende and Iroro Orife and Davis David and Samba Ngom and Tosin Adewumi and Paul Rayson and Mofetoluwa Adeyemi and Gerald Muriuki and Emmanuel Anebi and Chiamaka Chukwuneke and Nkiruka Odu and Eric Peter Wairagala and Samuel Oyerinde and Clemencia Siro and Tobius Saul Bateesa and Temilola Oloyede and Yvonne Wambui and Victor Akinode and Deborah Nabagereka and Maurice Katusiime and Ayodele Awokoya and Mouhamadane MBOUP and Dibora Gebreyohannes and Henok Tilaye and Kelechi Nwaike and Degaga Wolde and Abdoulaye Faye and Blessing Sibanda and Orevaoghene Ahia and Bonaventure F. P. Dossou and Kelechi Ogueji and Thierno Ibrahima DIOP and Abdoulaye Diallo and Adewale Akinfaderin and Tendai Marengereke and Salomey Osei},
journal = {Transactions of the Association for Computational Linguistics (TACL)},
month = {},
url = {https://arxiv.org/abs/2103.11811},
year = {2021}
}
```
|
Davlan/bert-base-multilingual-cased-finetuned-hausa
|
Davlan
| 2022-06-27T10:56:44Z | 108 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
Hugging Face's logo
---
language: ha
datasets:
---
# bert-base-multilingual-cased-finetuned-hausa
## Model description
**bert-base-multilingual-cased-finetuned-hausa** is a **Hausa BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Hausa language texts. It provides **better performance** than the multilingual BERT on text classification and named entity recognition datasets.
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Hausa corpus.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-hausa')
>>> unmasker("Shugaban [MASK] Muhammadu Buhari ya amince da shawarar da ma’aikatar sufuri karkashin jagoranci")
[{'sequence':
'[CLS] Shugaban Nigeria Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]',
'score': 0.9762618541717529,
'token': 22045,
'token_str': 'Nigeria'},
{'sequence': '[CLS] Shugaban Ka Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]', 'score': 0.007239189930260181,
'token': 25444,
'token_str': 'Ka'},
{'sequence': '[CLS] Shugaban, Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]', 'score': 0.001990817254409194,
'token': 117,
'token_str': ','},
{'sequence': '[CLS] Shugaban Ghana Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]', 'score': 0.001566368737258017,
'token': 28682,
'token_str': 'Ghana'},
{'sequence': '[CLS] Shugabanmu Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]', 'score': 0.0009375187801197171,
'token': 11717,
'token_str': '##mu'}]
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on [Hausa CC-100](http://data.statmt.org/cc-100/)
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (F-score, average over 5 runs)
Dataset| mBERT F1 | ha_bert F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 86.65 | 91.31
[VOA Hausa Textclass](https://huggingface.co/datasets/hausa_voa_topics) | 84.76 | 90.98
### BibTeX entry and citation info
By David Adelani
```
```
|
JeremiahZ/reproduce-unsup-roberta-base-avg
|
JeremiahZ
| 2022-06-27T10:19:27Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"generated_from_trainer",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T08:09:54Z |
---
language:
- en
license: mit
tags:
- generated_from_trainer
model-index:
- name: reproduce-unsup-roberta-base-avg
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. -->
# reproduce-unsup-roberta-base-avg
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 512
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
MaryaAI/opus-mt-ar-en-finetunedTanzil-v7-ar-to-en
|
MaryaAI
| 2022-06-27T10:10:12Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-05T06:04:57Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: opus-mt-ar-en-finetunedTanzil-v7-ar-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. -->
# opus-mt-ar-en-finetunedTanzil-v7-ar-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/Helsinki-NLP/opus-mt-ar-en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1919
- Validation Loss: 0.5047
- Train Rouge1: 49.6877
- Train Rouge2: 25.9574
- Train Rougel: 45.2590
- Train Rougelsum: 45.7464
- Train Gen Len: 85.57
- Epoch: 4
## 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 Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 0.1959 | 0.5105 | 48.2182 | 23.4978 | 44.1127 | 44.6422 | 87.45 | 0 |
| 0.1950 | 0.5114 | 49.5777 | 25.1663 | 45.7183 | 46.0930 | 86.72 | 1 |
| 0.1937 | 0.5074 | 49.1793 | 24.1899 | 45.3374 | 45.5902 | 84.805 | 2 |
| 0.1929 | 0.5075 | 49.1553 | 24.8199 | 44.7342 | 45.1392 | 87.495 | 3 |
| 0.1919 | 0.5047 | 49.6877 | 25.9574 | 45.2590 | 45.7464 | 85.57 | 4 |
### Framework versions
- Transformers 4.17.0.dev0
- TensorFlow 2.7.0
- Datasets 1.18.4.dev0
- Tokenizers 0.10.3
|
Laure996/bert-finetuned-ner
|
Laure996
| 2022-06-27T10:00:55Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-27T09:31:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9329136988570482
- name: Recall
type: recall
value: 0.9478290138000673
- name: F1
type: f1
value: 0.9403122130394858
- name: Accuracy
type: accuracy
value: 0.9855477718255137
---
<!-- 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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0663
- Precision: 0.9329
- Recall: 0.9478
- F1: 0.9403
- Accuracy: 0.9855
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0837 | 1.0 | 1756 | 0.0656 | 0.9151 | 0.9392 | 0.9270 | 0.9834 |
| 0.0388 | 2.0 | 3512 | 0.0619 | 0.9249 | 0.9475 | 0.9361 | 0.9855 |
| 0.0198 | 3.0 | 5268 | 0.0663 | 0.9329 | 0.9478 | 0.9403 | 0.9855 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
YuanWellspring/wav2vec2-nsc-final_1-google-colab
|
YuanWellspring
| 2022-06-27T09:21:32Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-27T07:57:07Z |
---
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-nsc-final_1-google-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-nsc-final_1-google-colab
This model was trained from scratch 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.10.3
|
facebook/convnext-xlarge-224-22k-1k
|
facebook
| 2022-06-27T08:55:36Z | 279 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-21k",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-21k
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# ConvNeXT (xlarge-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ConvNextFeatureExtractor, ConvNextForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
feature_extractor = ConvNextFeatureExtractor.from_pretrained("facebook/convnext-xlarge-224-22k-1k")
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-xlarge-224-22k-1k")
inputs = feature_extractor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2201-03545,
author = {Zhuang Liu and
Hanzi Mao and
Chao{-}Yuan Wu and
Christoph Feichtenhofer and
Trevor Darrell and
Saining Xie},
title = {A ConvNet for the 2020s},
journal = {CoRR},
volume = {abs/2201.03545},
year = {2022},
url = {https://arxiv.org/abs/2201.03545},
eprinttype = {arXiv},
eprint = {2201.03545},
timestamp = {Thu, 20 Jan 2022 14:21:35 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
hustvl/yolos-small-dwr
|
hustvl
| 2022-06-27T08:38:00Z | 11 | 4 |
transformers
|
[
"transformers",
"pytorch",
"yolos",
"object-detection",
"vision",
"dataset:coco",
"arxiv:2106.00666",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2022-04-26T10:15:57Z |
---
license: apache-2.0
tags:
- object-detection
- vision
datasets:
- coco
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg
example_title: Savanna
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg
example_title: Football Match
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg
example_title: Airport
---
# YOLOS (small-sized, fast model scaling) model
YOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Fang et al. and first released in [this repository](https://github.com/hustvl/YOLOS).
Disclaimer: The team releasing YOLOS did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
YOLOS is a Vision Transformer (ViT) trained using the DETR loss. Despite its simplicity, a base-sized YOLOS model is able to achieve 42 AP on COCO validation 2017 (similar to DETR and more complex frameworks such as Faster R-CNN).
The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.
## Intended uses & limitations
You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=hustvl/yolos) to look for all available YOLOS models.
### How to use
Here is how to use this model:
```python
from transformers import YolosFeatureExtractor, YolosForObjectDetection
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = YolosFeatureExtractor.from_pretrained('hustvl/yolos-small-dwr')
model = YolosForObjectDetection.from_pretrained('hustvl/yolos-small-dwr')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
# model predicts bounding boxes and corresponding COCO classes
logits = outputs.logits
bboxes = outputs.pred_boxes
```
Currently, both the feature extractor and model support PyTorch.
## Training data
The YOLOS model was pre-trained on [ImageNet-1k](https://huggingface.co/datasets/imagenet2012) and fine-tuned on [COCO 2017 object detection](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively.
### Training
The model was pre-trained for 300 epochs on ImageNet-1k and fine-tuned for 150 epochs on COCO.
## Evaluation results
This model achieves an AP (average precision) of **37.6** on COCO 2017 validation. For more details regarding evaluation results, we refer to table 1 of the original paper.
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2106-00666,
author = {Yuxin Fang and
Bencheng Liao and
Xinggang Wang and
Jiemin Fang and
Jiyang Qi and
Rui Wu and
Jianwei Niu and
Wenyu Liu},
title = {You Only Look at One Sequence: Rethinking Transformer in Vision through
Object Detection},
journal = {CoRR},
volume = {abs/2106.00666},
year = {2021},
url = {https://arxiv.org/abs/2106.00666},
eprinttype = {arXiv},
eprint = {2106.00666},
timestamp = {Fri, 29 Apr 2022 19:49:16 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2106-00666.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
TheRensselaerIDEA/gpt2-large-covid-tweet-response
|
TheRensselaerIDEA
| 2022-06-27T07:26:54Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"arxiv:2204.04353",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-26T19:56:35Z |
---
license: mit
---
Base model: [gpt2-large](https://huggingface.co/gpt2-large)
Fine-tuned to generate responses on a dataset of [COVID-19 public health tweets](https://github.com/TheRensselaerIDEA/generative-response-modeling). For more information about the dataset, task and training, see [our paper](https://arxiv.org/abs/2204.04353). This checkpoint corresponds to the lowest validation perplexity (3.36 at 2 epochs) seen during training. See Training metrics for Tensorboard logs.
Also see: our [Vaccine public health tweet response model](https://huggingface.co/TheRensselaerIDEA/gpt2-large-vaccine-tweet-response).
**Data input format:** <span style="color:red"><|message|></span>public health message<span style="color:red"><|author|></span>public health Twitter handle<span style="color:red"><|response|></span>
Example:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers.trainer_utils import set_seed
import torch
tokenizer = AutoTokenizer.from_pretrained("TheRensselaerIDEA/gpt2-large-covid-tweet-response")
model = AutoModelForCausalLM.from_pretrained("TheRensselaerIDEA/gpt2-large-covid-tweet-response")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
set_seed(33)
message = "Is your child worried about #COVID19? Learn the facts so you can answer your children’s questions."
author = "CDCgov"
num_responses = 2
author_token, message_token, response_token = tokenizer.additional_special_tokens
input_str = f"{message_token}{message}{author_token}{author}{response_token}"
inputs = tokenizer(input_str, return_tensors="pt").to(device)
responses_ids = model.generate(**inputs,
max_new_tokens=100,
pad_token_id=tokenizer.pad_token_id,
do_sample=True,
top_p=0.95,
temperature=1.5,
num_beams=3,
early_stopping=True,
num_return_sequences=num_responses)
responses = [tokenizer.decode(r[inputs.input_ids.shape[-1]:], skip_special_tokens=True) for r in responses_ids]
for i, resp in enumerate(responses):
print(f"Response {i}: {resp}\n")
```
Output:
```
Response 0: @CDCgov I'm not worried. I don't know who needs to hear this, but I have a feeling I know who will be listening.
It is not the virus. It is the media. I know you and CDC have been lying for months now, but the media will keep pushing this lie.
Response 1: #WashYourHands to help #StopTheSpread of #COVID19 and other diseases. Learn more about hand washing: #HandWashing
```
|
gopalkalpande/t5-small-finetuned-xsum
|
gopalkalpande
| 2022-06-27T06:29:10Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-27T06:26:01Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: gopalkalpande/t5-small-finetuned-xsum
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. -->
# gopalkalpande/t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.0422
- Validation Loss: 0.4407
- Train Rouge1: 19.5311
- Train Rouge2: 14.2402
- Train Rougel: 17.9781
- Train Rougelsum: 18.1546
- Train Gen Len: 19.0
- 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 Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 1.0422 | 0.4407 | 19.5311 | 14.2402 | 17.9781 | 18.1546 | 19.0 | 0 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
|
vebie91/dqn-SpaceInvadersNoFrameskip-v4
|
vebie91
| 2022-06-27T05:49:36Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-26T02:29:12Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 784.00 +/- 298.23
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga vebie91 -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga vebie91
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 3000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
robingeibel/longformer-large-finetuned-big_patent
|
robingeibel
| 2022-06-27T05:04:39Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"longformer",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-21T07:29:34Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: robingeibel/longformer-large-finetuned-big_patent
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. -->
# robingeibel/longformer-large-finetuned-big_patent
This model is a fine-tuned version of [robingeibel/longformer-large-finetuned-big_patent](https://huggingface.co/robingeibel/longformer-large-finetuned-big_patent) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.1706
- 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': 79030, '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 | Epoch |
|:----------:|:-----:|
| 1.1706 | 0 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jcmc/q-Taxi-v3
|
jcmc
| 2022-06-27T04:21:20Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-27T04:21:13Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.46 +/- 2.70
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **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="jcmc/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"])
```
|
TheRensselaerIDEA/gpt2-large-vaccine-tweet-response
|
TheRensselaerIDEA
| 2022-06-27T03:22:42Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"arxiv:2204.04353",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-27T03:03:38Z |
---
license: mit
---
Base model: [gpt2-large](https://huggingface.co/gpt2-large)
Fine-tuned to generate responses on a dataset of [Vaccine public health tweets](https://github.com/TheRensselaerIDEA/generative-response-modeling). For more information about the dataset, task and training, see [our paper](https://arxiv.org/abs/2204.04353). This checkpoint corresponds to the lowest validation perplexity (2.82 at 2 epochs) seen during training. See Training metrics for Tensorboard logs.
For input format and usage examples, see our [COVID-19 public health tweet response model](https://huggingface.co/TheRensselaerIDEA/gpt2-large-covid-tweet-response).
|
neweasterns/wav2vec2-base-timit-demo-google-colab
|
neweasterns
| 2022-06-27T02:49:23Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-27T00:01:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-google-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-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5206
- Wer: 0.3388
## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5597 | 1.0 | 500 | 2.3415 | 0.9991 |
| 0.9759 | 2.01 | 1000 | 0.5556 | 0.5382 |
| 0.4587 | 3.01 | 1500 | 0.7690 | 0.4781 |
| 0.3156 | 4.02 | 2000 | 0.7994 | 0.4412 |
| 0.2272 | 5.02 | 2500 | 0.8948 | 0.4120 |
| 0.1921 | 6.02 | 3000 | 0.7065 | 0.3940 |
| 0.1618 | 7.03 | 3500 | 0.4333 | 0.3855 |
| 0.1483 | 8.03 | 4000 | 0.4232 | 0.3872 |
| 0.156 | 9.04 | 4500 | 0.4172 | 0.3749 |
| 0.1138 | 10.04 | 5000 | 0.4084 | 0.3758 |
| 0.1045 | 11.04 | 5500 | 0.4665 | 0.3623 |
| 0.0908 | 12.05 | 6000 | 0.4416 | 0.3684 |
| 0.0788 | 13.05 | 6500 | 0.4801 | 0.3659 |
| 0.0773 | 14.06 | 7000 | 0.4560 | 0.3583 |
| 0.0684 | 15.06 | 7500 | 0.4878 | 0.3610 |
| 0.0645 | 16.06 | 8000 | 0.4635 | 0.3567 |
| 0.0577 | 17.07 | 8500 | 0.5245 | 0.3548 |
| 0.0547 | 18.07 | 9000 | 0.5265 | 0.3639 |
| 0.0466 | 19.08 | 9500 | 0.5161 | 0.3546 |
| 0.0432 | 20.08 | 10000 | 0.5263 | 0.3558 |
| 0.0414 | 21.08 | 10500 | 0.4874 | 0.3500 |
| 0.0365 | 22.09 | 11000 | 0.5266 | 0.3472 |
| 0.0321 | 23.09 | 11500 | 0.5422 | 0.3458 |
| 0.0325 | 24.1 | 12000 | 0.5201 | 0.3428 |
| 0.0262 | 25.1 | 12500 | 0.5208 | 0.3398 |
| 0.0249 | 26.1 | 13000 | 0.5034 | 0.3429 |
| 0.0262 | 27.11 | 13500 | 0.5055 | 0.3396 |
| 0.0248 | 28.11 | 14000 | 0.5164 | 0.3404 |
| 0.0222 | 29.12 | 14500 | 0.5206 | 0.3388 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
ra-XOr/Unity-Pyramids
|
ra-XOr
| 2022-06-27T02:36:12Z | 33 | 1 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2022-06-27T02:16:39Z |
---
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: ra-XOr/Unity-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
RUCAIBox/mvp-story
|
RUCAIBox
| 2022-06-27T02:28:15Z | 9 | 3 |
transformers
|
[
"transformers",
"pytorch",
"mvp",
"text-generation",
"text2text-generation",
"en",
"arxiv:2206.12131",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-02T11:55:25Z |
---
license: apache-2.0
language:
- en
tags:
- text-generation
- text2text-generation
pipeline_tag: text2text-generation
widget:
- text: "Given the story title: I think all public schools should have a uniform dress code."
example_title: "Example1"
- text: "Given the story title: My girlfriend and I decided to move to a new state. We packed everything in our cars and drove there."
example_title: "Example2"
---
# MVP-story
The MVP-story model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP](https://github.com/RUCAIBox/MVP).
## Model Description
MVP-story is a prompt-based model that MVP is further equipped with prompts pre-trained using labeled story generation datasets. It is a variant (MVP+S) of our main [MVP](https://huggingface.co/RUCAIBox/mvp) model. It follows a Transformer encoder-decoder architecture with layer-wise prompts.
MVP-story is specially designed for story generation tasks, such as ROCStories and WritingPrompts.
## Example
```python
>>> from transformers import MvpTokenizer, MvpForConditionalGeneration
>>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp")
>>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp-story")
>>> inputs = tokenizer(
... "Given the story title: I think all public schools should have a uniform dress code.",
... return_tensors="pt",
... )
>>> generated_ids = model.generate(**inputs, max_length=1024)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
['I think it would be a good idea to have uniform dress codes for all public schools. It would make it easier for students to dress appropriately.']
```
## Related Models
**MVP**: [https://huggingface.co/RUCAIBox/mvp](https://huggingface.co/RUCAIBox/mvp).
**Prompt-based models**:
- MVP-multi-task: [https://huggingface.co/RUCAIBox/mvp-multi-task](https://huggingface.co/RUCAIBox/mvp-multi-task).
- MVP-summarization: [https://huggingface.co/RUCAIBox/mvp-summarization](https://huggingface.co/RUCAIBox/mvp-summarization).
- MVP-open-dialog: [https://huggingface.co/RUCAIBox/mvp-open-dialog](https://huggingface.co/RUCAIBox/mvp-open-dialog).
- MVP-data-to-text: [https://huggingface.co/RUCAIBox/mvp-data-to-text](https://huggingface.co/RUCAIBox/mvp-data-to-text).
- MVP-story: [https://huggingface.co/RUCAIBox/mvp-story](https://huggingface.co/RUCAIBox/mvp-story).
- MVP-question-answering: [https://huggingface.co/RUCAIBox/mvp-question-answering](https://huggingface.co/RUCAIBox/mvp-question-answering).
- MVP-question-generation: [https://huggingface.co/RUCAIBox/mvp-question-generation](https://huggingface.co/RUCAIBox/mvp-question-generation).
- MVP-task-dialog: [https://huggingface.co/RUCAIBox/mvp-task-dialog](https://huggingface.co/RUCAIBox/mvp-task-dialog).
**Multi-task models**:
- MTL-summarization: [https://huggingface.co/RUCAIBox/mtl-summarization](https://huggingface.co/RUCAIBox/mtl-summarization).
- MTL-open-dialog: [https://huggingface.co/RUCAIBox/mtl-open-dialog](https://huggingface.co/RUCAIBox/mtl-open-dialog).
- MTL-data-to-text: [https://huggingface.co/RUCAIBox/mtl-data-to-text](https://huggingface.co/RUCAIBox/mtl-data-to-text).
- MTL-story: [https://huggingface.co/RUCAIBox/mtl-story](https://huggingface.co/RUCAIBox/mtl-story).
- MTL-question-answering: [https://huggingface.co/RUCAIBox/mtl-question-answering](https://huggingface.co/RUCAIBox/mtl-question-answering).
- MTL-question-generation: [https://huggingface.co/RUCAIBox/mtl-question-generation](https://huggingface.co/RUCAIBox/mtl-question-generation).
- MTL-task-dialog: [https://huggingface.co/RUCAIBox/mtl-task-dialog](https://huggingface.co/RUCAIBox/mtl-task-dialog).
## Citation
```bibtex
@article{tang2022mvp,
title={MVP: Multi-task Supervised Pre-training for Natural Language Generation},
author={Tang, Tianyi and Li, Junyi and Zhao, Wayne Xin and Wen, Ji-Rong},
journal={arXiv preprint arXiv:2206.12131},
year={2022},
url={https://arxiv.org/abs/2206.12131},
}
```
|
RUCAIBox/mvp-question-generation
|
RUCAIBox
| 2022-06-27T02:28:10Z | 7 | 1 |
transformers
|
[
"transformers",
"pytorch",
"mvp",
"text-generation",
"text2text-generation",
"en",
"arxiv:2206.12131",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-02T11:54:39Z |
---
license: apache-2.0
language:
- en
tags:
- text-generation
- text2text-generation
pipeline_tag: text2text-generation
widget:
- text: "Generate the question based on the answer: boxing [X_SEP] A bolo punch is a punch used in martial arts . A hook is a punch in boxing ."
example_title: "Example1"
- text: "Generate the question based on the answer: Arthur 's Magazine [X_SEP] Arthur 's Magazine ( 1844–1846 ) was an American literary periodical published in Philadelphia in the 19th century . First for Women is a woman 's magazine published by Bauer Media Group in the USA ."
example_title: "Example2"
---
# MVP-question-generation
The MVP-question-generation model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP](https://github.com/RUCAIBox/MVP).
## Model Description
MVP-question-generation is a prompt-based model that MVP is further equipped with prompts pre-trained using labeled question generation datasets. It is a variant (MVP+S) of our main [MVP](https://huggingface.co/RUCAIBox/mvp) model. It follows a Transformer encoder-decoder architecture with layer-wise prompts.
MVP-question-generation is specially designed for question generation tasks, such as SQuAD and CoQA.
## Example
```python
>>> from transformers import MvpTokenizer, MvpForConditionalGeneration
>>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp")
>>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp-question-generation")
>>> inputs = tokenizer(
... "Generate the question based on the answer: boxing [X_SEP] A bolo punch is a punch used in martial arts . A hook is a punch in boxing .",
... return_tensors="pt",
... )
>>> generated_ids = model.generate(**inputs)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
['A bolo punch and a hook are both punches used in what sport?']
```
## Related Models
**MVP**: [https://huggingface.co/RUCAIBox/mvp](https://huggingface.co/RUCAIBox/mvp).
**Prompt-based models**:
- MVP-multi-task: [https://huggingface.co/RUCAIBox/mvp-multi-task](https://huggingface.co/RUCAIBox/mvp-multi-task).
- MVP-summarization: [https://huggingface.co/RUCAIBox/mvp-summarization](https://huggingface.co/RUCAIBox/mvp-summarization).
- MVP-open-dialog: [https://huggingface.co/RUCAIBox/mvp-open-dialog](https://huggingface.co/RUCAIBox/mvp-open-dialog).
- MVP-data-to-text: [https://huggingface.co/RUCAIBox/mvp-data-to-text](https://huggingface.co/RUCAIBox/mvp-data-to-text).
- MVP-story: [https://huggingface.co/RUCAIBox/mvp-story](https://huggingface.co/RUCAIBox/mvp-story).
- MVP-question-answering: [https://huggingface.co/RUCAIBox/mvp-question-answering](https://huggingface.co/RUCAIBox/mvp-question-answering).
- MVP-question-generation: [https://huggingface.co/RUCAIBox/mvp-question-generation](https://huggingface.co/RUCAIBox/mvp-question-generation).
- MVP-task-dialog: [https://huggingface.co/RUCAIBox/mvp-task-dialog](https://huggingface.co/RUCAIBox/mvp-task-dialog).
**Multi-task models**:
- MTL-summarization: [https://huggingface.co/RUCAIBox/mtl-summarization](https://huggingface.co/RUCAIBox/mtl-summarization).
- MTL-open-dialog: [https://huggingface.co/RUCAIBox/mtl-open-dialog](https://huggingface.co/RUCAIBox/mtl-open-dialog).
- MTL-data-to-text: [https://huggingface.co/RUCAIBox/mtl-data-to-text](https://huggingface.co/RUCAIBox/mtl-data-to-text).
- MTL-story: [https://huggingface.co/RUCAIBox/mtl-story](https://huggingface.co/RUCAIBox/mtl-story).
- MTL-question-answering: [https://huggingface.co/RUCAIBox/mtl-question-answering](https://huggingface.co/RUCAIBox/mtl-question-answering).
- MTL-question-generation: [https://huggingface.co/RUCAIBox/mtl-question-generation](https://huggingface.co/RUCAIBox/mtl-question-generation).
- MTL-task-dialog: [https://huggingface.co/RUCAIBox/mtl-task-dialog](https://huggingface.co/RUCAIBox/mtl-task-dialog).
## Citation
```bibtex
@article{tang2022mvp,
title={MVP: Multi-task Supervised Pre-training for Natural Language Generation},
author={Tang, Tianyi and Li, Junyi and Zhao, Wayne Xin and Wen, Ji-Rong},
journal={arXiv preprint arXiv:2206.12131},
year={2022},
url={https://arxiv.org/abs/2206.12131},
}
```
|
RUCAIBox/mvp-data-to-text
|
RUCAIBox
| 2022-06-27T02:27:50Z | 38 | 4 |
transformers
|
[
"transformers",
"pytorch",
"mvp",
"text-generation",
"text2text-generation",
"en",
"arxiv:2206.12131",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-02T11:53:26Z |
---
license: apache-2.0
language:
- en
tags:
- text-generation
- text2text-generation
pipeline_tag: text2text-generation
widget:
- text: "Describe the following data: Iron Man | instance of | Superhero [SEP] Stan Lee | creator | Iron Man"
example_title: "Example1"
- text: "Describe the following data: First Clearing | LOCATION | On NYS 52 1 Mi. Youngsville [SEP] On NYS 52 1 Mi. Youngsville | CITY_OR_TOWN | Callicoon, New York"
example_title: "Example2"
---
# MVP-data-to-text
The MVP-data-to-text model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP](https://github.com/RUCAIBox/MVP).
## Model Description
MVP-data-to-text is a prompt-based model that MVP is further equipped with prompts pre-trained using labeled data-to-text datasets. It is a variant (MVP+S) of our main [MVP](https://huggingface.co/RUCAIBox/mvp) model. It follows a Transformer encoder-decoder architecture with layer-wise prompts.
MVP-data-to-text is specially designed for data-to-text generation tasks, such as KG-to-text generation (WebNLG, DART), table-to-text generation (WikiBio, ToTTo) and MR-to-text generation (E2E).
## Example
```python
>>> from transformers import MvpTokenizer, MvpForConditionalGeneration
>>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp")
>>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp-data-to-text")
>>> inputs = tokenizer(
... "Describe the following data: Iron Man | instance of | Superhero [SEP] Stan Lee | creator | Iron Man",
... return_tensors="pt",
... )
>>> generated_ids = model.generate(**inputs)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
['Iron Man is a fictional superhero appearing in American comic books published by Marvel Comics.']
```
## Related Models
**MVP**: [https://huggingface.co/RUCAIBox/mvp](https://huggingface.co/RUCAIBox/mvp).
**Prompt-based models**:
- MVP-multi-task: [https://huggingface.co/RUCAIBox/mvp-multi-task](https://huggingface.co/RUCAIBox/mvp-multi-task).
- MVP-summarization: [https://huggingface.co/RUCAIBox/mvp-summarization](https://huggingface.co/RUCAIBox/mvp-summarization).
- MVP-open-dialog: [https://huggingface.co/RUCAIBox/mvp-open-dialog](https://huggingface.co/RUCAIBox/mvp-open-dialog).
- MVP-data-to-text: [https://huggingface.co/RUCAIBox/mvp-data-to-text](https://huggingface.co/RUCAIBox/mvp-data-to-text).
- MVP-story: [https://huggingface.co/RUCAIBox/mvp-story](https://huggingface.co/RUCAIBox/mvp-story).
- MVP-question-answering: [https://huggingface.co/RUCAIBox/mvp-question-answering](https://huggingface.co/RUCAIBox/mvp-question-answering).
- MVP-question-generation: [https://huggingface.co/RUCAIBox/mvp-question-generation](https://huggingface.co/RUCAIBox/mvp-question-generation).
- MVP-task-dialog: [https://huggingface.co/RUCAIBox/mvp-task-dialog](https://huggingface.co/RUCAIBox/mvp-task-dialog).
**Multi-task models**:
- MTL-summarization: [https://huggingface.co/RUCAIBox/mtl-summarization](https://huggingface.co/RUCAIBox/mtl-summarization).
- MTL-open-dialog: [https://huggingface.co/RUCAIBox/mtl-open-dialog](https://huggingface.co/RUCAIBox/mtl-open-dialog).
- MTL-data-to-text: [https://huggingface.co/RUCAIBox/mtl-data-to-text](https://huggingface.co/RUCAIBox/mtl-data-to-text).
- MTL-story: [https://huggingface.co/RUCAIBox/mtl-story](https://huggingface.co/RUCAIBox/mtl-story).
- MTL-question-answering: [https://huggingface.co/RUCAIBox/mtl-question-answering](https://huggingface.co/RUCAIBox/mtl-question-answering).
- MTL-question-generation: [https://huggingface.co/RUCAIBox/mtl-question-generation](https://huggingface.co/RUCAIBox/mtl-question-generation).
- MTL-task-dialog: [https://huggingface.co/RUCAIBox/mtl-task-dialog](https://huggingface.co/RUCAIBox/mtl-task-dialog).
## Citation
```bibtex
@article{tang2022mvp,
title={MVP: Multi-task Supervised Pre-training for Natural Language Generation},
author={Tang, Tianyi and Li, Junyi and Zhao, Wayne Xin and Wen, Ji-Rong},
journal={arXiv preprint arXiv:2206.12131},
year={2022},
url={https://arxiv.org/abs/2206.12131},
}
```
|
RUCAIBox/mvp
|
RUCAIBox
| 2022-06-27T02:27:44Z | 4,763 | 7 |
transformers
|
[
"transformers",
"pytorch",
"mvp",
"text-generation",
"text2text-generation",
"summarization",
"conversational",
"en",
"arxiv:2206.12131",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-29T08:21:56Z |
---
license: apache-2.0
language:
- en
tags:
- text-generation
- text2text-generation
- summarization
- conversational
pipeline_tag: text2text-generation
widget:
- text: "Summarize: You may want to stick it to your boss and leave your job, but don't do it if these are your reasons."
example_title: "Summarization"
- text: "Given the dialog: do you like dance? [SEP] Yes I do. Did you know Bruce Lee was a cha cha dancer?"
example_title: "Dialog"
- text: "Describe the following data: Iron Man | instance of | Superhero [SEP] Stan Lee | creator | Iron Man"
example_title: "Data-to-text"
- text: "Given the story title: I think all public schools should have a uniform dress code."
example_title: "Story Generation"
- text: "Answer the following question: From which country did Angola achieve independence in 1975?"
example_title: "Question Answering"
- text: "Generate the question based on the answer: boxing [X_SEP] A bolo punch is a punch used in martial arts . A hook is a punch in boxing ."
example_title: "Question Generaion"
---
# MVP
The MVP model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP](https://github.com/RUCAIBox/MVP).
## Model Description
MVP is supervised pre-trained using a mixture of labeled datasets. It follows a standard Transformer encoder-decoder architecture.
MVP is specially designed for natural language generation and can be adapted to a wide range of generation tasks, including but not limited to summarization, data-to-text generation, open-ended dialogue system, story generation, question answering, question generation, task-oriented dialogue system, commonsense generation, paraphrase generation, text style transfer, and text simplification. Our model can also be adapted to natural language understanding tasks such as sequence classification and (extractive) question answering.
## Examples
For summarization:
```python
>>> from transformers import MvpTokenizer, MvpForConditionalGeneration
>>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp")
>>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp")
>>> inputs = tokenizer(
... "Summarize: You may want to stick it to your boss and leave your job, but don't do it if these are your reasons.",
... return_tensors="pt",
... )
>>> generated_ids = model.generate(**inputs)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
["Why You Shouldn't Quit Your Job"]
```
For data-to-text generation:
```python
>>> from transformers import MvpTokenizerFast, MvpForConditionalGeneration
>>> tokenizer = MvpTokenizerFast.from_pretrained("RUCAIBox/mvp")
>>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp")
>>> inputs = tokenizer(
... "Describe the following data: Iron Man | instance of | Superhero [SEP] Stan Lee | creator | Iron Man",
... return_tensors="pt",
... )
>>> generated_ids = model.generate(**inputs)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
['Stan Lee created the character of Iron Man, a fictional superhero appearing in American comic']
```
## Related Models
**MVP**: [https://huggingface.co/RUCAIBox/mvp](https://huggingface.co/RUCAIBox/mvp).
**Prompt-based models**:
- MVP-multi-task: [https://huggingface.co/RUCAIBox/mvp-multi-task](https://huggingface.co/RUCAIBox/mvp-multi-task).
- MVP-summarization: [https://huggingface.co/RUCAIBox/mvp-summarization](https://huggingface.co/RUCAIBox/mvp-summarization).
- MVP-open-dialog: [https://huggingface.co/RUCAIBox/mvp-open-dialog](https://huggingface.co/RUCAIBox/mvp-open-dialog).
- MVP-data-to-text: [https://huggingface.co/RUCAIBox/mvp-data-to-text](https://huggingface.co/RUCAIBox/mvp-data-to-text).
- MVP-story: [https://huggingface.co/RUCAIBox/mvp-story](https://huggingface.co/RUCAIBox/mvp-story).
- MVP-question-answering: [https://huggingface.co/RUCAIBox/mvp-question-answering](https://huggingface.co/RUCAIBox/mvp-question-answering).
- MVP-question-generation: [https://huggingface.co/RUCAIBox/mvp-question-generation](https://huggingface.co/RUCAIBox/mvp-question-generation).
- MVP-task-dialog: [https://huggingface.co/RUCAIBox/mvp-task-dialog](https://huggingface.co/RUCAIBox/mvp-task-dialog).
**Multi-task models**:
- MTL-summarization: [https://huggingface.co/RUCAIBox/mtl-summarization](https://huggingface.co/RUCAIBox/mtl-summarization).
- MTL-open-dialog: [https://huggingface.co/RUCAIBox/mtl-open-dialog](https://huggingface.co/RUCAIBox/mtl-open-dialog).
- MTL-data-to-text: [https://huggingface.co/RUCAIBox/mtl-data-to-text](https://huggingface.co/RUCAIBox/mtl-data-to-text).
- MTL-story: [https://huggingface.co/RUCAIBox/mtl-story](https://huggingface.co/RUCAIBox/mtl-story).
- MTL-question-answering: [https://huggingface.co/RUCAIBox/mtl-question-answering](https://huggingface.co/RUCAIBox/mtl-question-answering).
- MTL-question-generation: [https://huggingface.co/RUCAIBox/mtl-question-generation](https://huggingface.co/RUCAIBox/mtl-question-generation).
- MTL-task-dialog: [https://huggingface.co/RUCAIBox/mtl-task-dialog](https://huggingface.co/RUCAIBox/mtl-task-dialog).
## Citation
```bibtex
@article{tang2022mvp,
title={MVP: Multi-task Supervised Pre-training for Natural Language Generation},
author={Tang, Tianyi and Li, Junyi and Zhao, Wayne Xin and Wen, Ji-Rong},
journal={arXiv preprint arXiv:2206.12131},
year={2022},
url={https://arxiv.org/abs/2206.12131},
}
```
|
RUCAIBox/mtl-summarization
|
RUCAIBox
| 2022-06-27T02:27:34Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mvp",
"text-generation",
"text2text-generation",
"summarization",
"en",
"arxiv:2206.12131",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-02T12:01:19Z |
---
license: apache-2.0
language:
- en
tags:
- text-generation
- text2text-generation
- summarization
pipeline_tag: text2text-generation
widget:
- text: "Summarize: You may want to stick it to your boss and leave your job, but don't do it if these are your reasons."
example_title: "Example1"
- text: "Summarize: Jorge Alfaro drove in two runs, Aaron Nola pitched seven innings of two-hit ball and the Philadelphia Phillies beat the Los Angeles Dodgers 2-1 Thursday, spoiling Clayton Kershaw's first start in almost a month. Hitting out of the No. 8 spot in the ..."
example_title: "Example2"
---
# MTL-summarization
The MTL-summarization model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP](https://github.com/RUCAIBox/MVP).
## Model Description
MTL-summarization is supervised pre-trained using a mixture of labeled summarization datasets. It is a variant (Single) of our main [MVP](https://huggingface.co/RUCAIBox/mvp) model. It follows a standard Transformer encoder-decoder architecture.
MTL-summarization is specially designed for summarization tasks, such as new summarization (CNN/DailyMail, XSum) and dialog summarization (SAMSum).
## Example
```python
>>> from transformers import MvpTokenizer, MvpForConditionalGeneration
>>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp")
>>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mtl-summarization")
>>> inputs = tokenizer(
... "Summarize: You may want to stick it to your boss and leave your job, but don't do it if these are your reasons.",
... return_tensors="pt",
... )
>>> generated_ids = model.generate(**inputs)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
["Don't do it if these are your reasons"]
```
## Related Models
**MVP**: [https://huggingface.co/RUCAIBox/mvp](https://huggingface.co/RUCAIBox/mvp).
**Prompt-based models**:
- MVP-multi-task: [https://huggingface.co/RUCAIBox/mvp-multi-task](https://huggingface.co/RUCAIBox/mvp-multi-task).
- MVP-summarization: [https://huggingface.co/RUCAIBox/mvp-summarization](https://huggingface.co/RUCAIBox/mvp-summarization).
- MVP-open-dialog: [https://huggingface.co/RUCAIBox/mvp-open-dialog](https://huggingface.co/RUCAIBox/mvp-open-dialog).
- MVP-data-to-text: [https://huggingface.co/RUCAIBox/mvp-data-to-text](https://huggingface.co/RUCAIBox/mvp-data-to-text).
- MVP-story: [https://huggingface.co/RUCAIBox/mvp-story](https://huggingface.co/RUCAIBox/mvp-story).
- MVP-question-answering: [https://huggingface.co/RUCAIBox/mvp-question-answering](https://huggingface.co/RUCAIBox/mvp-question-answering).
- MVP-question-generation: [https://huggingface.co/RUCAIBox/mvp-question-generation](https://huggingface.co/RUCAIBox/mvp-question-generation).
- MVP-task-dialog: [https://huggingface.co/RUCAIBox/mvp-task-dialog](https://huggingface.co/RUCAIBox/mvp-task-dialog).
**Multi-task models**:
- MTL-summarization: [https://huggingface.co/RUCAIBox/mtl-summarization](https://huggingface.co/RUCAIBox/mtl-summarization).
- MTL-open-dialog: [https://huggingface.co/RUCAIBox/mtl-open-dialog](https://huggingface.co/RUCAIBox/mtl-open-dialog).
- MTL-data-to-text: [https://huggingface.co/RUCAIBox/mtl-data-to-text](https://huggingface.co/RUCAIBox/mtl-data-to-text).
- MTL-story: [https://huggingface.co/RUCAIBox/mtl-story](https://huggingface.co/RUCAIBox/mtl-story).
- MTL-question-answering: [https://huggingface.co/RUCAIBox/mtl-question-answering](https://huggingface.co/RUCAIBox/mtl-question-answering).
- MTL-question-generation: [https://huggingface.co/RUCAIBox/mtl-question-generation](https://huggingface.co/RUCAIBox/mtl-question-generation).
- MTL-task-dialog: [https://huggingface.co/RUCAIBox/mtl-task-dialog](https://huggingface.co/RUCAIBox/mtl-task-dialog).
## Citation
```bibtex
@article{tang2022mvp,
title={MVP: Multi-task Supervised Pre-training for Natural Language Generation},
author={Tang, Tianyi and Li, Junyi and Zhao, Wayne Xin and Wen, Ji-Rong},
journal={arXiv preprint arXiv:2206.12131},
year={2022},
url={https://arxiv.org/abs/2206.12131},
}
```
|
RUCAIBox/mtl-story
|
RUCAIBox
| 2022-06-27T02:27:29Z | 1 | 1 |
transformers
|
[
"transformers",
"pytorch",
"mvp",
"text-generation",
"text2text-generation",
"en",
"arxiv:2206.12131",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-02T12:00:10Z |
---
license: apache-2.0
language:
- en
tags:
- text-generation
- text2text-generation
pipeline_tag: text2text-generation
widget:
- text: "Given the story title: I think all public schools should have a uniform dress code."
example_title: "Example1"
- text: "Given the story title: My girlfriend and I decided to move to a new state. We packed everything in our cars and drove there."
example_title: "Example2"
---
# MTL-story
The MTL-story model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP](https://github.com/RUCAIBox/MVP).
## Model Description
MTL-story is supervised pre-trained using a mixture of labeled story generation datasets. It is a variant (Single) of our main [MVP](https://huggingface.co/RUCAIBox/mvp) model. It follows a standard Transformer encoder-decoder architecture.
MTL-story is specially designed for story generation tasks, such as ROCStories and WritingPrompts.
## Example
```python
>>> from transformers import MvpTokenizer, MvpForConditionalGeneration
>>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp")
>>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mtl-story")
>>> inputs = tokenizer(
... "Given the story title: I think all public schools should have a uniform dress code.",
... return_tensors="pt",
... )
>>> generated_ids = model.generate(**inputs, max_length=1024)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
["I don't know about you, but I don't think it would be a good idea to have a uniform dress code in public schools. I think it's a waste of time and money. If you're going to have uniform dress codes, you need to make sure that the uniforms are appropriate for the school and that the students are comfortable in them. If they're not comfortable, then they shouldn't be allowed to wear them."]
```
## Related Models
**MVP**: [https://huggingface.co/RUCAIBox/mvp](https://huggingface.co/RUCAIBox/mvp).
**Prompt-based models**:
- MVP-multi-task: [https://huggingface.co/RUCAIBox/mvp-multi-task](https://huggingface.co/RUCAIBox/mvp-multi-task).
- MVP-summarization: [https://huggingface.co/RUCAIBox/mvp-summarization](https://huggingface.co/RUCAIBox/mvp-summarization).
- MVP-open-dialog: [https://huggingface.co/RUCAIBox/mvp-open-dialog](https://huggingface.co/RUCAIBox/mvp-open-dialog).
- MVP-data-to-text: [https://huggingface.co/RUCAIBox/mvp-data-to-text](https://huggingface.co/RUCAIBox/mvp-data-to-text).
- MVP-story: [https://huggingface.co/RUCAIBox/mvp-story](https://huggingface.co/RUCAIBox/mvp-story).
- MVP-question-answering: [https://huggingface.co/RUCAIBox/mvp-question-answering](https://huggingface.co/RUCAIBox/mvp-question-answering).
- MVP-question-generation: [https://huggingface.co/RUCAIBox/mvp-question-generation](https://huggingface.co/RUCAIBox/mvp-question-generation).
- MVP-task-dialog: [https://huggingface.co/RUCAIBox/mvp-task-dialog](https://huggingface.co/RUCAIBox/mvp-task-dialog).
**Multi-task models**:
- MTL-summarization: [https://huggingface.co/RUCAIBox/mtl-summarization](https://huggingface.co/RUCAIBox/mtl-summarization).
- MTL-open-dialog: [https://huggingface.co/RUCAIBox/mtl-open-dialog](https://huggingface.co/RUCAIBox/mtl-open-dialog).
- MTL-data-to-text: [https://huggingface.co/RUCAIBox/mtl-data-to-text](https://huggingface.co/RUCAIBox/mtl-data-to-text).
- MTL-story: [https://huggingface.co/RUCAIBox/mtl-story](https://huggingface.co/RUCAIBox/mtl-story).
- MTL-question-answering: [https://huggingface.co/RUCAIBox/mtl-question-answering](https://huggingface.co/RUCAIBox/mtl-question-answering).
- MTL-question-generation: [https://huggingface.co/RUCAIBox/mtl-question-generation](https://huggingface.co/RUCAIBox/mtl-question-generation).
- MTL-task-dialog: [https://huggingface.co/RUCAIBox/mtl-task-dialog](https://huggingface.co/RUCAIBox/mtl-task-dialog).
## Citation
```bibtex
@article{tang2022mvp,
title={MVP: Multi-task Supervised Pre-training for Natural Language Generation},
author={Tang, Tianyi and Li, Junyi and Zhao, Wayne Xin and Wen, Ji-Rong},
journal={arXiv preprint arXiv:2206.12131},
year={2022},
url={https://arxiv.org/abs/2206.12131},
}
```
|
RUCAIBox/mtl-question-answering
|
RUCAIBox
| 2022-06-27T02:27:20Z | 29 | 1 |
transformers
|
[
"transformers",
"pytorch",
"mvp",
"text-generation",
"text2text-generation",
"en",
"arxiv:2206.12131",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-02T12:00:27Z |
---
license: apache-2.0
language:
- en
tags:
- text-generation
- text2text-generation
pipeline_tag: text2text-generation
widget:
- text: "Answer the following question: From which country did Angola achieve independence in 1975?"
example_title: "Example1"
- text: "Answer the following question: what is ce certified [X_SEP] The CE marking is the manufacturer's declaration that the product meets the requirements of the applicable EC directives. Officially, CE is an abbreviation of Conformite Conformité, europeenne Européenne Meaning. european conformity"
example_title: "Example2"
---
# MTL-question-answering
The MTL-question-answering model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP](https://github.com/RUCAIBox/MVP).
## Model Description
MTL-question-answering is supervised pre-trained using a mixture of labeled question answering datasets. It is a variant (Single) of our main [MVP](https://huggingface.co/RUCAIBox/mvp) model. It follows a standard Transformer encoder-decoder architecture.
MTL-question-answering is specially designed for question answering tasks, such as reading comprehension (SQuAD), conversational question answering (CoQA) and closed-book question-answering (Natural Questions).
## Example
```python
>>> from transformers import MvpTokenizer, MvpForConditionalGeneration
>>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp")
>>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mtl-question-answering")
>>> inputs = tokenizer(
... "Answer the following question: From which country did Angola achieve independence in 1975?",
... return_tensors="pt",
... )
>>> generated_ids = model.generate(**inputs)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
['Portugal']
```
## Related Models
**MVP**: [https://huggingface.co/RUCAIBox/mvp](https://huggingface.co/RUCAIBox/mvp).
**Prompt-based models**:
- MVP-multi-task: [https://huggingface.co/RUCAIBox/mvp-multi-task](https://huggingface.co/RUCAIBox/mvp-multi-task).
- MVP-summarization: [https://huggingface.co/RUCAIBox/mvp-summarization](https://huggingface.co/RUCAIBox/mvp-summarization).
- MVP-open-dialog: [https://huggingface.co/RUCAIBox/mvp-open-dialog](https://huggingface.co/RUCAIBox/mvp-open-dialog).
- MVP-data-to-text: [https://huggingface.co/RUCAIBox/mvp-data-to-text](https://huggingface.co/RUCAIBox/mvp-data-to-text).
- MVP-story: [https://huggingface.co/RUCAIBox/mvp-story](https://huggingface.co/RUCAIBox/mvp-story).
- MVP-question-answering: [https://huggingface.co/RUCAIBox/mvp-question-answering](https://huggingface.co/RUCAIBox/mvp-question-answering).
- MVP-question-generation: [https://huggingface.co/RUCAIBox/mvp-question-generation](https://huggingface.co/RUCAIBox/mvp-question-generation).
- MVP-task-dialog: [https://huggingface.co/RUCAIBox/mvp-task-dialog](https://huggingface.co/RUCAIBox/mvp-task-dialog).
**Multi-task models**:
- MTL-summarization: [https://huggingface.co/RUCAIBox/mtl-summarization](https://huggingface.co/RUCAIBox/mtl-summarization).
- MTL-open-dialog: [https://huggingface.co/RUCAIBox/mtl-open-dialog](https://huggingface.co/RUCAIBox/mtl-open-dialog).
- MTL-data-to-text: [https://huggingface.co/RUCAIBox/mtl-data-to-text](https://huggingface.co/RUCAIBox/mtl-data-to-text).
- MTL-story: [https://huggingface.co/RUCAIBox/mtl-story](https://huggingface.co/RUCAIBox/mtl-story).
- MTL-question-answering: [https://huggingface.co/RUCAIBox/mtl-question-answering](https://huggingface.co/RUCAIBox/mtl-question-answering).
- MTL-question-generation: [https://huggingface.co/RUCAIBox/mtl-question-generation](https://huggingface.co/RUCAIBox/mtl-question-generation).
- MTL-task-dialog: [https://huggingface.co/RUCAIBox/mtl-task-dialog](https://huggingface.co/RUCAIBox/mtl-task-dialog).
## Citation
```bibtex
@article{tang2022mvp,
title={MVP: Multi-task Supervised Pre-training for Natural Language Generation},
author={Tang, Tianyi and Li, Junyi and Zhao, Wayne Xin and Wen, Ji-Rong},
journal={arXiv preprint arXiv:2206.12131},
year={2022},
url={https://arxiv.org/abs/2206.12131},
}
```
|
RUCAIBox/mtl-open-dialog
|
RUCAIBox
| 2022-06-27T02:27:15Z | 3 | 1 |
transformers
|
[
"transformers",
"pytorch",
"mvp",
"text-generation",
"text2text-generation",
"conversational",
"en",
"arxiv:2206.12131",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-02T12:02:35Z |
---
license: apache-2.0
language:
- en
tags:
- text-generation
- text2text-generation
- conversational
pipeline_tag: text2text-generation
widget:
- text: "Given the dialog: do you like dance? [SEP] Yes I do. Did you know Bruce Lee was a cha cha dancer?"
example_title: "Example1"
- text: "Given the dialog: i used to scare for darkness [X_SEP] it feels like hitting to blank wall when i see the darkness [SEP] Oh ya? I don't really see how [SEP] dont you feel so.. its a wonder [SEP] I do actually hit blank walls a lot of times but i get by"
example_title: "Example2"
---
# MTL-open-dialog
The MTL-open-dialog model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP](https://github.com/RUCAIBox/MVP).
## Model Description
MTL-open-dialog is supervised pre-trained using a mixture of labeled open dialogue system datasets. It is a variant (Single) of our main [MVP](https://huggingface.co/RUCAIBox/mvp) model. It follows a standard Transformer encoder-decoder architecture.
MTL-open-dialog is specially designed for open dialogue system (conversation) tasks, such as chitchat (PersonaChat, DailyDialog), knowledge grounded conversation (Topical-Chat, Wizard of Wikipedia) and visual dialog (DSTC7-AVSD).
## Example
```python
>>> from transformers import MvpTokenizer, MvpForConditionalGeneration
>>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp")
>>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mtl-open-dialog")
>>> inputs = tokenizer(
... "Given the dialog: do you like dance? [SEP] Yes I do. Did you know Bruce Lee was a cha cha dancer?",
... return_tensors="pt",
... )
>>> generated_ids = model.generate(**inputs)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
['Yes he won the Hong Kong Cha Cha championship in 1958']
```
## Related Models
**MVP**: [https://huggingface.co/RUCAIBox/mvp](https://huggingface.co/RUCAIBox/mvp).
**Prompt-based models**:
- MVP-multi-task: [https://huggingface.co/RUCAIBox/mvp-multi-task](https://huggingface.co/RUCAIBox/mvp-multi-task).
- MVP-summarization: [https://huggingface.co/RUCAIBox/mvp-summarization](https://huggingface.co/RUCAIBox/mvp-summarization).
- MVP-open-dialog: [https://huggingface.co/RUCAIBox/mvp-open-dialog](https://huggingface.co/RUCAIBox/mvp-open-dialog).
- MVP-data-to-text: [https://huggingface.co/RUCAIBox/mvp-data-to-text](https://huggingface.co/RUCAIBox/mvp-data-to-text).
- MVP-story: [https://huggingface.co/RUCAIBox/mvp-story](https://huggingface.co/RUCAIBox/mvp-story).
- MVP-question-answering: [https://huggingface.co/RUCAIBox/mvp-question-answering](https://huggingface.co/RUCAIBox/mvp-question-answering).
- MVP-question-generation: [https://huggingface.co/RUCAIBox/mvp-question-generation](https://huggingface.co/RUCAIBox/mvp-question-generation).
- MVP-task-dialog: [https://huggingface.co/RUCAIBox/mvp-task-dialog](https://huggingface.co/RUCAIBox/mvp-task-dialog).
**Multi-task models**:
- MTL-summarization: [https://huggingface.co/RUCAIBox/mtl-summarization](https://huggingface.co/RUCAIBox/mtl-summarization).
- MTL-open-dialog: [https://huggingface.co/RUCAIBox/mtl-open-dialog](https://huggingface.co/RUCAIBox/mtl-open-dialog).
- MTL-data-to-text: [https://huggingface.co/RUCAIBox/mtl-data-to-text](https://huggingface.co/RUCAIBox/mtl-data-to-text).
- MTL-story: [https://huggingface.co/RUCAIBox/mtl-story](https://huggingface.co/RUCAIBox/mtl-story).
- MTL-question-answering: [https://huggingface.co/RUCAIBox/mtl-question-answering](https://huggingface.co/RUCAIBox/mtl-question-answering).
- MTL-question-generation: [https://huggingface.co/RUCAIBox/mtl-question-generation](https://huggingface.co/RUCAIBox/mtl-question-generation).
- MTL-task-dialog: [https://huggingface.co/RUCAIBox/mtl-task-dialog](https://huggingface.co/RUCAIBox/mtl-task-dialog).
## Citation
```bibtex
@article{tang2022mvp,
title={MVP: Multi-task Supervised Pre-training for Natural Language Generation},
author={Tang, Tianyi and Li, Junyi and Zhao, Wayne Xin and Wen, Ji-Rong},
journal={arXiv preprint arXiv:2206.12131},
year={2022},
url={https://arxiv.org/abs/2206.12131},
}
```
|
luomingshuang/icefall_asr_tal-csasr_pruned_transducer_stateless5
|
luomingshuang
| 2022-06-27T01:54:36Z | 0 | 3 | null |
[
"tensorboard",
"region:us"
] | null | 2022-06-23T04:14:43Z |
Note: This recipe is trained with the codes from this PR https://github.com/k2-fsa/icefall/pull/428
# Pre-trained Transducer-Stateless5 models for the TAL_CSASR dataset with icefall.
The model was trained on the far data of [TAL_CSASR](https://ai.100tal.com/dataset) with the scripts in [icefall](https://github.com/k2-fsa/icefall) based on the latest version k2.
## Training procedure
The main repositories are list below, we will update the training and decoding scripts with the update of version.
k2: https://github.com/k2-fsa/k2
icefall: https://github.com/k2-fsa/icefall
lhotse: https://github.com/lhotse-speech/lhotse
* Install k2 and lhotse, k2 installation guide refers to https://k2.readthedocs.io/en/latest/installation/index.html, lhotse refers to https://lhotse.readthedocs.io/en/latest/getting-started.html#installation. I think the latest version would be ok. And please also install the requirements listed in icefall.
* Clone icefall(https://github.com/k2-fsa/icefall) and check to the commit showed above.
```
git clone https://github.com/k2-fsa/icefall
cd icefall
```
* Preparing data.
```
cd egs/tal_csasr/ASR
bash ./prepare.sh
```
* Training
```
export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5"
./pruned_transducer_stateless5/train.py \
--world-size 6 \
--num-epochs 30 \
--start-epoch 1 \
--exp-dir pruned_transducer_stateless5/exp \
--lang-dir data/lang_char \
--max-duration 90
```
## Evaluation results
The decoding results (CER%) on TAL_CSASR(dev and test) are listed below:
|decoding-method | epoch(iter) | avg | dev | test |
|--|--|--|--|--|
|greedy_search | 30 | 24 | 7.49 | 7.58|
|modified_beam_search | 30 | 24 | 7.33 | 7.38|
|fast_beam_search | 30 | 24 | 7.32 | 7.42|
|greedy_search(use-averaged-model=True) | 30 | 24 | 7.30 | 7.39|
|modified_beam_search(use-averaged-model=True) | 30 | 24 | 7.15 | 7.22|
|fast_beam_search(use-averaged-model=True) | 30 | 24 | 7.18 | 7.27|
|greedy_search | 348000 | 30 | 7.46 | 7.54|
|modified_beam_search | 348000 | 30 | 7.24 | 7.36|
|fast_beam_search | 348000 | 30 | 7.25 | 7.39 |
The results (CER(%) and WER(%)) for Chinese CER and English WER respectivly (zh: Chinese, en: English):
|decoding-method | epoch(iter) | avg | dev | dev_zh | dev_en | test | test_zh | test_en |
|--|--|--|--|--|--|--|--|--|
|greedy_search(use-averaged-model=True) | 30 | 24 | 7.30 | 6.48 | 19.19 |7.39| 6.66 | 19.13|
|modified_beam_search(use-averaged-model=True) | 30 | 24 | 7.15 | 6.35 | 18.95 | 7.22| 6.50 | 18.70 |
|fast_beam_search(use-averaged-model=True) | 30 | 24 | 7.18 | 6.39| 18.90 | 7.27| 6.55 | 18.77|
|
tjscollins/atari-dqn
|
tjscollins
| 2022-06-27T01:45:40Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-27T01:44:58Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 565.50 +/- 141.39
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga tjscollins -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga tjscollins
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Corianas/ppo-QbertNoFrameskip-v4_4
|
Corianas
| 2022-06-27T01:07:03Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"QbertNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-27T01:05:46Z |
---
library_name: stable-baselines3
tags:
- QbertNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 19340.00 +/- 862.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: QbertNoFrameskip-v4
type: QbertNoFrameskip-v4
---
# **PPO** Agent playing **QbertNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **QbertNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo ppo --env QbertNoFrameskip-v4 -orga Corianas -f logs/
python enjoy.py --algo ppo --env QbertNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo ppo --env QbertNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo ppo --env QbertNoFrameskip-v4 -f logs/ -orga Corianas
```
## Hyperparameters
```python
OrderedDict([('batch_size', 256),
('clip_range', 'lin_0.1'),
('ent_coef', 0.01),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('frame_stack', 4),
('learning_rate', 'lin_2.5e-4'),
('n_envs', 8),
('n_epochs', 4),
('n_steps', 128),
('n_timesteps', 10000000.0),
('policy', 'CnnPolicy'),
('vf_coef', 0.5),
('normalize', False)])
```
|
sudo-s/exper_batch_32_e8
|
sudo-s
| 2022-06-26T23:45:06Z | 52 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-26T22:48:05Z |
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: exper_batch_32_e8
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. -->
# exper_batch_32_e8
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the sudo-s/herbier_mesuem1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3520
- Accuracy: 0.9113
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 32
- 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
- mixed_precision_training: Apex, opt level O1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.3787 | 0.31 | 100 | 3.3100 | 0.3566 |
| 2.3975 | 0.62 | 200 | 2.3196 | 0.5717 |
| 1.5578 | 0.94 | 300 | 1.6764 | 0.6461 |
| 1.0291 | 1.25 | 400 | 1.1713 | 0.7463 |
| 0.8185 | 1.56 | 500 | 0.9292 | 0.7953 |
| 0.6181 | 1.88 | 600 | 0.7732 | 0.8169 |
| 0.3873 | 2.19 | 700 | 0.6877 | 0.8277 |
| 0.2979 | 2.5 | 800 | 0.6250 | 0.8404 |
| 0.2967 | 2.81 | 900 | 0.6151 | 0.8365 |
| 0.1874 | 3.12 | 1000 | 0.5401 | 0.8608 |
| 0.2232 | 3.44 | 1100 | 0.5032 | 0.8712 |
| 0.1109 | 3.75 | 1200 | 0.4635 | 0.8774 |
| 0.0539 | 4.06 | 1300 | 0.4495 | 0.8843 |
| 0.0668 | 4.38 | 1400 | 0.4273 | 0.8951 |
| 0.0567 | 4.69 | 1500 | 0.4427 | 0.8867 |
| 0.0285 | 5.0 | 1600 | 0.4092 | 0.8955 |
| 0.0473 | 5.31 | 1700 | 0.3720 | 0.9071 |
| 0.0225 | 5.62 | 1800 | 0.3691 | 0.9063 |
| 0.0196 | 5.94 | 1900 | 0.3775 | 0.9048 |
| 0.0173 | 6.25 | 2000 | 0.3641 | 0.9040 |
| 0.0092 | 6.56 | 2100 | 0.3551 | 0.9090 |
| 0.008 | 6.88 | 2200 | 0.3591 | 0.9125 |
| 0.0072 | 7.19 | 2300 | 0.3542 | 0.9121 |
| 0.007 | 7.5 | 2400 | 0.3532 | 0.9106 |
| 0.007 | 7.81 | 2500 | 0.3520 | 0.9113 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.5.1
- Datasets 2.3.2
- Tokenizers 0.12.1
|
kaisuke/finetuning-sentiment-model-3000-samples
|
kaisuke
| 2022-06-26T21:39:32Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-26T21:27:20Z |
---
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
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.87
- name: F1
type: f1
value: 0.8695652173913044
---
<!-- 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: 0.3120
- Accuracy: 0.87
- F1: 0.8696
## 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 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
KayKozaronek/xlm-roberta-base-finetuned-panx-de
|
KayKozaronek
| 2022-06-26T20:13:36Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-23T10:13:29Z |
---
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: 0.8620945214069894
- task:
type: token-classification
name: Token Classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.899981559318585
verified: true
- name: Precision
type: precision
value: 0.9009035072102546
verified: true
- name: Recall
type: recall
value: 0.9206610317249744
verified: true
- name: F1
type: f1
value: 0.9106751198809252
verified: true
- name: loss
type: loss
value: 0.3754884600639343
verified: true
---
<!-- 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: 0.1372
- F1: 0.8621
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2575 | 1.0 | 525 | 0.1621 | 0.8292 |
| 0.1287 | 2.0 | 1050 | 0.1378 | 0.8526 |
| 0.0831 | 3.0 | 1575 | 0.1372 | 0.8621 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
sinhprous/dqn-SpaceInvadersNoFrameskip-v4
|
sinhprous
| 2022-06-26T19:51:15Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-22T18:19:11Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 925.00 +/- 356.35
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga sinhprous -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga sinhprous
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
sanjay-m1/grammar-corrector-v2
|
sanjay-m1
| 2022-06-26T19:10:37Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-26T19:00:10Z |
**This model is part of the Gramformer library** please refer to https://github.com/PrithivirajDamodaran/Gramformer/
|
sudo-s/exper_batch_8_e8
|
sudo-s
| 2022-06-26T18:24:00Z | 72 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-26T15:35:19Z |
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: exper_batch_8_e8
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. -->
# exper_batch_8_e8
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the sudo-s/herbier_mesuem1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4608
- Accuracy: 0.9052
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 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
- mixed_precision_training: Apex, opt level O1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 4.2202 | 0.08 | 100 | 4.1245 | 0.1237 |
| 3.467 | 0.16 | 200 | 3.5622 | 0.2143 |
| 3.3469 | 0.23 | 300 | 3.1688 | 0.2675 |
| 2.8086 | 0.31 | 400 | 2.8965 | 0.3034 |
| 2.6291 | 0.39 | 500 | 2.5858 | 0.4025 |
| 2.2382 | 0.47 | 600 | 2.2908 | 0.4133 |
| 1.9259 | 0.55 | 700 | 2.2007 | 0.4676 |
| 1.8088 | 0.63 | 800 | 2.0419 | 0.4742 |
| 1.9462 | 0.7 | 900 | 1.6793 | 0.5578 |
| 1.5392 | 0.78 | 1000 | 1.5460 | 0.6079 |
| 1.561 | 0.86 | 1100 | 1.5793 | 0.5690 |
| 1.2135 | 0.94 | 1200 | 1.4663 | 0.5929 |
| 1.0725 | 1.02 | 1300 | 1.2974 | 0.6534 |
| 0.8696 | 1.1 | 1400 | 1.2406 | 0.6569 |
| 0.8758 | 1.17 | 1500 | 1.2127 | 0.6623 |
| 1.1737 | 1.25 | 1600 | 1.2243 | 0.6550 |
| 0.8242 | 1.33 | 1700 | 1.1371 | 0.6735 |
| 1.0141 | 1.41 | 1800 | 1.0536 | 0.7024 |
| 0.9855 | 1.49 | 1900 | 0.9885 | 0.7205 |
| 0.805 | 1.57 | 2000 | 0.9048 | 0.7479 |
| 0.7207 | 1.64 | 2100 | 0.8842 | 0.7490 |
| 0.7101 | 1.72 | 2200 | 0.8954 | 0.7436 |
| 0.5946 | 1.8 | 2300 | 0.9174 | 0.7386 |
| 0.6937 | 1.88 | 2400 | 0.7818 | 0.7760 |
| 0.5593 | 1.96 | 2500 | 0.7449 | 0.7934 |
| 0.4139 | 2.04 | 2600 | 0.7787 | 0.7830 |
| 0.2929 | 2.11 | 2700 | 0.7122 | 0.7945 |
| 0.4159 | 2.19 | 2800 | 0.7446 | 0.7907 |
| 0.4079 | 2.27 | 2900 | 0.7354 | 0.7938 |
| 0.516 | 2.35 | 3000 | 0.7499 | 0.8007 |
| 0.2728 | 2.43 | 3100 | 0.6851 | 0.8061 |
| 0.4159 | 2.51 | 3200 | 0.7258 | 0.7999 |
| 0.3396 | 2.58 | 3300 | 0.7455 | 0.7972 |
| 0.1918 | 2.66 | 3400 | 0.6793 | 0.8119 |
| 0.1228 | 2.74 | 3500 | 0.6696 | 0.8134 |
| 0.2671 | 2.82 | 3600 | 0.6306 | 0.8285 |
| 0.4986 | 2.9 | 3700 | 0.6111 | 0.8296 |
| 0.3699 | 2.98 | 3800 | 0.5600 | 0.8508 |
| 0.0444 | 3.05 | 3900 | 0.6021 | 0.8331 |
| 0.1489 | 3.13 | 4000 | 0.5599 | 0.8516 |
| 0.15 | 3.21 | 4100 | 0.6377 | 0.8365 |
| 0.2535 | 3.29 | 4200 | 0.5752 | 0.8543 |
| 0.2679 | 3.37 | 4300 | 0.5677 | 0.8608 |
| 0.0989 | 3.45 | 4400 | 0.6325 | 0.8396 |
| 0.0825 | 3.52 | 4500 | 0.5979 | 0.8524 |
| 0.0427 | 3.6 | 4600 | 0.5903 | 0.8516 |
| 0.1806 | 3.68 | 4700 | 0.5323 | 0.8628 |
| 0.2672 | 3.76 | 4800 | 0.5688 | 0.8604 |
| 0.2674 | 3.84 | 4900 | 0.5369 | 0.8635 |
| 0.2185 | 3.92 | 5000 | 0.4743 | 0.8820 |
| 0.2195 | 3.99 | 5100 | 0.5340 | 0.8709 |
| 0.0049 | 4.07 | 5200 | 0.5883 | 0.8608 |
| 0.0204 | 4.15 | 5300 | 0.6102 | 0.8539 |
| 0.0652 | 4.23 | 5400 | 0.5659 | 0.8670 |
| 0.028 | 4.31 | 5500 | 0.4916 | 0.8840 |
| 0.0423 | 4.39 | 5600 | 0.5706 | 0.8736 |
| 0.0087 | 4.46 | 5700 | 0.5653 | 0.8697 |
| 0.0964 | 4.54 | 5800 | 0.5423 | 0.8755 |
| 0.0841 | 4.62 | 5900 | 0.5160 | 0.8743 |
| 0.0945 | 4.7 | 6000 | 0.5532 | 0.8697 |
| 0.0311 | 4.78 | 6100 | 0.4947 | 0.8867 |
| 0.0423 | 4.86 | 6200 | 0.5063 | 0.8843 |
| 0.1348 | 4.93 | 6300 | 0.5619 | 0.8743 |
| 0.049 | 5.01 | 6400 | 0.5800 | 0.8732 |
| 0.0053 | 5.09 | 6500 | 0.5499 | 0.8770 |
| 0.0234 | 5.17 | 6600 | 0.5102 | 0.8874 |
| 0.0192 | 5.25 | 6700 | 0.5447 | 0.8836 |
| 0.0029 | 5.32 | 6800 | 0.4787 | 0.8936 |
| 0.0249 | 5.4 | 6900 | 0.5232 | 0.8870 |
| 0.0671 | 5.48 | 7000 | 0.4766 | 0.8975 |
| 0.0056 | 5.56 | 7100 | 0.5136 | 0.8894 |
| 0.003 | 5.64 | 7200 | 0.5085 | 0.8882 |
| 0.0015 | 5.72 | 7300 | 0.4832 | 0.8971 |
| 0.0014 | 5.79 | 7400 | 0.4648 | 0.8998 |
| 0.0065 | 5.87 | 7500 | 0.4739 | 0.8978 |
| 0.0011 | 5.95 | 7600 | 0.5349 | 0.8867 |
| 0.0021 | 6.03 | 7700 | 0.5460 | 0.8847 |
| 0.0012 | 6.11 | 7800 | 0.5309 | 0.8890 |
| 0.0011 | 6.19 | 7900 | 0.4852 | 0.8998 |
| 0.0093 | 6.26 | 8000 | 0.4751 | 0.8998 |
| 0.003 | 6.34 | 8100 | 0.4934 | 0.8963 |
| 0.0027 | 6.42 | 8200 | 0.4882 | 0.9029 |
| 0.0009 | 6.5 | 8300 | 0.4806 | 0.9021 |
| 0.0009 | 6.58 | 8400 | 0.4974 | 0.9029 |
| 0.0009 | 6.66 | 8500 | 0.4748 | 0.9075 |
| 0.0008 | 6.73 | 8600 | 0.4723 | 0.9094 |
| 0.001 | 6.81 | 8700 | 0.4692 | 0.9098 |
| 0.0007 | 6.89 | 8800 | 0.4726 | 0.9075 |
| 0.0011 | 6.97 | 8900 | 0.4686 | 0.9067 |
| 0.0006 | 7.05 | 9000 | 0.4653 | 0.9056 |
| 0.0006 | 7.13 | 9100 | 0.4755 | 0.9029 |
| 0.0007 | 7.2 | 9200 | 0.4633 | 0.9036 |
| 0.0067 | 7.28 | 9300 | 0.4611 | 0.9036 |
| 0.0007 | 7.36 | 9400 | 0.4608 | 0.9052 |
| 0.0007 | 7.44 | 9500 | 0.4623 | 0.9044 |
| 0.0005 | 7.52 | 9600 | 0.4621 | 0.9056 |
| 0.0005 | 7.6 | 9700 | 0.4615 | 0.9056 |
| 0.0005 | 7.67 | 9800 | 0.4612 | 0.9059 |
| 0.0005 | 7.75 | 9900 | 0.4626 | 0.9075 |
| 0.0004 | 7.83 | 10000 | 0.4626 | 0.9075 |
| 0.0005 | 7.91 | 10100 | 0.4626 | 0.9075 |
| 0.0006 | 7.99 | 10200 | 0.4626 | 0.9079 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.5.1
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ryanblak/PPO-CarRacing-v0
|
ryanblak
| 2022-06-26T18:23:33Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-26T18:21:51Z |
---
library_name: stable-baselines3
tags:
- CarRacing-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 38.16 +/- 93.54
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CarRacing-v0
type: CarRacing-v0
---
# **PPO** Agent playing **CarRacing-v0**
This is a trained model of a **PPO** agent playing **CarRacing-v0**
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
...
```
|
p123/autotrain-my-sum-1040935781
|
p123
| 2022-06-26T18:02:45Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain",
"zh",
"dataset:p123/autotrain-data-my-sum",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-26T15:19:08Z |
---
tags: autotrain
language: zh
widget:
- text: "I love AutoTrain 🤗"
datasets:
- p123/autotrain-data-my-sum
co2_eq_emissions: 326.52733725745725
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1040935781
- CO2 Emissions (in grams): 326.52733725745725
## Validation Metrics
- Loss: 1.9157543182373047
- Rouge1: 0.4843
- Rouge2: 0.0
- RougeL: 0.4843
- RougeLsum: 0.4843
- Gen Len: 10.9718
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/p123/autotrain-my-sum-1040935781
```
|
prahlad/rotten_model
|
prahlad
| 2022-06-26T16:52:37Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-23T23:46:48Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: prahlad/rotten_model
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. -->
# prahlad/rotten_model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on rotten_tomatoes movie review dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4876
- Train Accuracy: 0.7620
- Validation Loss: 0.5001
- Validation Accuracy: 0.7842
- 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': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 12795, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4876 | 0.7620 | 0.5001 | 0.7842 | 0 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ivanlau/ppo-mlppolicy-LunarLander-v2
|
ivanlau
| 2022-06-26T16:39:24Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-26T16:38:42Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 0.39 +/- 42.53
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
sudo-s/exper_batch_8_e4
|
sudo-s
| 2022-06-26T15:33:41Z | 53 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-26T14:18:20Z |
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: exper_batch_8_e4
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. -->
# exper_batch_8_e4
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the sudo-s/herbier_mesuem1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3353
- Accuracy: 0.9183
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 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: 4
- mixed_precision_training: Apex, opt level O1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2251 | 0.08 | 100 | 4.1508 | 0.1203 |
| 3.4942 | 0.16 | 200 | 3.5566 | 0.2082 |
| 3.2871 | 0.23 | 300 | 3.0942 | 0.3092 |
| 2.7273 | 0.31 | 400 | 2.8338 | 0.3308 |
| 2.4984 | 0.39 | 500 | 2.4860 | 0.4341 |
| 2.3423 | 0.47 | 600 | 2.2201 | 0.4796 |
| 1.8785 | 0.55 | 700 | 2.1890 | 0.4653 |
| 1.8012 | 0.63 | 800 | 1.9901 | 0.4865 |
| 1.7236 | 0.7 | 900 | 1.6821 | 0.5736 |
| 1.4949 | 0.78 | 1000 | 1.5422 | 0.6083 |
| 1.5573 | 0.86 | 1100 | 1.5436 | 0.6110 |
| 1.3241 | 0.94 | 1200 | 1.4077 | 0.6207 |
| 1.0773 | 1.02 | 1300 | 1.1417 | 0.6916 |
| 0.7935 | 1.1 | 1400 | 1.1194 | 0.6931 |
| 0.7677 | 1.17 | 1500 | 1.0727 | 0.7167 |
| 0.9468 | 1.25 | 1600 | 1.0707 | 0.7136 |
| 0.7563 | 1.33 | 1700 | 0.9427 | 0.7390 |
| 0.8471 | 1.41 | 1800 | 0.8906 | 0.7571 |
| 0.9998 | 1.49 | 1900 | 0.8098 | 0.7845 |
| 0.6039 | 1.57 | 2000 | 0.7244 | 0.8034 |
| 0.7052 | 1.64 | 2100 | 0.7881 | 0.7953 |
| 0.6753 | 1.72 | 2200 | 0.7458 | 0.7926 |
| 0.3758 | 1.8 | 2300 | 0.6987 | 0.8022 |
| 0.4985 | 1.88 | 2400 | 0.6286 | 0.8265 |
| 0.4122 | 1.96 | 2500 | 0.5949 | 0.8358 |
| 0.1286 | 2.04 | 2600 | 0.5691 | 0.8385 |
| 0.1989 | 2.11 | 2700 | 0.5535 | 0.8389 |
| 0.3304 | 2.19 | 2800 | 0.5261 | 0.8520 |
| 0.3415 | 2.27 | 2900 | 0.5504 | 0.8477 |
| 0.4066 | 2.35 | 3000 | 0.5418 | 0.8497 |
| 0.1208 | 2.43 | 3100 | 0.5156 | 0.8612 |
| 0.1668 | 2.51 | 3200 | 0.5655 | 0.8539 |
| 0.0727 | 2.58 | 3300 | 0.4971 | 0.8658 |
| 0.0929 | 2.66 | 3400 | 0.4962 | 0.8635 |
| 0.0678 | 2.74 | 3500 | 0.4903 | 0.8670 |
| 0.1212 | 2.82 | 3600 | 0.4357 | 0.8867 |
| 0.1579 | 2.9 | 3700 | 0.4642 | 0.8739 |
| 0.2625 | 2.98 | 3800 | 0.3994 | 0.8951 |
| 0.024 | 3.05 | 3900 | 0.3953 | 0.8971 |
| 0.0696 | 3.13 | 4000 | 0.3883 | 0.9056 |
| 0.0169 | 3.21 | 4100 | 0.3755 | 0.9086 |
| 0.023 | 3.29 | 4200 | 0.3685 | 0.9109 |
| 0.0337 | 3.37 | 4300 | 0.3623 | 0.9109 |
| 0.0123 | 3.45 | 4400 | 0.3647 | 0.9067 |
| 0.0159 | 3.52 | 4500 | 0.3630 | 0.9082 |
| 0.0154 | 3.6 | 4600 | 0.3522 | 0.9094 |
| 0.0112 | 3.68 | 4700 | 0.3439 | 0.9163 |
| 0.0219 | 3.76 | 4800 | 0.3404 | 0.9194 |
| 0.0183 | 3.84 | 4900 | 0.3371 | 0.9183 |
| 0.0103 | 3.92 | 5000 | 0.3362 | 0.9183 |
| 0.0357 | 3.99 | 5100 | 0.3353 | 0.9183 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.5.1
- Datasets 2.3.2
- Tokenizers 0.12.1
|
l3cube-pune/marathi-albert-v2
|
l3cube-pune
| 2022-06-26T15:13:43Z | 7 | 1 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"fill-mask",
"mr",
"dataset:L3Cube-MahaCorpus",
"arxiv:2202.01159",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-04T17:42:49Z |
---
license: cc-by-4.0
language: mr
datasets:
- L3Cube-MahaCorpus
---
## MahaAlBERT
MahaAlBERT is a Marathi AlBERT model trained on L3Cube-MahaCorpus and other publicly available Marathi monolingual datasets.
[dataset link] (https://github.com/l3cube-pune/MarathiNLP)
More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2202.01159)
```
@InProceedings{joshi:2022:WILDRE6,
author = {Joshi, Raviraj},
title = {L3Cube-MahaCorpus and MahaBERT: Marathi Monolingual Corpus, Marathi BERT Language Models, and Resources},
booktitle = {Proceedings of The WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference},
month = {June},
year = {2022},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {97--101}
}
```
|
allegro/herbert-large-cased
|
allegro
| 2022-06-26T14:18:54Z | 1,073 | 6 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"herbert",
"pl",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language: pl
tags:
- herbert
license: cc-by-4.0
---
# HerBERT
**[HerBERT](https://en.wikipedia.org/wiki/Zbigniew_Herbert)** is a BERT-based Language Model trained on Polish corpora
using Masked Language Modelling (MLM) and Sentence Structural Objective (SSO) with dynamic masking of whole words. For more details, please refer to: [HerBERT: Efficiently Pretrained Transformer-based Language Model for Polish](https://www.aclweb.org/anthology/2021.bsnlp-1.1/).
Model training and experiments were conducted with [transformers](https://github.com/huggingface/transformers) in version 2.9.
## Corpus
HerBERT was trained on six different corpora available for Polish language:
| Corpus | Tokens | Documents |
| :------ | ------: | ------: |
| [CCNet Middle](https://github.com/facebookresearch/cc_net) | 3243M | 7.9M |
| [CCNet Head](https://github.com/facebookresearch/cc_net) | 2641M | 7.0M |
| [National Corpus of Polish](http://nkjp.pl/index.php?page=14&lang=1)| 1357M | 3.9M |
| [Open Subtitles](http://opus.nlpl.eu/OpenSubtitles-v2018.php) | 1056M | 1.1M
| [Wikipedia](https://dumps.wikimedia.org/) | 260M | 1.4M |
| [Wolne Lektury](https://wolnelektury.pl/) | 41M | 5.5k |
## Tokenizer
The training dataset was tokenized into subwords using a character level byte-pair encoding (``CharBPETokenizer``) with
a vocabulary size of 50k tokens. The tokenizer itself was trained with a [tokenizers](https://github.com/huggingface/tokenizers) library.
We kindly encourage you to use the ``Fast`` version of the tokenizer, namely ``HerbertTokenizerFast``.
## Usage
Example code:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("allegro/herbert-large-cased")
model = AutoModel.from_pretrained("allegro/herbert-large-cased")
output = model(
**tokenizer.batch_encode_plus(
[
(
"A potem szedł środkiem drogi w kurzawie, bo zamiatał nogami, ślepy dziad prowadzony przez tłustego kundla na sznurku.",
"A potem leciał od lasu chłopak z butelką, ale ten ujrzawszy księdza przy drodze okrążył go z dala i biegł na przełaj pól do karczmy."
)
],
padding='longest',
add_special_tokens=True,
return_tensors='pt'
)
)
```
## License
CC BY 4.0
## Citation
If you use this model, please cite the following paper:
```
@inproceedings{mroczkowski-etal-2021-herbert,
title = "{H}er{BERT}: Efficiently Pretrained Transformer-based Language Model for {P}olish",
author = "Mroczkowski, Robert and
Rybak, Piotr and
Wr{\'o}blewska, Alina and
Gawlik, Ireneusz",
booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing",
month = apr,
year = "2021",
address = "Kiyv, Ukraine",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.bsnlp-1.1",
pages = "1--10",
}
```
## Authors
The model was trained by [**Machine Learning Research Team at Allegro**](https://ml.allegro.tech/) and [**Linguistic Engineering Group at Institute of Computer Science, Polish Academy of Sciences**](http://zil.ipipan.waw.pl/).
You can contact us at: <a href="mailto:klejbenchmark@allegro.pl">klejbenchmark@allegro.pl</a>
|
Nikkisora/q-FrozenLake-v1-4x4-noSlippery
|
Nikkisora
| 2022-06-26T13:58:56Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-26T13:58:48Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Nikkisora/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
sumedh/pretrained-word-embeddings
|
sumedh
| 2022-06-26T13:43:27Z | 0 | 1 |
keras
|
[
"keras",
"tensorboard",
"tf-keras",
"multiclass-classification",
"newsgroup",
"en",
"dataset:newsgroup",
"region:us"
] | null | 2022-06-26T03:04:29Z |
---
library_name: keras
language: en
datasets: newsgroup
tags:
- multiclass-classification
- newsgroup
---
## Model description
Text classification on the Newsgroup20 dataset using pre-trained [GloVe](https://nlp.stanford.edu/projects/glove/) word embeddings.
This repo contains the model [to this Keras example using pre-trained word embeddings](https://keras.io/examples/nlp/pretrained_word_embeddings/).
Full credits to : [fchollet](https://twitter.com/fchollet)
Model reproduced by : [Sumedh](https://huggingface.co/sumedh)
## Training and evaluation data
Newsgroup20 dataset, a set of 20,000 message board messages belonging to 20 different topic categories
### Training hyperparameters
The following hyperparameters were used during training:
| name | learning_rate | decay | rho | momentum | epsilon | centered | training_precision |
|----|-------------|-----|---|--------|-------|--------|------------------|
|RMSprop|0.0010000000474974513|0.0|0.8999999761581421|0.0|1e-07|False|float32|
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
FreelancerFel/dqn-SpaceInvadersNoFrameskip-v4
|
FreelancerFel
| 2022-06-26T11:39:32Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-26T11:38:55Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 692.50 +/- 193.97
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga FreelancerFel -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga FreelancerFel
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
FreelancerFel/dqn_SpaceInvader
|
FreelancerFel
| 2022-06-26T11:35:59Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-26T11:35:21Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 692.50 +/- 193.97
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga FreelancerFel -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga FreelancerFel
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
shafin/distilbert-similarity-b32-3
|
shafin
| 2022-06-26T11:24:03Z | 4 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-06-26T11:23:53Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# shafin/distilbert-similarity-b32-3
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 3 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('shafin/distilbert-similarity-b32-3')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=shafin/distilbert-similarity-b32-3)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 56250 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.OnlineContrastiveLoss.OnlineContrastiveLoss`
Parameters of the fit()-Method:
```
{
"epochs": 3,
"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": null,
"warmup_steps": 5000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Dense({'in_features': 768, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
(3): Dense({'in_features': 256, 'out_features': 32, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
(4): Dense({'in_features': 32, 'out_features': 3, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
onlplab/alephbert-base
|
onlplab
| 2022-06-26T09:32:47Z | 65,559 | 17 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"language model",
"he",
"dataset:oscar",
"dataset:wikipedia",
"dataset:twitter",
"arxiv:1810.04805",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language:
- he
tags:
- language model
license: apache-2.0
datasets:
- oscar
- wikipedia
- twitter
---
# AlephBERT
## Hebrew Language Model
State-of-the-art language model for Hebrew.
Based on Google's BERT architecture [(Devlin et al. 2018)](https://arxiv.org/abs/1810.04805).
#### How to use
```python
from transformers import BertModel, BertTokenizerFast
alephbert_tokenizer = BertTokenizerFast.from_pretrained('onlplab/alephbert-base')
alephbert = BertModel.from_pretrained('onlplab/alephbert-base')
# if not finetuning - disable dropout
alephbert.eval()
```
## Training data
1. OSCAR [(Ortiz, 2019)](https://oscar-corpus.com/) Hebrew section (10 GB text, 20 million sentences).
2. Hebrew dump of [Wikipedia](https://dumps.wikimedia.org/hewiki/latest/) (650 MB text, 3 million sentences).
3. Hebrew Tweets collected from the Twitter sample stream (7 GB text, 70 million sentences).
## Training procedure
Trained on a DGX machine (8 V100 GPUs) using the standard huggingface training procedure.
Since the larger part of our training data is based on tweets we decided to start by optimizing using Masked Language Model loss only.
To optimize training time we split the data into 4 sections based on max number of tokens:
1. num tokens < 32 (70M sentences)
2. 32 <= num tokens < 64 (12M sentences)
3. 64 <= num tokens < 128 (10M sentences)
4. 128 <= num tokens < 512 (1.5M sentences)
Each section was first trained for 5 epochs with an initial learning rate set to 1e-4. Then each section was trained for another 5 epochs with an initial learning rate set to 1e-5, for a total of 10 epochs.
Total training time was 8 days.
|
romainlhardy/roberta-large-finetuned-ner
|
romainlhardy
| 2022-06-26T09:20:58Z | 122 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-26T08:07:48Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-large-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9476811355009077
- name: Recall
type: recall
value: 0.9663412992258499
- name: F1
type: f1
value: 0.9569202566452795
- name: Accuracy
type: accuracy
value: 0.990656929827253
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-finetuned-ner
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0495
- Precision: 0.9477
- Recall: 0.9663
- F1: 0.9569
- Accuracy: 0.9907
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.078 | 1.0 | 1756 | 0.0577 | 0.9246 | 0.9536 | 0.9389 | 0.9865 |
| 0.0382 | 2.0 | 3512 | 0.0528 | 0.9414 | 0.9620 | 0.9516 | 0.9890 |
| 0.021 | 3.0 | 5268 | 0.0495 | 0.9477 | 0.9663 | 0.9569 | 0.9907 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
TextCortex/product_description_generator
|
TextCortex
| 2022-06-26T09:20:16Z | 26 | 1 |
transformers
|
[
"transformers",
"gpt_neo",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-26T08:55:26Z |
---
license: mit
---
## TextCortex AI - Product Description Generator - Electronics Model
This is one of our legacy models that was used for generating product descriptions for Electronic products. Because of the inference times, we trained this model on a very small version of the GPT-NEO with 125M parameters.
Due to its small size, we had to train a model for each product category for our users.\
We will be releasing other trained models on other categories soon.
### How to Prompt:
Just give your product name and add 'Product Description:' at the end of it to generate product descriptions.\
Here is an example prompt:\
`Product name: USB Dongle for video capture Product Description:`
### TextCortex API
If you want to generate product descriptions programatically, you can check out our API, hemingwAI at this link: https://textcortex.com/documentation/api
|
vebie91/dqn-SpaceInvadersNoFrameskip-v4-v1.1
|
vebie91
| 2022-06-26T08:48:25Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-26T08:47:44Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 650.00 +/- 154.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga vebie91 -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga vebie91
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 2000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
kidzy/distilbert-base-uncased-finetuned-emotion
|
kidzy
| 2022-06-26T08:19:59Z | 7 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-23T13:17:25Z |
---
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
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9245
- name: F1
type: f1
value: 0.9246037761691881
---
<!-- 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: 0.2240
- Accuracy: 0.9245
- F1: 0.9246
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8521 | 1.0 | 250 | 0.3285 | 0.904 | 0.9017 |
| 0.2546 | 2.0 | 500 | 0.2240 | 0.9245 | 0.9246 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ashhyun/distilbert-base-uncased-finetuned-squad
|
ashhyun
| 2022-06-26T06:25:36Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-26T05:20:17Z |
---
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:
- eval_loss: 1.1563
- eval_runtime: 141.535
- eval_samples_per_second: 76.193
- eval_steps_per_second: 4.762
- epoch: 1.0
- step: 5533
## 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
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
hyan97/distilbert-base-uncased-finetuned-squad
|
hyan97
| 2022-06-26T05:55:35Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-26T03:31:05Z |
---
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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3517
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2094 | 1.0 | 8235 | 1.2174 |
| 0.9515 | 2.0 | 16470 | 1.1923 |
| 0.7687 | 3.0 | 24705 | 1.3517 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
|
romainlhardy/bert-finetuned-ner
|
romainlhardy
| 2022-06-26T04:50:31Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-26T00:21:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9292895994725564
- name: Recall
type: recall
value: 0.9488387748232918
- name: F1
type: f1
value: 0.9389624448330418
- name: Accuracy
type: accuracy
value: 0.9863572143403779
---
<!-- 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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0602
- Precision: 0.9293
- Recall: 0.9488
- F1: 0.9390
- Accuracy: 0.9864
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0827 | 1.0 | 1756 | 0.0639 | 0.9167 | 0.9359 | 0.9262 | 0.9828 |
| 0.0413 | 2.0 | 3512 | 0.0565 | 0.9262 | 0.9465 | 0.9362 | 0.9859 |
| 0.0188 | 3.0 | 5268 | 0.0602 | 0.9293 | 0.9488 | 0.9390 | 0.9864 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
danielcfho/MLAgents-Pyramids
|
danielcfho
| 2022-06-26T02:13:30Z | 15 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2022-06-26T02:11:23Z |
---
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: danielcfho/MLAgents-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
anas-awadalla/opt-125m-squad
|
anas-awadalla
| 2022-06-25T23:56:38Z | 65 | 0 |
transformers
|
[
"transformers",
"pytorch",
"opt",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-19T23:01:14Z |
A facebook/opt-125m model trained on SQUAD for extractive question answering.
To use the model format input in the following manner:
"(Context Text)\nQuestion:(Question Text)\nAnswer:"
|
robertodtg/wav2vec2-large-xls-r-300m-pt-colab
|
robertodtg
| 2022-06-25T21:25:10Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_9_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-24T11:52:02Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_9_0
model-index:
- name: wav2vec2-large-xls-r-300m-pt-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-large-xls-r-300m-pt-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_9_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2975
- Wer: 0.1736
## 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
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 5.179 | 0.49 | 400 | 1.4554 | 0.9349 |
| 0.7545 | 0.98 | 800 | 0.5594 | 0.5174 |
| 0.4485 | 1.47 | 1200 | 0.3964 | 0.3749 |
| 0.4118 | 1.96 | 1600 | 0.3547 | 0.3172 |
| 0.3282 | 2.45 | 2000 | 0.3372 | 0.3061 |
| 0.3199 | 2.94 | 2400 | 0.3466 | 0.2910 |
| 0.2847 | 3.44 | 2800 | 0.3651 | 0.3310 |
| 0.2713 | 3.93 | 3200 | 0.3509 | 0.3016 |
| 0.2414 | 4.42 | 3600 | 0.3451 | 0.2908 |
| 0.2473 | 4.91 | 4000 | 0.3253 | 0.2747 |
| 0.2168 | 5.4 | 4400 | 0.3243 | 0.2680 |
| 0.219 | 5.89 | 4800 | 0.3067 | 0.2540 |
| 0.196 | 6.38 | 5200 | 0.3268 | 0.2824 |
| 0.1934 | 6.87 | 5600 | 0.3252 | 0.2736 |
| 0.1808 | 7.36 | 6000 | 0.3422 | 0.2737 |
| 0.177 | 7.85 | 6400 | 0.3292 | 0.2707 |
| 0.1626 | 8.34 | 6800 | 0.3089 | 0.2524 |
| 0.1605 | 8.83 | 7200 | 0.3062 | 0.2471 |
| 0.1505 | 9.32 | 7600 | 0.3229 | 0.2474 |
| 0.1491 | 9.82 | 8000 | 0.3098 | 0.2491 |
| 0.1433 | 10.31 | 8400 | 0.3449 | 0.2681 |
| 0.1431 | 10.8 | 8800 | 0.3439 | 0.2532 |
| 0.1349 | 11.29 | 9200 | 0.3112 | 0.2413 |
| 0.1236 | 11.78 | 9600 | 0.3248 | 0.2378 |
| 0.1253 | 12.27 | 10000 | 0.3393 | 0.2394 |
| 0.1195 | 12.76 | 10400 | 0.3050 | 0.2336 |
| 0.1194 | 13.25 | 10800 | 0.3494 | 0.2550 |
| 0.1125 | 13.74 | 11200 | 0.3332 | 0.2395 |
| 0.1063 | 14.23 | 11600 | 0.3134 | 0.2365 |
| 0.1044 | 14.72 | 12000 | 0.3101 | 0.2303 |
| 0.0999 | 15.21 | 12400 | 0.3162 | 0.2248 |
| 0.0986 | 15.71 | 12800 | 0.3183 | 0.2260 |
| 0.0958 | 16.2 | 13200 | 0.3300 | 0.2279 |
| 0.0907 | 16.69 | 13600 | 0.3136 | 0.2260 |
| 0.0875 | 17.18 | 14000 | 0.3492 | 0.2203 |
| 0.0823 | 17.67 | 14400 | 0.3214 | 0.2259 |
| 0.0839 | 18.16 | 14800 | 0.3194 | 0.2145 |
| 0.0783 | 18.65 | 15200 | 0.3122 | 0.2180 |
| 0.0789 | 19.14 | 15600 | 0.3158 | 0.2127 |
| 0.0732 | 19.63 | 16000 | 0.3076 | 0.2109 |
| 0.0715 | 20.12 | 16400 | 0.3216 | 0.2150 |
| 0.0649 | 20.61 | 16800 | 0.2958 | 0.2051 |
| 0.0647 | 21.1 | 17200 | 0.3022 | 0.2014 |
| 0.0649 | 21.59 | 17600 | 0.3045 | 0.2033 |
| 0.0621 | 22.09 | 18000 | 0.3194 | 0.2035 |
| 0.0561 | 22.58 | 18400 | 0.3197 | 0.2022 |
| 0.0582 | 23.07 | 18800 | 0.3109 | 0.1978 |
| 0.0533 | 23.56 | 19200 | 0.3121 | 0.1932 |
| 0.0515 | 24.05 | 19600 | 0.3125 | 0.1939 |
| 0.0484 | 24.54 | 20000 | 0.3081 | 0.1908 |
| 0.0485 | 25.03 | 20400 | 0.3042 | 0.1896 |
| 0.0444 | 25.52 | 20800 | 0.3038 | 0.1886 |
| 0.0426 | 26.01 | 21200 | 0.2985 | 0.1868 |
| 0.0415 | 26.5 | 21600 | 0.3066 | 0.1858 |
| 0.0398 | 26.99 | 22000 | 0.3117 | 0.1828 |
| 0.0397 | 27.48 | 22400 | 0.2980 | 0.1795 |
| 0.0394 | 27.97 | 22800 | 0.2950 | 0.1791 |
| 0.0364 | 28.47 | 23200 | 0.3025 | 0.1773 |
| 0.0365 | 28.96 | 23600 | 0.3022 | 0.1747 |
| 0.0376 | 29.45 | 24000 | 0.2978 | 0.1738 |
| 0.0344 | 29.94 | 24400 | 0.2975 | 0.1736 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.0+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
|
cambridgeltl/simctg_writingprompts
|
cambridgeltl
| 2022-06-25T19:21:26Z | 7 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"arxiv:1805.04833",
"arxiv:2202.06417",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-06T20:07:36Z |
This model provides a GPT-2 language model trained with SimCTG on the WritingPrompts benchmark [(Fan et al., 2018)](https://arxiv.org/abs/1805.04833) based on our paper [_A Contrastive Framework for Neural Text Generation_](https://arxiv.org/abs/2202.06417).
We provide a detailed tutorial on how to apply SimCTG and Contrastive Search in our [project repo](https://github.com/yxuansu/SimCTG#4-huggingface-style-tutorials-back-to-top). In the following, we illustrate a brief tutorial on how to use our approach to perform text generation.
## 1. Installation of SimCTG:
```yaml
pip install simctg --upgrade
```
## 2. Initialize SimCTG Model:
```python
import torch
# load SimCTG language model
from simctg.simctggpt import SimCTGGPT
model_name = r'cambridgeltl/simctg_writingprompts'
model = SimCTGGPT(model_name)
model.eval()
tokenizer = model.tokenizer
```
## 3. Prepare the Text Prefix:
```python
prefix_text = r"[ WP ] A kid doodling in a math class accidentally creates the world 's first functional magic circle in centuries . <|endoftext|>"
print ('Prefix is: {}'.format(prefix_text))
tokens = tokenizer.tokenize(prefix_text)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
input_ids = torch.LongTensor(input_ids).view(1,-1)
```
## 4. Generate Text with Contrastive Search:
```python
beam_width, alpha, decoding_len = 5, 0.6, 200
output = model.fast_contrastive_search(input_ids=input_ids, beam_width=beam_width,
alpha=alpha, decoding_len=decoding_len)
print("Output:\n" + 100 * '-')
print(tokenizer.decode(output))
'''
Prefix is: [ WP ] A kid doodling in a math class accidentally creates the world 's first functional magic circle in centuries . <|endoftext|>
Output:
----------------------------------------------------------------------------------------------------
[ WP ] A kid doodling in a math class accidentally creates the world's first functional magic circle in centuries. <|endoftext|> I looked at
the circle, it wasn't there. I couldn't see it, and my eyes were watering from the rain that had fallen over the school, the wind howling through
the windows and making a wispy noise as it passed through the air. `` What is it? '' I asked, trying to find the source of the noise. `` It's a
circle, '' the teacher said in a voice that sounded like it was from an old TV show or something like that. `` You can't make it out of there. ''
I looked around the room, there was no one there. It was as if I was in a dream, but no one seemed to notice me. Then I saw a flash of light, and
the circle appeared in front of me. I turned around to see what was going on, I had never seen anything like it before in my life. I ran up to the
teacher and asked, `` Are you sure this is real?
'''
```
For more details of our work, please refer to our main [project repo](https://github.com/yxuansu/SimCTG).
## 5. Citation:
If you find our paper and resources useful, please kindly leave a star and cite our paper. Thanks!
```bibtex
@article{su2022contrastive,
title={A Contrastive Framework for Neural Text Generation},
author={Su, Yixuan and Lan, Tian and Wang, Yan and Yogatama, Dani and Kong, Lingpeng and Collier, Nigel},
journal={arXiv preprint arXiv:2202.06417},
year={2022}
}
```
|
SusBioRes-UBC/dqn-SpaceInvadersNoFrameskip-v4
|
SusBioRes-UBC
| 2022-06-25T19:10:42Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-25T19:10:14Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 241.50 +/- 137.02
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga SusBioRes-UBC -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga SusBioRes-UBC
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
sumedh/lstm-seq2seq
|
sumedh
| 2022-06-25T17:06:30Z | 0 | 0 |
keras
|
[
"keras",
"tensorboard",
"tf-keras",
"seq2seq",
"translation",
"en",
"fr",
"license:apache-2.0",
"region:us"
] |
translation
| 2022-06-21T20:21:20Z |
---
library_name: keras
license: apache-2.0
tags:
- seq2seq
- translation
language:
- en
- fr
---
## Keras Implementation of Character-level recurrent sequence-to-sequence model
This repo contains the model and the notebook [to this Keras example on Character-level recurrent sequence-to-sequence model](https://keras.io/examples/nlp/lstm_seq2seq/).
Full credits to : [fchollet](https://twitter.com/fchollet)
Model reproduced by : [Sumedh](https://huggingface.co/sumedh)
## Intended uses & limitations
This model implements a basic character-level recurrent sequence-to-sequence network for translating short English sentences into short French sentences, character-by-character. Note that it is fairly unusual to do character-level machine translation, as word-level models are more common in this domain. It works best on text of length <= 15 characters.
## Training and evaluation data
English to French translation data from
https://www.manythings.org/anki/
## Training procedure
- We start with input sequences from a domain (e.g. English sentences) and corresponding target sequences from another domain (e.g. French sentences).
- An encoder LSTM turns input sequences to 2 state vectors (we keep the last LSTM state and discard the outputs).
- A decoder LSTM is trained to turn the target sequences into the same sequence but offset by one timestep in the future, a training process called "teacher forcing" in this context. It uses as initial state the state vectors from the encoder. Effectively, the decoder learns to generate targets[t+1...] given targets[...t], conditioned on the input sequence.
- In inference mode, when we want to decode unknown input sequences, we: - Encode the input sequence into state vectors - Start with a target sequence of size 1 (just the start-of-sequence character) - Feed the state vectors and 1-char target sequence to the decoder to produce predictions for the next character - Sample the next character using these predictions (we simply use argmax). - Append the sampled character to the target sequence - Repeat until we generate the end-of-sequence character or we hit the character limit.
### Training hyperparameters
The following hyperparameters were used during training:
| name | learning_rate | decay | rho | momentum | epsilon | centered | training_precision |
|----|-------------|-----|---|--------|-------|--------|------------------|
|RMSprop|0.0010000000474974513|0.0|0.8999999761581421|0.0|1e-07|False|float32|
```python
batch_size = 64 # Batch size for training.
epochs = 100 # Number of epochs to train for.
latent_dim = 256 # Latent dimensionality of the encoding space.
num_samples = 10000 # Number of samples to train on.
```
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
patrickvonplaten/opt_metaseq_6700m
|
patrickvonplaten
| 2022-06-25T15:56:09Z | 8 | 0 |
transformers
|
[
"transformers",
"opt",
"feature-extraction",
"opt_metasq",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-05-10T17:32:24Z |
---
tags:
- opt_metasq
---
# This repo let's you run the following checkpoint using facebookresearch/metaseq.
Do the following:
## 1. Install PyTorch
```
pip3 install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
```
## 2. Install Megatron
```
git clone https://github.com/patrickvonplaten/Megatron-LM.git
cd Megatron-LM
pip3 install six regex
pip3 install -e .
```
## 3. Install fairscale
```
git clone https://github.com/facebookresearch/fairscale.git
cd fairscale
git checkout prefetch_fsdp_params_simple
pip3 install -e .
```
## 4. Install metaseq
```
git clone https://github.com/patrickvonplaten/metaseq.git
cd metaseq
pip3 install -e .
```
## 5. Clone this repo (click top right on "How to clone")
## 6. Run the following:
```bash
cd <path/to/cloned/repo>
bash run.sh
```
|
domenicrosati/deberta-v3-large-finetuned-DAGPap22
|
domenicrosati
| 2022-06-25T15:42:46Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-25T02:26:36Z |
---
license: mit
tags:
- text-classification
- generated_from_trainer
model-index:
- name: deberta-v3-large-finetuned-DAGPap22
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. -->
# deberta-v3-large-finetuned-DAGPap22
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 20
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
bousejin/xlm-roberta-base-finetuned-panx-de-fr
|
bousejin
| 2022-06-25T15:06:04Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-25T05:23:24Z |
---
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: 0.1631
- F1: 0.8579
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2878 | 1.0 | 715 | 0.1840 | 0.8247 |
| 0.1456 | 2.0 | 1430 | 0.1596 | 0.8473 |
| 0.0925 | 3.0 | 2145 | 0.1631 | 0.8579 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
NikitaErmolaev/dqn-SpaceInvadersNoFrameskip-v4
|
NikitaErmolaev
| 2022-06-25T12:19:51Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-25T12:19:14Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 598.00 +/- 147.67
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga NikitaErmolaev -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga NikitaErmolaev
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
KoboldAI/fairseq-dense-2.7B-Nerys
|
KoboldAI
| 2022-06-25T11:23:23Z | 153 | 10 |
transformers
|
[
"transformers",
"pytorch",
"xglm",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-13T13:40:07Z |
---
language: en
license: mit
---
# Fairseq-dense 2.7B - Nerys
## Model Description
Fairseq-dense 2.7B-Nerys is a finetune created using Fairseq's MoE dense model.
## Training data
The training data contains around 2500 ebooks in various genres (the "Pike" dataset), a CYOA dataset called "CYS" and 50 Asian "Light Novels" (the "Manga-v1" dataset).
Most parts of the dataset have been prepended using the following text: `[Genre: <genre1>, <genre2>]`
### How to use
You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
```py
>>> from transformers import pipeline
>>> generator = pipeline('text-generation', model='KoboldAI/fairseq-dense-2.7B-Nerys')
>>> generator("Welcome Captain Janeway, I apologize for the delay.", do_sample=True, min_length=50)
[{'generated_text': 'Welcome Captain Janeway, I apologize for the delay."\nIt's all right," Janeway said. "I'm certain that you're doing your best to keep me informed of what\'s going on."'}]
```
### Limitations and Biases
Based on known problems with NLP technology, potential relevant factors include bias (gender, profession, race and religion).
### BibTeX entry and citation info
```
Artetxe et al. (2021): Efficient Large Scale Language Modeling with Mixtures of Experts
```
|
ubermenchh/ppo-LunarLander-v2
|
ubermenchh
| 2022-06-25T09:38:19Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-21T19:29:57Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 117.65 +/- 104.03
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
QuickSilver007/MLAgents-Pyramids
|
QuickSilver007
| 2022-06-25T09:30:46Z | 9 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2022-06-25T09:30:41Z |
---
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: QuickSilver007/MLAgents-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
gdario/distilbert-base-uncased-finetuned-emotion
|
gdario
| 2022-06-25T09:24:49Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-06T08:25:21Z |
---
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
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8955
- name: F1
type: f1
value: 0.8918003951340884
---
<!-- 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: 0.3662
- Accuracy: 0.8955
- F1: 0.8918
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 125 | 0.5675 | 0.8265 | 0.8067 |
| 0.7565 | 2.0 | 250 | 0.3662 | 0.8955 | 0.8918 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
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Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.