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Jeevesh8/feather_berts_67
f34380360b06875350e289ad2b57c68b0048ba69
2022-04-20T13:42:17.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
Jeevesh8
null
Jeevesh8/feather_berts_67
5
null
transformers
17,100
Entry not found
Jeevesh8/feather_berts_77
b01bc9716ee80d9a97749d145e26e841f3691869
2022-04-20T13:46:52.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
Jeevesh8
null
Jeevesh8/feather_berts_77
5
null
transformers
17,101
Entry not found
Jeevesh8/feather_berts_85
dc483f0a95045023946a22e3403a343825e83e32
2022-04-20T13:50:29.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
Jeevesh8
null
Jeevesh8/feather_berts_85
5
null
transformers
17,102
Entry not found
Jeevesh8/feather_berts_93
73468add0c9bc27e7684f66d1a1b25d98ec3139c
2022-04-20T13:54:29.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
Jeevesh8
null
Jeevesh8/feather_berts_93
5
null
transformers
17,103
Entry not found
ktangri/autotrain-financial-sentiment-765323474
c335af66226f04ad16feebee47ac01fca22d1970
2022-04-20T14:35:01.000Z
[ "pytorch", "bert", "text-classification", "en", "dataset:ktangri/autotrain-data-financial-sentiment", "transformers", "autotrain", "co2_eq_emissions" ]
text-classification
false
ktangri
null
ktangri/autotrain-financial-sentiment-765323474
5
null
transformers
17,104
--- tags: autotrain language: en widget: - text: "I love AutoTrain 🤗" datasets: - ktangri/autotrain-data-financial-sentiment co2_eq_emissions: 0.007501354635994803 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 765323474 - CO2 Emissions (in grams): 0.007501354635994803 ## Validation Metrics - Loss: 0.0447433702647686 - Accuracy: 0.9823788546255506 - Macro F1: 0.974405452470854 - Micro F1: 0.9823788546255506 - Weighted F1: 0.9823043153179869 - Macro Precision: 0.978208375548801 - Micro Precision: 0.9823788546255506 - Weighted Precision: 0.9823204968555985 - Macro Recall: 0.9707159078140736 - Micro Recall: 0.9823788546255506 - Weighted Recall: 0.9823788546255506 ## 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/ktangri/autotrain-financial-sentiment-765323474 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("ktangri/autotrain-financial-sentiment-765323474", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("ktangri/autotrain-financial-sentiment-765323474", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
masapasa/deberta_amazon_reviews_v1
8f57a60fd5e9eb876c58ed68f6452b2b7558ec3a
2022-04-20T15:23:24.000Z
[ "pytorch", "tensorboard", "deberta-v2", "text-classification", "transformers", "generated_from_trainer", "license:mit", "model-index" ]
text-classification
false
masapasa
null
masapasa/deberta_amazon_reviews_v1
5
null
transformers
17,105
--- license: mit tags: - generated_from_trainer model-index: - name: deberta_amazon_reviews_v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # deberta_amazon_reviews_v1 This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 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 - lr_scheduler_warmup_steps: 20 - num_epochs: 1 ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2+cpu - Datasets 1.18.3 - Tokenizers 0.11.0
nirmalkumar/gpt2-cric-commentary
00eb0226526890f3340fda6638c1034eb94a8b18
2022-04-20T21:11:38.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
nirmalkumar
null
nirmalkumar/gpt2-cric-commentary
5
null
transformers
17,106
Entry not found
kabelomalapane/model_zu-en_updated
9fbd9cdc1db3fb72c83e37c667cefe25e9eec7c4
2022-04-22T02:55:18.000Z
[ "pytorch", "tensorboard", "marian", "text2text-generation", "transformers", "translation", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
translation
false
kabelomalapane
null
kabelomalapane/model_zu-en_updated
5
null
transformers
17,107
--- license: apache-2.0 tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: model_zu-en_updated 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. --> # model_zu-en_updated This model is a fine-tuned version of [Helsinki-NLP/opus-mt-mul-en](https://huggingface.co/Helsinki-NLP/opus-mt-mul-en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8306 - Bleu: 27.1218 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2 - Datasets 1.18.3 - Tokenizers 0.11.0
Plaban81/results
ceb0df33034ad4a5ee993b19397280286af31f19
2022-04-21T13:53:54.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers" ]
text-classification
false
Plaban81
null
Plaban81/results
5
null
transformers
17,108
Entry not found
jackmleitch/distilbert-base-uncased-finetuned-clinc
1f3549d70bef634b40270a5dc617ff26cd3b9f07
2022-04-21T18:22:26.000Z
[ "pytorch", "distilbert", "text-classification", "dataset:clinc_oos", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
jackmleitch
null
jackmleitch/distilbert-base-uncased-finetuned-clinc
5
null
transformers
17,109
--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9183870967741935 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7702 - Accuracy: 0.9184 ## 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: 48 - eval_batch_size: 48 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2984 | 1.0 | 318 | 3.2941 | 0.7490 | | 2.6352 | 2.0 | 636 | 1.8755 | 0.8410 | | 1.5468 | 3.0 | 954 | 1.1587 | 0.8913 | | 1.0086 | 4.0 | 1272 | 0.8541 | 0.9123 | | 0.7941 | 5.0 | 1590 | 0.7702 | 0.9184 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0 - Datasets 2.0.0 - Tokenizers 0.11.6
caurdy/wav2vec2-large-960h-lv60-self_MIDIARIES_72H_FT
053555bab3bb2f1304920ce1c0f3ea11553c791c
2022-04-22T16:45:56.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers", "license:afl-3.0" ]
automatic-speech-recognition
false
caurdy
null
caurdy/wav2vec2-large-960h-lv60-self_MIDIARIES_72H_FT
5
null
transformers
17,110
--- license: afl-3.0 --- FineTuned wav2vec2 large 960H lv60 self pre-trained facebook model on 72 Hours of MI Diaries Data WER 13 % -> 9.7% on 20 min test set of MI Diaries audio clips (https://mi-diaries.org/) ### Usage ### model = Wav2Vec2ForCTC.from_pretrained("caurdy/wav2vec2-large-960h-lv60-self_MIDIARIES_72H_FT") processor = Wav2Vec2Processor.from_pretrained("caurdy/wav2vec2-large-960h-lv60-self_MIDIARIES_72H_FT")
Sarim24/xlm-roberta-base-finetuned-panx-de
7f0cbff81d4d46c0a7037b321a23e42350ad896c
2022-04-21T23:12:20.000Z
[ "pytorch", "tensorboard", "xlm-roberta", "token-classification", "dataset:xtreme", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]
token-classification
false
Sarim24
null
Sarim24/xlm-roberta-base-finetuned-panx-de
5
null
transformers
17,111
--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.862669465085938 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1374 - F1: 0.8627 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2596 | 1.0 | 525 | 0.1571 | 0.8302 | | 0.1292 | 2.0 | 1050 | 0.1416 | 0.8455 | | 0.0809 | 3.0 | 1575 | 0.1374 | 0.8627 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1
PrasunMishra/prasun
df9b7886dbe7a9a74a27dcef98ff950f1f39a240
2022-04-22T05:30:15.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers" ]
text-classification
false
PrasunMishra
null
PrasunMishra/prasun
5
null
transformers
17,112
Entry not found
huggingtweets/it_its_are_are-miyarepostbot-unbridled_id
ff4932f5e957ad108d66ed8cc249abd2d8190ec4
2022-04-22T19:04:30.000Z
[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/it_its_are_are-miyarepostbot-unbridled_id
5
null
transformers
17,113
--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1376263696389914629/_FzhUcTW_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1480214799539740676/S3W8I0f2_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1400304659688878088/Lbb8zMZE_400x400.jpg&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Sierra Armour 𝔼𝕣𝕚𝕤 & angelicism2727272628 & Miya</div> <div style="text-align: center; font-size: 14px;">@it_its_are_are-miyarepostbot-unbridled_id</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Sierra Armour 𝔼𝕣𝕚𝕤 & angelicism2727272628 & Miya. | Data | Sierra Armour 𝔼𝕣𝕚𝕤 | angelicism2727272628 | Miya | | --- | --- | --- | --- | | Tweets downloaded | 3146 | 179 | 1840 | | Retweets | 545 | 28 | 23 | | Short tweets | 413 | 20 | 214 | | Tweets kept | 2188 | 131 | 1603 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/wlae4njw/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @it_its_are_are-miyarepostbot-unbridled_id's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2xs5iik1) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2xs5iik1/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/it_its_are_are-miyarepostbot-unbridled_id') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
dapang/distilroberta-base-etc-nlp
13c436b12cbbeba56150e8cde95ff3ba39f15800
2022-04-23T04:20:09.000Z
[ "pytorch", "tensorboard", "roberta", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
dapang
null
dapang/distilroberta-base-etc-nlp
5
null
transformers
17,114
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilroberta-base-etc-nlp 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. --> # distilroberta-base-etc-nlp This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0039 - Accuracy: 0.9993 - F1: 0.9993 ## 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: 2.740146306575944e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 262 | 0.0025 | 0.9997 | 0.9997 | | No log | 2.0 | 524 | 0.0039 | 0.9993 | 0.9993 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.12.0.dev20220422+cu116 - Datasets 2.1.0 - Tokenizers 0.12.1
dapang/distilroberta-base-mrl-sym
285b546d7c2619189c01d461c727350f211c1f7a
2022-04-23T04:30:29.000Z
[ "pytorch", "tensorboard", "roberta", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
dapang
null
dapang/distilroberta-base-mrl-sym
5
null
transformers
17,115
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilroberta-base-mrl-sym 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. --> # distilroberta-base-mrl-sym This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0001 - Accuracy: 1.0 - F1: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.740146306575944e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---:| | No log | 1.0 | 150 | 0.0001 | 1.0 | 1.0 | | No log | 2.0 | 300 | 0.0001 | 1.0 | 1.0 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.12.0.dev20220422+cu116 - Datasets 2.1.0 - Tokenizers 0.12.1
mofyrt/bert-base-uncased-finetuned-cola
b931d00c9a1a9425809e9eab7073e0ef290ac975
2022-04-23T18:04:55.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "dataset:glue", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
mofyrt
null
mofyrt/bert-base-uncased-finetuned-cola
5
null
transformers
17,116
--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: bert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5905946625710334 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-finetuned-cola This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7445 - Matthews Correlation: 0.5906 ## 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 | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4926 | 1.0 | 535 | 0.5155 | 0.4941 | | 0.2971 | 2.0 | 1070 | 0.5561 | 0.5320 | | 0.1947 | 3.0 | 1605 | 0.7230 | 0.5677 | | 0.1293 | 4.0 | 2140 | 0.7445 | 0.5906 | | 0.0867 | 5.0 | 2675 | 0.8836 | 0.5788 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1
brad1141/bertBasev2
27638dd491d99b3f885abb2ef746195eb0464c2e
2022-04-23T14:44:30.000Z
[ "pytorch", "bert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
brad1141
null
brad1141/bertBasev2
5
null
transformers
17,117
--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bertBasev2 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. --> # bertBasev2 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0328 - Precision: 0.9539 - Recall: 0.9707 - F1: 0.9622 - Accuracy: 0.9911 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 1.2004 | 1.0 | 1012 | 0.9504 | 0.2620 | 0.3519 | 0.3004 | 0.6856 | | 1.0265 | 2.0 | 2024 | 0.6205 | 0.4356 | 0.5161 | 0.4725 | 0.7956 | | 0.6895 | 3.0 | 3036 | 0.3269 | 0.6694 | 0.7302 | 0.6985 | 0.9044 | | 0.44 | 4.0 | 4048 | 0.1325 | 0.8356 | 0.9091 | 0.8708 | 0.9667 | | 0.2585 | 5.0 | 5060 | 0.0717 | 0.9259 | 0.9531 | 0.9393 | 0.9844 | | 0.1722 | 6.0 | 6072 | 0.0382 | 0.9480 | 0.9619 | 0.9549 | 0.99 | | 0.0919 | 7.0 | 7084 | 0.0328 | 0.9539 | 0.9707 | 0.9622 | 0.9911 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1
SophieTr/RM_incr_lr_v1
a588e9c54ca3820d201509ebdb9a5f30a7aeb2b8
2022-04-24T07:13:52.000Z
[ "pytorch", "pegasus", "feature-extraction", "transformers" ]
feature-extraction
false
SophieTr
null
SophieTr/RM_incr_lr_v1
5
null
transformers
17,118
Entry not found
M-junaid-A/wav2vec-speech-project
6a03235e6373e43725e6172219e30482a7f7446c
2022-04-26T06:53:17.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
M-junaid-A
null
M-junaid-A/wav2vec-speech-project
5
null
transformers
17,119
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec-speech-project 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. --> # wav2vec-speech-project This model is a fine-tuned version of [kingabzpro/wav2vec2-large-xls-r-300m-Urdu](https://huggingface.co/kingabzpro/wav2vec2-large-xls-r-300m-Urdu) 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: 32 - 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: 60 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
gagan3012/ArOCRv2
3a7741b48feae17405dc563d43362c60c258389f
2022-04-26T22:33:08.000Z
[ "pytorch", "tensorboard", "vision-encoder-decoder", "transformers", "generated_from_trainer", "model-index" ]
null
false
gagan3012
null
gagan3012/ArOCRv2
5
null
transformers
17,120
--- tags: - generated_from_trainer model-index: - name: ArOCRv2 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. --> # ArOCRv2 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8990 - Cer: 0.0722 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.9659 | 1.18 | 1000 | 1.6020 | 0.3575 | | 0.1571 | 2.36 | 2000 | 0.8990 | 0.0722 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.9.1 - Datasets 2.1.0 - Tokenizers 0.11.6
Shashidhar/distilbert-base-uncased-finetuned-squad
475d800a47dc4b1afbd985eae48abcd2953a6938
2022-05-13T00:57:08.000Z
[ "pytorch", "tensorboard", "distilbert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
question-answering
false
Shashidhar
null
Shashidhar/distilbert-base-uncased-finetuned-squad
5
null
transformers
17,121
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1080 ## 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: 7e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.1205 | 1.0 | 5533 | 1.1080 | ### Framework versions - Transformers 4.19.0 - Pytorch 1.11.0+cu113 - Datasets 2.2.1 - Tokenizers 0.12.1
MatthewAlanPow1/distilbert-base-uncased-finetuned-cola
e94c73de559e522a2e0dfc75ae08eccf9cd5cdc9
2022-04-25T17:26:25.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:glue", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
MatthewAlanPow1
null
MatthewAlanPow1/distilbert-base-uncased-finetuned-cola
5
null
transformers
17,122
--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5421747077088894 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7994 - Matthews Correlation: 0.5422 ## 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 | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.42 | 1.0 | 535 | 0.4631 | 0.5242 | | 0.2823 | 2.0 | 1070 | 0.5755 | 0.5056 | | 0.1963 | 3.0 | 1605 | 0.6767 | 0.5478 | | 0.1441 | 4.0 | 2140 | 0.7742 | 0.5418 | | 0.1069 | 5.0 | 2675 | 0.7994 | 0.5422 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1
swcrazyfan/Kingify-2Way-T5-Large-v1_1
babc95f246800aa131a2a8db04b709a99b736c05
2022-04-26T09:15:58.000Z
[ "pytorch", "t5", "text2text-generation", "english", "transformers", "autotrain_compatible" ]
text2text-generation
false
swcrazyfan
null
swcrazyfan/Kingify-2Way-T5-Large-v1_1
5
null
transformers
17,123
--- language: english tags: - t5 widget: - text: "dekingify: " example_title: "Translate 17th-century English to modern English" - text: "kingify: " example_title: "Translate modern English to 17th-century English" --- # Kingify 2Way This is a custom AI model that translates modern English into 17th-century English or "King James" English. ## Details of the model This model is a fine-tuned version of [google/t5-v1_1-large] on a dataset of a modern Bible translation with matching King James Bible verses. ## Intended uses & limitations At times, despite sharing the same language and general grammatical rules, English from previous centuries can be easily misunderstood. The purpose of this was to explore ways to understand texts from the 17th-century more clearly. #### How to use ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("swcrazyfan/Kingify-2Way") model = AutoModelWithLMHead.from_pretrained("swcrazyfan/Kingify-2Way") ``` #### Limitations and bias - The model is trained on the King James Version of the Bible, so it will work best with Christian-style language (or even clichés). - Before the 18th and 19th centuries, English spelling was inconsistent. Because of this, the model often does not recognize spellings different from those in the KJV. - The model was trained on a relatively small amount of data, so it will not be as accurate as a model trained on a larger data set. ## Training data The data used to train this model is from the New English Translation and the King James Version of the Bible. ## Training procedure The model was trained on Kaggle using the Hugging Face Transformers library. ### Training hyperparameters The following hyperparameters were used during training: - num_train_epochs: 4 - learning_rate: 5e-04 - train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear ## Eval results The model was evaluated using a human test. A human was asked to evaluate the translation quality of the model. The human was not told which sentences were translated by the model and which sentences were written by a human. ## BibTeX entry and citation info ```bibtex @inproceedings{, title={Kingify 2Way}, author={Joshua Kaufmann}, year={2022}, url={https://huggingface.co/swcrazyfan/Kingify-2Way-T5-Large-v1_1} } ```
Cheatham/xlm-roberta-large-finetuned-dA-001
07d279c89a998a95d2b96d8ffc5d94c12f053892
2022-04-25T12:35:46.000Z
[ "pytorch", "xlm-roberta", "text-classification", "transformers" ]
text-classification
false
Cheatham
null
Cheatham/xlm-roberta-large-finetuned-dA-001
5
null
transformers
17,124
Entry not found
bullmount/quanIta_t5
7425406d2c6aa253a9f52f8f1112ebcf110ad80e
2022-04-26T05:32:32.000Z
[ "pytorch", "t5", "text2text-generation", "it", "transformers", "text2text_generation", "question_answering", "model-index", "autotrain_compatible" ]
text2text-generation
false
bullmount
null
bullmount/quanIta_t5
5
null
transformers
17,125
--- tags: - text2text_generation - question_answering language: - it model-index: - name: quanIta_t5 results: [] widget: - text: "Quante torri ha Bologna? La torre degli Asinelli è una delle cosiddette due torri di Bologna, simbolo della città, situate in piazza di porta Ravegnana, all'incrocio tra le antiche strade San Donato (ora via Zamboni), San Vitale, Maggiore e Castiglione. Eretta, secondo la tradizione, fra il 1109 e il 1119 dal nobile Gherardo Asinelli, la torre è alta 97,20 metri, pende verso ovest per 2,23 metri e presenta all'interno una scalinata composta da 498 gradini. Ancora non si può dire con certezza quando e da chi fu costruita la torre degli Asinelli. Si presume che la torre debba il proprio nome a Gherardo Asinelli, il nobile cavaliere di fazione ghibellina al quale se ne attribuisce la costruzione, iniziata secondo una consolidata tradizione l'11 ottobre 1109 e terminata dieci anni dopo, nel 1119." - text: "Chi costruì la torre degli Asinelli? La torre degli Asinelli è una delle cosiddette due torri di Bologna, simbolo della città, situate in piazza di porta Ravegnana, all'incrocio tra le antiche strade San Donato (ora via Zamboni), San Vitale, Maggiore e Castiglione. Eretta, secondo la tradizione, fra il 1109 e il 1119 dal nobile Gherardo Asinelli, la torre è alta 97,20 metri, pende verso ovest per 2,23 metri e presenta all'interno una scalinata composta da 498 gradini. Ancora non si può dire con certezza quando e da chi fu costruita la torre degli Asinelli. Si presume che la torre debba il proprio nome a Gherardo Asinelli, il nobile cavaliere di fazione ghibellina al quale se ne attribuisce la costruzione, iniziata secondo una consolidata tradizione l'11 ottobre 1109 e terminata dieci anni dopo, nel 1119." - text: "Chi è l'autore della Gioconda? La torre degli Asinelli è una delle cosiddette due torri di Bologna, simbolo della città, situate in piazza di porta Ravegnana, all'incrocio tra le antiche strade San Donato (ora via Zamboni), San Vitale, Maggiore e Castiglione. Eretta, secondo la tradizione, fra il 1109 e il 1119 dal nobile Gherardo Asinelli, la torre è alta 97,20 metri, pende verso ovest per 2,23 metri e presenta all'interno una scalinata composta da 498 gradini. Ancora non si può dire con certezza quando e da chi fu costruita la torre degli Asinelli. Si presume che la torre debba il proprio nome a Gherardo Asinelli, il nobile cavaliere di fazione ghibellina al quale se ne attribuisce la costruzione, iniziata secondo una consolidata tradizione l'11 ottobre 1109 e terminata dieci anni dopo, nel 1119." - text: "Chi fece accostare Seneca agli insegnamenti di Pitagora? Seneca seguì molto intensamente gli insegnamenti dei maestri, che esercitarono su di lui un profondo influsso sia con la parola sia con l'esempio di una vita vissuta in coerenza con gli ideali professati. Da Attalo imparò i principi dello stoicismo e l'abitudine alle pratiche ascetiche. Da Sozione, oltre ad apprendere i principi delle dottrine di Pitagora, fu avviato per qualche tempo verso la pratica vegetariana; venne distolto però dal padre che non amava la filosofia e dal fatto che l'imperatore Tiberio proibisse di seguire consuetudini di vita non romane." --- This model is a fine-tuned version of [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) on [Thoroughly Cleaned Italian mC4 Corpus](https://huggingface.co/datasets/gsarti/clean_mc4_it) (~41B words, ~275GB).<br/> This is an mt5-based Question Answering model for the Italian language. <br/> Training is done on translated subset of SQuAD 2.0 dataset (of about 100k questions).<br/> Thus, this model not only attempts to answer questions through reading comprehension, but also refrains when presented with a question that cannot be answered based on the paragraph provided. You can test the model by entering question + context like the string shown below: ``` In quale anno si è verificato il terremoto nel Sichuan? Il terremoto del Sichuan del 2008 o il terremoto del Gran Sichuan, misurato a 8.0 Ms e 7.9 Mw, e si è verificato alle 02:28:01 PM China Standard Time all' epicentro (06:28:01 UTC) il 12 maggio nella provincia del Sichuan, ha ucciso 69.197 persone e lasciato 18.222 dispersi. ``` The train achieves the following results: - EM: 78.69 - F1: 84.69 - rouge1: precision=0.862, recall=0.849, fmeasure=0.845 - rouge2: precision=0.309, recall=0.300, fmeasure=0.298 - rougeL: precision=0.862, recall=0.849, fmeasure=0.845 - rougeLsum: precision=0.862, recall=0.849, fmeasure=0.845
Cheatham/xlm-roberta-large-finetuned-dAB-001
904460ee4f12700e795b30296348f15e8eb804f2
2022-04-25T18:06:58.000Z
[ "pytorch", "xlm-roberta", "text-classification", "transformers" ]
text-classification
false
Cheatham
null
Cheatham/xlm-roberta-large-finetuned-dAB-001
5
null
transformers
17,126
Entry not found
anshr/distilgpt2_reward_model_03
3138ff252f01e83f746e83b95ccf6f208182b84d
2022-04-26T01:02:21.000Z
[ "pytorch", "gpt2", "text-classification", "transformers" ]
text-classification
false
anshr
null
anshr/distilgpt2_reward_model_03
5
null
transformers
17,127
Entry not found
crcb/isear_bert
64b3cc2932cab99acd4e47f6a506d7533f005749
2022-04-26T03:14:10.000Z
[ "pytorch", "roberta", "text-classification", "unk", "dataset:crcb/autotrain-data-isear_bert", "transformers", "autotrain", "co2_eq_emissions" ]
text-classification
false
crcb
null
crcb/isear_bert
5
null
transformers
17,128
--- tags: autotrain language: unk widget: - text: "I love AutoTrain 🤗" datasets: - crcb/autotrain-data-isear_bert co2_eq_emissions: 0.026027055434994496 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 786224257 - CO2 Emissions (in grams): 0.026027055434994496 ## Validation Metrics - Loss: 0.8348872065544128 - Accuracy: 0.7272727272727273 - Macro F1: 0.7230931630686932 - Micro F1: 0.7272727272727273 - Weighted F1: 0.7236599456423468 - Macro Precision: 0.7328252157220334 - Micro Precision: 0.7272727272727273 - Weighted Precision: 0.7336599708829821 - Macro Recall: 0.7270448163292604 - Micro Recall: 0.7272727272727273 - Weighted Recall: 0.7272727272727273 ## 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/crcb/autotrain-isear_bert-786224257 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("crcb/autotrain-isear_bert-786224257", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("crcb/autotrain-isear_bert-786224257", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
stefan-it/it5-efficient-small-el32
15c23b9731b7a71e714e5a06d9de0ecf662b2c94
2022-04-26T07:27:25.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "license:mit", "autotrain_compatible" ]
text2text-generation
false
stefan-it
null
stefan-it/it5-efficient-small-el32
5
2
transformers
17,129
--- license: mit ---
manueltonneau/bert-twitter-es-lost-job
f86a65eafd0949b374b402952cc30d929e0819c9
2022-04-26T16:04:49.000Z
[ "pytorch", "bert", "text-classification", "es", "arxiv:2203.09178", "transformers" ]
text-classification
false
manueltonneau
null
manueltonneau/bert-twitter-es-lost-job
5
null
transformers
17,130
--- language: es # <-- my language widget: - text: "Hoy perdí mi trabajo..." --- # Detection of employment status disclosures on Twitter ## Model main characteristics: - class: Lost Job (1), else (0) - country: MX - language: Spanish - architecture: BERT base ## Model description This model is a version of `dccuchile/bert-base-spanish-wwm-cased` finetuned to recognize Spanish tweets where a user mentions that she lost her job in the past month. It was trained on Spanish tweets from users based in Mexico. The task is framed as a binary classification problem with: - the positive class referring to tweets mentioning that a user recently lost her job (label=1) - the negative class referring to all other tweets (label=0) ## Resources The dataset of Spanish tweets on which this classifier was trained is open-sourced [here](https://github.com/manueltonneau/twitter-unemployment). Details on the performance can be found in our [ACL 2022 paper](https://arxiv.org/abs/2203.09178). ## Citation If you find this model useful, please cite our paper (citation to come soon).
UT/BRTW_DEBIAS
87bfb8a61e6413436ee2f3a357b5d63d1ff4db8f
2022-04-27T08:54:01.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
UT
null
UT/BRTW_DEBIAS
5
null
transformers
17,131
Entry not found
caush/Clickbait2
28940b18770367d40953ad17123758727f900139
2022-04-26T21:15:28.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers", "generated_from_trainer", "model-index" ]
text-classification
false
caush
null
caush/Clickbait2
5
null
transformers
17,132
--- tags: - generated_from_trainer model-index: - name: Clickbait2 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. --> # Clickbait2 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0212 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 0.05 | 50 | 0.0213 | | No log | 0.09 | 100 | 0.0213 | | No log | 0.14 | 150 | 0.0213 | | No log | 0.18 | 200 | 0.0216 | | No log | 0.23 | 250 | 0.0214 | | No log | 0.27 | 300 | 0.0212 | | No log | 0.32 | 350 | 0.0214 | | No log | 0.36 | 400 | 0.0212 | | No log | 0.41 | 450 | 0.0218 | | 0.0219 | 0.46 | 500 | 0.0219 | | 0.0219 | 0.5 | 550 | 0.0214 | | 0.0219 | 0.55 | 600 | 0.0216 | | 0.0219 | 0.59 | 650 | 0.0217 | | 0.0219 | 0.64 | 700 | 0.0214 | | 0.0219 | 0.68 | 750 | 0.0214 | | 0.0219 | 0.73 | 800 | 0.0214 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0a0+17540c5 - Datasets 2.1.0 - Tokenizers 0.12.1
manueltonneau/bert-twitter-es-job-offer
f32aa9dd33ad6a0db5ed0c300630fc0cfea2a94d
2022-04-26T20:10:22.000Z
[ "pytorch", "bert", "text-classification", "es", "arxiv:2203.09178", "transformers" ]
text-classification
false
manueltonneau
null
manueltonneau/bert-twitter-es-job-offer
5
null
transformers
17,133
--- language: es # <-- my language widget: - text: "Difunde a contactos: #trabajo: Cajeros Zona Taxqueña- Turnos fijos. Oaxaca" --- # Detection of employment status disclosures on Twitter ## Model main characteristics: - class: Job Offer (1), else (0) - country: MX - language: Spanish - architecture: BERT base ## Model description This model is a version of `dccuchile/bert-base-spanish-wwm-cased` finetuned to recognize Spanish tweets containing job offers. It was trained on Spanish tweets from users based in Mexico. The task is framed as a binary classification problem with: - the positive class referring to tweets containing job offers (label=1) - the negative class referring to all other tweets (label=0) ## Resources The dataset of Spanish tweets on which this classifier was trained is open-sourced [here](https://github.com/manueltonneau/twitter-unemployment). Details on the performance can be found in our [ACL 2022 paper](https://arxiv.org/abs/2203.09178). ## Citation If you find this model useful, please cite our paper (citation to come soon).
huggingtweets/ai_curio_bot
9b0b9a3c6031e889f63bb65f3e9c3abbe482759b
2022-04-27T09:37:54.000Z
[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/ai_curio_bot
5
null
transformers
17,134
--- language: en thumbnail: http://www.huggingtweets.com/ai_curio_bot/1651052269778/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1516142458660401154/YdxpLcQj_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">ai_curio_bot (ADMISSIONS OPEN)(GUIDED DIFFUSION)</div> <div style="text-align: center; font-size: 14px;">@ai_curio_bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from ai_curio_bot (ADMISSIONS OPEN)(GUIDED DIFFUSION). | Data | ai_curio_bot (ADMISSIONS OPEN)(GUIDED DIFFUSION) | | --- | --- | | Tweets downloaded | 3250 | | Retweets | 51 | | Short tweets | 716 | | Tweets kept | 2483 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3os17v54/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ai_curio_bot's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2krcmz6f) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2krcmz6f/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/ai_curio_bot') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
pistachiocow/product_description_generator_bad
1184e697a236f95352a51dc20b6c1bca48cdd956
2022-04-27T14:07:16.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
pistachiocow
null
pistachiocow/product_description_generator_bad
5
null
transformers
17,135
Entry not found
Brawl/UKRI_DistilBERT
d9477c2b13282ba58b18a0cf89dd0121d131fdf8
2022-04-27T15:54:51.000Z
[ "pytorch", "distilbert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Brawl
null
Brawl/UKRI_DistilBERT
5
null
transformers
17,136
Entry not found
LiYuan/amazon-cross-encoder
f8018f8b5c6ed3c18cec26cc6432d76f18d68b7e
2022-04-27T18:36:36.000Z
[ "pytorch", "roberta", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
LiYuan
null
LiYuan/amazon-cross-encoder
5
null
transformers
17,137
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-mnli results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-mnli 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: 0.8244 - Accuracy: 0.6617 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.8981 | 1.0 | 35702 | 0.8662 | 0.6371 | | 0.7837 | 2.0 | 71404 | 0.8244 | 0.6617 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
gagan3012/ArOCRv4
efe7fcf23bf8a053fab2b5ab8831e55816808ae4
2022-04-27T20:23:52.000Z
[ "pytorch", "tensorboard", "vision-encoder-decoder", "transformers", "generated_from_trainer", "model-index" ]
null
false
gagan3012
null
gagan3012/ArOCRv4
5
null
transformers
17,138
--- tags: - generated_from_trainer model-index: - name: ArOCRv4 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. --> # ArOCRv4 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5811 - Cer: 0.1249 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 3.103 | 1.18 | 1000 | 8.0852 | 11.5974 | | 1.2535 | 2.36 | 2000 | 2.0400 | 0.4904 | | 0.5682 | 3.55 | 3000 | 1.9336 | 0.2145 | | 0.3038 | 4.73 | 4000 | 1.5811 | 0.1249 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.9.1 - Datasets 2.1.0 - Tokenizers 0.11.6
Mim/pro-cell-expert
904f1325b1236902ae279da1babd8887366dd8bc
2022-04-29T11:36:58.000Z
[ "pytorch", "bert", "text-classification", "unk", "dataset:Mim/autotrain-data-procell-expert", "transformers", "autotrain", "co2_eq_emissions" ]
text-classification
false
Mim
null
Mim/pro-cell-expert
5
null
transformers
17,139
--- tags: autotrain language: unk widget: - text: "ACE2 overexpression in AAV cell lines" datasets: - Mim/autotrain-data-procell-expert co2_eq_emissions: 0.004814823138367317 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 800724769 - CO2 Emissions (in grams): 0.004814823138367317 ## Validation Metrics - Loss: 0.4749071002006531 - Accuracy: 0.9 - Precision: 0.8928571428571429 - Recall: 0.9615384615384616 - AUC: 0.9065934065934066 - F1: 0.9259259259259259 ## 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/Mim/autotrain-procell-expert-800724769 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("Mim/autotrain-procell-expert-800724769", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("Mim/autotrain-procell-expert-800724769", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
TehranNLP-org/electra-base-hateXplain
63e6e822a1e229a4ff3a4291f57f2b93104714d8
2022-05-03T17:00:31.000Z
[ "pytorch", "electra", "text-classification", "en", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
TehranNLP-org
null
TehranNLP-org/electra-base-hateXplain
5
null
transformers
17,140
--- language: - en license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: SEED0042 results: - task: name: Text Classification type: text-classification dataset: name: HATEXPLAIN type: '' args: hatexplain metrics: - name: Accuracy type: accuracy value: 0.4162330905306972 --- <!-- 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. --> # SEED0042 This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the HATEXPLAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.7667 - Accuracy: 0.4162 - Accuracy 0: 0.8145 - Accuracy 1: 0.1895 - Accuracy 2: 0.3084 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - distributed_type: not_parallel - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 150 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Accuracy 0 | Accuracy 1 | Accuracy 2 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:----------:|:----------:| | No log | 1.0 | 481 | 0.7431 | 0.4152 | 0.7707 | 0.1805 | 0.3650 | | No log | 2.0 | 962 | 0.7346 | 0.4152 | 0.8010 | 0.2190 | 0.2774 | | No log | 3.0 | 1443 | 0.7667 | 0.4162 | 0.8145 | 0.1895 | 0.3084 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu113 - Datasets 2.1.0 - Tokenizers 0.11.6
astremo/JAINU
913b2349d1ea03f69bc95c690fff80ddbefbe2c6
2022-05-22T05:51:12.000Z
[ "pytorch", "t5", "text2text-generation", "ja", "ain", "transformers", "japanese", "ainu", "license:cc-by-4.0", "autotrain_compatible" ]
text2text-generation
false
astremo
null
astremo/JAINU
5
4
transformers
17,141
--- language: - ja - ain license: cc-by-4.0 tags: - japanese - ainu --- # JAINU-Model (T5 fine-tuned model) JAINU is a Japanese - Ainu language machine translation model. ⚠️ Attention! The model is still experimental and needs to be refined! # Examples | input | output| |---|---| |こんにちは|イランカラプテ| |ありがとうございます|イヤイライケレ| |熊は神ですか|キムンカムイアナクカムイネヤ?| |熊は怖いのか|キムンカムイアナクアシトマプネヤ?| |フクロウは鳥です|イソサンケカムイアナクチカプネ| |分かりません!|ケラムシカレ!| |勉強した?|ヤイホノッカエキプネヤ?| |してないです|クキカソモキ| |さようなら|アプンノオカヤン| # References t5 japanese pre-trained model: sonoisa t5-base-japanese (https://huggingface.co/sonoisa/t5-base-japanese) # License Shield: [![CC BY 4.0][cc-by-shield]][cc-by] This work is licensed under a [Creative Commons Attribution 4.0 International License][cc-by]. [![CC BY 4.0][cc-by-image]][cc-by] [cc-by]: http://creativecommons.org/licenses/by/4.0/ [cc-by-image]: https://i.creativecommons.org/l/by/4.0/88x31.png [cc-by-shield]: https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg
PHISSTOOD/codet5-small-code-summarization-python
432cdede8e92ce45969eea7450f9733157688424
2022-05-01T03:55:05.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
PHISSTOOD
null
PHISSTOOD/codet5-small-code-summarization-python
5
null
transformers
17,142
Entry not found
behroz/sp_proj
3f56ff0c0d3dd20dd7d2a6e9e59dda534ce8e98b
2022-05-06T20:39:46.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
behroz
null
behroz/sp_proj
5
null
transformers
17,143
--- license: apache-2.0 tags: - generated_from_trainer model-index: name: sp_proj --- <!-- 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. --> # sp_proj This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.10.3
Raffay/my_final_wav2vec2-urdu-asr-project
29ba84945453b41bb17b9b499ce2122c85e48416
2022-05-01T16:09:24.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "model-index" ]
automatic-speech-recognition
false
Raffay
null
Raffay/my_final_wav2vec2-urdu-asr-project
5
null
transformers
17,144
--- tags: - generated_from_trainer model-index: - name: my_final_wav2vec2-urdu-asr-project results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_final_wav2vec2-urdu-asr-project This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.4680 - 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 7.8981 | 1.41 | 200 | 5.5809 | 1.0 | | 5.254 | 2.82 | 400 | 5.4720 | 1.0 | | 5.2209 | 4.23 | 600 | 5.4862 | 1.0 | | 5.256 | 5.63 | 800 | 5.4716 | 1.0 | | 5.1244 | 7.04 | 1000 | 5.4912 | 1.0 | | 5.0641 | 8.45 | 1200 | 5.4797 | 1.0 | | 5.0923 | 9.86 | 1400 | 5.5290 | 1.0 | | 5.0166 | 11.27 | 1600 | 5.4722 | 1.0 | | 5.1251 | 12.68 | 1800 | 5.4690 | 1.0 | | 5.0201 | 14.08 | 2000 | 5.4684 | 1.0 | | 5.1285 | 15.49 | 2200 | 5.4745 | 1.0 | | 5.0853 | 16.9 | 2400 | 5.4734 | 1.0 | | 5.0112 | 18.31 | 2600 | 5.4668 | 1.0 | | 5.0372 | 19.72 | 2800 | 5.4680 | 1.0 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1
Muennighoff/t5-small-finetuned-xsum-512
772370a0af3b8cc51b4bc30a6d3af4913a495385
2022-05-01T10:55:33.000Z
[ "pytorch", "t5", "text2text-generation", "dataset:xsum", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
Muennighoff
null
Muennighoff/t5-small-finetuned-xsum-512
5
null
transformers
17,145
--- license: apache-2.0 tags: - generated_from_trainer datasets: - xsum metrics: - rouge model-index: - name: t5-small-finetuned-xsum-512 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: xsum type: xsum args: default metrics: - name: Rouge1 type: rouge value: 28.8448 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-xsum-512 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.4706 - Rouge1: 28.8448 - Rouge2: 7.9819 - Rougel: 22.8686 - Rougelsum: 22.8754 - Gen Len: 18.7654 T5, zero-shot on the same evaluation set: `{'rouge1': 19.2304, 'rouge2': 2.5842, 'rougeL': 13.9683, 'rougeLsum': 15.516}` ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.7057 | 1.0 | 7854 | 2.4706 | 28.8448 | 7.9819 | 22.8686 | 22.8754 | 18.7654 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2 - Datasets 2.1.0 - Tokenizers 0.12.1
charly/autotrain-sentiment-4-812425472
87a7f46843bc10dd154132eaa6ba81a7dba882c8
2022-05-02T00:38:00.000Z
[ "pytorch", "bert", "text-classification", "en", "dataset:charly/autotrain-data-sentiment-4", "transformers", "autotrain", "co2_eq_emissions" ]
text-classification
false
charly
null
charly/autotrain-sentiment-4-812425472
5
null
transformers
17,146
--- tags: autotrain language: en widget: - text: "I love AutoTrain 🤗" datasets: - charly/autotrain-data-sentiment-4 co2_eq_emissions: 0.007597570744740809 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 812425472 - CO2 Emissions (in grams): 0.007597570744740809 ## Validation Metrics - Loss: 0.5105093121528625 - Accuracy: 0.8268156424581006 - Macro F1: 0.6020923520923521 - Micro F1: 0.8268156424581006 - Weighted F1: 0.8021395116367184 - Macro Precision: 0.5907986111111111 - Micro Precision: 0.8268156424581006 - Weighted Precision: 0.7792248603351954 - Macro Recall: 0.6141625496464206 - Micro Recall: 0.8268156424581006 - Weighted Recall: 0.8268156424581006 ## 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/charly/autotrain-sentiment-4-812425472 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("charly/autotrain-sentiment-4-812425472", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("charly/autotrain-sentiment-4-812425472", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
DioLiu/distilbert-base-uncased-finetuned-sst2-newdata
215b4a8854a277a1128063ac78318ac6af22ab95
2022-05-02T12:40:09.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
DioLiu
null
DioLiu/distilbert-base-uncased-finetuned-sst2-newdata
5
null
transformers
17,147
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-sst2-newdata results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-sst2-newdata 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: 0.0588 - Accuracy: 0.9911 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0543 | 1.0 | 1116 | 0.0307 | 0.9911 | | 0.0235 | 2.0 | 2232 | 0.0372 | 0.9911 | | 0.0102 | 3.0 | 3348 | 0.0486 | 0.9914 | | 0.0003 | 4.0 | 4464 | 0.0563 | 0.9914 | | 0.0008 | 5.0 | 5580 | 0.0588 | 0.9911 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
ali2066/DistilBERTFINAL_ctxSentence_TRAIN_editorials_TEST_NULL_second_train_set_null_False
26f6448d463617db95e50e7a0e0c7de3f6a570df
2022-05-02T13:43:39.000Z
[ "pytorch", "tensorboard", "roberta", "text-classification", "transformers", "generated_from_trainer", "model-index" ]
text-classification
false
ali2066
null
ali2066/DistilBERTFINAL_ctxSentence_TRAIN_editorials_TEST_NULL_second_train_set_null_False
5
null
transformers
17,148
--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: DistilBERTFINAL_ctxSentence_TRAIN_editorials_TEST_NULL_second_train_set_null_False 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. --> # DistilBERTFINAL_ctxSentence_TRAIN_editorials_TEST_NULL_second_train_set_null_False This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.4527 - Precision: 0.2844 - Recall: 0.9676 - F1: 0.4395 - Accuracy: 0.2991 ## 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: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 166 | 0.1044 | 0.9742 | 1.0 | 0.9869 | 0.9742 | | No log | 2.0 | 332 | 0.1269 | 0.9742 | 1.0 | 0.9869 | 0.9742 | | No log | 3.0 | 498 | 0.1028 | 0.9742 | 1.0 | 0.9869 | 0.9742 | | 0.0947 | 4.0 | 664 | 0.0836 | 0.9826 | 0.9971 | 0.9898 | 0.9799 | | 0.0947 | 5.0 | 830 | 0.0884 | 0.9854 | 0.9912 | 0.9883 | 0.9771 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/DistilBERTFINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False
58612b52e712c1f0d2369ca0a91d4b0a023be80f
2022-05-02T14:00:18.000Z
[ "pytorch", "tensorboard", "roberta", "text-classification", "transformers", "generated_from_trainer", "model-index" ]
text-classification
false
ali2066
null
ali2066/DistilBERTFINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False
5
null
transformers
17,149
--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: DistilBERTFINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False 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. --> # DistilBERTFINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0557 - Precision: 0.9930 - Recall: 0.9878 - F1: 0.9904 - Accuracy: 0.9814 ## 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: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 479 | 0.3334 | 0.9041 | 0.9041 | 0.9041 | 0.8550 | | 0.3756 | 2.0 | 958 | 0.3095 | 0.8991 | 0.9251 | 0.9119 | 0.8649 | | 0.2653 | 3.0 | 1437 | 0.3603 | 0.8929 | 0.9527 | 0.9218 | 0.8779 | | 0.1991 | 4.0 | 1916 | 0.3907 | 0.8919 | 0.9540 | 0.9219 | 0.8779 | | 0.1586 | 5.0 | 2395 | 0.3642 | 0.9070 | 0.9356 | 0.9211 | 0.8788 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
kurama/bert-finetuned-ner
b81a0fbe1b4dafb25c182b6a01bbb3c650de0ce6
2022-05-02T14:02:58.000Z
[ "pytorch", "tensorboard", "bert", "token-classification", "dataset:conll2003", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
token-classification
false
kurama
null
kurama/bert-finetuned-ner
5
null
transformers
17,150
--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 args: conll2003 metrics: - name: Precision type: precision value: 0.9321865696328151 - name: Recall type: recall value: 0.9485021878155503 - name: F1 type: f1 value: 0.9402736069402736 - name: Accuracy type: accuracy value: 0.9860187201977983 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0617 - Precision: 0.9322 - Recall: 0.9485 - F1: 0.9403 - Accuracy: 0.9860 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0831 | 1.0 | 1756 | 0.0652 | 0.9213 | 0.9392 | 0.9302 | 0.9835 | | 0.0413 | 2.0 | 3512 | 0.0567 | 0.9292 | 0.9495 | 0.9392 | 0.9861 | | 0.0192 | 3.0 | 5268 | 0.0617 | 0.9322 | 0.9485 | 0.9403 | 0.9860 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
ali2066/DistilBERT_FINAL_ctxSentence_TRAIN_editorials_TEST_NULL_second_train_set_null_False
5ff1da3d87729b255bee9476168bbd774b764f7d
2022-05-02T18:29:59.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
ali2066
null
ali2066/DistilBERT_FINAL_ctxSentence_TRAIN_editorials_TEST_NULL_second_train_set_null_False
5
null
transformers
17,151
--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: DistilBERT_FINAL_ctxSentence_TRAIN_editorials_TEST_NULL_second_train_set_null_False results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # DistilBERT_FINAL_ctxSentence_TRAIN_editorials_TEST_NULL_second_train_set_null_False This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.8119 - Precision: 0.2752 - Recall: 0.9522 - F1: 0.4270 - Accuracy: 0.2849 ## 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: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 166 | 0.0726 | 0.9827 | 1.0 | 0.9913 | 0.9828 | | No log | 2.0 | 332 | 0.0569 | 0.9827 | 1.0 | 0.9913 | 0.9828 | | No log | 3.0 | 498 | 0.0434 | 0.9884 | 1.0 | 0.9942 | 0.9885 | | 0.1021 | 4.0 | 664 | 0.0505 | 0.9884 | 1.0 | 0.9942 | 0.9885 | | 0.1021 | 5.0 | 830 | 0.0472 | 0.9884 | 1.0 | 0.9942 | 0.9885 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
maesneako/gpt2-fr_paco-cheese_e3
cf332f315fe883965766052c6cb6552f3b9afbc1
2022-05-02T20:06:47.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
maesneako
null
maesneako/gpt2-fr_paco-cheese_e3
5
null
transformers
17,152
Entry not found
mdroth/bert-finetuned-ner
d10310f3cb882bacbb31bd8d08d4f85d4700a75a
2022-05-26T18:32:46.000Z
[ "pytorch", "bert", "token-classification", "dataset:conll2003", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
token-classification
false
mdroth
null
mdroth/bert-finetuned-ner
5
null
transformers
17,153
--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 args: conll2003 metrics: - name: Precision type: precision value: 0.9331020812685827 - name: Recall type: recall value: 0.9506900033658701 - name: F1 type: f1 value: 0.9418139379793263 - name: Accuracy type: accuracy value: 0.9865926885265203 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0589 - Precision: 0.9331 - Recall: 0.9507 - F1: 0.9418 - Accuracy: 0.9866 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0857 | 1.0 | 1756 | 0.0621 | 0.9181 | 0.9382 | 0.9281 | 0.9836 | | 0.0308 | 2.0 | 3512 | 0.0611 | 0.9228 | 0.9458 | 0.9342 | 0.9846 | | 0.0223 | 3.0 | 5268 | 0.0589 | 0.9331 | 0.9507 | 0.9418 | 0.9866 | ### Framework versions - Transformers 4.12.0 - Pytorch 1.9.0+cu111 - Datasets 2.0.0 - Tokenizers 0.10.3
chebmarcel/sun
84df9fbf3758b30ab1ade57d0a558970aeaf2e51
2022-05-03T12:41:23.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
false
chebmarcel
null
chebmarcel/sun
5
null
transformers
17,154
Entry not found
moma1820/sen_pair_cluster4
4c0806ae6c529dd985156818239a18b772be85c9
2022-05-03T12:34:53.000Z
[ "pytorch", "xlm-roberta", "feature-extraction", "transformers" ]
feature-extraction
false
moma1820
null
moma1820/sen_pair_cluster4
5
null
transformers
17,155
Entry not found
TweebankNLP/bertweet-tb2-pos-tagging
41aaa15a9052135239fcb4b3f8cc77f686fc63be
2022-05-05T00:23:38.000Z
[ "pytorch", "roberta", "token-classification", "arxiv:2201.07281", "transformers", "license:cc-by-nc-4.0", "autotrain_compatible" ]
token-classification
false
TweebankNLP
null
TweebankNLP/bertweet-tb2-pos-tagging
5
null
transformers
17,156
--- license: cc-by-nc-4.0 --- ## Model Specification - This is a **baseline Twitter POS tagging model (with 95.21\% Accuracy)** on Tweebank V2's NER benchmark (also called `Tweebank-NER`), trained on the Tweebank-NER training data. - **If you are looking for the SOTA Twitter POS tagger**, please go to this [HuggingFace hub link](https://huggingface.co/TweebankNLP/bertweet-tb2_ewt-pos-tagging). - For more details about the `TweebankNLP` project, please refer to this [our paper](https://arxiv.org/pdf/2201.07281.pdf) and [github](https://github.com/social-machines/TweebankNLP) page. - In the paper, it is referred as `HuggingFace-BERTweet (TB2)` in the POS table. ## How to use the model - **PRE-PROCESSING**: when you apply the model on tweets, please make sure that tweets are preprocessed by the [TweetTokenizer](https://github.com/VinAIResearch/BERTweet/blob/master/TweetNormalizer.py) to get the best performance. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("TweebankNLP/bertweet-tb2-pos-tagging") model = AutoModelForTokenClassification.from_pretrained("TweebankNLP/bertweet-tb2-pos-tagging") ``` ## References If you use this repository in your research, please kindly cite [our paper](https://arxiv.org/pdf/2201.07281.pdf): ```bibtex @article{jiang2022tweetnlp, title={Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis}, author={Jiang, Hang and Hua, Yining and Beeferman, Doug and Roy, Deb}, journal={In Proceedings of the 13th Language Resources and Evaluation Conference (LREC)}, year={2022} } ```
enimai/opus-mt-en-ru-finetuned-en-to-ru
f25fdac198c1137b384d0d838ae6309e4397bb31
2022-05-03T16:56:39.000Z
[ "pytorch", "marian", "text2text-generation", "transformers", "license:apache-2.0", "autotrain_compatible" ]
text2text-generation
false
enimai
null
enimai/opus-mt-en-ru-finetuned-en-to-ru
5
null
transformers
17,157
--- license: apache-2.0 ---
Nakul24/RoBERTa-emotion-extraction
43abab03b92f84ca618992ea26084d641b294c5e
2022-05-04T16:23:29.000Z
[ "pytorch", "roberta", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
Nakul24
null
Nakul24/RoBERTa-emotion-extraction
5
1
transformers
17,158
Entry not found
DioLiu/distilbert-base-uncased-finetuned-sst2-shake-wiki
9a10bf42d90374928f1c7f1e1f21302a99cc3112
2022-05-05T06:39:28.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
DioLiu
null
DioLiu/distilbert-base-uncased-finetuned-sst2-shake-wiki
5
null
transformers
17,159
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-sst2-shake-wiki results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-sst2-shake-wiki This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0096 - Accuracy: 0.9994 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.001 | 1.0 | 5029 | 0.0120 | 0.9988 | | 0.0017 | 2.0 | 10058 | 0.0028 | 0.9996 | | 0.0 | 3.0 | 15087 | 0.0094 | 0.9992 | | 0.0 | 4.0 | 20116 | 0.0091 | 0.9994 | | 0.0 | 5.0 | 25145 | 0.0096 | 0.9994 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
cradle-bio/thermo-predictor-thermo-evotuning-prot_bert
a46e54855eef78a766b3bb83bf7f5e803d3b0f98
2022-05-06T12:46:22.000Z
[ "pytorch", "bert", "text-classification", "transformers", "generated_from_trainer", "model-index" ]
text-classification
false
cradle-bio
null
cradle-bio/thermo-predictor-thermo-evotuning-prot_bert
5
null
transformers
17,160
--- tags: - generated_from_trainer metrics: - spearmanr model-index: - name: thermo-predictor-thermo-evotuning-prot_bert 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. --> # thermo-predictor-thermo-evotuning-prot_bert This model is a fine-tuned version of [thundaa/thermo-evotuning-prot_bert](https://huggingface.co/thundaa/thermo-evotuning-prot_bert) on the cradle-bio/tape-thermostability dataset. It achieves the following results on the evaluation set: - Loss: 0.1617 - Spearmanr: 0.6914 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 16384 - 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 | Spearmanr | |:-------------:|:-----:|:----:|:---------------:|:---------:| | 0.4734 | 0.68 | 2 | 0.3146 | 0.3359 | | 0.4392 | 1.68 | 4 | 0.2936 | 0.3407 | | 0.4034 | 2.68 | 6 | 0.2633 | 0.3696 | | 0.3669 | 3.68 | 8 | 0.2437 | 0.3903 | | 0.3496 | 4.68 | 10 | 0.2377 | 0.4102 | | 0.3351 | 5.68 | 12 | 0.2285 | 0.4204 | | 0.3289 | 6.68 | 14 | 0.2267 | 0.4180 | | 0.3267 | 7.68 | 16 | 0.2258 | 0.4242 | | 0.3177 | 8.68 | 18 | 0.2206 | 0.4295 | | 0.3116 | 9.68 | 20 | 0.2150 | 0.4365 | | 0.3039 | 10.68 | 22 | 0.2115 | 0.4365 | | 0.2985 | 11.68 | 24 | 0.2062 | 0.4469 | | 0.2927 | 12.68 | 26 | 0.2045 | 0.4531 | | 0.2885 | 13.68 | 28 | 0.2005 | 0.4603 | | 0.2838 | 14.68 | 30 | 0.1987 | 0.4690 | | 0.2806 | 15.68 | 32 | 0.1975 | 0.4744 | | 0.2772 | 16.68 | 34 | 0.1970 | 0.4765 | | 0.2728 | 17.68 | 36 | 0.1939 | 0.4845 | | 0.2684 | 18.68 | 38 | 0.1931 | 0.4858 | | 0.2641 | 19.68 | 40 | 0.1925 | 0.4936 | | 0.2608 | 20.68 | 42 | 0.1905 | 0.4929 | | 0.2566 | 21.68 | 44 | 0.1886 | 0.5049 | | 0.2518 | 22.68 | 46 | 0.1875 | 0.5095 | | 0.2467 | 23.68 | 48 | 0.1869 | 0.5141 | | 0.2424 | 24.68 | 50 | 0.1859 | 0.5161 | | 0.2375 | 25.68 | 52 | 0.1850 | 0.5223 | | 0.2329 | 26.68 | 54 | 0.1851 | 0.5210 | | 0.2279 | 27.68 | 56 | 0.1850 | 0.5294 | | 0.2226 | 28.68 | 58 | 0.1837 | 0.5310 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
masakhane/m2m100_418M_en_lug_rel
ce802ba7a7e5ee6d7b99c91942d4b9d41e89eb55
2022-05-05T14:29:05.000Z
[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/m2m100_418M_en_lug_rel
5
null
transformers
17,161
--- license: afl-3.0 ---
benjamin/gpt2-wechsel-scottish-gaelic
0eab6ebb4e06512d302aa76a89f1e958310c0d58
2022-07-13T23:39:53.000Z
[ "pytorch", "gpt2", "text-generation", "gd", "transformers", "license:mit" ]
text-generation
false
benjamin
null
benjamin/gpt2-wechsel-scottish-gaelic
5
1
transformers
17,162
--- language: gd license: mit --- # gpt2-wechsel-scottish-gaelic Model trained with WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models. See the code here: https://github.com/CPJKU/wechsel And the paper here: https://aclanthology.org/2022.naacl-main.293/ ## Performance | Model | PPL | |---|---| | `gpt2-wechsel-sundanese` | **111.72** | | `gpt2` (retrained from scratch) | 149.46 | | Model | PPL | |---|---| | `gpt2-wechsel-scottish-gaelic` | **16.43** | | `gpt2` (retrained from scratch) | 19.53 | | Model | PPL | |---|---| | `gpt2-wechsel-uyghur` | **34.33** | | `gpt2` (retrained from scratch) | 42.82 | | Model | PPL | |---|---| | `gpt2-wechsel-malagasy` | **14.01** | | `gpt2` (retrained from scratch) | 15.93 | See our paper for details. ## Citation Please cite WECHSEL as ``` @inproceedings{minixhofer-etal-2022-wechsel, title = "{WECHSEL}: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models", author = "Minixhofer, Benjamin and Paischer, Fabian and Rekabsaz, Navid", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", address = "Seattle, United States", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.naacl-main.293", pages = "3992--4006", abstract = "Large pretrained language models (LMs) have become the central building block of many NLP applications. Training these models requires ever more computational resources and most of the existing models are trained on English text only. It is exceedingly expensive to train these models in other languages. To alleviate this problem, we introduce a novel method {--} called WECHSEL {--} to efficiently and effectively transfer pretrained LMs to new languages. WECHSEL can be applied to any model which uses subword-based tokenization and learns an embedding for each subword. The tokenizer of the source model (in English) is replaced with a tokenizer in the target language and token embeddings are initialized such that they are semantically similar to the English tokens by utilizing multilingual static word embeddings covering English and the target language. We use WECHSEL to transfer the English RoBERTa and GPT-2 models to four languages (French, German, Chinese and Swahili). We also study the benefits of our method on very low-resource languages. WECHSEL improves over proposed methods for cross-lingual parameter transfer and outperforms models of comparable size trained from scratch with up to 64x less training effort. Our method makes training large language models for new languages more accessible and less damaging to the environment. We make our code and models publicly available.", } ```
benjamin/gpt2-wechsel-sundanese
73e26088c229d0a624e91137b159089d27a299c9
2022-07-13T23:45:18.000Z
[ "pytorch", "gpt2", "text-generation", "su", "transformers", "license:mit" ]
text-generation
false
benjamin
null
benjamin/gpt2-wechsel-sundanese
5
null
transformers
17,163
--- language: su license: mit --- # gpt2-wechsel-sundanese Model trained with WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models. See the code here: https://github.com/CPJKU/wechsel And the paper here: https://aclanthology.org/2022.naacl-main.293/ ## Performance | Model | PPL | |---|---| | `gpt2-wechsel-sundanese` | **111.72** | | `gpt2` (retrained from scratch) | 149.46 | | Model | PPL | |---|---| | `gpt2-wechsel-scottish-gaelic` | **16.43** | | `gpt2` (retrained from scratch) | 19.53 | | Model | PPL | |---|---| | `gpt2-wechsel-uyghur` | **34.33** | | `gpt2` (retrained from scratch) | 42.82 | | Model | PPL | |---|---| | `gpt2-wechsel-malagasy` | **14.01** | | `gpt2` (retrained from scratch) | 15.93 | See our paper for details. ## Citation Please cite WECHSEL as ``` @inproceedings{minixhofer-etal-2022-wechsel, title = "{WECHSEL}: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models", author = "Minixhofer, Benjamin and Paischer, Fabian and Rekabsaz, Navid", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", address = "Seattle, United States", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.naacl-main.293", pages = "3992--4006", abstract = "Large pretrained language models (LMs) have become the central building block of many NLP applications. Training these models requires ever more computational resources and most of the existing models are trained on English text only. It is exceedingly expensive to train these models in other languages. To alleviate this problem, we introduce a novel method {--} called WECHSEL {--} to efficiently and effectively transfer pretrained LMs to new languages. WECHSEL can be applied to any model which uses subword-based tokenization and learns an embedding for each subword. The tokenizer of the source model (in English) is replaced with a tokenizer in the target language and token embeddings are initialized such that they are semantically similar to the English tokens by utilizing multilingual static word embeddings covering English and the target language. We use WECHSEL to transfer the English RoBERTa and GPT-2 models to four languages (French, German, Chinese and Swahili). We also study the benefits of our method on very low-resource languages. WECHSEL improves over proposed methods for cross-lingual parameter transfer and outperforms models of comparable size trained from scratch with up to 64x less training effort. Our method makes training large language models for new languages more accessible and less damaging to the environment. We make our code and models publicly available.", } ```
Khalsuu/english-filipino-wav2vec2-l-xls-r-test-02
88fa97ae20acdec8b64ad64f5eac44d7ea3172b9
2022-05-05T22:56:01.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "dataset:filipino_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
Khalsuu
null
Khalsuu/english-filipino-wav2vec2-l-xls-r-test-02
5
null
transformers
17,164
--- license: apache-2.0 tags: - generated_from_trainer datasets: - filipino_voice model-index: - name: english-filipino-wav2vec2-l-xls-r-test-02 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # english-filipino-wav2vec2-l-xls-r-test-02 This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the filipino_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4561 - Wer: 0.2632 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.1707 | 2.09 | 400 | 0.8006 | 0.8224 | | 0.4801 | 4.19 | 800 | 0.3363 | 0.4329 | | 0.2541 | 6.28 | 1200 | 0.3365 | 0.3676 | | 0.1851 | 8.38 | 1600 | 0.3485 | 0.3739 | | 0.1408 | 10.47 | 2000 | 0.3628 | 0.3420 | | 0.1098 | 12.57 | 2400 | 0.3979 | 0.3277 | | 0.1019 | 14.66 | 2800 | 0.4031 | 0.2896 | | 0.0887 | 16.75 | 3200 | 0.3977 | 0.3024 | | 0.0798 | 18.85 | 3600 | 0.3959 | 0.3129 | | 0.0671 | 20.94 | 4000 | 0.4489 | 0.3241 | | 0.0633 | 23.04 | 4400 | 0.4455 | 0.3026 | | 0.055 | 25.13 | 4800 | 0.4668 | 0.2910 | | 0.0523 | 27.23 | 5200 | 0.4670 | 0.2960 | | 0.0468 | 29.32 | 5600 | 0.4536 | 0.2781 | | 0.0392 | 31.41 | 6000 | 0.4612 | 0.2860 | | 0.0381 | 33.51 | 6400 | 0.4651 | 0.2841 | | 0.034 | 35.6 | 6800 | 0.4723 | 0.2716 | | 0.0315 | 37.7 | 7200 | 0.4546 | 0.2642 | | 0.0294 | 39.79 | 7600 | 0.4561 | 0.2632 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
jimregan/wav2vec-ljspeech-splits
401eeb2f6f79661b3c4ddd2ac2d1cc5dfb9fcbf2
2022-05-06T19:56:52.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "license:apache-2.0" ]
automatic-speech-recognition
false
jimregan
null
jimregan/wav2vec-ljspeech-splits
5
null
transformers
17,165
--- license: apache-2.0 ---
birgermoell/liepa-lithuanian
b56c3adf3543901f3e4986e3155ea251daf77fc8
2022-05-06T13:10:45.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
birgermoell
null
birgermoell/liepa-lithuanian
5
null
transformers
17,166
Entry not found
allenai/tk-instruct-3b-def-pos-neg
252a02ec7005f103a212f359684a6b83456af558
2022-05-27T06:30:55.000Z
[ "pytorch", "t5", "text2text-generation", "en", "dataset:natural instructions v2.0", "arxiv:1910.10683", "arxiv:2204.07705", "transformers", "license:apache-2.0", "autotrain_compatible" ]
text2text-generation
false
allenai
null
allenai/tk-instruct-3b-def-pos-neg
5
null
transformers
17,167
--- language: en license: apache-2.0 datasets: - natural instructions v2.0 --- # Model description Tk-Instruct is a series of encoder-decoder Transformer models that are trained to solve various NLP tasks by following in-context instructions (plain language task definitions, k-shot examples, explanations, etc). Built upon the pre-trained [T5 models](https://arxiv.org/abs/1910.10683), they are fine-tuned on a large number of tasks & instructions that are collected in the [Natural Instructions benchmark](https://github.com/allenai/natural-instructions), which contains 1600+ tasks in 70+ broach categories in total. This enables the model to not only process the training tasks, but also generalize to many unseen tasks without further parameter update. More resources for using the model: - **Paper**: [link](https://arxiv.org/abs/2204.07705) - **Code repository**: [Tk-Instruct](https://github.com/yizhongw/Tk-Instruct) - **Official Website**: [Natural Instructions](https://instructions.apps.allenai.org/) - **All released models**: [allenai/tk-instruct](https://huggingface.co/models?search=allenai/tk-instruct) ## Intended uses & limitations Tk-Instruct can be used to do many NLP tasks by following instructions. ### How to use When instructing the model, task definition or demonstration examples or explanations should be prepended to the original input and fed into the model. You can easily try Tk-Instruct models as follows: ```python >>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM >>> tokenizer = AutoTokenizer.from_pretrained("allenai/tk-instruct-3b-def") >>> model = AutoModelForSeq2SeqLM.from_pretrained("allenai/tk-instruct-3b-def") >>> input_ids = tokenizer.encode( "Definition: return the currency of the given country. Now complete the following example - Input: India. Output:", return_tensors="pt") >>> output = model.generate(input_ids, max_length=10) >>> output = tokenizer.decode(output[0], skip_special_tokens=True) # model should output 'Indian Rupee' >>> input_ids = tokenizer.encode( "Definition: negate the following sentence. Input: John went to school. Output:", return_tensors="pt") >>> output = model.generate(input_ids, max_length=10) >>> output = tokenizer.decode(output[0], skip_special_tokens=True) # model should output 'John did not go to shool.' ``` ### Limitations We are still working on understanding the behaviors of these models, but here are several issues we have found: - Models are generally sensitive to the instruction. Sometimes rewording the instruction can lead to very different output. - Models are not always compliant to the instruction. Sometimes the model don't follow your instruction (e.g., when you ask the model to generate one sentence, it might still generate one word or a long story). - Models might totally fail on some tasks. If you find serious issues or any interesting result, you are welcome to share with us! ## Training data Tk-Instruct is trained using the tasks & instructions in [Natural Instructions benchmark](https://github.com/allenai/natural-instructions), which contains 1600+ tasks in 70+ broach categories in total. We follow the official train/test split. Tk-Instruct model series were trained using 757 tasks, and mTk-Instruct series were trained using 1271 tasks (including some non-English tasks). The training tasks are in 64 broad categories, such as text categorization / question answering / sentiment analysis / summarization / grammar error detection / dialogue generation / etc. The other 12 categories are selected for evaluation. ## Training procedure All our models are initialized from either T5 models or mT5 models. Because generating the output can be regarded as a form of language modeling, we used their [LM adapted version](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-t511lm100k). All data is converted into a text-to-text format, and models are fine-tuned to maximize the likelihood of the output sequence. Our [released models](https://huggingface.co/models?search=allenai/tk-instruct) are in different sizes, and each of them was trained with a specific type of instruction encoding. For instance, `tk-instruct-3b-def-pos` was initialized from [t5-xl-lm-adapt](https://huggingface.co/google/t5-xl-lm-adapt), and it saw task definition & 2 positive examples as the instruction during training time. Although they are trained with only one type of instruction encodings, we found they can usually work with other type of encodings at test time (see more in our paper). ### BibTeX entry and citation info ```bibtex @article{wang2022benchmarking, title={Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks}, author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and A. Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and M. Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddharth Deepak Mishra and Sujan C. Reddy and Sumanta Patro and Tanay Dixit and Xu-dong Shen and Chitta Baral and Yejin Choi and Hannaneh Hajishirzi and Noah A. Smith and Daniel Khashabi}, year={2022}, archivePrefix={arXiv}, eprint={2204.07705}, primaryClass={cs.CL}, } ```
chrishistewandb/hugging-face
3236dd0ec6901dc8e38f48606c234e0a2c79ec80
2022-05-11T19:49:11.000Z
[ "pytorch", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
chrishistewandb
null
chrishistewandb/hugging-face
5
null
transformers
17,168
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: hugging-face 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. --> # hugging-face This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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 ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.2.1 - Tokenizers 0.12.1
omar47/wav2vec2-large-xls-r-300m-urdu-v2
e9346a4ed9380cd80d5078de2081c1b26322b288
2022-05-14T04:53:01.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
omar47
null
omar47/wav2vec2-large-xls-r-300m-urdu-v2
5
null
transformers
17,169
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xls-r-300m-urdu-CV_8_0-and-PRUS_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. --> # wav2vec2-large-xls-r-300m-urdu-CV_8_0-and-PRUS_v2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3541 - Wer: 0.6532 ## 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: 25 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 14.8521 | 0.52 | 32 | 20.0617 | 1.0 | | 9.2152 | 1.05 | 64 | 7.8943 | 1.0 | | 4.8598 | 1.57 | 96 | 5.1558 | 1.0 | | 3.866 | 2.1 | 128 | 3.9680 | 1.0 | | 3.3517 | 2.62 | 160 | 3.4201 | 1.0 | | 3.2029 | 3.15 | 192 | 3.2355 | 1.0 | | 3.1509 | 3.67 | 224 | 3.2337 | 1.0 | | 3.1399 | 4.2 | 256 | 3.1627 | 1.0 | | 3.0848 | 4.72 | 288 | 3.0550 | 1.0 | | 2.9806 | 5.25 | 320 | 2.8343 | 0.9996 | | 2.3814 | 5.77 | 352 | 2.0685 | 0.9523 | | 1.2936 | 6.3 | 384 | 1.5907 | 0.8657 | | 0.8656 | 6.82 | 416 | 1.3810 | 0.8235 | | 0.7014 | 7.34 | 448 | 1.3838 | 0.7920 | | 0.6015 | 7.87 | 480 | 1.3479 | 0.8046 | | 0.5341 | 8.39 | 512 | 1.2613 | 0.7757 | | 0.5031 | 8.92 | 544 | 1.2818 | 0.7890 | | 0.4349 | 9.44 | 576 | 1.3171 | 0.7739 | | 0.4198 | 9.97 | 608 | 1.2420 | 0.7750 | | 0.3593 | 10.49 | 640 | 1.2991 | 0.7587 | | 0.3252 | 11.02 | 672 | 1.2653 | 0.7228 | | 0.2715 | 11.54 | 704 | 1.2488 | 0.7350 | | 0.2733 | 12.07 | 736 | 1.2639 | 0.7110 | | 0.2338 | 12.59 | 768 | 1.3733 | 0.7454 | | 0.2403 | 13.11 | 800 | 1.3908 | 0.7228 | | 0.2106 | 13.64 | 832 | 1.3384 | 0.7224 | | 0.2041 | 14.16 | 864 | 1.3770 | 0.7050 | | 0.1814 | 14.69 | 896 | 1.3526 | 0.6932 | | 0.1742 | 15.21 | 928 | 1.3486 | 0.6895 | | 0.1658 | 15.74 | 960 | 1.3210 | 0.6936 | | 0.1455 | 16.26 | 992 | 1.3292 | 0.6858 | | 0.1399 | 16.79 | 1024 | 1.3521 | 0.6828 | | 0.1325 | 17.31 | 1056 | 1.3339 | 0.6876 | | 0.1256 | 17.84 | 1088 | 1.3389 | 0.6836 | | 0.1219 | 18.36 | 1120 | 1.3496 | 0.6769 | | 0.1212 | 18.89 | 1152 | 1.3277 | 0.6776 | | 0.1097 | 19.41 | 1184 | 1.3594 | 0.6762 | | 0.1129 | 19.93 | 1216 | 1.3448 | 0.6688 | | 0.1036 | 20.46 | 1248 | 1.3295 | 0.6710 | | 0.1035 | 20.98 | 1280 | 1.3243 | 0.6577 | | 0.094 | 21.51 | 1312 | 1.3832 | 0.6591 | | 0.0912 | 22.03 | 1344 | 1.3857 | 0.6584 | | 0.0815 | 22.56 | 1376 | 1.3739 | 0.6547 | | 0.0864 | 23.08 | 1408 | 1.3649 | 0.6554 | | 0.0772 | 23.61 | 1440 | 1.3791 | 0.6458 | | 0.0894 | 24.13 | 1472 | 1.3630 | 0.6488 | | 0.0776 | 24.66 | 1504 | 1.3541 | 0.6532 | ### Framework versions - Transformers 4.19.1 - Pytorch 1.11.0+cu113 - Datasets 2.2.1 - Tokenizers 0.12.1
miazhao/deberta_base_model_s3_ccnet_airbnb_dat_continue
328d9fb472f7bb6b4dd742f7ee25197ca927b9a3
2022-05-12T22:02:30.000Z
[ "pytorch", "deberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
miazhao
null
miazhao/deberta_base_model_s3_ccnet_airbnb_dat_continue
5
null
transformers
17,170
Entry not found
Jiexing/spider_relation_t5_3b-2624
bea41b2abb1734e76cf318bf38380b3a6a44fd9e
2022-05-08T01:49:12.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
Jiexing
null
Jiexing/spider_relation_t5_3b-2624
5
null
transformers
17,171
Entry not found
Jeevesh8/bert_ft_cola-0
ee1d00855b173f564ff2d47a4e9a9f1f10443e81
2022-05-09T08:58:40.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
Jeevesh8
null
Jeevesh8/bert_ft_cola-0
5
null
transformers
17,172
Entry not found
Chramer/remote-sensing-distilbert-cased
1cd7f7c7a7ca9aafce748696610d6b9e6356f055
2022-05-10T10:11:16.000Z
[ "pytorch", "distilbert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Chramer
null
Chramer/remote-sensing-distilbert-cased
5
1
transformers
17,173
--- widget: - text: "Earth [MASK] is a growing field." - text: "Multiple [MASK] channels enable full polarimetry" - text: "The [MASK] is capable of measuring in limb and nadir geometry" --- # RemoteSensing Distilbert ![alt text](https://media.istockphoto.com/photos/space-communications-satellite-in-low-orbit-around-the-earth-elements-picture-id1062473882?b=1&k=20&m=1062473882&s=170667a&w=0&h=KWJwGSiXBffLgKdaQTxY-eY7ljJE5_3khXgQyAQHPbU=) The field of earth observation is increasingly growing. More and more data scientists are interested about this domain, and they're developing computer vision applications that do amazing things, while NLP doesn't seem to be given much consideration in this area That's why I posted [Chramer/remote-sensing-distilbert-cased](https://huggingface.co/Chramer/remote-sensing-distilbert-cased). This is masked language model trained on a corpus of technical information about space missions, instruments, and sensors. The model is based on [distilbert-base-cased](https://huggingface.co/distilbert-base-uncased), but I didn't have the chance to play with the hyperparameters of the model because of the limited computational capabilities I have. So there's a lot to improve! 😆 It was fun to publish my first model on hugging face! 🤩 **Author:** Marcello Politi ([Twitter 🐦](https://twitter.com/_March08_) ,[LinkedIn 💼](https://www.linkedin.com/in/marcello-politi/)). # Perplexity Test set: 4.5k sentences about technical space stuff. | Model | Perplexity | | ------ | ------ | | remote-sensing-distilbert-cased | **6.45** | | distilbert-base-cased | 33.77 | # Usage ```python from transformers import AutoModel, AutoTokenizer model_name = "Chramer/remote-sensing-distilbert-cased" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModel.from_pretrained(model_name) ```
Jeevesh8/bert_ft_cola-11
b56b405dd4cc966cd9c23a029cd9bb791099baff
2022-05-09T14:01:29.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
Jeevesh8
null
Jeevesh8/bert_ft_cola-11
5
null
transformers
17,174
Entry not found
Jeevesh8/bert_ft_cola-12
4bb3b2d0a54bbb05295ed4bbb56c8ddbdd4d04c5
2022-05-09T14:02:09.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
Jeevesh8
null
Jeevesh8/bert_ft_cola-12
5
null
transformers
17,175
Entry not found
Jeevesh8/bert_ft_cola-20
d2a74272ba07f781c89c2d078cbf5a210c7c5bd7
2022-05-09T14:07:28.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
Jeevesh8
null
Jeevesh8/bert_ft_cola-20
5
null
transformers
17,176
Entry not found
Jeevesh8/bert_ft_cola-21
472ec9ff54bd0c00ce1dba748921df5ae0147b6a
2022-05-09T14:08:07.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
Jeevesh8
null
Jeevesh8/bert_ft_cola-21
5
null
transformers
17,177
Entry not found
Jeevesh8/bert_ft_cola-39
e76f64290dd9a535305e7653697ee729e76617fc
2022-05-09T14:19:54.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
Jeevesh8
null
Jeevesh8/bert_ft_cola-39
5
null
transformers
17,178
Entry not found
Jeevesh8/bert_ft_cola-45
35b56409280e7376f4282b22c5d5d380d8d9ac43
2022-05-09T14:24:05.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
Jeevesh8
null
Jeevesh8/bert_ft_cola-45
5
null
transformers
17,179
Entry not found
Jeevesh8/bert_ft_cola-55
e829ad8278d15b64515a73117aa93574c30aa7ae
2022-05-09T14:30:43.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
Jeevesh8
null
Jeevesh8/bert_ft_cola-55
5
null
transformers
17,180
Entry not found
Jeevesh8/bert_ft_cola-59
6533ddef00b478ed6f530405181183b294801624
2022-05-09T14:33:25.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
Jeevesh8
null
Jeevesh8/bert_ft_cola-59
5
null
transformers
17,181
Entry not found
Jeevesh8/bert_ft_cola-62
538d03ffb3426b8bda53b00326026936f35ae30c
2022-05-09T14:35:24.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
Jeevesh8
null
Jeevesh8/bert_ft_cola-62
5
null
transformers
17,182
Entry not found
Jeevesh8/bert_ft_cola-63
954328c12b11a1bb0818d5b8c5412a7d5fa0ec8e
2022-05-09T14:36:03.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
Jeevesh8
null
Jeevesh8/bert_ft_cola-63
5
null
transformers
17,183
Entry not found
Jeevesh8/bert_ft_cola-65
a5b1b57e1b53fadf8169e11d411dd585acdba5bf
2022-05-09T14:37:23.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
Jeevesh8
null
Jeevesh8/bert_ft_cola-65
5
null
transformers
17,184
Entry not found
Jeevesh8/bert_ft_cola-79
bd9284f703518135f76668bbd0c7492f5459eba6
2022-05-09T14:46:46.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
Jeevesh8
null
Jeevesh8/bert_ft_cola-79
5
null
transformers
17,185
Entry not found
Jeevesh8/bert_ft_cola-83
75ab1e6d032428b19fb28e5e0d16caf818fe1bf0
2022-05-09T14:49:28.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
Jeevesh8
null
Jeevesh8/bert_ft_cola-83
5
null
transformers
17,186
Entry not found
Jeevesh8/bert_ft_cola-85
7ff84c04bc55248e9962963cca0263b454dff793
2022-05-09T14:50:48.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
Jeevesh8
null
Jeevesh8/bert_ft_cola-85
5
null
transformers
17,187
Entry not found
princeton-nlp/CoFi-CoLA-s95
bd503bc66975127fb1985209f66864f5cc751db3
2022-05-09T15:24:06.000Z
[ "pytorch", "bert", "text-classification", "arxiv:2204.00408", "transformers" ]
text-classification
false
princeton-nlp
null
princeton-nlp/CoFi-CoLA-s95
5
null
transformers
17,188
This is a model checkpoint for "[Structured Pruning Learns Compact and Accurate Models](https://arxiv.org/pdf/2204.00408.pdf)". The model is pruned from `bert-base-uncased` to a 95% sparsity on dataset CoLA. Please go to [our repository](https://github.com/princeton-nlp/CoFiPruning) for more details on how to use the model for inference. Note that you would have to use the model class specified in our repository to load the model.
Santiagot1105/wav2vec2-large-xlsr-es-col-pro
55d281c6d325b83fac1b8e63295ea2044504357e
2022-05-10T11:19:34.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
Santiagot1105
null
Santiagot1105/wav2vec2-large-xlsr-es-col-pro
5
null
transformers
17,189
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xlsr-es-col-pro results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-es-col-pro This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-spanish](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0636 - Wer: 0.0507 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1032 | 7.4 | 400 | 0.0618 | 0.0656 | | 0.0687 | 14.81 | 800 | 0.0670 | 0.0619 | | 0.0402 | 22.22 | 1200 | 0.0693 | 0.0573 | | 0.0252 | 29.62 | 1600 | 0.0636 | 0.0507 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.1+cu102 - Datasets 1.13.3 - Tokenizers 0.10.3
mrm8488/electricidad-base-finetuned-parmex
67664a9a539bcf3618abd53a6bf638cd825090eb
2022-05-10T08:18:19.000Z
[ "pytorch", "tensorboard", "electra", "text-classification", "transformers", "generated_from_trainer", "model-index" ]
text-classification
false
mrm8488
null
mrm8488/electricidad-base-finetuned-parmex
5
1
transformers
17,190
--- tags: - generated_from_trainer metrics: - f1 model-index: - name: electricidad-base-finetuned-parmex 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. --> # electricidad-base-finetuned-parmex This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0372 - F1: 0.9764 ## 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: 8.309269976237555e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 208 | 0.0377 | 0.9801 | | No log | 2.0 | 416 | 0.0372 | 0.9764 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
selimonder/gptj-bswiki-8bit
3a856072f7f76f8157d92de53aff058103642dd7
2022-05-10T09:30:07.000Z
[ "pytorch", "gptj", "text-generation", "transformers" ]
text-generation
false
selimonder
null
selimonder/gptj-bswiki-8bit
5
null
transformers
17,191
Entry not found
lucifermorninstar011/autotrain-defector_ner-846726994
bdbb11b3fb6d723a93fb92d176279ca1f3868c0f
2022-05-10T11:58:10.000Z
[ "pytorch", "distilbert", "token-classification", "en", "dataset:lucifermorninstar011/autotrain-data-defector_ner", "transformers", "autotrain", "co2_eq_emissions", "autotrain_compatible" ]
token-classification
false
lucifermorninstar011
null
lucifermorninstar011/autotrain-defector_ner-846726994
5
null
transformers
17,192
--- tags: autotrain language: en widget: - text: "I love AutoTrain 🤗" datasets: - lucifermorninstar011/autotrain-data-defector_ner co2_eq_emissions: 101.31873212485134 --- # Model Trained Using AutoTrain - Problem type: Entity Extraction - Model ID: 846726994 - CO2 Emissions (in grams): 101.31873212485134 ## Validation Metrics - Loss: 0.032001420855522156 - Accuracy: 0.9895226362258249 - Precision: 0.9431602948450375 - Recall: 0.9486306771989856 - F1: 0.945887576828147 ## 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/lucifermorninstar011/autotrain-defector_ner-846726994 ``` Or Python API: ``` from transformers import AutoModelForTokenClassification, AutoTokenizer model = AutoModelForTokenClassification.from_pretrained("lucifermorninstar011/autotrain-defector_ner-846726994", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("lucifermorninstar011/autotrain-defector_ner-846726994", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
Kepa/exist_task1_es
67a2156a1b82c35fd0a7fcc78f34829991294c66
2022-05-10T14:08:10.000Z
[ "pytorch", "xlm-roberta", "transformers" ]
null
false
Kepa
null
Kepa/exist_task1_es
5
null
transformers
17,193
florentgbelidji/setfit_emotion
63f01ba380f0c0672d48aa25674ac048493cd975
2022-05-10T12:57:31.000Z
[ "pytorch", "mpnet", "feature-extraction", "sentence-transformers", "sentence-similarity" ]
sentence-similarity
false
florentgbelidji
null
florentgbelidji/setfit_emotion
5
null
sentence-transformers
17,194
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # florentgbelidji/setfit_emotion 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('florentgbelidji/setfit_emotion') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=florentgbelidji/setfit_emotion) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 203 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.dataloader._InfiniteConstantSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.BatchHardTripletLoss.BatchHardTripletLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 4060, "warmup_steps": 406, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
taln-ls2n/POET
633769c260b7f4116dc0e0fa3068b69083f04964
2022-07-06T23:49:35.000Z
[ "pytorch", "camembert", "token-classification", "fr", "dataset:qanastek/ANTILLES", "arxiv:1911.03894", "transformers", "Transformers", "sequence-tagger-model", "autotrain_compatible" ]
token-classification
false
taln-ls2n
null
taln-ls2n/POET
5
1
transformers
17,195
--- tags: - Transformers - token-classification - sequence-tagger-model language: fr datasets: - qanastek/ANTILLES widget: - text: "George Washington est allé à Washington" --- # POET: A French Extended Part-of-Speech Tagger - Corpora: [ANTILLES](https://github.com/qanastek/ANTILLES) - Embeddings & Sequence Labelling: [CamemBERT](https://arxiv.org/abs/1911.03894) - Number of Epochs: 115 **People Involved** * [LABRAK Yanis](https://www.linkedin.com/in/yanis-labrak-8a7412145/) (1) * [DUFOUR Richard](https://cv.archives-ouvertes.fr/richard-dufour) (2) **Affiliations** 1. [LIA, NLP team](https://lia.univ-avignon.fr/), Avignon University, Avignon, France. 2. [LS2N, TALN team](https://www.ls2n.fr/equipe/taln/), Nantes University, Nantes, France. ## Demo: How to use in HuggingFace Transformers Requires [transformers](https://pypi.org/project/transformers/): ```pip install transformers``` ```python from transformers import CamembertTokenizer, CamembertForTokenClassification, TokenClassificationPipeline tokenizer = CamembertTokenizer.from_pretrained('taln-ls2n/POET') model = CamembertForTokenClassification.from_pretrained('taln-ls2n/POET') pos = TokenClassificationPipeline(model=model, tokenizer=tokenizer) def make_prediction(sentence): labels = [l['entity'] for l in pos(sentence)] return list(zip(sentence.split(" "), labels)) res = make_prediction("George Washington est allé à Washington") ``` Output: ![Preview Output](preview.PNG) ## Training data `ANTILLES` is a part-of-speech tagging corpora based on [UD_French-GSD](https://universaldependencies.org/treebanks/fr_gsd/index.html) which was originally created in 2015 and is based on the [universal dependency treebank v2.0](https://github.com/ryanmcd/uni-dep-tb). Originally, the corpora consists of 400,399 words (16,341 sentences) and had 17 different classes. Now, after applying our tags augmentation we obtain 60 different classes which add linguistic and semantic information such as the gender, number, mood, person, tense or verb form given in the different CoNLL-03 fields from the original corpora. We based our tags on the level of details given by the [LIA_TAGG](http://pageperso.lif.univ-mrs.fr/frederic.bechet/download.html) statistical POS tagger written by [Frédéric Béchet](http://pageperso.lif.univ-mrs.fr/frederic.bechet/index-english.html) in 2001. The corpora used for this model is available on [Github](https://github.com/qanastek/ANTILLES) at the [CoNLL-U format](https://universaldependencies.org/format.html). Training data are fed to the model as free language and doesn't pass a normalization phase. Thus, it's made the model case and punctuation sensitive. ## Original Tags ```plain PRON VERB SCONJ ADP CCONJ DET NOUN ADJ AUX ADV PUNCT PROPN NUM SYM PART X INTJ ``` ## New additional POS tags | Abbreviation | Description | Examples | |:--------:|:--------:|:--------:| | PREP | Preposition | de | | AUX | Auxiliary Verb | est | | ADV | Adverb | toujours | | COSUB | Subordinating conjunction | que | | COCO | Coordinating Conjunction | et | | PART | Demonstrative particle | -t | | PRON | Pronoun | qui ce quoi | | PDEMMS | Demonstrative Pronoun - Singular Masculine | ce | | PDEMMP | Demonstrative Pronoun - Plural Masculine | ceux | | PDEMFS | Demonstrative Pronoun - Singular Feminine | cette | | PDEMFP | Demonstrative Pronoun - Plural Feminine | celles | | PINDMS | Indefinite Pronoun - Singular Masculine | tout | | PINDMP | Indefinite Pronoun - Plural Masculine | autres | | PINDFS | Indefinite Pronoun - Singular Feminine | chacune | | PINDFP | Indefinite Pronoun - Plural Feminine | certaines | | PROPN | Proper noun | Houston | | XFAMIL | Last name | Levy | | NUM | Numerical Adjective | trentaine vingtaine | | DINTMS | Masculine Numerical Adjective | un | | DINTFS | Feminine Numerical Adjective | une | | PPOBJMS | Pronoun complements of objects - Singular Masculine | le lui | | PPOBJMP | Pronoun complements of objects - Plural Masculine | eux y | | PPOBJFS | Pronoun complements of objects - Singular Feminine | moi la | | PPOBJFP | Pronoun complements of objects - Plural Feminine | en y | | PPER1S | Personal Pronoun First-Person - Singular | je | | PPER2S | Personal Pronoun Second-Person - Singular | tu | | PPER3MS | Personal Pronoun Third-Person - Singular Masculine | il | | PPER3MP | Personal Pronoun Third-Person - Plural Masculine | ils | | PPER3FS | Personal Pronoun Third-Person - Singular Feminine | elle | | PPER3FP | Personal Pronoun Third-Person - Plural Feminine | elles | | PREFS | Reflexive Pronoun First-Person - Singular | me m' | | PREF | Reflexive Pronoun Third-Person - Singular | se s' | | PREFP | Reflexive Pronoun First / Second-Person - Plural | nous vous | | VERB | Verb | obtient | | VPPMS | Past Participle - Singular Masculine | formulé | | VPPMP | Past Participle - Plural Masculine | classés | | VPPFS | Past Participle - Singular Feminine | appelée | | VPPFP | Past Participle - Plural Feminine | sanctionnées | | DET | Determinant | les l' | | DETMS | Determinant - Singular Masculine | les | | DETFS | Determinant - Singular Feminine | la | | ADJ | Adjective | capable sérieux | | ADJMS | Adjective - Singular Masculine | grand important | | ADJMP | Adjective - Plural Masculine | grands petits | | ADJFS | Adjective - Singular Feminine | française petite | | ADJFP | Adjective - Plural Feminine | légères petites | | NOUN | Noun | temps | | NMS | Noun - Singular Masculine | drapeau | | NMP | Noun - Plural Masculine | journalistes | | NFS | Noun - Singular Feminine | tête | | NFP | Noun - Plural Feminine | ondes | | PREL | Relative Pronoun | qui dont | | PRELMS | Relative Pronoun - Singular Masculine | lequel | | PRELMP | Relative Pronoun - Plural Masculine | lesquels | | PRELFS | Relative Pronoun - Singular Feminine | laquelle | | PRELFP | Relative Pronoun - Plural Feminine | lesquelles | | INTJ | Interjection | merci bref | | CHIF | Numbers | 1979 10 | | SYM | Symbol | € % | | YPFOR | Endpoint | . | | PUNCT | Ponctuation | : , | | MOTINC | Unknown words | Technology Lady | | X | Typos & others | sfeir 3D statu | ## Evaluation results The test corpora used for this evaluation is available on [Github](https://github.com/qanastek/ANTILLES/blob/main/ANTILLES/test.conllu). ```plain precision recall f1-score support ADJ 0.9040 0.8828 0.8933 128 ADJFP 0.9811 0.9585 0.9697 434 ADJFS 0.9606 0.9826 0.9715 918 ADJMP 0.9613 0.9357 0.9483 451 ADJMS 0.9561 0.9611 0.9586 952 ADV 0.9870 0.9948 0.9908 1524 AUX 0.9956 0.9964 0.9960 1124 CHIF 0.9798 0.9774 0.9786 1239 COCO 1.0000 0.9989 0.9994 884 COSUB 0.9939 0.9939 0.9939 328 DET 0.9972 0.9972 0.9972 2897 DETFS 0.9990 1.0000 0.9995 1007 DETMS 1.0000 0.9993 0.9996 1426 DINTFS 0.9967 0.9902 0.9934 306 DINTMS 0.9923 0.9948 0.9935 387 INTJ 0.8000 0.8000 0.8000 5 MOTINC 0.5049 0.5827 0.5410 266 NFP 0.9807 0.9675 0.9740 892 NFS 0.9778 0.9699 0.9738 2588 NMP 0.9687 0.9495 0.9590 1367 NMS 0.9759 0.9560 0.9659 3181 NOUN 0.6164 0.8673 0.7206 113 NUM 0.6250 0.8333 0.7143 6 PART 1.0000 0.9375 0.9677 16 PDEMFP 1.0000 1.0000 1.0000 3 PDEMFS 1.0000 1.0000 1.0000 89 PDEMMP 1.0000 1.0000 1.0000 20 PDEMMS 1.0000 1.0000 1.0000 222 PINDFP 1.0000 1.0000 1.0000 3 PINDFS 0.8571 1.0000 0.9231 12 PINDMP 0.9000 1.0000 0.9474 9 PINDMS 0.9286 0.9701 0.9489 67 PINTFS 0.0000 0.0000 0.0000 2 PPER1S 1.0000 1.0000 1.0000 62 PPER2S 0.7500 1.0000 0.8571 3 PPER3FP 1.0000 1.0000 1.0000 9 PPER3FS 1.0000 1.0000 1.0000 96 PPER3MP 1.0000 1.0000 1.0000 31 PPER3MS 1.0000 1.0000 1.0000 377 PPOBJFP 1.0000 0.7500 0.8571 4 PPOBJFS 0.9167 0.8919 0.9041 37 PPOBJMP 0.7500 0.7500 0.7500 12 PPOBJMS 0.9371 0.9640 0.9504 139 PREF 1.0000 1.0000 1.0000 332 PREFP 1.0000 1.0000 1.0000 64 PREFS 1.0000 1.0000 1.0000 13 PREL 0.9964 0.9964 0.9964 277 PRELFP 1.0000 1.0000 1.0000 5 PRELFS 0.8000 1.0000 0.8889 4 PRELMP 1.0000 1.0000 1.0000 3 PRELMS 1.0000 1.0000 1.0000 11 PREP 0.9971 0.9977 0.9974 6161 PRON 0.9836 0.9836 0.9836 61 PROPN 0.9468 0.9503 0.9486 4310 PUNCT 1.0000 1.0000 1.0000 4019 SYM 0.9394 0.8158 0.8732 76 VERB 0.9956 0.9921 0.9938 2273 VPPFP 0.9145 0.9469 0.9304 113 VPPFS 0.9562 0.9597 0.9580 273 VPPMP 0.8827 0.9728 0.9256 147 VPPMS 0.9778 0.9794 0.9786 630 VPPRE 0.0000 0.0000 0.0000 1 X 0.9604 0.9935 0.9766 1073 XFAMIL 0.9386 0.9113 0.9248 1342 YPFOR 1.0000 1.0000 1.0000 2750 accuracy 0.9778 47574 macro avg 0.9151 0.9285 0.9202 47574 weighted avg 0.9785 0.9778 0.9780 47574 ``` ## BibTeX Citations Please cite the following paper when using this model. ANTILLES corpus and POET taggers: ```latex @inproceedings{labrak:hal-03696042, TITLE = {{ANTILLES: An Open French Linguistically Enriched Part-of-Speech Corpus}}, AUTHOR = {Labrak, Yanis and Dufour, Richard}, URL = {https://hal.archives-ouvertes.fr/hal-03696042}, BOOKTITLE = {{25th International Conference on Text, Speech and Dialogue (TSD)}}, ADDRESS = {Brno, Czech Republic}, PUBLISHER = {{Springer}}, YEAR = {2022}, MONTH = Sep, KEYWORDS = {Part-of-speech corpus ; POS tagging ; Open tools ; Word embeddings ; Bi-LSTM ; CRF ; Transformers}, PDF = {https://hal.archives-ouvertes.fr/hal-03696042/file/ANTILLES_A_freNch_linguisTIcaLLy_Enriched_part_of_Speech_corpus.pdf}, HAL_ID = {hal-03696042}, HAL_VERSION = {v1}, } ``` UD_French-GSD corpora: ```latex @misc{ universaldependencies, title={UniversalDependencies/UD_French-GSD}, url={https://github.com/UniversalDependencies/UD_French-GSD}, journal={GitHub}, author={UniversalDependencies} } ``` LIA TAGG: ```latex @techreport{LIA_TAGG, author = {Frédéric Béchet}, title = {LIA_TAGG: a statistical POS tagger + syntactic bracketer}, institution = {Aix-Marseille University & CNRS}, year = {2001} } ``` Flair Embeddings: ```latex @inproceedings{akbik2018coling, title={Contextual String Embeddings for Sequence Labeling}, author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland}, booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics}, pages = {1638--1649}, year = {2018} } ``` ## Acknowledgment This work was financially supported by [Zenidoc](https://zenidoc.fr/) and the [ANR project DIETS](https://anr-diets.univ-avignon.fr) under the contract [ANR-20-CE23-0005](https://anr.fr/en/funded-projects-and-impact/funded-projects/project/funded/project/b2d9d3668f92a3b9fbbf7866072501ef-fd7e69d902/?tx_anrprojects_funded%5Bcontroller%5D=Funded&cHash=cb6d54d24c9e21e0d50fabf46bd56646).
wvangils/DistilGPT2-Beatles-Lyrics-finetuned
66ff22af3ea4ff3018bc409df0532b86bbeb21fa
2022-05-11T11:44:35.000Z
[ "pytorch", "tensorboard", "gpt2", "text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-generation
false
wvangils
null
wvangils/DistilGPT2-Beatles-Lyrics-finetuned
5
null
transformers
17,196
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: DistilGPT2-Beatles-Lyrics-finetuned results: [] widget: - text: "Last night in Kiev the" example_title: "Kiev" - text: "It hasn't rained in weeks" example_title: "Rain" --- # DistilGPT2-Beatles-Lyrics-finetuned This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the [Huggingartists - beatles](https://huggingface.co/datasets/huggingartists/the-beatles) dataset. It will complete an input prompt with Beatles-like text. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.748 | 1.0 | 165 | 2.3732 | | 2.4395 | 2.0 | 330 | 2.1938 | | 2.2968 | 3.0 | 495 | 2.1118 | | 2.2075 | 4.0 | 660 | 2.0721 | | 2.1393 | 5.0 | 825 | 2.0571 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.2.0 - Tokenizers 0.12.1
Matthijs/mobilevit-x-small
98efb39f22843428ba6d78d5999f9fbdb844e1e3
2022-05-11T14:43:30.000Z
[ "pytorch", "mobilevit", "image-classification", "transformers" ]
image-classification
false
Matthijs
null
Matthijs/mobilevit-x-small
5
null
transformers
17,197
Entry not found
ceggian/sbert_pt_reddit_mnr_64
c014a59c01c7db8448544513616736b18e50ecfb
2022-05-11T20:10:24.000Z
[ "pytorch", "bert", "feature-extraction", "sentence-transformers", "sentence-similarity", "transformers" ]
sentence-similarity
false
ceggian
null
ceggian/sbert_pt_reddit_mnr_64
5
null
sentence-transformers
17,198
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 39289 with parameters: ``` {'batch_size': 8} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 3928, "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 -->
CleveGreen/FieldClassifier_v3_gpt
b932ba76842f85b240025869a901f4a81d7db79a
2022-05-11T20:39:29.000Z
[ "pytorch", "gpt2", "text-classification", "transformers" ]
text-classification
false
CleveGreen
null
CleveGreen/FieldClassifier_v3_gpt
5
null
transformers
17,199
Entry not found