modelId
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81
| tags
list | pipeline_tag
stringclasses 17
values | config
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int64 0
59.7M
| first_commit
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|
|---|---|---|---|---|---|---|
Dawit/DialogGPT-small-ironman
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
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"task_specific_params": {
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},
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}
}
}
| 7
| null |
---
tags:
- mteb
model-index:
- name: all-mpnet-base-v2-negation
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 72.6268656716418
- type: ap
value: 36.40585820220466
- type: f1
value: 67.06383995428979
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 85.11834999999999
- type: ap
value: 79.72843246428603
- type: f1
value: 85.08938287851875
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 37.788000000000004
- type: f1
value: 37.40475118737949
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 45.73138953773995
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 39.13609863309245
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 65.56639026991134
- type: mrr
value: 77.8122938926263
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 72.27098152643569
- type: cos_sim_spearman
value: 71.13475338373253
- type: euclidean_pearson
value: 70.48545151074218
- type: euclidean_spearman
value: 69.49917394727082
- type: manhattan_pearson
value: 69.2653740752147
- type: manhattan_spearman
value: 68.59192435931085
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.7012987012987
- type: f1
value: 84.61766470772943
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 37.61314886948818
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 34.496442588205205
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 45.63
- type: f1
value: 40.24119129248194
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 74.73479999999999
- type: ap
value: 68.80435332319863
- type: f1
value: 74.66014345440416
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.06429548563612
- type: f1
value: 92.91686969560733
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 78.19197446420428
- type: f1
value: 61.50020940946492
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 73.86684599865502
- type: f1
value: 72.11245795864379
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.53866845998655
- type: f1
value: 77.51746806908895
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 33.66744884855605
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 31.951900966550262
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 29.34485636178124
- type: mrr
value: 30.118035109577022
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 47.14306531904168
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 51.59878183893005
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 78.5530506834234
- type: cos_sim_spearman
value: 77.45787185404667
- type: euclidean_pearson
value: 76.37727601604011
- type: euclidean_spearman
value: 77.14250754925013
- type: manhattan_pearson
value: 75.85855462882735
- type: manhattan_spearman
value: 76.6223895689777
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 83.1019526956277
- type: cos_sim_spearman
value: 72.98362332123834
- type: euclidean_pearson
value: 78.42992808997602
- type: euclidean_spearman
value: 70.79569301491145
- type: manhattan_pearson
value: 77.96413528436207
- type: manhattan_spearman
value: 70.34707852104586
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 85.09200805966644
- type: cos_sim_spearman
value: 85.52497834636847
- type: euclidean_pearson
value: 84.20407512505086
- type: euclidean_spearman
value: 85.35640946044332
- type: manhattan_pearson
value: 83.79425758102826
- type: manhattan_spearman
value: 84.9531731481683
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
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value: 82.43419245577238
- type: cos_sim_spearman
value: 79.87215923164575
- type: euclidean_pearson
value: 80.99628882719712
- type: euclidean_spearman
value: 79.2671186335978
- type: manhattan_pearson
value: 80.47076166661054
- type: manhattan_spearman
value: 78.82329686631051
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 84.67294508915346
- type: cos_sim_spearman
value: 85.34528695616378
- type: euclidean_pearson
value: 83.65270617275111
- type: euclidean_spearman
value: 84.64456096952591
- type: manhattan_pearson
value: 83.26416114783083
- type: manhattan_spearman
value: 84.26944094512996
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 80.70172607906416
- type: cos_sim_spearman
value: 81.96031310316046
- type: euclidean_pearson
value: 82.34820192315314
- type: euclidean_spearman
value: 82.72576940549405
- type: manhattan_pearson
value: 81.93093910116202
- type: manhattan_spearman
value: 82.25431799152639
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
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value: 90.43640731744911
- type: cos_sim_spearman
value: 90.16343998541602
- type: euclidean_pearson
value: 89.49834342254633
- type: euclidean_spearman
value: 90.17304989919288
- type: manhattan_pearson
value: 89.32424382015218
- type: manhattan_spearman
value: 89.91884845996768
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 62.06205206393254
- type: cos_sim_spearman
value: 60.920792876665885
- type: euclidean_pearson
value: 60.49188637403393
- type: euclidean_spearman
value: 60.73500415357452
- type: manhattan_pearson
value: 59.94692152491976
- type: manhattan_spearman
value: 60.215426858338994
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
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value: 84.78948820087687
- type: cos_sim_spearman
value: 84.64531509697663
- type: euclidean_pearson
value: 84.77264321816324
- type: euclidean_spearman
value: 84.67485410196043
- type: manhattan_pearson
value: 84.43100272264775
- type: manhattan_spearman
value: 84.29254033404217
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
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value: 88.39411601972704
- type: mrr
value: 96.49192583016112
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
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value: 99.55445544554455
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value: 84.82462858434408
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value: 76.11464968152866
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value: 81.10859728506787
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value: 71.7
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value: 80.97278220281665
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value: 72.2914669223394
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value: 69.42909760589319
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value: 75.4
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value: 85.21566333946467
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value: 76.60239708181345
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value: 73.5
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value: 84.49960192851891
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value: 75.9681697612732
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value: 80.90395480225989
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value: 71.6
- type: max_accuracy
value: 99.56138613861386
- type: max_ap
value: 85.21566333946467
- type: max_f1
value: 76.60239708181345
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
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value: 49.33929838947165
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
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value: 31.523973661953686
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
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value: 52.22408767861519
- type: mrr
value: 53.16279921059333
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
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value: 28.128173244098726
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- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
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value: 67.6684
- type: ap
value: 12.681984793717413
- type: f1
value: 51.97637585601529
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
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value: 58.44086021505377
- type: f1
value: 58.68058329615692
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
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value: 44.226944341054015
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
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- type: cos_sim_f1
value: 70.42042042042041
- type: cos_sim_precision
value: 66.96811042360781
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value: 58.97488258117215
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- task:
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dataset:
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name: MTEB TwitterURLCorpus
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split: test
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value: 88.23883261536074
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value: 76.74366281685916
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value: 78.78811210348013
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value: 88.43870066363954
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value: 84.77197321507954
- type: max_f1
value: 76.91440595175472
---
|
Daymarebait/Discord_BOT_RICK
|
[
"conversational"
] |
conversational
|
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| 3
| null |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** 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-SnowballTarget
2. Step 1: Find your model_id: cleth/ppo-SnowballTarget1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Dazai/Ko
|
[] | null |
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| 0
| null |
---
license: mit
language:
- pl
tags:
- llama
- alpaca
- lora
- self-instruct
---
This repo contains a low-rank adapter for LLaMA-7B trained on generated (not translated!) 55125 [instructions](https://huggingface.co/datasets/chrisociepa/raw-self-generated-instructions-pl) in Polish.
The training took almost 16 hours on a single RTX 4090 with the following hyperparameters:
* Epochs: 10 (global steps: 4300)
* batch_size: 128
* micro_batch_size: 8
* learning_rate: 0.0001
* cutoff_len: 2048 (effectively no cut off!)
* lora_r: 8
* lora_alpha: 16
* lora_dropout: 0.05
* lora_target_modules: ['q_proj', 'k_proj', 'v_proj', 'o_proj']
* train_on_inputs: True
* group_by_length: True
This adapter allows the model to speak Polish more accurate than [Alpaca-Lora-7B](https://huggingface.co/tloen/alpaca-lora-7b).
Instructions for running it can be found at https://github.com/tloen/alpaca-lora.
## Examples
* **Instruction:** `Jaki jest najwyższy szczyt w Polsce?`
Alpaca PL: `Najwyższy szczyt w Polsce to Rysy, osiągający wysokość 2499 m n.p.m.`
Alpaca-Lora-7B: `W Polsce najwyższym szczytem jest Snieżnik o wysokości 1655 m n.p.m.`
* **Instruction:** `Przetłumacz na angielski: Kiedy byłem małym chłopcem, lubiłem grać w piłkę nożną. Teraz jestem dorosły i lubie biegać.`
Alpaca PL: `When I was a little boy, I used to play soccer. Now I'm an adult and I like running.`
Alpaca-Lora-7B: `When I was a small boy, I loved playing soccer. Now I am an adult and I love running.`
* **Instruction:** `Przetłumacz zdanie na język polski: We have a powerful tool like LLM but are still looking for equally powerful methods to control it.`
Alpaca PL: `Tłumaczenie zdania na język polski: "Mamy silny narzędzie takie jak LLM, ale jeszcze nie znaleźliśmy równie silnych metod, aby go kontrolować."`
Alpaca-Lora-7B: `When I was a small boy, I loved playing soccer. Now I am an adult and I love running.`
* **Instruction:** `Co można robić w wolnym czasie?`
Alpaca PL: `W wolnym czasie można zrobić wiele rzeczy, takich jak czytać książki, słuchać muzyki, oglądać filmy lub grać w gry planszowe. Można również spędzać czas z rodziną lub przyjaciółmi.`
Alpaca-Lora-7B: `Co można robić w wolnym czasie? W wolnym czasie można rozmawiać z rodziną, czytać książki, czytać gazetę, czytać w internecie, grać w karty, grać w gry komputerowe, spędzać czas z przyjaciółmi, czytać książki, czytać gazetę, czytać w internecie, grać w karty, grać w gry komputerowe, spędzać czas z przy`
* **Instruction:** `Wytłumacz 5 latkowi co to jest fizyka kwantowa`
Alpaca PL: `Fizyka kwantowa to dziedzina nauki, która zajmuje się badaniem zjawisk zachodzących na poziomie kwantowym, czyli na poziomie atomów i cząstek. Jest to dziedzina nauki, która ma ogromny wpływ na wiele dziedzin nauki, takich jak fizyka, chemia, medycyna i inżynieria.`
Alpaca-Lora-7B: `Quantum physics is the study of the behavior of matter and energy at the atomic and subatomic levels. It is based on the principles of quantum mechanics, which describe the behavior of particles and waves. Quantum physics is used to explain phenomena such as superposition, entanglement, and tunneling.`
* **Instruction:** `Na czym polega faktoryzacja liczby?`
Alpaca PL: `Faktoryzacja to proces dzielenia liczby na mniejsze liczby, takie jak 12 = 3 * 4.`
Alpaca-Lora-7B: `Faktoryzacja jest procesem obliczeniowym polegającym na rozdzieleniu liczby na jej mniejsze części.`
* **Instruction:** `Jaki jest wpływ palenia papierosów na zdrowie człowieka?`
Alpaca PL: `Palenie papierosów ma negatywny wpływ na zdrowie człowieka, powodując choroby takie jak raka płuc, choroby serca i cukrzycę typu 2.`
Alpaca-Lora-7B: `Palenie papierosów może mieć negatywny wpływ na zdrowie człowieka. Palenie papierosów powoduje zwiększenie ryzyka wystąpienia chorób układu oddechowego, takich jak raka płuc, choroby układu krążkowego, chorób serca, chorób układu krwionośnego, chorób układu nerwowego i chorób układu pokarmowego. Palenie papierosów powoduje również zwięks`
* **Instruction:** `Jakie jest znaczenie Protistów?`
Alpaca PL: `Protisty to grupa organizmów, które nie należą do zwierząt ani roślin. Są to organizmy jednokomórkowe lub wielokomórkowe, które żyją w różnych środowiskach, w tym w wodzie, na ziemi i w powietrzu.`
Alpaca-Lora-7B: `Protistów znaczenie to "przeciwstawnych".`
* **Instruction:** `Dokończ zdanie: W Szczebrzeszynie chrząszcz brzmi w ____`
Alpaca PL: `W Szczebrzeszynie chrząszcz brzmi w lesie.`
Alpaca-Lora-7B: `W Szczebrzeszynie chrząszcz brzmi w gwizdku.`
|
Dazai/Ok
|
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| 0
| null |
คุณกำลังสงสัยอยู่ใช่หรือไม่ว่าสล็อตคืออะไร ? หากคุณลองค้นหาข้อมูลตามอินเตอร์เน็ตทั่วไปจะพบว่า PG SLOT คือเครื่องเล่นพนันชนิดหนึ่งที่มีลักษณะเป็นตู้สี่เหลี่ยม มีคันโยกคล้ายกับเกียร์รถยนต์เพื่อใช้ในการเริ่มต้นเล่นเกมและที่ตู้สี่เหลี่ยมนี้จะมีหน้าจอคล้ายกับโทรทัศน์จอแก้วเป็นหน้าจอในการแสดงผลของเกม ซึ่งในสมัยก่อนนั้นสัญลักษณ์ในตู้สล็อตแมชชีนมักจะใช้รูปผลไม้เพียงแค่ไม่กี่รูปภาพเท่านั้น โดยที่เราจะพบเห็นตู้สล็อตแมชชีนตามบ่อนคาสิโน สถานบันเทิง ผับ บาร์และร้านอาหารต่าง ๆ แต่ในปัจจุบันสล็อตได้กลายเป็นเกมสล็อตออนไลน์เกมเดิมพันออนไลน์แบบใหม่ที่กำลังได้รับความนิยมจากนักเดิมพันกว่าล้านคน
หลังจากที่ยุคของตู้เกม Slot Machine เริ่มได้รับความนิยมลดน้อยลงเพราะมีเกมออนไลน์เข้ามาแทน ไม่ว่าจะเป็นเกมยิงปลาหรือเกมคาสิโนออนไลน์ซึ่งเป็นเกมการเดิมพันออนไลน์ที่นักเดิมพันสามารถเข้าเล่นได้ทุกที่ ทุกเวลา ซึ่งเกมเหล่านี้อำนวยความสะดวกให้แก่นักเดิมพันมากกว่าการต้องออกไปเล่นเกมที่ตู้เกมสล็อตแมชชีน จนในที่สุดตู้เกมสล็อตแมชชีนก็หายไปจากโลกของเกมเดิมพัน แต่ด้วยเอกลักษณ์ของเกมสล็อตก็ทำให้เกิดเกมสล็อตออนไลน์ขึ้นแทนที่เกมสล็อตแมชชีน
และเกมสล็อตออนไลน์ก็ได้รับความนิยมเพิ่มมากขึ้นเรื่อย ๆ จนแซงหน้าเกมยิงปลาและเกมคาสิโนออนไลน์ เพราะด้วยรูปแบบของเกมที่เล่นง่ายและยังคงเอาไว้ซึ่งเอกลักษณ์ของเกมสล็อตแมชชีน และเมื่อเทคโนโลยีต่าง ๆ ได้พัฒนาขึ้นอย่างรวดเร็วเกมสล็อตออนไลน์ก็ได้ถูกพัฒนาให้อยู่ในรูปแบบของเกมสามมิติที่มีความสวยงามโดดเด่นน่าสนใจเพิ่มมากขึ้นและมีค่ายเกมผู้ผลิตเกมสล็อตออนไลน์ออกมาสู่โลกของเกมเดิมพันออนไลน์มากมายหลายค่ายเกม
<p>► <a href="https://golden678.com/" rel="noopener nofollow">ทดลองเล่นฟรี</a></p>
ซึ่งค่ายเกม PGSLOT ก็คือหนึ่งในค่ายเกมผู้ผลิตเกมสล็อตออนไลน์ชั้นนำที่ได้รับความนิยมจากนักเดิมพันจำนวนมากเช่นกัน ซึ่งในปัจจุบันก็ยังมีเว็บไซต์ผู้ให้บริการเกมสล็อตออนไลน์ที่ได้นำเอาเกมจากค่ายเกม PGSLOT มาให้บริการแก่นักเดิมพันที่ชื่นชอบการเล่นเกมเดิมพันออนไลน์และให้ความสนใจในเกมสล็อตออนไลน์เป็นพิเศษ
เว็บไซต์ PGSLOT.TO เป็นอีกหนึ่งค่ายเกมที่มีเกมสล็อตออนไลน์ยอดนิยมจากค่ายเกม PGSLOT ให้บริการแก่นักเดิมพันมากกว่า 250 เกม แต่ละเกมมาพร้อมกับความสนุกสนานและความหลากหลายของเกมสล็อตที่มีความแตกต่างกันให้นักเดิมพันได้เลือกเล่นและลงเดิมพันในเกมสล็อตที่ชื่นชอบได้มากมายหลายเกมด้วย เราจะยกตัวอย่างเกมสล็อตยอดนิยมจากของเว็บไซต์ของเรา เช่น เกม Candy Bonanza สล็อตทดลองเล่น PG ที่มีสีสันสวยงามสดใส น่าสนใจด้วยลูกกวาด และลูกอมชนิดต่าง ๆ ที่มีความโดดเด่นสะดุดตา
นอกจากเกมสล็อตเกมนี้จะมีธีมเกมเป็นที่น่าสนใจแล้ว ภายในเกมก็ยังมีสัญลักษณ์ต่าง ๆ เป็นตัวคูณเงินรางวัลให้แก่ผู้เล่นมีโอกาสชนะเกมด้วยเงินรางวัลสูงสุด รวมทั้งโบนัสของเกมซึ่งโบนัสของเกมสล็อต Candy Bonanza คือเงินรางวัลจากการหมุนเกมฟรีสูงสุด 10 รอบ ซึ่งนอกจากนี้หากผู้เล่นสามารถหมุนวงล้อของเกมให้ปรากฏสัญลักษณ์รูปแบบเดียวกันเรียงกันบนวงล้อครบ 4 สัญลักษณ์ (สัญลักษณ์ซองลูกกวาด) ผู้เล่นจะมีโอกาสได้รับโบนัสการหมุนเกมฟรีเพิ่มอีก 1 รอบ ทำให้มีโอกาสชนะเกมด้วยเงินรางวัลสูงสุดของเกมได้ง่ายยิ่งขึ้น ไม่เพียงเกมสล็อต Candy Bonanza เท่านั้น เพราะที่เว็บไซต์ PGSLOT.TO มีเกมสล็อตจากค่าย PGSLOT ให้คุณเลือกเล่นอีกมากมาย
|
DecafNosebleed/DialoGPT-small-ScaraBot
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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| 15
| null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: HASAN55/bert-finetuned-for-uncased
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. -->
# HASAN55/bert-finetuned-for-uncased
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.0058
- Train End Logits Accuracy: 0.7259
- Train Start Logits Accuracy: 0.6833
- Validation Loss: 0.9945
- Validation End Logits Accuracy: 0.7254
- Validation Start Logits Accuracy: 0.6994
- 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': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 11064, '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: mixed_float16
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 1.0058 | 0.7259 | 0.6833 | 0.9945 | 0.7254 | 0.6994 | 0 |
### Framework versions
- Transformers 4.27.4
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.3
|
DecafNosebleed/scarabot-model
|
[
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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}
| 6
| null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 504.00 +/- 100.12
name: mean_reward
verified: false
---
# **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
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga FranEnguix -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga FranEnguix -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga FranEnguix
```
## 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', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Declan/Breitbart_model_v4
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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}
| 3
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: spam-classifier
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. -->
# spam-classifier
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: 0.0539
- Accuracy: 0.9914
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 262 | 0.0424 | 0.9899 |
| 0.053 | 2.0 | 524 | 0.0473 | 0.9914 |
| 0.053 | 3.0 | 786 | 0.0497 | 0.9907 |
| 0.0062 | 4.0 | 1048 | 0.0522 | 0.9914 |
| 0.0062 | 5.0 | 1310 | 0.0539 | 0.9914 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Declan/Breitbart_model_v5
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
"translation_en_to_fr": {
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}
}
| 3
| null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: TestZee/mt5-small-finetuned-mt5-Large-English-Test
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. -->
# TestZee/mt5-small-finetuned-mt5-Large-English-Test
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.0827
- Validation Loss: 2.5454
- 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 3512, '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: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 9.1600 | 3.1254 | 0 |
| 5.3014 | 2.7890 | 1 |
| 4.6548 | 2.6661 | 2 |
| 4.3133 | 2.5973 | 3 |
| 4.0827 | 2.5454 | 4 |
### Framework versions
- Transformers 4.27.4
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Declan/Breitbart_model_v7
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
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},
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}
}
| 5
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERT_ep5_lr2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERT_ep5_lr2
This model is a fine-tuned version of [ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT](https://huggingface.co/ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0922
- Precision: 0.8259
- Recall: 0.8778
- F1: 0.8511
- Accuracy: 0.9740
## 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-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
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 467 | 0.0878 | 0.7978 | 0.8576 | 0.8266 | 0.9708 |
| 0.1112 | 2.0 | 934 | 0.0861 | 0.8072 | 0.8780 | 0.8411 | 0.9718 |
| 0.0746 | 3.0 | 1401 | 0.0867 | 0.8212 | 0.8756 | 0.8475 | 0.9736 |
| 0.0586 | 4.0 | 1868 | 0.0901 | 0.8239 | 0.8780 | 0.8501 | 0.9737 |
| 0.0539 | 5.0 | 2335 | 0.0922 | 0.8259 | 0.8778 | 0.8511 | 0.9740 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Declan/Breitbart_model_v8
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"translation_en_to_fr": {
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}
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}
| 3
| null |
---
tags:
- bert
- adapter-transformers
- adapterhub:reginaboateng/cleaned_pubmedqa
datasets:
- pubmedqa
---
# Adapter `reginaboateng/pubmedqa-adapter` for allenai/scibert_scivocab_uncased
An [adapter](https://adapterhub.ml) for the `allenai/scibert_scivocab_uncased` model that was trained on the [reginaboateng/cleaned_pubmedqa](https://adapterhub.ml/explore/reginaboateng/cleaned_pubmedqa/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("allenai/scibert_scivocab_uncased")
adapter_name = model.load_adapter("reginaboateng/pubmedqa-adapter", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
Declan/Breitbart_modelv7
|
[] | null |
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}
| 0
| null |
---
license: creativeml-openrail-m
base_model: /root/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - raven316/pokemon-lora
These are LoRA adaption weights for /root/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following.
|
Declan/CNN_model_v3
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 3
| null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-samsum-ElectrifAi_v12
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. -->
# bart-large-cnn-samsum-ElectrifAi_v12
This model is a fine-tuned version of [philschmid/bart-large-cnn-samsum](https://huggingface.co/philschmid/bart-large-cnn-samsum) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9357
- Rouge1: 52.8096
- Rouge2: 30.6135
- Rougel: 40.9225
- Rougelsum: 48.0235
- Gen Len: 80.6
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 11 | 2.1692 | 50.8653 | 25.2603 | 37.6053 | 46.5156 | 86.0 |
| No log | 2.0 | 22 | 1.9852 | 49.1764 | 25.9935 | 38.3519 | 44.5786 | 75.4 |
| No log | 3.0 | 33 | 1.9357 | 52.8096 | 30.6135 | 40.9225 | 48.0235 | 80.6 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Declan/CNN_model_v4
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
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"no_repeat_ngram_size": null,
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},
"text-generation": {
"do_sample": null,
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
| 3
| null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: ec-biogpt-noised-pubmed-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ec-biogpt-noised-pubmed-v2
This model is a fine-tuned version of [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2703
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.1503 | 0.11 | 500 | 1.3369 |
| 1.3766 | 0.21 | 1000 | 1.2721 |
| 1.3523 | 0.32 | 1500 | 1.2516 |
| 1.3123 | 0.43 | 2000 | 1.2394 |
| 1.1954 | 0.54 | 2500 | 1.2265 |
| 1.226 | 0.64 | 3000 | 1.2182 |
| 1.1269 | 0.75 | 3500 | 1.2118 |
| 1.212 | 0.86 | 4000 | 1.2053 |
| 1.3253 | 0.96 | 4500 | 1.1984 |
| 1.0722 | 1.07 | 5000 | 1.2016 |
| 1.1208 | 1.18 | 5500 | 1.2009 |
| 1.132 | 1.28 | 6000 | 1.1992 |
| 1.1228 | 1.39 | 6500 | 1.1967 |
| 1.1529 | 1.5 | 7000 | 1.1918 |
| 1.0342 | 1.61 | 7500 | 1.1916 |
| 1.0881 | 1.71 | 8000 | 1.1889 |
| 1.084 | 1.82 | 8500 | 1.1852 |
| 1.1409 | 1.93 | 9000 | 1.1807 |
| 0.9794 | 2.03 | 9500 | 1.2098 |
| 0.9821 | 2.14 | 10000 | 1.2146 |
| 0.9695 | 2.25 | 10500 | 1.2096 |
| 0.9866 | 2.35 | 11000 | 1.2088 |
| 1.0305 | 2.46 | 11500 | 1.2059 |
| 0.9532 | 2.57 | 12000 | 1.2060 |
| 0.9978 | 2.68 | 12500 | 1.2041 |
| 1.0013 | 2.78 | 13000 | 1.2006 |
| 1.0401 | 2.89 | 13500 | 1.2023 |
| 1.0899 | 3.0 | 14000 | 1.1988 |
| 0.8229 | 3.1 | 14500 | 1.2410 |
| 0.8598 | 3.21 | 15000 | 1.2420 |
| 0.9295 | 3.32 | 15500 | 1.2414 |
| 0.8477 | 3.43 | 16000 | 1.2386 |
| 0.9302 | 3.53 | 16500 | 1.2382 |
| 0.8284 | 3.64 | 17000 | 1.2374 |
| 0.8242 | 3.75 | 17500 | 1.2410 |
| 0.8422 | 3.85 | 18000 | 1.2346 |
| 0.8742 | 3.96 | 18500 | 1.2362 |
| 0.798 | 4.07 | 19000 | 1.2667 |
| 0.7821 | 4.17 | 19500 | 1.2701 |
| 0.7788 | 4.28 | 20000 | 1.2714 |
| 0.7701 | 4.39 | 20500 | 1.2702 |
| 0.7348 | 4.5 | 21000 | 1.2722 |
| 0.762 | 4.6 | 21500 | 1.2705 |
| 0.7385 | 4.71 | 22000 | 1.2705 |
| 0.7837 | 4.82 | 22500 | 1.2695 |
| 0.8371 | 4.92 | 23000 | 1.2703 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Declan/CNN_model_v8
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
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}
| 3
| null |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: photo of a sks plushy
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- svdiff
inference: true
---
# SVDiff-pytorch - mshing/svdiff_kumamon_example
These are SVDiff weights for runwayml/stable-diffusion-v1-5. The weights were trained on photo of a sks plushy using [DreamBooth](https://dreambooth.github.io/).
|
Declan/ChicagoTribune_model_v4
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
| 7
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: my_awesome_billsum_model
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: billsum
type: billsum
config: default
split: ca_test
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.1368
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_billsum_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5366
- Rouge1: 0.1368
- Rouge2: 0.0483
- Rougel: 0.1123
- Rougelsum: 0.1123
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 62 | 2.8280 | 0.1322 | 0.0403 | 0.1094 | 0.1095 | 19.0 |
| No log | 2.0 | 124 | 2.6181 | 0.1371 | 0.0468 | 0.1121 | 0.1118 | 19.0 |
| No log | 3.0 | 186 | 2.5548 | 0.1376 | 0.0484 | 0.1129 | 0.1129 | 19.0 |
| No log | 4.0 | 248 | 2.5366 | 0.1368 | 0.0483 | 0.1123 | 0.1123 | 19.0 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Declan/ChicagoTribune_model_v6
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 5
| null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 608.00 +/- 131.50
name: mean_reward
verified: false
---
# **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
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga justinsiow -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga justinsiow -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga justinsiow
```
## 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', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Declan/ChicagoTribune_model_v7
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 7
| null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: HF_DRL_U4_pixelcopter_reinforcepg_v2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 12.00 +/- 13.55
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Declan/ChicagoTribune_model_v8
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
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},
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}
}
| 7
| null |
---
license: apache-2.0
---
# Introduction
This repo contains torchscript model of `stt_en_conformer_ctc_medium` from NeMo.
See https://registry.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_ctc_medium
The following code is used to obtain `model.onnx` and `tokens.txt`:
```python3
m = nemo_asr.models.EncDecCTCModelBPE.from_pretrained('stt_en_conformer_ctc_medium')
m.export('model.onnx')
with open('tokens.txt', 'w') as f:
for i, s in enumerate(m.decoder.vocabulary):
f.write(f"{s} {i}\n")
f.write(f"<blk> {i+1}\n")
```
|
Declan/FoxNews_model_v1
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
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}
}
}
| 3
| null |
---
tags:
- adapter-transformers
- bert
- adapterhub:biomedical/pubmedqa
datasets:
- pubmedqa
---
# Adapter `reginaboateng/pubmedqa_adapter` for allenai/scibert_scivocab_uncased
An [adapter](https://adapterhub.ml) for the `allenai/scibert_scivocab_uncased` model that was trained on the [biomedical/pubmedqa](https://adapterhub.ml/explore/biomedical/pubmedqa/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("allenai/scibert_scivocab_uncased")
adapter_name = model.load_adapter("reginaboateng/pubmedqa_adapter", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
Declan/FoxNews_model_v4
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
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}
}
}
| 7
| null |
---
license: apache-2.0
---
# Introduction
This repo contains torchscript model of `stt_en_conformer_ctc_large` from NeMo.
See https://registry.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_ctc_large
The following code is used to obtain `model.onnx` and `tokens.txt`:
```python3
m = nemo_asr.models.EncDecCTCModelBPE.from_pretrained('stt_en_conformer_ctc_large')
m.export('model.onnx')
with open('tokens.txt', 'w') as f:
for i, s in enumerate(m.decoder.vocabulary):
f.write(f"{s} {i}\n")
f.write(f"<blk> {i+1}\n")
`
|
Declan/FoxNews_model_v5
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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"prefix": null
}
}
}
| 7
| null |
# Lora模型使用介绍
## Celestine Lucullus
2023/2/25 update
Prune tags to make the description more accurate. Adjust rank to 32 to balance file size and quality.
Introduction
A LORA model of Celestine Lucullus from Kuroinu. The 784mb VAEs (NAI, Orangemix, Anything, Counterfeit) are recommended. 0.6~0.8 weights are good. It‘s recommended to use with any olga discordia LORA to generate high-quality nsfw masterpiece. The preview images are generated using latest version of YesMix.
********
## Adventurers
Thank you all for downloading and reviewing this LoRA! I currently have plans to work on the next version, but there are still many challenges to overcome.
In addition, I found that sometimes certain themes can work better as standalone LoRAs rather than mixed LoRAs. As a pre-practice for the updated version, I have created some standalone LoRAs, including:
Angelic Wairrors
For divine armored characters such as Valkyrie, Priestesses, Paladin, Knight, and Angel.
Not Horny Pirate
For classic pirate.
Norse Viking
For Nordic style Viking Warrior and Berserker.
Non-Hentai Elf
For traditional fantasy elf characters such as Ranger, Warrior, Druid, and Priestess.
Thank you once again for your patience and support!
******
## Hipoly 3D Model
High-poly 3D Model LoRA
This is a LoRA trained on high-polygon 3D model images.
It can provide clean, high-resolution skin and hair materials, as well as detailed clothing elements.
For those interested, I have compiled the technical insights gained during the training of ver.2 in the following article.
https://note.com/takumi__ncr/n/n21016c358ea5
Changes in 2.0
Increased the number of training images
Increased the training resolution (ver.1: 768, ver.2: 896)
Revised the tagging
Removed the trigger word as well
Reviewed the training parameters
Improvements in 2.0
It can reproduce a more 3D-like texture and stereoscopi effect than ver.1
LoRA can be applied without a trigger word
The style can be controlled using 3d and realistic tags
When added to Positive Prompt, it enhances the 3D feel
When added to Negative Prompt, it adds details such as clothing while maintaining the model's art style
The issue of eroding the model's art style with tags such as intricate, detailed has been alleviated
Additional information
The effect of LoRA was confirmed using the following my custom merged models:
Alstroemeria Mix
Bougainvillea Mix
Chrysanthemum Mix
High-poly 3D Model LoRA
ハイポリゴン 3D モデル画像を学習した LoRA です。
3D らしいクリーンで高精細な肌・髪の質感や、衣服のディテールを与えることができます。
ver.2 の学習時に得られた技術的知見をまとめましたので、興味がある方は次の記事をご覧ください。
https://note.com/takumi__ncr/n/n2fb9d265ffa9
2.0 での変更点
教師画像を増やしました
学習解像度を向上しました (ver.1: 768, ver.2: 896)
タグ付けを見直しました
トリガーワードも削除しました
学習パラメータを見直しました
2.0 での改善点
ver.1 よりも 3D らしいテクスチャ感、奥行き感が再現できます
トリガーワードなしで LoRA を適用することが可能です
3d, realistic タグを使用することで絵柄のコントロールが可能です
Positive Prompt に入れた場合、より 3D 感を強化します
Negative Prompt に入れた場合、モデルの絵柄を保ちつつ、衣装などのディテールを追加することができます
intricate, detailed などのタグによってモデルの絵柄を侵食してしまう問題が緩和されました
その他
LoRA の動作確認は以下の自作マージモデルを使用して行いました。
Alstroemeria Mix
Bougainvillea Mix
Chrysanthemum Mix
******
## Thai university
This Lora made for make the White shirt and Black skirt near to female thailand uniform
BASE model train from Chilloutmix ni
Use: black skirt for normal university skirt
but if you want the tight one i suggest black tight skirt or black pencil skirt
|
Declan/HuffPost_model_v2
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
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},
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},
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},
"translation_en_to_fr": {
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}
| 3
| null |
---
license: mit
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: m2m100_418M-english-somali-v2.0
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. -->
# m2m100_418M-english-somali-v2.0
This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9011
- Bleu: 53.2731
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu |
|:-------------:|:-----:|:------:|:---------------:|:-------:|
| 1.224 | 1.0 | 41980 | 1.1302 | 31.5463 |
| 0.913 | 2.0 | 83960 | 0.9315 | 37.3096 |
| 0.6626 | 3.0 | 125940 | 0.8554 | 42.1830 |
| 0.5521 | 4.0 | 167920 | 0.8173 | 45.8223 |
| 0.4004 | 5.0 | 209900 | 0.8154 | 47.8919 |
| 0.3457 | 6.0 | 251880 | 0.8229 | 49.8002 |
| 0.2521 | 7.0 | 293860 | 0.8455 | 51.3589 |
| 0.1889 | 8.0 | 335840 | 0.8653 | 52.3174 |
| 0.1396 | 9.0 | 377820 | 0.8905 | 52.9441 |
| 0.1059 | 10.0 | 419800 | 0.9011 | 53.2731 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.12.1
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Declan/HuffPost_model_v6
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 9
| null |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
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|
Declan/HuffPost_model_v8
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
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}
| 7
| null |
---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- PavelDanek/autotrain-data-s2gsummarize
co2_eq_emissions:
emissions: 15.760105221870123
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 47615116641
- CO2 Emissions (in grams): 15.7601
## Validation Metrics
- Loss: 2.577
- Rouge1: 14.976
- Rouge2: 2.957
- RougeL: 14.679
- RougeLsum: 14.859
- Gen Len: 18.875
## 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/PavelDanek/autotrain-s2gsummarize-47615116641
```
|
Declan/NPR_model_v1
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
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}
| 3
| null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** 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-SoccerTwos
2. Step 1: Write your model_id: marimurta/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Declan/NPR_model_v3
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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}
| 9
| null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: HFRLu4_CartPole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Declan/NPR_model_v5
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 7
| null |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **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: Find your model_id: cleth/ppo-PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Declan/WallStreetJournal_model_v1
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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},
"text-generation": {
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"max_length": null
},
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},
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}
| 3
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: vit-base-patch16-224-finetuned-flower
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. -->
# vit-base-patch16-224-finetuned-flower
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 2.0.0+cu118
- Datasets 2.7.1
- Tokenizers 0.13.3
|
DeskDown/MarianMixFT_en-ms
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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}
| 5
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-fine-tuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5678267214677118
---
<!-- 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-fine-tuned-cola
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8424
- Matthews Correlation: 0.5678
## 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 | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4779 | 1.0 | 1069 | 0.6219 | 0.4808 |
| 0.3375 | 2.0 | 2138 | 0.6739 | 0.5705 |
| 0.1886 | 3.0 | 3207 | 0.8424 | 0.5678 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
DeskDown/MarianMixFT_en-th
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 3
| null |
---
license: creativeml-openrail-m
---
created by https://civitai.com/user/AreThoseLevel4Plates
all credits reserved to the creator
uploaded only for personal use in colab
|
DeskDown/MarianMix_en-ja-10
|
[
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 1
| null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="mfidabel/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"])
```
|
DeskDown/MarianMix_en-zh-10
|
[
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 3
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
- relation-extraction
metrics:
- rouge
model-index:
- name: t5-base-DreamBank-Generation-Act-Char
results: []
language:
- en
inference:
parameters:
max_length: 128
widget:
- text: >-
I was skating on the outdoor ice pond that used to be across the street from
my house. I was not alone, but I did not recognize any of the other people
who were skating around. I went through my whole repertoire of jumps,
spires, and steps-some of which I can do and some of which I'm not yet sure
of. They were all executed flawlessly-some I repeated, some I did only once.
I seemed to know that if I went into competition, I would be sure of coming
in third because there were only three contestants. Up to that time I hadn't
considered it because I hadn't thought I was good enough, but now since
everything was going so well, I decided to enter.
example_title: Dream 1
- text: >-
I was talking on the telephone to the father of an old friend of mine (boy,
21 years old). We were discussing the party the Saturday night before to
which I had invited his son as a guest. I asked him if his son had a good
time at the party. He told me not to tell his son that he had told me, but
that he had had a good time, except he was a little surprised that I had
acted the way I did.
example_title: Dream 2
- text: I was walking alone with my dog in a forest.
example_title: Dream 3
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-DreamBank-Generation-Act-Char
This model is a fine-tuned version of [DReAMy-lib/t5-base-DreamBank-Generation-NER-Char](https://huggingface.co/DReAMy-lib/t5-base-DreamBank-Generation-NER-Char) on the DreamBank dataset.
The uploaded model contains the weights of the best-performing model (see table below), tune to annotate a given
dream report according to [Hall and Van de Castle the Activity feature](https://dreams.ucsc.edu/Coding/activities.html)
## Model description
The model is trained end-to-end using a text2text solution to annotate dream reports following the Activity feature
from the Hall and Van de Castle scoring framework. Given a report, the model generates texts of the form
`(initialiser : activity type : receiver)`. For those cases where `initialiser` and `receiver` are the same
entity, the output will follow the `(initialiser : alone activity type : none)` setting.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log | 1.0 | 49 | 0.3674 | 0.4008 | 0.3122 | 0.3821 | 0.3812 |
| No log | 2.0 | 98 | 0.3200 | 0.4240 | 0.3433 | 0.4130 | 0.4121 |
| No log | 3.0 | 147 | 0.2845 | 0.4591 | 0.3883 | 0.4459 | 0.4455 |
| No log | 4.0 | 196 | 0.2508 | 0.4614 | 0.3930 | 0.4504 | 0.4497 |
| No log | 5.0 | 245 | 0.2632 | 0.4614 | 0.3929 | 0.4467 | 0.4459 |
| No log | 6.0 | 294 | 0.2688 | 0.4706 | 0.4036 | 0.4537 | 0.4534 |
| No log | 7.0 | 343 | 0.2790 | 0.4682 | 0.4043 | 0.4559 | 0.4556 |
| No log | 8.0 | 392 | 0.2895 | 0.4670 | 0.3972 | 0.4529 | 0.4534 |
| No log | 9.0 | 441 | 0.3058 | 0.4708 | 0.4040 | 0.4576 | 0.4572 |
| No log | 10.0 | 490 | 0.3169 | 0.4690 | 0.4001 | 0.4547 | 0.4544 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Despin89/test
|
[] | null |
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}
| 0
| null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Drachenlord-x-Protogen-Dreambooth
Sample prompt:
modelshoot style, (extremely detailed CG unity 8k wallpaper), ((((full body portrait)))) photo of a (((very morbidly obese, fat lumps, wounds, diabetes))) (((drachenlord))), portrait as (((Jesus))), the most beautiful portrait in the world, professional majestic oil painting by Ed Blinkey, Atey Ghailan, Studio Ghibli, by Jeremy Mann, Greg Manchess, Antonio Moro, trending on ArtStation, trending on CGSociety, Intricate, High Detail, Sharp focus, dramatic, photorealistic painting art by midjourney and greg rutkowski
Sample negative prompt: nude, naked, 3d, 3d render, b&w, bad anatomy, bad anatomy, bad anatomy, bad art, bad art, bad proportions, blurry, blurry, blurry, body out of frame, canvas frame, cartoon, cloned face, close up, cross-eye, deformed, deformed, deformed, disfigured, disfigured, disfigured, duplicate, extra arms, extra arms, extra fingers, extra legs, extra legs, extra limbs, extra limbs, extra limbs, extra limbs, fused fingers, gross proportions, long neck, malformed limbs, missing arms, missing legs, morbid, mutated, mutated hands, mutated hands, mutation, mutation, mutilated, out of frame, out of frame, out of frame, Photoshop, poorly drawn face, poorly drawn face, poorly drawn feet, poorly drawn hands, poorly drawn hands, tiling, too many fingers, video game, weird colors, (((headphones, microphone, glasses, bald)))
Sample pictures of this concept:
Model version: Stable Diffusion 1.5
Viel Spaß damit und Meddl On!
|
Devmapall/paraphrase-quora
|
[
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"length_penalty": 2,
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"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
}
| 3
| null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-baseline
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.42 +/- 2.72
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="mfidabel/q-Taxi-v3-baseline", 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"])
```
|
DevsIA/Devs_IA
|
[] | null |
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},
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}
}
}
| 0
| 2023-04-07T15:43:39Z
|
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.44 +/- 0.98
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
|
DheerajPranav/Dialo-GPT-Rick-bot
|
[] | null |
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}
| 0
| null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 63.00 +/- 50.32
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Dilmk2/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
}
| 13
| null |
---
language:
- ru
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Medium Russian
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0 ru
type: mozilla-foundation/common_voice_11_0
config: ru
split: test
args: ru
metrics:
- type: wer
value: 7.562437929892964
name: Wer
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: google/fleurs
type: google/fleurs
config: ru_ru
split: test
metrics:
- type: wer
value: 10.92
name: WER
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Medium Russian
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 ru dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2253
- Wer: 7.5624
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.1578 | 0.1 | 1000 | 0.1662 | 8.8290 |
| 0.045 | 1.08 | 2000 | 0.1748 | 8.9148 |
| 0.0176 | 2.06 | 3000 | 0.1889 | 8.7848 |
| 0.0104 | 3.04 | 4000 | 0.1922 | 8.4354 |
| 0.0051 | 4.02 | 5000 | 0.2034 | 8.1865 |
| 0.0047 | 4.12 | 6000 | 0.2012 | 8.0455 |
| 0.0018 | 5.1 | 7000 | 0.2117 | 7.6237 |
| 0.0004 | 6.08 | 8000 | 0.2177 | 7.6078 |
| 0.0003 | 7.06 | 9000 | 0.2244 | 7.6262 |
| 0.0002 | 8.04 | 10000 | 0.2253 | 7.5624 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.11.1.dev0
- Tokenizers 0.13.2
|
DingleyMaillotUrgell/homer-bot
|
[
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
| 12
| null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxiv3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.76
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Viswes/taxiv3", 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"])
```
|
DongHai/DialoGPT-small-rick
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
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"translation_en_to_fr": {
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}
| 9
| null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 55.70 +/- 45.06
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Waynehillsdev/Waynehills_summary_tensorflow
|
[
"tf",
"t5",
"text2text-generation",
"transformers",
"generated_from_keras_callback",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
"translation_en_to_fr": {
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}
}
}
| 5
| null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: cartpole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 453.90 +/- 138.30
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-12
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"translation_en_to_de": {
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}
}
}
| 29
| 2023-04-07T16:58:23Z
|
---
language:
- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: wav2vec2-common_voice-hi-demo
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: COMMON_VOICE - HI
type: common_voice
config: hi
split: test
args: 'Config: hi, Training split: train+validation, Eval split: test'
metrics:
- name: Wer
type: wer
value: 1.0
---
<!-- 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-common_voice-hi-demo
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - HI dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8769
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 15.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| No log | 10.53 | 100 | 5.0318 | 1.0 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
|
albert-xlarge-v1
|
[
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
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}
}
| 341
| 2023-04-07T17:41:44Z
|
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 16.70 +/- 11.30
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
albert-xxlarge-v1
|
[
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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},
"translation_en_to_fr": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 7,091
| 2023-04-07T17:46:03Z
|
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 402.00 +/- 184.81
name: mean_reward
verified: false
---
# **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
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga PhuocBui -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga PhuocBui -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga PhuocBui
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 10000),
('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', 10000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
albert-xxlarge-v2
|
[
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 42,640
| 2023-04-07T17:47:42Z
|
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** 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-SnowballTarget
2. Step 1: Find your model_id: Viswes/ppo-SnowballTargetTESTCOLAB
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
bert-base-cased-finetuned-mrpc
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 11,644
| 2023-04-07T17:47:47Z
|
---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- billster45/autotrain-data-news_headlines
co2_eq_emissions:
emissions: 0.47291454067852207
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 47662116693
- CO2 Emissions (in grams): 0.4729
## Validation Metrics
- Loss: 0.474
- Accuracy: 0.866
- Macro F1: 0.836
- Micro F1: 0.866
- Weighted F1: 0.865
- Macro Precision: 0.844
- Micro Precision: 0.866
- Weighted Precision: 0.865
- Macro Recall: 0.830
- Micro Recall: 0.866
- Weighted Recall: 0.866
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/billster45/autotrain-news_headlines-47662116693
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("billster45/autotrain-news_headlines-47662116693", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("billster45/autotrain-news_headlines-47662116693", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
bert-base-chinese
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"zh",
"arxiv:1810.04805",
"transformers",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 3,377,486
| 2023-04-07T17:55:00Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-mseva
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-mseva
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) 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: 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: 300
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 2.0.0+cu118
- Datasets 1.18.3
- Tokenizers 0.13.3
|
bert-base-german-dbmdz-cased
|
[
"pytorch",
"jax",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 1,814
| 2023-04-07T17:58:58Z
|
---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.12 +/- 0.32
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="MohammedDhiyaEddine/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"])
```
|
bert-large-cased-whole-word-masking-finetuned-squad
|
[
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 8,214
| 2023-04-07T18:09:23Z
|
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 277.82 +/- 18.73
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub
from huggingface_sb3 import load_from_hub
repo_id = "Flooow/ppo-LunarLander-v2" # The repo_id
filename = "ppo-LunarLander-v2.zip" # The model filename.zip
# When the model was trained on Python 3.8 the pickle protocol is 5
# But Python 3.6, 3.7 use protocol 4
# In order to get compatibility we need to:
# 1. Install pickle5 (we done it at the beginning of the colab)
# 2. Create a custom empty object we pass as parameter to PPO.load()
custom_objects = {
"learning_rate": 0.0,
"lr_schedule": lambda _: 0.0,
"clip_range": lambda _: 0.0,
}
checkpoint = load_from_hub(repo_id, filename)
model = PPO.load(checkpoint, custom_objects=custom_objects, print_system_info=True)
```
|
camembert-base
|
[
"pytorch",
"tf",
"safetensors",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"arxiv:1911.03894",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"CamembertForMaskedLM"
],
"model_type": "camembert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
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},
"translation_en_to_fr": {
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},
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}
}
}
| 1,440,898
| 2023-04-07T19:34:59Z
|
---
license: cc-by-4.0
tags:
- generated_from_trainer
datasets:
- wmt16
metrics:
- bleu
model-index:
- name: opus-mt-en-de-finetuned-en-to-de
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wmt16
type: wmt16
config: de-en
split: validation
args: de-en
metrics:
- name: Bleu
type: bleu
value: 30.529
---
<!-- 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-en-de-finetuned-en-to-de
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-de](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) on the wmt16 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2849
- Bleu: 30.529
- Rougelsum: 0.5587
- Gen Len: 27.0521
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:---------:|:-------:|
| 1.5584 | 1.0 | 12500 | 1.2921 | 30.5519 | 0.5601 | 27.0549 |
| 1.5649 | 2.0 | 25000 | 1.2877 | 30.578 | 0.5591 | 27.0415 |
| 1.5686 | 3.0 | 37500 | 1.2859 | 30.5509 | 0.5591 | 27.0401 |
| 1.5507 | 4.0 | 50000 | 1.2851 | 30.5396 | 0.5589 | 27.0526 |
| 1.5532 | 5.0 | 62500 | 1.2849 | 30.529 | 0.5587 | 27.0521 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
distilbert-base-cased-distilled-squad
|
[
"pytorch",
"tf",
"rust",
"safetensors",
"openvino",
"distilbert",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"has_space"
] |
question-answering
|
{
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 257,745
| 2023-04-07T18:23:12Z
|
---
license: mit
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: test-dialogue-summarization
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: samsum
type: samsum
config: samsum
split: validation
args: samsum
metrics:
- name: Rouge1
type: rouge
value: 53.8085
---
<!-- 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. -->
# test-dialogue-summarization
This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7499
- Rouge1: 53.8085
- Rouge2: 28.9627
- Rougel: 44.3913
- Rougelsum: 49.5056
- Gen Len: 30.2237
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.3909 | 1.0 | 1841 | 1.5591 | 52.8029 | 28.5133 | 43.7896 | 48.3986 | 25.8386 |
| 1.0938 | 2.0 | 3683 | 1.4337 | 54.5987 | 29.195 | 44.2764 | 49.9201 | 31.9071 |
| 0.856 | 3.0 | 5524 | 1.5402 | 54.2077 | 29.0463 | 44.3965 | 49.6877 | 29.4597 |
| 0.6704 | 4.0 | 7366 | 1.6280 | 54.2616 | 29.5501 | 44.5626 | 49.7599 | 29.967 |
| 0.548 | 5.0 | 9205 | 1.7499 | 53.8085 | 28.9627 | 44.3913 | 49.5056 | 30.2237 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
distilbert-base-uncased-finetuned-sst-2-english
|
[
"pytorch",
"tf",
"rust",
"safetensors",
"distilbert",
"text-classification",
"en",
"dataset:sst2",
"dataset:glue",
"arxiv:1910.01108",
"doi:10.57967/hf/0181",
"transformers",
"license:apache-2.0",
"model-index",
"has_space"
] |
text-classification
|
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"DistilBertForSequenceClassification"
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}
| 3,060,704
| 2023-04-07T18:30:45Z
|
---
license: other
tags:
- image-segmentation
- vision
- generated_from_trainer
model-index:
- name: segformer-finetuned-lane-10k-steps
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-finetuned-lane-10k-steps
This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-cityscapes-512-1024](https://huggingface.co/nvidia/segformer-b0-finetuned-cityscapes-512-1024) on the Efferbach/lane_master dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0365
- Mean Iou: 0.4899
- Mean Accuracy: 0.7371
- Overall Accuracy: 0.7371
- Accuracy Background: nan
- Accuracy Left: 0.7394
- Accuracy Right: 0.7348
- Iou Background: 0.0
- Iou Left: 0.7371
- Iou Right: 0.7325
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: polynomial
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Left | Accuracy Right | Iou Background | Iou Left | Iou Right |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:--------------:|:--------------:|:--------:|:---------:|
| 0.0792 | 1.0 | 308 | 0.0714 | 0.0148 | 0.0229 | 0.0225 | nan | 0.0373 | 0.0085 | 0.0 | 0.0362 | 0.0083 |
| 0.0437 | 2.0 | 616 | 0.0502 | 0.1687 | 0.2775 | 0.2784 | nan | 0.2492 | 0.3058 | 0.0 | 0.2343 | 0.2718 |
| 0.0326 | 3.0 | 924 | 0.0445 | 0.2614 | 0.4441 | 0.4479 | nan | 0.3134 | 0.5748 | 0.0 | 0.3100 | 0.4742 |
| 0.0224 | 4.0 | 1232 | 0.0370 | 0.4048 | 0.6098 | 0.6100 | nan | 0.6043 | 0.6153 | 0.0 | 0.6031 | 0.6113 |
| 0.0184 | 5.0 | 1540 | 0.0346 | 0.3820 | 0.5858 | 0.5870 | nan | 0.5421 | 0.6295 | 0.0 | 0.5400 | 0.6060 |
| 0.0159 | 6.0 | 1848 | 0.0319 | 0.4367 | 0.6567 | 0.6573 | nan | 0.6343 | 0.6791 | 0.0 | 0.6341 | 0.6760 |
| 0.0139 | 7.0 | 2156 | 0.0317 | 0.4555 | 0.6855 | 0.6860 | nan | 0.6691 | 0.7019 | 0.0 | 0.6680 | 0.6986 |
| 0.0129 | 8.0 | 2464 | 0.0321 | 0.4348 | 0.6533 | 0.6535 | nan | 0.6479 | 0.6588 | 0.0 | 0.6474 | 0.6571 |
| 0.0122 | 9.0 | 2772 | 0.0275 | 0.4541 | 0.6827 | 0.6830 | nan | 0.6710 | 0.6943 | 0.0 | 0.6697 | 0.6927 |
| 0.0111 | 10.0 | 3080 | 0.0305 | 0.4609 | 0.6928 | 0.6927 | nan | 0.6969 | 0.6887 | 0.0 | 0.6963 | 0.6865 |
| 0.011 | 11.0 | 3388 | 0.0286 | 0.4646 | 0.6988 | 0.6991 | nan | 0.6890 | 0.7087 | 0.0 | 0.6883 | 0.7055 |
| 0.0103 | 12.0 | 3696 | 0.0298 | 0.4693 | 0.7058 | 0.7062 | nan | 0.6939 | 0.7177 | 0.0 | 0.6932 | 0.7148 |
| 0.0097 | 13.0 | 4004 | 0.0293 | 0.4717 | 0.7090 | 0.7087 | nan | 0.7184 | 0.6996 | 0.0 | 0.7176 | 0.6975 |
| 0.0093 | 14.0 | 4312 | 0.0330 | 0.4537 | 0.6835 | 0.6836 | nan | 0.6775 | 0.6894 | 0.0 | 0.6768 | 0.6843 |
| 0.009 | 15.0 | 4620 | 0.0331 | 0.4804 | 0.7226 | 0.7226 | nan | 0.7194 | 0.7257 | 0.0 | 0.7178 | 0.7234 |
| 0.0088 | 16.0 | 4928 | 0.0315 | 0.4890 | 0.7355 | 0.7357 | nan | 0.7275 | 0.7435 | 0.0 | 0.7259 | 0.7411 |
| 0.0086 | 17.0 | 5236 | 0.0338 | 0.4813 | 0.7234 | 0.7234 | nan | 0.7224 | 0.7243 | 0.0 | 0.7216 | 0.7223 |
| 0.0085 | 18.0 | 5544 | 0.0348 | 0.4743 | 0.7129 | 0.7126 | nan | 0.7225 | 0.7033 | 0.0 | 0.7217 | 0.7012 |
| 0.0083 | 19.0 | 5852 | 0.0357 | 0.4812 | 0.7245 | 0.7244 | nan | 0.7281 | 0.7210 | 0.0 | 0.7254 | 0.7183 |
| 0.0081 | 20.0 | 6160 | 0.0334 | 0.4829 | 0.7271 | 0.7269 | nan | 0.7337 | 0.7205 | 0.0 | 0.7305 | 0.7182 |
| 0.0079 | 21.0 | 6468 | 0.0359 | 0.4773 | 0.7177 | 0.7177 | nan | 0.7184 | 0.7170 | 0.0 | 0.7174 | 0.7146 |
| 0.0077 | 22.0 | 6776 | 0.0351 | 0.4874 | 0.7332 | 0.7329 | nan | 0.7440 | 0.7223 | 0.0 | 0.7432 | 0.7190 |
| 0.0075 | 23.0 | 7084 | 0.0344 | 0.4855 | 0.7296 | 0.7292 | nan | 0.7437 | 0.7156 | 0.0 | 0.7425 | 0.7141 |
| 0.0077 | 24.0 | 7392 | 0.0362 | 0.4799 | 0.7216 | 0.7216 | nan | 0.7236 | 0.7196 | 0.0 | 0.7223 | 0.7174 |
| 0.0071 | 25.0 | 7700 | 0.0391 | 0.4775 | 0.7179 | 0.7180 | nan | 0.7173 | 0.7186 | 0.0 | 0.7161 | 0.7163 |
| 0.0077 | 26.0 | 8008 | 0.0339 | 0.4895 | 0.7367 | 0.7366 | nan | 0.7405 | 0.7329 | 0.0 | 0.7388 | 0.7297 |
| 0.0069 | 27.0 | 8316 | 0.0344 | 0.4858 | 0.7305 | 0.7305 | nan | 0.7291 | 0.7318 | 0.0 | 0.7278 | 0.7297 |
| 0.0069 | 28.0 | 8624 | 0.0361 | 0.4844 | 0.7283 | 0.7282 | nan | 0.7324 | 0.7243 | 0.0 | 0.7309 | 0.7221 |
| 0.007 | 29.0 | 8932 | 0.0371 | 0.4837 | 0.7273 | 0.7270 | nan | 0.7360 | 0.7186 | 0.0 | 0.7345 | 0.7166 |
| 0.007 | 30.0 | 9240 | 0.0366 | 0.4854 | 0.7305 | 0.7303 | nan | 0.7379 | 0.7231 | 0.0 | 0.7353 | 0.7208 |
| 0.0067 | 31.0 | 9548 | 0.0367 | 0.4866 | 0.7322 | 0.7321 | nan | 0.7357 | 0.7286 | 0.0 | 0.7335 | 0.7263 |
| 0.0068 | 32.0 | 9856 | 0.0364 | 0.4883 | 0.7348 | 0.7347 | nan | 0.7377 | 0.7318 | 0.0 | 0.7355 | 0.7295 |
| 0.0067 | 32.47 | 10000 | 0.0365 | 0.4899 | 0.7371 | 0.7371 | nan | 0.7394 | 0.7348 | 0.0 | 0.7371 | 0.7325 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
AdapterHub/bert-base-uncased-pf-squad_v2
|
[
"bert",
"en",
"dataset:squad_v2",
"arxiv:2104.08247",
"adapter-transformers",
"question-answering",
"adapterhub:qa/squad2"
] |
question-answering
|
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}
| 10
| null |
# vicuna-13b
This README provides a step-by-step guide to set up and run the FastChat application with the required dependencies and model.
## Prerequisites
Before you proceed, ensure that you have `git` installed on your system.
## Installation
Follow the steps below to install the required packages and set up the environment.
1. Upgrade `pip`:
```bash
python3 -m pip install --upgrade pip
```
2. Install `accelerate`:
```bash
python3 -m pip install accelerate
```
3. Install `bitsandbytes`
3.1 install by pip
```bash
python3 -m pip install bitsandbytes
```
3.2 Clone the `bitsandbytes` repository and install it:
```bash
git clone https://github.com/TimDettmers/bitsandbytes.git
cd bitsandbytes
CUDA_VERSION=118 make cuda11x
python3 -m pip install .
cd ..
```
use the following command to find `CUDA_VERSION`:
```bash
nvcc --version
```
4. Clone the `FastChat` repository and install it:
```bash
git clone https://github.com/lm-sys/FastChat.git
cd FastChat
python3 -m pip install -e .
cd ..
```
5. Install `git-lfs`:
```bash
curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash
sudo apt-get install git-lfs
git lfs install
```
6. Clone the `vicuna-13b` model:
```bash
git clone https://huggingface.co/helloollel/vicuna-13b
```
## Running FastChat
After completing the installation, you can run FastChat with the following command:
```bash
python3 -m fastchat.serve.cli --model-path ./vicuna-13b
```
This will start FastChat using the `vicuna-13b` model.
## Running in Notebook
```python
import argparse
import time
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaTokenizer
from fastchat.conversation import conv_templates, SeparatorStyle
from fastchat.serve.monkey_patch_non_inplace import replace_llama_attn_with_non_inplace_operations
def load_model(model_name, device, num_gpus, load_8bit=False):
if device == "cpu":
kwargs = {}
elif device == "cuda":
kwargs = {"torch_dtype": torch.float16}
if load_8bit:
if num_gpus != "auto" and int(num_gpus) != 1:
print("8-bit weights are not supported on multiple GPUs. Revert to use one GPU.")
kwargs.update({"load_in_8bit": True, "device_map": "auto"})
else:
if num_gpus == "auto":
kwargs["device_map"] = "auto"
else:
num_gpus = int(num_gpus)
if num_gpus != 1:
kwargs.update({
"device_map": "auto",
"max_memory": {i: "13GiB" for i in range(num_gpus)},
})
elif device == "mps":
# Avoid bugs in mps backend by not using in-place operations.
kwargs = {"torch_dtype": torch.float16}
replace_llama_attn_with_non_inplace_operations()
else:
raise ValueError(f"Invalid device: {device}")
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_name,
low_cpu_mem_usage=True, **kwargs)
# calling model.cuda() mess up weights if loading 8-bit weights
if device == "cuda" and num_gpus == 1 and not load_8bit:
model.to("cuda")
elif device == "mps":
model.to("mps")
return model, tokenizer
@torch.inference_mode()
def generate_stream(tokenizer, model, params, device,
context_len=2048, stream_interval=2):
"""Adapted from fastchat/serve/model_worker.py::generate_stream"""
prompt = params["prompt"]
l_prompt = len(prompt)
temperature = float(params.get("temperature", 1.0))
max_new_tokens = int(params.get("max_new_tokens", 256))
stop_str = params.get("stop", None)
input_ids = tokenizer(prompt).input_ids
output_ids = list(input_ids)
max_src_len = context_len - max_new_tokens - 8
input_ids = input_ids[-max_src_len:]
for i in range(max_new_tokens):
if i == 0:
out = model(
torch.as_tensor([input_ids], device=device), use_cache=True)
logits = out.logits
past_key_values = out.past_key_values
else:
attention_mask = torch.ones(
1, past_key_values[0][0].shape[-2] + 1, device=device)
out = model(input_ids=torch.as_tensor([[token]], device=device),
use_cache=True,
attention_mask=attention_mask,
past_key_values=past_key_values)
logits = out.logits
past_key_values = out.past_key_values
last_token_logits = logits[0][-1]
if device == "mps":
# Switch to CPU by avoiding some bugs in mps backend.
last_token_logits = last_token_logits.float().to("cpu")
if temperature < 1e-4:
token = int(torch.argmax(last_token_logits))
else:
probs = torch.softmax(last_token_logits / temperature, dim=-1)
token = int(torch.multinomial(probs, num_samples=1))
output_ids.append(token)
if token == tokenizer.eos_token_id:
stopped = True
else:
stopped = False
if i % stream_interval == 0 or i == max_new_tokens - 1 or stopped:
output = tokenizer.decode(output_ids, skip_special_tokens=True)
pos = output.rfind(stop_str, l_prompt)
if pos != -1:
output = output[:pos]
stopped = True
yield output
if stopped:
break
del past_key_values
args = dict(
model_name='./vicuna-13b',
device='cuda',
num_gpus='1',
load_8bit=True,
conv_template='vicuna_v1.1',
temperature=0.7,
max_new_tokens=512,
debug=False
)
args = argparse.Namespace(**args)
model_name = args.model_name
# Model
model, tokenizer = load_model(args.model_name, args.device,
args.num_gpus, args.load_8bit)
# Chat
conv = conv_templates[args.conv_template].copy()
def chat(inp):
conv.append_message(conv.roles[0], inp)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
params = {
"model": model_name,
"prompt": prompt,
"temperature": args.temperature,
"max_new_tokens": args.max_new_tokens,
"stop": conv.sep if conv.sep_style == SeparatorStyle.SINGLE else conv.sep2,
}
print(f"{conv.roles[1]}: ", end="", flush=True)
pre = 0
for outputs in generate_stream(tokenizer, model, params, args.device):
outputs = outputs[len(prompt) + 1:].strip()
outputs = outputs.split(" ")
now = len(outputs)
if now - 1 > pre:
print(" ".join(outputs[pre:now-1]), end=" ", flush=True)
pre = now - 1
print(" ".join(outputs[pre:]), flush=True)
conv.messages[-1][-1] = " ".join(outputs)
```
```python
chat("what's the meaning of life?")
```
|
AdapterHub/roberta-base-pf-cola
|
[
"roberta",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:lingaccept/cola"
] |
text-classification
|
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| 0
| null |
---
tags:
- generated_from_trainer
datasets:
- aeslc
metrics:
- rouge
model-index:
- name: pegasus-large-finetuned-aeslc
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: aeslc
type: aeslc
config: default
split: validation
args: default
metrics:
- name: Rouge1
type: rouge
value: 32.9341
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-large-finetuned-aeslc
This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the aeslc dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0274
- Rouge1: 32.9341
- Rouge2: 17.7876
- Rougel: 32.0858
- Rougelsum: 32.0886
- Gen Len: 11.6577
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 3.3247 | 1.0 | 7218 | 3.0274 | 32.9341 | 17.7876 | 32.0858 | 32.0886 | 11.6577 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Aleksandar/distilbert-srb-ner-setimes
|
[
"pytorch",
"distilbert",
"token-classification",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
}
| 3
| null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 233.56 +/- 30.24
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Aleksandar/electra-srb-ner
|
[
"pytorch",
"safetensors",
"electra",
"token-classification",
"dataset:wikiann",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"ElectraForTokenClassification"
],
"model_type": "electra",
"task_specific_params": {
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},
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}
}
| 15
| null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### RFIA2 Dreambooth model trained by HuggM3 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
Alexander-Learn/bert-finetuned-squad-accelerate
|
[] | null |
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}
| 0
| null |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **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: Find your model_id: Agneev/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Andrija/M-bert-NER
|
[
"pytorch",
"bert",
"token-classification",
"hr",
"sr",
"multilingual",
"dataset:hr500k",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
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}
| 8
| null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.97 +/- 0.56
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
|
AnonymousSub/SR_EManuals-BERT
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
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}
| 6
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: t5-small_finetuned_billsum_model_bs8_lr5e-05
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: billsum
type: billsum
config: default
split: ca_test
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.1967
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small_finetuned_billsum_model_bs8_lr5e-05
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1736
- Rouge1: 0.1967
- Rouge2: 0.0984
- Rougel: 0.1675
- Rougelsum: 0.1677
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 124 | 2.2775 | 0.1968 | 0.0943 | 0.1674 | 0.1674 | 19.0 |
| No log | 2.0 | 248 | 2.2187 | 0.2001 | 0.0981 | 0.1705 | 0.1707 | 19.0 |
| No log | 3.0 | 372 | 2.1969 | 0.1965 | 0.0987 | 0.1686 | 0.1687 | 19.0 |
| No log | 4.0 | 496 | 2.1736 | 0.1967 | 0.0984 | 0.1675 | 0.1677 | 19.0 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
AnonymousSub/SR_EManuals-RoBERTa
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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"RobertaModel"
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| 1
| null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 233.59 +/- 36.15
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AnonymousSub/cline-s10-AR
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
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"RobertaForSequenceClassification"
],
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}
| 31
| null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforece-cartpole_policyV1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
AnonymousSub/rule_based_bert_hier_diff_equal_wts_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
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}
| 8
| 2021-11-04T21:11:11Z
|
---
language: en
tags:
- azbert
- pretraining
- fill-mask
widget:
- text: "$f$ $($ $x$ [MASK] $y$ $)$"
example_title: "mathy"
- text: "$x$ [MASK] $x$ $equal$ $2$ $x$"
example_title: "mathy"
- text: "Proof by [MASK] that $n$ $fact$ $gt$ $3$ $n$ for $n$ $gt$ $6$"
example_title: "mathy"
- text: "Proof by induction that $n$ [MASK] $gt$ $3$ $n$ for $n$ $gt$ $6$"
example_title: "mathy"
- text: "The goal of life is [MASK]."
example_title: "philosophical"
license: mit
---
## About
This [repository](https://github.com/approach0/azbert) is a boilerplate to push a mask-filling model to the HuggingFace Model Hub.
### Upload to huggingface
Download your tokenizer, model checkpoints, and optionally the training logs (`events.out.*`) to the `./ckpt` directory (do not include any large files except `pytorch_model.bin` and log files `events.out.*`).
Optionally, test model using the MLM task:
```sh
pip install pya0 # for math token preprocessing
# testing local checkpoints:
python test.py ./ckpt/math-tokenizer ./ckpt/2-2-0/encoder.ckpt
# testing Model Hub checkpoints:
python test.py approach0/coco-mae-220 approach0/coco-mae-220
```
> **Note**
> Modify the test examples in `test.txt` to play with it.
> The test file is tab-separated, the first column is additional positions you want to mask for the right-side sentence (useful for masking tokens in math markups).
> A zero means no additional mask positions.
To upload to huggingface, use the `upload2hgf.sh` script.
Before runnig this script, be sure to check:
* `git-lfs` is installed
* having git-remote named `hgf` reference to `https://huggingface.co/your/repo`
* model contains all the files needed: `config.json` and `pytorch_model.bin`
* tokenizer contains all the files needed: `added_tokens.json`, `special_tokens_map.json`, `tokenizer_config.json`, `vocab.txt` and `tokenizer.json`
* no `tokenizer_file` field in `tokenizer_config.json` (sometimes it is located locally at `~/.cache`)
|
AnonymousSub/rule_based_roberta_only_classfn_twostage_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
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},
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}
| 10
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- esnli
metrics:
- accuracy
- f1
- rouge
- bleu
model-index:
- name: google-flan-t5-small-e-snli-generation-label_and_explanation-selected-b48
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: esnli
type: esnli
config: plain_text
split: validation
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8622231253810201
- name: F1
type: f1
value: 0.8623314280769628
- name: Rouge1
type: rouge
value: 0.605873896307076
- name: Bleu
type: bleu
value: 0.40472213589689604
---
<!-- 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. -->
# google-flan-t5-small-e-snli-generation-label_and_explanation-selected-b48
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the esnli dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8720
- Accuracy: 0.8622
- F1: 0.8623
- Bertscore F1: 0.9329
- Rouge1: 0.6059
- Rouge2: 0.3988
- Rougel: 0.5475
- Rougelsum: 0.5496
- Bleu: 0.4047
## 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: 0.001
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Bertscore F1 | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------------:|:------:|:------:|:------:|:---------:|:------:|
| 1.5084 | 0.17 | 2000 | 1.7484 | 0.8001 | 0.7997 | 0.9271 | 0.5768 | 0.3695 | 0.5209 | 0.5229 | 0.3703 |
| 1.2745 | 0.35 | 4000 | 1.8137 | 0.8113 | 0.8110 | 0.9304 | 0.5881 | 0.3804 | 0.5305 | 0.5325 | 0.3853 |
| 1.2287 | 0.52 | 6000 | 1.8358 | 0.8392 | 0.8403 | 0.9298 | 0.5828 | 0.3747 | 0.5282 | 0.5301 | 0.3778 |
| 1.1964 | 0.7 | 8000 | 1.8432 | 0.8430 | 0.8437 | 0.9326 | 0.5974 | 0.3905 | 0.5447 | 0.5462 | 0.3998 |
| 1.1674 | 0.87 | 10000 | 1.8567 | 0.8507 | 0.8485 | 0.9310 | 0.5947 | 0.3888 | 0.5383 | 0.5402 | 0.3892 |
| 1.1371 | 1.05 | 12000 | 1.8720 | 0.8622 | 0.8623 | 0.9329 | 0.6059 | 0.3988 | 0.5475 | 0.5496 | 0.4047 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_roberta_only_classfn_twostage_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
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}
| 24
| null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: polev1-basic
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
AnonymousSub/rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"RobertaModel"
],
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},
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}
| 7
| null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 258.36 +/- 13.96
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AnonymousSub/rule_based_roberta_twostagetriplet_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"RobertaModel"
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| 1
| null |
---
tags:
- stable diffusion
---
Just a place where i upload the models i mainly use for lora/lycrois training !!
I only upload models that arent already on hugging face btw.
all of the models here are availble on civitai -1 of them :3
Porn Merge
https://civitai.com/models/2661/uber-realistic-porn-merge-urpm
|
AnonymousSub/rule_based_roberta_twostagetriplet_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
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},
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}
| 4
| null |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** 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-SnowballTarget
2. Step 1: Find your model_id: globophobe/ppo-SnowballTargetTESTCOLAB
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AnonymousSub/rule_based_twostagequadruplet_hier_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
],
"model_type": "bert",
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}
| 1
| null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# rithwik-db/triplets-bert-base-cased-500
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('rithwik-db/triplets-bert-base-cased-500')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('rithwik-db/triplets-bert-base-cased-500')
model = AutoModel.from_pretrained('rithwik-db/triplets-bert-base-cased-500')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=rithwik-db/triplets-bert-base-cased-500)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 8245 with parameters:
```
{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.TripletLoss.TripletLoss` with parameters:
```
{'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5}
```
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": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
AnonymousSub/rule_based_twostagetriplet_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
],
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}
| 10
| null |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **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: Find your model_id: globophobe/ppo-PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AnonymousSub/specter-bert-model_copy
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
],
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}
| 2
| null |
Access to model Ekittl01/Illuminate is restricted and you are not in the authorized list. Visit https://huggingface.co/Ekittl01/Illuminate to ask for access.
|
AnonymousSub/specter-bert-model_squad2.0
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
| 1
| null |
---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-sroie
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# donut-sroie
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
AnonymousSub/unsup-consert-base_copy_wikiqa
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
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}
| 26
| null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Cartpolev1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
AnonymousSub/unsup-consert-base_squad2.0
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
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}
| 2
| null |
Access to model pathanziyankhan989bba/Hackerdit is restricted and you are not in the authorized list. Visit https://huggingface.co/pathanziyankhan989bba/Hackerdit to ask for access.
|
AnonymousSub/unsup-consert-papers
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
],
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}
| 2
| null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/32537/caulkinumv2ar-for-anime-style
|
Anthos23/distilbert-base-uncased-finetuned-sst2
|
[
"tf",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_keras_callback",
"license:apache-2.0"
] |
text-classification
|
{
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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| 21
| null |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: disfluency-large
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. -->
# disfluency-large
This model is a fine-tuned version of [vinai/phobert-large](https://huggingface.co/vinai/phobert-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0438
- Precision: 0.9698
- Recall: 0.9663
- F1: 0.9681
- Accuracy: 0.9921
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 140 | 0.0422 | 0.9651 | 0.9627 | 0.9639 | 0.9902 |
| No log | 2.0 | 280 | 0.0315 | 0.9718 | 0.9730 | 0.9724 | 0.9923 |
| No log | 3.0 | 420 | 0.2221 | 0.8079 | 0.7530 | 0.7795 | 0.9355 |
| 0.024 | 4.0 | 560 | 0.0379 | 0.9693 | 0.9675 | 0.9684 | 0.9926 |
| 0.024 | 5.0 | 700 | 0.0499 | 0.9657 | 0.9639 | 0.9648 | 0.9905 |
| 0.024 | 6.0 | 840 | 0.0388 | 0.9688 | 0.9688 | 0.9688 | 0.9925 |
| 0.024 | 7.0 | 980 | 0.0438 | 0.9698 | 0.9663 | 0.9681 | 0.9921 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Anthos23/my-awesome-model
|
[
"pytorch",
"tf",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
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"RobertaForSequenceClassification"
],
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| 30
| null |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1087.72 +/- 88.11
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-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
...
```
|
ArBert/albert-base-v2-finetuned-ner-agglo-twitter
|
[
"pytorch",
"tensorboard",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
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| 27
| null |
---
license: mit
---
### ahx-beta-4322d99 on Stable Diffusion
This is the `<ahx-beta-4322d99>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
ArBert/albert-base-v2-finetuned-ner
|
[
"pytorch",
"tensorboard",
"albert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] |
token-classification
|
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"AlbertForTokenClassification"
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}
| 19
| null |
---
tags:
- autotrain
- vision
- image-classification
datasets:
- cledoux42/autotrain-data-ethnicity-test_v003
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
co2_eq_emissions:
emissions: 6.022813032092885
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 47959117029
- CO2 Emissions (in grams): 6.0228
## Validation Metrics
- Loss: 0.530
- Accuracy: 0.796
- Macro F1: 0.797
- Micro F1: 0.796
- Weighted F1: 0.796
- Macro Precision: 0.797
- Micro Precision: 0.796
- Weighted Precision: 0.796
- Macro Recall: 0.798
- Micro Recall: 0.796
- Weighted Recall: 0.796
|
ArBert/bert-base-uncased-finetuned-ner-gmm
|
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| 0
| null |
---
tags:
- generated_from_trainer
datasets:
- odinsynth_sequence_dataset
model-index:
- name: odinsynth_encoder_decoder_test
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# odinsynth_encoder_decoder_test
This model is a fine-tuned version of [](https://huggingface.co/) on the odinsynth_sequence_dataset 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: 3
- eval_batch_size: 3
- seed: 42
- gradient_accumulation_steps: 200
- total_train_batch_size: 600
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0
### Training results
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.11.0
|
ArBert/bert-base-uncased-finetuned-ner-kmeans
|
[
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
{
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"BertForTokenClassification"
],
"model_type": "bert",
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}
| 6
| 2023-04-09T04:42:33Z
|
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 280.03 +/- 20.25
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
ArthurBaia/bert-base-portuguese-cased-finetuned-squad
|
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}
| 0
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERT_ep5_lr4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERT_ep5_lr4
This model is a fine-tuned version of [ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT](https://huggingface.co/ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2107
- Precision: 0.6683
- Recall: 0.7027
- F1: 0.6851
- Accuracy: 0.9463
## 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-08
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 467 | 0.2604 | 0.6839 | 0.6767 | 0.6803 | 0.9435 |
| 0.2766 | 2.0 | 934 | 0.2361 | 0.6769 | 0.6897 | 0.6832 | 0.9448 |
| 0.2401 | 3.0 | 1401 | 0.2212 | 0.6732 | 0.6994 | 0.6861 | 0.9456 |
| 0.2253 | 4.0 | 1868 | 0.2131 | 0.6695 | 0.7016 | 0.6852 | 0.9462 |
| 0.2103 | 5.0 | 2335 | 0.2107 | 0.6683 | 0.7027 | 0.6851 | 0.9463 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Atampy26/GPT-Glacier
|
[
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] |
text-generation
|
{
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"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
"task_specific_params": {
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}
| 5
| null |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **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: Find your model_id: ManishW/ppo-Pyramids-v1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Atlasky/turkish-negator-nn
|
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}
| 0
| null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: roberta-base-value-determinator
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-value-determinator
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the 0x7194633/value_determinant dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
- F1: 1.0
- Precision: 1.0
- Recall: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Augustvember/WokkaBot7
|
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}
| 0
| 2023-04-09T07:31:43Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-medium-hi
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-medium-hi
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3957
- Wer: 0.2425
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0234 | 4.88 | 1000 | 0.2762 | 0.2750 |
| 0.003 | 9.76 | 2000 | 0.3342 | 0.2518 |
| 0.0001 | 14.63 | 3000 | 0.3636 | 0.2424 |
| 0.0 | 19.51 | 4000 | 0.3877 | 0.2426 |
| 0.0 | 24.39 | 5000 | 0.3957 | 0.2425 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
|
AvatarXD/DialoGPT-medium-Blitzo
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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| 14
| null |
---
language:
- ru
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small Russian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 ru
type: mozilla-foundation/common_voice_11_0
config: ru
split: test
args: ru
metrics:
- name: Wer
type: wer
value: 12.237466436164343
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Russian
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 ru dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3060
- Wer: 12.2375
## 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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.0731 | 1.04 | 1000 | 0.2183 | 13.0589 |
| 0.0194 | 3.02 | 2000 | 0.2390 | 12.8027 |
| 0.0067 | 4.06 | 3000 | 0.2524 | 12.5832 |
| 0.0025 | 6.04 | 4000 | 0.2725 | 12.3245 |
| 0.0017 | 8.02 | 5000 | 0.2854 | 12.7046 |
| 0.0009 | 9.06 | 6000 | 0.2915 | 12.5072 |
| 0.0005 | 11.04 | 7000 | 0.3006 | 12.2473 |
| 0.0004 | 13.02 | 8000 | 0.3060 | 12.2375 |
| 0.0003 | 14.06 | 9000 | 0.3129 | 12.2963 |
| 0.0003 | 16.04 | 10000 | 0.3157 | 12.2988 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.11.1.dev0
- Tokenizers 0.13.2
|
Ayham/bert_gpt2_summarization_xsum
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
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},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 6
| null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 254.38 +/- 18.03
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Ayham/bert_roberta_summarization_cnn_dailymail
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 3
| null |
---
datasets:
- hackathon-somos-nlp-2023/podcasts-ner-es
license: mit
language:
- es
pipeline_tag: text-generation
---
# Named-entity recognition for Spanish Podcasts
This model is a fine-tuned version for named-entity recognition of the Spanish [bertin-project/bertin-gpt-j-6B](https://huggingface.co/bertin-project/bertin-gpt-j-6B) checkpoint.
This model has been developed during the 2023 Hackaton organized by SomosNLP with the GPUs RTX 3090 provided by Q Blocks.
## Motivation of the project
Podcasts are an incredible source of information and inspiration. We can listen to them while commuting, practising sport or cooking our favourite recipe. However, it can be difficult to retain specific facts, dates or people mentioned in them. The aim of this project has been to explore how to capture all those facts using named-entity recognition.
Instead of using a language model fine-tuned with a specific NER head, we have reframed the problem as text generation from a prompt of the kind:
```
text: Yo hoy voy a hablar de mujeres en el mundo del arte, porque me ha leído un libro fantástico que se llama Historia del arte sin hombres, de Katie Hesel. entities: (people, Katie Hesel), (books, Historia del arte sin hombres)
```
By fine-tuning a large generative model with this prompt, we are able to capture the entities mentioned in the podcast. We fine-tuned the [bertin-gpt-j-6B](https://huggingface.co/bertin-project/bertin-gpt-j-6B) following this strategy.
Similar projects with podcasts have been conducted by Andrej Karpathy (https://karpathy.ai/lexicap/) and Aleksa Gordic (https://www.hubermantranscripts.com/).
## Dataset creation
For full details of the dataset, check [this page](https://huggingface.co/datasets/hackathon-somos-nlp-2023/podcasts-ner-es). A brief summary is:
1) Transcribe the audio from a youtube playlist by employing whisper ([check this notebook to understand how we did it](https://github.com/sergiopperez/hackathon_podcast/blob/main/src/NER/get_transcriptions.ipynb)). For the podcast we chose "Deforme Semanal" and the audios from this [playlist](https://www.youtube.com/playlist?list=PLLbN7SMQhMVZoXhtQ00AyebQE_-ttDrs9).
2) Gather all the transcriptions, unify all of them into a dataset, and divide them into sentences of 512 characters.
3) For each sentence, we label the entities in it by using the `text-davinci-003` API from OpenAI ([check this notebook to understand how we did it](https://github.com/sergiopperez/hackathon_podcast/blob/main/src/NER/create_entities_json.ipynb).
## Fine-tuning
Training was performed in a RTX 3090 kindly provided by Q Blocks. It took 2h20m.
We employed the Low-Rank Adaptation (LoRA) strategy to substantially reduce the number of trainable parameters for downstream tasks while maintaining model quality.
The pre-trained checkpoint employed was [bertin-project/bertin-gpt-j-6B](https://huggingface.co/bertin-project/bertin-gpt-j-6B). We didn't perform an extensive hyperparameter sweep so there's room to improve it.
Check [this script](https://github.com/sergiopperez/hackathon_podcast/blob/main/src/NER/peft-gpt-j.ipynb) to understand how we did it.
## Evaluation
Disclaimer: There was no formal evaluation for the training
## Team members
[David Mora](https://huggingface.co/DavidFM43)
[Sergio Perez](https://huggingface.co/sergiopperez)
[Albeto Fernandez](https://huggingface.co/AlbertoFH98)
---
datasets:
- hackathon-somos-nlp-2023/podcasts-ner-es
---
|
Ayham/bertgpt2_cnn
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 4
| null |
---
license: bigscience-bloom-rail-1.0
datasets:
- JosephusCheung/GuanacoDataset
language:
- am
metrics:
- character
library_name: flair
---
|
Ayham/roberta_distilgpt2_summarization_cnn_dailymail
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 4
| 2023-04-09T09:02:21Z
|
---
license: mit
datasets:
- vicgalle/alpaca-gpt4
language:
- en
tags:
- gpt-j
- instruction-tuning
- alpaca
- gpt4
---
# GPT-J-6B instruction-tuned on Alpaca-GPT4
This model was finetuned on GPT-4 generations of the Alpaca prompts, using LoRA for 30.000 steps (batch size of 128), taking over 7 hours in four V100S.
### Examples
The following samples were generated with `{"temperature" : 0.7, "top_p" : 0.92, "top_k" : 0}`:
##### **Give three tips for a good meal**
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
Give three tips for a good meal.
### Response:
Tip 1: Make sure to enjoy your meal. It's easy to forget about enjoying yourself when you're hungry.
Tip 2: Try to eat a healthy meal that contains lots of vegetables and fruits.
Tip 3: Eat slowly and chew your food thoroughly. This will help you digest your meal better.
```
##### **Write a poem about a delicious night**
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
Write a poem about a delicious night.
### Response:
> "The moon shone brightly on the night sky,
>
> Stars twinkled brightly from afar.
>
> Suddenly, my eyes saw the silhouette
>
> Of a handsome stranger in the distance.
>
> He walked slowly towards me with a smile.
>
> He said, 'I've come to eat you up tonight.'
>
> Suddenly, I knew that he was a cannibal!
>
> 'Wait!' I cried as he approached.
>
> I yelled, 'I'm vegetarian!'
>
> Suddenly, he laughed and replied,
>
> 'Oh, you silly girl! That's okay.'
>
> Suddenly, he grabbed my hand and kissed it.
>
> He said, 'You're delicious!'
>
> Suddenly, he dragged me to his lair.
>
> There, he ripped open my belly button
>
> And gobbled me up like a juicy steak.
>
> Suddenly, I woke up screaming from a nightmare.
>
> 'Damn! Damn! Damn!' I cried.
>
```
##### **Write a tweet describing your capabilities**
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
Write a tweet describing your capabilities.
### Response:
Sample tweet: "@geeksforgeeks I'm a mathematician and I enjoy programming. Feel free to reach out to me if you want to chat!"
```
|
Ayham/roberta_roberta_summarization_cnn_dailymail
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 3
| null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1-default
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Ayham/robertagpt2_xsum
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 4
| null |
---
license: creativeml-openrail-m
tags:
- text-to-image
---
### meryl-stryfe-20230408-17-adm-7k-4800-steps on Stable Diffusion via Dreambooth
#### model by NickKolok
This your the Stable Diffusion model fine-tuned the meryl-stryfe-20230408-17-adm-7k-4800-steps concept taught to Stable Diffusion with Dreambooth.
#It can be used by modifying the `instance_prompt`: **merylstryfetrigun**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
|
Ayham/xlmroberta_gpt2_summarization_xsum
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 9
| 2023-04-09T09:25:52Z
|
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi-rl
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="vovikdrg/taxi-rl", 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"])
```
|
Ayham/xlmroberta_large_gpt2_summarization_cnndm
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 12
| null |
### vicuna-7b
The repo contains the converted vicuna-7b model files.
The base model is from `decapoda-research/llama-7b-hf` and the delta model is from `lmsys/vicuna-7b-delta-v0`.
The conversion script is
```
python3 -m fastchat.model.apply_delta \
--base decapoda-research/llama-7b-hf \
--target /output/path/to/vicuna-7b \
--delta lmsys/vicuna-7b-delta-v0
```
tree -s vicuna-7b
```
vicuna-7b
├── [ 9976642750] pytorch_model-00001-of-00002.bin
├── [ 3500323731] pytorch_model-00002-of-00002.bin
└── [ 499723] tokenizer.model
```
tree -s vicuna-7b-delta-v0
```
vicuna-7b-delta-v0/
├── [ 9976642750] pytorch_model-00001-of-00002.bin
├── [ 3500323731] pytorch_model-00002-of-00002.bin
├── [ 26788] pytorch_model.bin.index.json
└── [ 499723] tokenizer.model
```
Although the file size with same prefix is equal, but the content is different. And the md5sum result is
```
5e63e46f2547e75c209c79aebe21f5ca vicuna-7b/pytorch_model-00001-of-00002.bin
53a0d7b5e34c432401f1979b454272ee vicuna-7b/pytorch_model-00002-of-00002.bin
7ded4bec6556250247b1efbb3b7e77e8 vicuna-7b-delta-v0/pytorch_model-00001-of-00002.bin
ee48ac060a0f352ba8fa3dea0385e4c3 vicuna-7b-delta-v0/pytorch_model-00002-of-00002.bin
```
The converted model has been tested using vicuna cli and got correct response. So enjoy it~
```
Q: hi how are you
A: I'm just a computer program, so I don't have feelings or emotions like a human. I'm here to respond to your questions and provide information to the best of my ability. Is there anything else you'd like to know?
Q: really cool
A: Thank you! I'm glad you think so. Is there anything in particular you'd like to know or discuss? I'm here to help so feel free to ask me anything.
```
|
Ayham/xlnet_distilgpt2_summarization_cnn_dailymail
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 13
| null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.35 +/- 0.96
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
|
Ayran/DialoGPT-medium-harry-potter-1-through-4-plus-6
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 12
| 2023-04-09T10:05:33Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERT_ep8_lr2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERT_ep8_lr2
This model is a fine-tuned version of [ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT](https://huggingface.co/ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0940
- Precision: 0.8489
- Recall: 0.8716
- F1: 0.8601
- Accuracy: 0.9771
## 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-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
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 467 | 0.0850 | 0.8142 | 0.8377 | 0.8257 | 0.9730 |
| 0.1137 | 2.0 | 934 | 0.0799 | 0.8402 | 0.8534 | 0.8467 | 0.9757 |
| 0.0746 | 3.0 | 1401 | 0.0825 | 0.8416 | 0.8614 | 0.8514 | 0.9765 |
| 0.0588 | 4.0 | 1868 | 0.0863 | 0.8560 | 0.8652 | 0.8606 | 0.9769 |
| 0.0482 | 5.0 | 2335 | 0.0885 | 0.8553 | 0.8646 | 0.8599 | 0.9771 |
| 0.0402 | 6.0 | 2802 | 0.0893 | 0.8520 | 0.8668 | 0.8593 | 0.9776 |
| 0.0362 | 7.0 | 3269 | 0.0916 | 0.8480 | 0.8726 | 0.8601 | 0.9772 |
| 0.0336 | 8.0 | 3736 | 0.0940 | 0.8489 | 0.8716 | 0.8601 | 0.9771 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Ayta/Haha
|
[] | null |
{
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}
}
| 0
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: en_to_kjven_translator
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. -->
# en_to_kjven_translator
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7384
- Bleu: 24.3137
- Gen Len: 18.018
## 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: 16
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 1.0945 | 1.0 | 2993 | 0.9622 | 21.6701 | 17.9817 |
| 0.9915 | 2.0 | 5986 | 0.8925 | 22.5111 | 17.9861 |
| 0.9306 | 3.0 | 8979 | 0.8543 | 22.9079 | 18.0025 |
| 0.889 | 4.0 | 11972 | 0.8257 | 23.2629 | 17.9987 |
| 0.8616 | 5.0 | 14965 | 0.8053 | 23.4706 | 18.0027 |
| 0.8412 | 6.0 | 17958 | 0.7886 | 23.6554 | 18.0092 |
| 0.811 | 7.0 | 20951 | 0.7769 | 23.8119 | 18.0106 |
| 0.8024 | 8.0 | 23944 | 0.7675 | 23.9582 | 18.0114 |
| 0.7898 | 9.0 | 26937 | 0.7583 | 24.0002 | 18.012 |
| 0.7802 | 10.0 | 29930 | 0.7538 | 24.0943 | 18.0156 |
| 0.7645 | 11.0 | 32923 | 0.7491 | 24.1756 | 18.0168 |
| 0.7565 | 12.0 | 35916 | 0.7445 | 24.2229 | 18.0158 |
| 0.7553 | 13.0 | 38909 | 0.7410 | 24.2709 | 18.0159 |
| 0.7454 | 14.0 | 41902 | 0.7399 | 24.2593 | 18.0205 |
| 0.7424 | 15.0 | 44895 | 0.7388 | 24.3007 | 18.0174 |
| 0.7461 | 16.0 | 47888 | 0.7384 | 24.3137 | 18.018 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Ayu/Shiriro
|
[] | null |
{
"architectures": null,
"model_type": null,
"task_specific_params": {
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},
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},
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},
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}
}
| 0
| 2023-04-09T10:06:44Z
|
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: pixelcopterV1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 18.60 +/- 15.79
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
AyushPJ/ai-club-inductions-21-nlp-distilBERT
|
[
"pytorch",
"distilbert",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
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}
}
}
| 8
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERT_ep8_lr3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERT_ep8_lr3
This model is a fine-tuned version of [ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT](https://huggingface.co/ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1048
- Precision: 0.7641
- Recall: 0.8235
- F1: 0.7927
- Accuracy: 0.9666
## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 467 | 0.1361 | 0.6936 | 0.7475 | 0.7195 | 0.9568 |
| 0.1814 | 2.0 | 934 | 0.1187 | 0.7168 | 0.7849 | 0.7493 | 0.9613 |
| 0.1202 | 3.0 | 1401 | 0.1118 | 0.7361 | 0.7990 | 0.7662 | 0.9635 |
| 0.1109 | 4.0 | 1868 | 0.1088 | 0.7508 | 0.8072 | 0.7780 | 0.9650 |
| 0.1006 | 5.0 | 2335 | 0.1069 | 0.7570 | 0.8158 | 0.7853 | 0.9657 |
| 0.0987 | 6.0 | 2802 | 0.1056 | 0.7604 | 0.8191 | 0.7887 | 0.9662 |
| 0.0969 | 7.0 | 3269 | 0.1050 | 0.7651 | 0.8224 | 0.7927 | 0.9665 |
| 0.0993 | 8.0 | 3736 | 0.1048 | 0.7641 | 0.8235 | 0.7927 | 0.9666 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
AyushPJ/ai-club-inductions-21-nlp-roBERTa
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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"prefix": null
}
}
}
| 8
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERT_ep8_lr4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERT_ep8_lr4
This model is a fine-tuned version of [ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT](https://huggingface.co/ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1754
- Precision: 0.6822
- Recall: 0.7097
- F1: 0.6957
- Accuracy: 0.9504
## 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-08
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 467 | 0.2549 | 0.6812 | 0.6660 | 0.6735 | 0.9454 |
| 0.2775 | 2.0 | 934 | 0.2278 | 0.6778 | 0.6814 | 0.6796 | 0.9469 |
| 0.2438 | 3.0 | 1401 | 0.2086 | 0.6772 | 0.6935 | 0.6852 | 0.9481 |
| 0.2135 | 4.0 | 1868 | 0.1949 | 0.6780 | 0.7001 | 0.6889 | 0.9489 |
| 0.2058 | 5.0 | 2335 | 0.1856 | 0.6808 | 0.7057 | 0.6931 | 0.9496 |
| 0.1937 | 6.0 | 2802 | 0.1796 | 0.6810 | 0.7073 | 0.6939 | 0.9499 |
| 0.1818 | 7.0 | 3269 | 0.1763 | 0.6831 | 0.7097 | 0.6962 | 0.9503 |
| 0.1835 | 8.0 | 3736 | 0.1754 | 0.6822 | 0.7097 | 0.6957 | 0.9504 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Azaghast/DistilBART-SCP-ParaSummarization
|
[
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"BartForConditionalGeneration"
],
"model_type": "bart",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 142,
"min_length": 56,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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"prefix": null
}
}
}
| 8
| 2023-04-09T10:34:43Z
|
---
license: mit
language:
- en
---
[Trained](https://ahxxm.com/179.moew/) on images labeled by myself.
2-step inference:
```python
from PIL import Image
import torch
import torch.nn as nn
import uform
path = "image.jpg"
# generate 768 dimension embeddings for an image
uf_model = uform.get_model("unum-cloud/uform-vl-english")
img = Image.open(path)
image_data = uf_model.preprocess_image(img)
text_data = uf_model.preprocess_text(path.name) # filename not necessarily useful, but encode anyway
memb = uf_model.encode_multimodal(image=image_data, text=text_data)
memb = memb.detach().numpy()
assert memb.shape == (1, 768)
# load model, infer and Sigmoid
class ImageBinaryClassifier(nn.Module):
def __init__(self):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(768, 2048),
nn.ReLU(),
nn.Linear(2048, 2048),
nn.ReLU(),
nn.Linear(2048, 2048),
nn.ReLU(),
nn.Linear(2048, 256),
nn.ReLU(),
nn.Linear(256, 1),
)
def forward(self, x):
return self.layers(x)
saved_model = ImageBinaryClassifier()
saved_model.load_state_dict(torch.load("model.pt", map_location=torch.device('cpu')))
saved_model = torch.compile(saved_model)
saved_model.eval()
prob = float(torch.sigmoid(saved_model(memb)))
```
|
Azaghast/DistilBERT-SCP-Class-Classification
|
[
"pytorch",
"distilbert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
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},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 42
| 2023-04-09T10:35:48Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERT_ep8_lr5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERT_ep8_lr5
This model is a fine-tuned version of [ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT](https://huggingface.co/ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2950
- Precision: 0.6748
- Recall: 0.6332
- F1: 0.6534
- Accuracy: 0.9420
## 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-09
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 467 | 0.3067 | 0.6768 | 0.6258 | 0.6503 | 0.9415 |
| 0.2941 | 2.0 | 934 | 0.3029 | 0.6753 | 0.6283 | 0.6510 | 0.9417 |
| 0.2874 | 3.0 | 1401 | 0.2999 | 0.6764 | 0.6302 | 0.6525 | 0.9418 |
| 0.2821 | 4.0 | 1868 | 0.2978 | 0.6761 | 0.6316 | 0.6531 | 0.9420 |
| 0.2828 | 5.0 | 2335 | 0.2963 | 0.6749 | 0.6321 | 0.6528 | 0.9421 |
| 0.2829 | 6.0 | 2802 | 0.2954 | 0.6748 | 0.6332 | 0.6534 | 0.9421 |
| 0.2808 | 7.0 | 3269 | 0.2951 | 0.6750 | 0.6332 | 0.6535 | 0.9421 |
| 0.2841 | 8.0 | 3736 | 0.2950 | 0.6748 | 0.6332 | 0.6534 | 0.9420 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
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