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quantresearch/tst_t1_base_20_1
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quantresearch/tst_t1_base_20_2
9956f16c64a4e4582dd030229d76ffcd0665c860
2021-09-16T04:54:28.000Z
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quantresearch/tst_t2_reweight_10_2
76c59ea8cc9bb0c5390fcefebd5e164581006ee1
2021-09-16T09:36:55.000Z
[ "pytorch", "transformers" ]
null
false
quantresearch
null
quantresearch/tst_t2_reweight_10_2
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transformers
Entry not found
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rafagudinov/ru_rent_estate_ads
86a3803bd2c2094fbe14505edb1af26b412a8556
2022-01-14T00:00:40.000Z
[ "pytorch", "roberta", "transformers" ]
null
false
rafagudinov
null
rafagudinov/ru_rent_estate_ads
0
null
transformers
Entry not found
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rafanegrette/t5_spa_gua
097d3937eece017c1c63d0a2b3cfce5dff250f25
2021-11-21T17:53:33.000Z
[ "pytorch", "mt5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
rafanegrette
null
rafanegrette/t5_spa_gua
0
null
transformers
## Translator of Spanish/Wayuunaiki with T5 model ## This is a finetuned model based on T5 using a corpus of spanish-wayuunaiki. Wayuunaiki is the native language of the Wayuus, the major indigenous people in the north of Colombia.
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rafiulrumy/wav2vec2-large-xlsr-53-demo-colab
9437b304e45ae0f9779188a7fb147c1d607f0579
2021-12-16T05:09:16.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
rafiulrumy
null
rafiulrumy/wav2vec2-large-xlsr-53-demo-colab
0
null
transformers
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xlsr-53-demo-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-53-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 6.7860 - Wer: 1.1067 ## 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: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 8.2273 | 44.42 | 400 | 3.3544 | 1.0 | | 0.9228 | 88.84 | 800 | 4.7054 | 1.1601 | | 0.1423 | 133.32 | 1200 | 5.9489 | 1.1578 | | 0.0751 | 177.74 | 1600 | 5.5939 | 1.1717 | | 0.0554 | 222.21 | 2000 | 6.1230 | 1.1717 | | 0.0356 | 266.63 | 2400 | 6.2845 | 1.1613 | | 0.0288 | 311.11 | 2800 | 6.6109 | 1.2100 | | 0.0223 | 355.53 | 3200 | 6.5605 | 1.1299 | | 0.0197 | 399.95 | 3600 | 7.1242 | 1.1682 | | 0.0171 | 444.42 | 4000 | 7.2452 | 1.1578 | | 0.0149 | 488.84 | 4400 | 7.4048 | 1.0684 | | 0.0118 | 533.32 | 4800 | 6.6227 | 1.1172 | | 0.011 | 577.74 | 5200 | 6.7909 | 1.1566 | | 0.0095 | 622.21 | 5600 | 6.8088 | 1.1102 | | 0.0077 | 666.63 | 6000 | 7.4451 | 1.1311 | | 0.0062 | 711.11 | 6400 | 6.8486 | 1.0777 | | 0.0051 | 755.53 | 6800 | 6.8812 | 1.1241 | | 0.0051 | 799.95 | 7200 | 6.9987 | 1.1450 | | 0.0041 | 844.42 | 7600 | 7.3048 | 1.1323 | | 0.0044 | 888.84 | 8000 | 6.6644 | 1.1125 | | 0.0031 | 933.32 | 8400 | 6.6298 | 1.1148 | | 0.0027 | 977.74 | 8800 | 6.7860 | 1.1067 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3
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rafiulrumy/wav2vec2-large-xlsr-hindi-demo-colab
9632da4365a55d7a913f77da1d5613b15a63fc96
2021-12-08T07:47:56.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
rafiulrumy
null
rafiulrumy/wav2vec2-large-xlsr-hindi-demo-colab
0
null
transformers
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xlsr-hindi-demo-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-hindi-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
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rahulchakwate/albert-base-finetuned-squad
374ffc1c2128dc039d4e0aaf566e543b16d48bbc
2021-12-14T19:03:34.000Z
[ "pytorch", "albert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
rahulchakwate
null
rahulchakwate/albert-base-finetuned-squad
0
null
transformers
Entry not found
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rahulchakwate/albert-xxlarge-finetuned-squad
1186f0c98573d7da2f9793a583e92d9bf9a64758
2021-12-13T04:04:51.000Z
[ "pytorch", "albert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
rahulchakwate
null
rahulchakwate/albert-xxlarge-finetuned-squad
0
null
transformers
Entry not found
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rahulchakwate/bert-finetuned-squad-cased
2a1d1c467dcc0a6343079eec8a60e718a362e4d4
2021-12-10T01:31:27.000Z
[ "pytorch", "bert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
rahulchakwate
null
rahulchakwate/bert-finetuned-squad-cased
0
null
transformers
Entry not found
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rahulchakwate/distilbert-base-finetuned-squad
f135643a04f048abda0c2242e29d4212d534f453
2021-12-14T19:07:25.000Z
[ "pytorch", "distilbert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
rahulchakwate
null
rahulchakwate/distilbert-base-finetuned-squad
0
null
transformers
Entry not found
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rahulchakwate/roberta-base-finetuned-squad
cf9c92a4f36b2b441d31e9c49d90d794d594c2ad
2021-12-13T02:56:58.000Z
[ "pytorch", "roberta", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
rahulchakwate
null
rahulchakwate/roberta-base-finetuned-squad
0
null
transformers
Entry not found
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rahulchakwate/roberta-large-finetuned-squad
1bf9a766200962d996bb73f236ec7ce8552043d5
2021-12-14T21:10:05.000Z
[ "pytorch", "roberta", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
rahulchakwate
null
rahulchakwate/roberta-large-finetuned-squad
0
null
transformers
Entry not found
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rajivratn/gupshup_e2e_mbart
5f66adb8b3d10d5fd5d8f7f02e4c851d1ba9449d
2021-11-06T17:42:01.000Z
[ "pytorch" ]
null
false
rajivratn
null
rajivratn/gupshup_e2e_mbart
0
null
null
Entry not found
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rajratnpranesh/DCS_sanskrit_albert
2d6007c9028513123ee421480661a930b84aec7e
2020-07-25T15:53:47.000Z
[ "pytorch", "albert", "feature-extraction", "transformers" ]
feature-extraction
false
rajratnpranesh
null
rajratnpranesh/DCS_sanskrit_albert
0
null
transformers
Entry not found
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ravinyu/codeparrot-small
d894cfc9317f54779b74a0fcd66cdc63f28ebba6
2022-01-23T06:53:10.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
ravinyu
null
ravinyu/codeparrot-small
0
null
transformers
Entry not found
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ravinyu/codeparrot
555ce5341da3dad37ee7c58cfa1627a28669274d
2022-01-20T05:21:49.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
ravinyu
null
ravinyu/codeparrot
0
null
transformers
Entry not found
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ravirajoshi/wav2vec2-large-xls-r-300m-hindi-lm-boosted
a257e1bb57514e7456e057cfffe7361a50267c22
2022-03-24T11:54:10.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "hi", "transformers", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
ravirajoshi
null
ravirajoshi/wav2vec2-large-xls-r-300m-hindi-lm-boosted
0
null
transformers
--- language: - hi license: apache-2.0 tags: - generated_from_trainer - robust-speech-event - hf-asr-leaderboard model-index: - name: wav2vec2-large-xls-r-300m-hindi 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-hindi 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: 0.7049 - Wer: 0.3200
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ravirajoshi/wav2vec2-large-xls-r-300m-marathi-lm-boosted
54b3f920a7567bd0948b77aec3c141c2cb45fa60
2022-03-24T11:58:30.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "mr", "transformers", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
ravirajoshi
null
ravirajoshi/wav2vec2-large-xls-r-300m-marathi-lm-boosted
0
null
transformers
--- language: - mr license: apache-2.0 tags: - generated_from_trainer - robust-speech-event - hf-asr-leaderboard model-index: - name: wav2vec2-large-xls-r-300m-marathi 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-marathi 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: 0.5656 - Wer: 0.2156
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ravishs/wav2vec2-large-xls-r-300m-tamil-colab
8ffae7b779f75b280d6467abbd6e73ab0bfa15df
2022-02-03T12:06:20.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
ravishs
null
ravishs/wav2vec2-large-xls-r-300m-tamil-colab
0
null
transformers
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-tamil-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-tamil-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
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raybin/model_out
7318e66c5933db166bd6cd112ab262c7d74de134
2021-05-20T04:03:40.000Z
[ "pytorch", "bert", "transformers" ]
null
false
raybin
null
raybin/model_out
0
null
transformers
Entry not found
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rays2pix/dummy-model
5aaf07d6a5666f2857ecd82b900d78bf87d9fbeb
2021-07-03T01:15:26.000Z
[ "pytorch", "camembert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
rays2pix
null
rays2pix/dummy-model
0
null
transformers
Entry not found
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reach-vb/wav2vec2-large-xls-r-1B-common_voice7-lt-ft
8893bef750751a30902bd8450351423172682134
2022-02-14T13:39:07.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
reach-vb
null
reach-vb/wav2vec2-large-xls-r-1B-common_voice7-lt-ft
0
1
transformers
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-1B-common_voice7-lt-ft 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-1B-common_voice7-lt-ft This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 2.5101 - 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: 3e-05 - train_batch_size: 36 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 72 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 900 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 2.3491 | 31.24 | 500 | 3.9827 | 1.0 | | 0.0421 | 62.48 | 1000 | 2.9544 | 1.0 | | 0.0163 | 93.73 | 1500 | 2.5101 | 1.0 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.10.3
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rebeccakoganlee/distilbert-base-uncased-finetuned-ner
1947f907b8cd0b3fba6257a4114df43abd148001
2021-11-24T16:17:06.000Z
[ "pytorch", "distilbert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
rebeccakoganlee
null
rebeccakoganlee/distilbert-base-uncased-finetuned-ner
0
null
transformers
Entry not found
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recobo/agri-sentence-transformer
c7c8254c17487fe0f30ff0dc9b651fc1064fea23
2022-01-24T17:36:17.000Z
[ "pytorch", "bert", "feature-extraction", "english", "sentence-transformers", "sentence-similarity", "transformers" ]
sentence-similarity
false
recobo
null
recobo/agri-sentence-transformer
0
2
sentence-transformers
--- pipeline_tag: sentence-similarity language: english tags: - sentence-transformers - sentence-similarity - transformers --- # recobo/agri-sentence-transformer This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. This model was built using [recobo/agriculture-bert-uncased](https://huggingface.co/recobo/agriculture-bert-uncased), which is a BERT model trained on 6.5 million passages from the agricultural domain. Hence, this model is expected to perform well on sentence similarity tasks specifically for agricultural text data. ## 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 = ["A man is eating food.", "A man is eating a piece of bread"] model = SentenceTransformer('recobo/agri-sentence-transformer') embeddings = model.encode(sentences) print(embeddings)
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redadmiral/headlines_test_small_example
6d341a03b1b9064f4fc2ef5af48b10edb063a65a
2021-12-30T10:07:34.000Z
[ "pytorch", "mt5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
redadmiral
null
redadmiral/headlines_test_small_example
0
null
transformers
This Model is a fine-tuned version of T-systems [summarization model v1](https://huggingface.co/deutsche-telekom/mt5-small-sum-de-en-v1). We used 1000 examples of headline-content pairs from BR24 articles for the fine-tuning process. Despite the small amount of training data, the tonality of the summarizations has changed significantly. Many of the resulting summaries do sound like a headline. ## Training We used the following parameters for training this model: + base model: deutsche-telekom/mt5-small-sum-de-en-v1 + source_prefix: "summarize: " + batch size: 4 + max_source_length: 400 + max_target_length: 35 + weight_decay: 0.01 + number of train epochs: 1 + learning rate: 5e-5 ## License Since the base model is trained on the MLSUM dataset, this model may not be used for commercial use. ## Stats | Model | Rouge1 | Rouge2 | RougeL | RougeLSum | |-----------------------------------------|-----------|----------|-----------|-----------| | headlines_test_small_example | 13.573500 | 3.694700 | 12.560600 | 12.60000 | | deutsche-telekom/mt5-small-sum-de-en-v1 | 10.6488 | 2.9313 | 10.0527 | 10.0523 |
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renBaikau/alphaDelay
b99a0c3da21a024afc07f61696ee2996ada07780
2021-11-22T12:21:47.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
renBaikau
null
renBaikau/alphaDelay
0
null
transformers
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: alphaDelay 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. --> # alphaDelay This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6648 - 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: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 20 - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 82.3335 | 5.0 | 25 | 14.0648 | 1.0 | | 6.1049 | 10.0 | 50 | 3.7145 | 1.0 | | 3.9873 | 15.0 | 75 | 3.6648 | 1.0 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3
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reshinthadith/FlashFill-T5
ca57600cac916819746481d7952449095312fe56
2021-11-07T05:01:45.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
reshinthadith
null
reshinthadith/FlashFill-T5
0
null
transformers
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rewardsignal/behavior_cloning
6e2b26939d3095c6ba996833fceab8362a48c469
2021-06-03T15:41:19.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
rewardsignal
null
rewardsignal/behavior_cloning
0
null
transformers
This model was trained using prompt_responses_full.csv which you can read more about [here](https://huggingface.co/datasets/rewardsignal/reddit_writing_prompts). All other training parameters and settings are accessible via the config.json and trainer_state.json files of the individual checkpoints
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rewardsignal/reddit_reward_model
4b9112d42e66b4e4999a36abd284b93e83e266ac
2021-06-04T01:35:01.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
rewardsignal
null
rewardsignal/reddit_reward_model
0
null
transformers
This model was trained using comparisons_train.csv which you can read more about [here](https://huggingface.co/datasets/projectaligned/reddit_writingprompts_full). All other training parameters and settings are accessible via the config.json and trainer_state.json files of the individual checkpoints
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ricardo-filho/bert_base_faquad
309ea34ead2d24ab4303a6267a15a22d4edccf25
2021-08-31T18:38:51.000Z
[ "pytorch", "bert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
ricardo-filho
null
ricardo-filho/bert_base_faquad
0
null
transformers
Entry not found
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ricardo-filho/bert_large_faquad
256a34a3a6908cbbab59d5eb21cd5b964a22f095
2021-08-31T18:28:34.000Z
[ "pytorch", "bert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
ricardo-filho
null
ricardo-filho/bert_large_faquad
0
null
transformers
Entry not found
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ricardo-filho/sbertimbau-base-allnli-mnrl
7bded7e55732aa4db0c3fd322afbe699d63147ec
2021-08-10T21:09:32.000Z
[ "pytorch", "bert", "feature-extraction", "sentence-transformers", "sentence-similarity", "transformers" ]
sentence-similarity
false
ricardo-filho
null
ricardo-filho/sbertimbau-base-allnli-mnrl
0
null
sentence-transformers
--- 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, max 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 8066 with parameters: ``` {'batch_size': 64} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "callback": null, "epochs": 1, "evaluation_steps": 806, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 807, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 75, '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 -->
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ricardo-filho/sbertimbau-base-nli-sts
09f415953f9ad6b5bf81bbf71af052198c8770d2
2021-08-11T03:04:08.000Z
[ "pytorch", "bert", "feature-extraction", "sentence-transformers", "sentence-similarity", "transformers" ]
sentence-similarity
false
ricardo-filho
null
ricardo-filho/sbertimbau-base-nli-sts
0
null
sentence-transformers
--- 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, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 356 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "callback": null, "epochs": 4, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 143, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 75, '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 -->
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richiellei/Childe
b169ac84420b3818a086659f1ea57c2a1d4287f2
2022-01-18T20:32:46.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
richiellei
null
richiellei/Childe
0
null
transformers
--- tags: - conversational --- # Childe DialoGPT Model
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richiellei/Childe3
3b41aa0dc998e6e77272d93d6fc5fcb2fb70a4fb
2022-01-18T21:38:14.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
richiellei
null
richiellei/Childe3
0
null
transformers
--- tags: - conversational --- # Childe3 DialoGPT Model
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rifkat/robert_BPE_zinc100k
c84f6ab4938e7b4941c369ff198f985bfffc0ba0
2021-07-23T17:04:02.000Z
[ "pytorch", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
rifkat
null
rifkat/robert_BPE_zinc100k
0
null
transformers
Entry not found
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rjrohit/wav2vec2-base-rj-try-5
bcd5e9d8478fdcb6a063aba7a3cfc250fca82a8b
2022-02-07T09:59:45.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
rjrohit
null
rjrohit/wav2vec2-base-rj-try-5
0
null
transformers
Entry not found
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rlagusrlagus123/XTC20000
e727e36646205a8d5c74a11d34dc671ddf50e343
2021-12-19T11:00:28.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
rlagusrlagus123
null
rlagusrlagus123/XTC20000
0
null
transformers
--- tags: - conversational --- --- #12 epochs, each batch size 2, gradient accumulation steps 2, tail 20000
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rlagusrlagus123/XTC4096
82793f6e119deca4b07206b99f3e1e4f46d25ff9
2021-12-19T11:19:34.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
rlagusrlagus123
null
rlagusrlagus123/XTC4096
0
null
transformers
--- tags: - conversational --- --- #12 epochs, each batch size 4, gradient accumulation steps 1, tail 4096. #THIS SEEMS TO BE THE OPTIMAL SETUP.
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rmicheal48/DialoGPT-small-steven_universe
f4eb531024e38c05edb8018847db6806ce827be7
2022-01-02T12:39:37.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
rmicheal48
null
rmicheal48/DialoGPT-small-steven_universe
0
null
transformers
--- tags: - conversational --- # Steven Universe DialoGPT Model
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rndlr96/Focalbest
6339d85a8871403ec715b1b625ba6453b37e81dd
2021-05-20T04:29:24.000Z
[ "pytorch", "bert", "transformers" ]
null
false
rndlr96
null
rndlr96/Focalbest
0
null
transformers
Entry not found
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rndlr96/Nfocal_label_v2
52a5f18644d162520c0c59e58030e1b4ae5479c2
2021-05-20T04:29:46.000Z
[ "pytorch", "bert", "transformers" ]
null
false
rndlr96
null
rndlr96/Nfocal_label_v2
0
null
transformers
Entry not found
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rndlr96/Nfocal_label_v2_512
9815cbe94af104ebbf411de5df763afe28541765
2021-05-20T04:30:09.000Z
[ "pytorch", "bert", "transformers" ]
null
false
rndlr96
null
rndlr96/Nfocal_label_v2_512
0
null
transformers
Entry not found
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rndlr96/bce_cls_5e_512
2e820e34d4e080b732844202283e9f38b5437ccd
2021-05-20T04:30:32.000Z
[ "pytorch", "bert", "transformers" ]
null
false
rndlr96
null
rndlr96/bce_cls_5e_512
0
null
transformers
Entry not found
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rndlr96/cls_256
1096926de1cfd3b0ff530186126314eb407abd88
2021-05-20T04:30:54.000Z
[ "pytorch", "bert", "transformers" ]
null
false
rndlr96
null
rndlr96/cls_256
0
null
transformers
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rndlr96/kobert_cls_ipc
8d2b761709cc8212db14ae3c408417f435fcf52d
2021-05-20T04:31:13.000Z
[ "pytorch", "bert", "transformers" ]
null
false
rndlr96
null
rndlr96/kobert_cls_ipc
0
null
transformers
Entry not found
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rndlr96/kobert_label_ipc
ca58c53a954dc8a85a6776aeb8ba51e95e39a742
2021-05-20T04:31:33.000Z
[ "pytorch", "bert", "transformers" ]
null
false
rndlr96
null
rndlr96/kobert_label_ipc
0
null
transformers
Entry not found
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robot-bengali-2/sahajbert2
dccdc6429c694eb6d4ba27c641136cbb663ec09d
2021-09-03T05:49:14.000Z
[ "pytorch", "albert", "transformers" ]
null
false
robot-bengali-2
null
robot-bengali-2/sahajbert2
0
null
transformers
Entry not found
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rodrigodz/DialoGPT-medium-dxd
94cb0ba287584b70c02f30422fbeb7d0309e1e2e
2021-09-07T04:50:17.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
rodrigodz
null
rodrigodz/DialoGPT-medium-dxd
0
null
transformers
--- tags: - conversational --- # Issei DialoGPT Model
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rohitsroch/hybrid_hbh_bart-base_icsi_sum
978220e767d7287a5f0671af37d3215eeaf92c58
2022-06-12T23:10:15.000Z
[ "pytorch", "bart", "text2text-generation", "en", "dataset:icsi", "transformers", "dialogue-summarization", "license:apache-2.0", "autotrain_compatible" ]
text2text-generation
false
rohitsroch
null
rohitsroch/hybrid_hbh_bart-base_icsi_sum
0
null
transformers
--- language: - en license: apache-2.0 tags: - dialogue-summarization model_index: - name: hybrid_hbh_bart-base_icsi_sum results: - task: name: Summarization type: summarization datasets: - icsi --- ## Paper ## [Domain Adapted Abstractive Summarization of Dialogue using Transfer Learning](https://dl.acm.org/doi/10.1145/3508546.3508640) Authors: *Rohit Sroch* ## Abstract Recently, the abstractive dialogue summarization task has been gaining a lot of attention from researchers. Also, unlike news articles and documents with well-structured text, dialogue differs in the sense that it often comes from two or more interlocutors, exchanging information with each other and having an inherent hierarchical structure based on the sequence of utterances by different speakers. This paper proposes a simple but effective hybrid approach that consists of two modules and uses transfer learning by leveraging pretrained language models (PLMs) to generate an abstractive summary. The first module highlights important utterances, capturing the utterance level relationship by adapting an auto-encoding model like BERT based on the unsupervised or supervised method. And then, the second module generates a concise abstractive summary by adapting encoder-decoder models like T5, BART, and PEGASUS. Experiment results on benchmark datasets show that our approach achieves a state-of-the-art performance by adapting to dialogue scenarios and can also be helpful in low-resource settings for domain adaptation. *Rohit Sroch. 2021. Domain Adapted Abstractive Summarization of Dialogue using Transfer Learning. In 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence (ACAI'21). Association for Computing Machinery, New York, NY, USA, Article 94, 1–6. https://doi.org/10.1145/3508546.3508640* # hybrid_hbh_bart-base_icsi_sum This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on ICSI dataset for dialogue summarization task. ## Model description More information needed ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-4 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100.0 - label_smoothing_factor: 0.1 ### Results on Test Set - predict_gen_len = 480.0 - predict_rouge1 = **46.8707** - predict_rouge2 = **10.1337** - predict_rougeL = **19.3386** - predict_rougeLsum = **43.6989** - predict_samples = 6 - predict_samples_per_second = 0.54 - predict_steps_per_second = 0.27 ### Framework versions - Transformers>=4.8.0 - Pytorch>=1.6.0 - Datasets>=1.10.2 - Tokenizers>=0.10.3 If you use this model, please cite the following paper: ``` @inproceedings{10.1145/3508546.3508640, author = {Sroch, Rohit}, title = {Domain Adapted Abstractive Summarization of Dialogue Using Transfer Learning}, year = {2021}, isbn = {9781450385053}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3508546.3508640}, doi = {10.1145/3508546.3508640}, articleno = {94}, numpages = {6}, keywords = {encoder-decoder, T5, abstractive summary, PEGASUS, BART, dialogue summarization, PLMs, BERT}, location = {Sanya, China}, series = {ACAI'21} } ```
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rohitsroch/hybrid_hbh_t5-small_ami_sum
7e5d23b69adbee43090733a412ed96d8a5129604
2022-06-12T23:23:05.000Z
[ "pytorch", "t5", "text2text-generation", "en", "dataset:ami", "transformers", "dialogue-summarization", "license:apache-2.0", "autotrain_compatible" ]
text2text-generation
false
rohitsroch
null
rohitsroch/hybrid_hbh_t5-small_ami_sum
0
null
transformers
--- language: - en license: apache-2.0 tags: - dialogue-summarization model_index: - name: hybrid_hbh_t5-small_ami_sum results: - task: name: Summarization type: summarization datasets: - ami --- ## Paper ## [Domain Adapted Abstractive Summarization of Dialogue using Transfer Learning](https://dl.acm.org/doi/10.1145/3508546.3508640) Authors: *Rohit Sroch* ## Abstract Recently, the abstractive dialogue summarization task has been gaining a lot of attention from researchers. Also, unlike news articles and documents with well-structured text, dialogue differs in the sense that it often comes from two or more interlocutors, exchanging information with each other and having an inherent hierarchical structure based on the sequence of utterances by different speakers. This paper proposes a simple but effective hybrid approach that consists of two modules and uses transfer learning by leveraging pretrained language models (PLMs) to generate an abstractive summary. The first module highlights important utterances, capturing the utterance level relationship by adapting an auto-encoding model like BERT based on the unsupervised or supervised method. And then, the second module generates a concise abstractive summary by adapting encoder-decoder models like T5, BART, and PEGASUS. Experiment results on benchmark datasets show that our approach achieves a state-of-the-art performance by adapting to dialogue scenarios and can also be helpful in low-resource settings for domain adaptation. *Rohit Sroch. 2021. Domain Adapted Abstractive Summarization of Dialogue using Transfer Learning. In 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence (ACAI'21). Association for Computing Machinery, New York, NY, USA, Article 94, 1–6. https://doi.org/10.1145/3508546.3508640* # hybrid_hbh_t5-small_ami_sum This model is a fine-tuned version of [t5-small](https://huggingface.co/best-models/H) on an AMI dataset for dialogue summarization task. ## Model description More information needed ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-4 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50.0 - label_smoothing_factor: 0.1 ### Results on Test Set - predict_gen_len = 329.2 - predict_rouge1 = **48.7673** - predict_rouge2 = **18.1832** - predict_rougeL = **26.1713** - predict_rougeLsum = **46.8434** - predict_samples = 20 - predict_samples_per_second = 1.098 - predict_steps_per_second = 0.274 ### Framework versions - Transformers>=4.8.0 - Pytorch>=1.6.0 - Datasets>=1.10.2 - Tokenizers>=0.10.3 If you use this model, please cite the following paper: ``` @inproceedings{10.1145/3508546.3508640, author = {Sroch, Rohit}, title = {Domain Adapted Abstractive Summarization of Dialogue Using Transfer Learning}, year = {2021}, isbn = {9781450385053}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3508546.3508640}, doi = {10.1145/3508546.3508640}, articleno = {94}, numpages = {6}, keywords = {encoder-decoder, T5, abstractive summary, PEGASUS, BART, dialogue summarization, PLMs, BERT}, location = {Sanya, China}, series = {ACAI'21} } ```
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romuNoob/Mine
cf21154f5f446d69a44342d21fc24edf6f9efcf4
2021-12-16T11:39:38.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
romuNoob
null
romuNoob/Mine
0
null
transformers
--- tags: - conversational --- # mine
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romuNoob/test
2877c246abbc65f388e1fab203d39991abe105f8
2021-12-16T16:15:03.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
romuNoob
null
romuNoob/test
0
null
transformers
--- tags: - conversational --- # mine
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ronanki/ml_mpnet_768_MNR_10
4b1c04c9d4408450df0095ad02e84a0d0f8e0a67
2022-02-22T18:14:36.000Z
[ "pytorch", "xlm-roberta", "feature-extraction", "sentence-transformers", "sentence-similarity", "transformers" ]
sentence-similarity
false
ronanki
null
ronanki/ml_mpnet_768_MNR_10
0
null
sentence-transformers
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # ronanki/ml_mpnet_768_MNR_10 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('ronanki/ml_mpnet_768_MNR_10') 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('ronanki/ml_mpnet_768_MNR_10') model = AutoModel.from_pretrained('ronanki/ml_mpnet_768_MNR_10') # 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=ronanki/ml_mpnet_768_MNR_10) ## Training The model was trained with the parameters: **DataLoader**: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 29 with parameters: ``` {'batch_size': 32} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 5, "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": 2, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (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 -->
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ronanki/ml_use_512_MNR_10
d2aab0286074eb9c70ed02b94459607d7d2a22ec
2022-02-22T18:12:25.000Z
[ "pytorch", "distilbert", "feature-extraction", "sentence-transformers", "sentence-similarity" ]
sentence-similarity
false
ronanki
null
ronanki/ml_use_512_MNR_10
0
null
sentence-transformers
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # ronanki/ml_use_512_MNR_10 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 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('ronanki/ml_use_512_MNR_10') 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=ronanki/ml_use_512_MNR_10) ## Training The model was trained with the parameters: **DataLoader**: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 29 with parameters: ``` {'batch_size': 32} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 10, "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": 2, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
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ronanki/xlmr_02-02-2022
57a80b46629a8fd98462dda8780ecd261d43d5a6
2022-01-03T13:48:37.000Z
[ "pytorch", "xlm-roberta", "feature-extraction", "sentence-transformers", "sentence-similarity", "transformers" ]
sentence-similarity
false
ronanki
null
ronanki/xlmr_02-02-2022
0
null
sentence-transformers
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # ronanki/xlmr_02-02-2022 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('ronanki/xlmr_02-02-2022') 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('ronanki/xlmr_02-02-2022') model = AutoModel.from_pretrained('ronanki/xlmr_02-02-2022') # 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=ronanki/xlmr_02-02-2022) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 160 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.TripletLoss.TripletLoss` with parameters: ``` {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5} ``` Parameters of the fit()-Method: ``` { "epochs": 5, "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": 16, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (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 -->
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ronanki/xlmr_17-01-2022_v3
f01d3a3afd44c78049b418bdef158a4ec1ded508
2022-01-17T20:34:20.000Z
[ "pytorch", "xlm-roberta", "feature-extraction", "sentence-transformers", "sentence-similarity", "transformers" ]
sentence-similarity
false
ronanki
null
ronanki/xlmr_17-01-2022_v3
0
null
sentence-transformers
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # ronanki/xlmr_17-01-2022_v3 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('ronanki/xlmr_17-01-2022_v3') 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('ronanki/xlmr_17-01-2022_v3') model = AutoModel.from_pretrained('ronanki/xlmr_17-01-2022_v3') # 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=ronanki/xlmr_17-01-2022_v3) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 40 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 10, "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": 4, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (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 -->
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rossanez/t5-small-finetuned-de-en-nofp16
47dbe88ac8e7db6b177cd910ec5669e84a1dd2b4
2021-12-04T13:59:26.000Z
[ "pytorch", "tensorboard", "t5", "text2text-generation", "dataset:wmt14", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
rossanez
null
rossanez/t5-small-finetuned-de-en-nofp16
0
null
transformers
--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt14 metrics: - bleu model-index: - name: t5-small-finetuned-de-en-nofp16 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wmt14 type: wmt14 args: de-en metrics: - name: Bleu type: bleu value: 9.5801 --- <!-- 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-de-en-nofp16 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt14 dataset. It achieves the following results on the evaluation set: - Loss: 2.1460 - Bleu: 9.5801 - Gen Len: 17.333 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 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 | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 188 | 2.1899 | 9.4821 | 17.312 | | No log | 2.0 | 376 | 2.1986 | 9.5705 | 17.3853 | | 1.2118 | 3.0 | 564 | 2.1933 | 9.448 | 17.3293 | | 1.2118 | 4.0 | 752 | 2.1607 | 9.563 | 17.336 | | 1.2118 | 5.0 | 940 | 2.1460 | 9.5801 | 17.333 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
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royeis/T5-FlowNLG-Planner
fc45f8c2f98bdb5690f5f5f40280f0306a04db8f
2021-12-26T17:35:56.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
royeis
null
royeis/T5-FlowNLG-Planner
0
null
transformers
Entry not found
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royeis/T5-FlowNLG-Realizer
00618656582f50e53442b6400a128bf536e91116
2021-12-26T17:28:10.000Z
[ "pytorch", "jax", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
royeis
null
royeis/T5-FlowNLG-Realizer
0
null
transformers
Entry not found
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rpeng35/DialoGPT-small-erenyeager
4ef76067eb8f7311da5fa9d5e8743262b48bcec0
2021-08-27T22:40:01.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
rpeng35
null
rpeng35/DialoGPT-small-erenyeager
0
null
transformers
--- tags: - conversational --- #Eren Yeager DialoGPT Model
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rpv/distilbert-base-uncased-finetuned-squad
c0ffad5c963ca4bfb1f05a2b9e81e4c1da2f0b3d
2022-01-29T15:44:17.000Z
[ "pytorch", "distilbert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
question-answering
false
rpv
null
rpv/distilbert-base-uncased-finetuned-squad
0
null
transformers
--- 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. ## 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: 6 ### Framework versions - Transformers 4.16.1 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0
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rtoguchi/t5-small-finetuned-en-to-ro-fp16_off
ef08c61810ae7e4128c89cfbc3dcebb992cd06f1
2021-12-03T13:18:24.000Z
[ "pytorch", "tensorboard", "t5", "text2text-generation", "dataset:wmt16", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
rtoguchi
null
rtoguchi/t5-small-finetuned-en-to-ro-fp16_off
0
null
transformers
--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 metrics: - bleu model-index: - name: t5-small-finetuned-en-to-ro-fp16_off results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wmt16 type: wmt16 args: ro-en metrics: - name: Bleu type: bleu value: 7.3056 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-en-to-ro-fp16_off This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.4078 - Bleu: 7.3056 - Gen Len: 18.2556 ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | 0.6037 | 1.0 | 7629 | 1.4078 | 7.3056 | 18.2556 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
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ruselkomp/distilbert-base-multilingual-cased-finetuned-squad
d321023866d51fdb272ec8dff35caa25c747d7f2
2021-12-20T16:14:10.000Z
[ "pytorch", "tensorboard", "distilbert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
ruselkomp
null
ruselkomp/distilbert-base-multilingual-cased-finetuned-squad
0
null
transformers
Entry not found
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ruselkomp/sbert_large_nlu_ru-finetuned-squad-full
2701b2bd2f1212b469213484984b644275fc0226
2021-12-22T18:06:38.000Z
[ "pytorch", "tensorboard", "bert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
ruselkomp
null
ruselkomp/sbert_large_nlu_ru-finetuned-squad-full
0
null
transformers
Entry not found
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ruselkomp/sbert_large_nlu_ru-finetuned-squad
ff0438f601cdc24a2cb47496eb2bb57a10fb84da
2021-12-22T12:15:18.000Z
[ "pytorch", "tensorboard", "bert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
ruselkomp
null
ruselkomp/sbert_large_nlu_ru-finetuned-squad
0
null
transformers
Entry not found
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rwang97/wav2vec2-base-timit-demo-colab
3009c10c9287cc4f94becfacb4332ac7b059c86d
2021-12-08T22:12:30.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
rwang97
null
rwang97/wav2vec2-base-timit-demo-colab
0
null
transformers
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4473 - Wer: 0.3380 ## 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: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1048 | 4.0 | 500 | 0.4370 | 0.3475 | | 0.0871 | 8.0 | 1000 | 0.4489 | 0.3405 | | 0.0651 | 12.0 | 1500 | 0.4473 | 0.3380 | | 0.0703 | 16.0 | 2000 | 0.4473 | 0.3380 | | 0.0676 | 20.0 | 2500 | 0.4473 | 0.3380 | | 0.0714 | 24.0 | 3000 | 0.4473 | 0.3380 | | 0.0742 | 28.0 | 3500 | 0.4473 | 0.3380 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
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s3h/mt5-small-finetuned-gec
14d6cfd85ef3bf088ec1decaf6f747b74b6153aa
2021-12-17T21:47:04.000Z
[ "pytorch", "mt5", "feature-extraction", "transformers" ]
feature-extraction
false
s3h
null
s3h/mt5-small-finetuned-gec
0
null
transformers
Entry not found
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s3h/mt5-small-finetuned-src-to-trg
087e1b5f31b9907f71a98adcaabb11ac58b367c1
2021-12-18T20:34:32.000Z
[ "pytorch", "tensorboard", "mt5", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
s3h
null
s3h/mt5-small-finetuned-src-to-trg
0
null
transformers
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: mt5-small-finetuned-src-to-trg results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mt5-small-finetuned-src-to-trg This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 40 | nan | 0.1737 | 3.1818 | ### Framework versions - Transformers 4.14.1 - Pytorch 1.6.0 - Datasets 1.16.1 - Tokenizers 0.10.3
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saattrupdan/xlmr-base-texas-squad-is
097e69dc7b77b84d5f37b4ac4d41e3d5d26d42a2
2022-01-31T21:28:56.000Z
[ "pytorch", "tensorboard", "xlm-roberta", "question-answering", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]
question-answering
false
saattrupdan
null
saattrupdan/xlmr-base-texas-squad-is
0
null
transformers
--- license: mit tags: - generated_from_trainer model-index: - name: xlmr-base-texas-squad-is results: [] widget: - text: "Hvenær var Halldór Laxness í menntaskóla ?" context: "Halldór Laxness ( Halldór Kiljan ) fæddist í Reykjavík 23. apríl árið 1902 og átti í fyrstu heima við Laugaveg en árið 1905 settist fjölskyldan að í Laxnesi í Mosfellssveit . Þar ólst Halldór upp en sótti skóla í Reykjavík á unglingsárum . Ungur hélt hann síðan utan og var langdvölum erlendis um árabil – í ýmsum Evrópulöndum og síðar í Ameríku . Þegar hann var heima bjó hann í Reykjavík þar til hann og kona hans , Auður Sveinsdóttir , byggðu sér húsið Gljúfrastein í Mosfellssveit og fluttu þangað árið 1945 . Þar var heimili þeirra alla tíð síðan og þar er nú safn til minningar um þau . Halldór lést 8. febrúar 1998 . Skólaganga Halldórs varð ekki löng . Árið 1918 hóf hann nám við Menntaskólann í Reykjavík en hafði lítinn tíma til að læra , enda var hann að skrifa skáldsögu , Barn náttúrunnar , sem kom út haustið 1919 – þá þegar var höfundurinn ungi farinn af landi brott . Sagan vakti þó nokkra athygli og í Alþýðublaðinu sagði m.a. : „ Og hver veit nema að Halldór frá Laxnesi eigi eftir að verða óskabarn íslensku þjóðarinnar . “ Upp frá þessu sendi Halldór frá sér bók nánast á hverju ári , stundum fleiri en eina , í yfir sex áratugi . Afköst hans voru með eindæmum ; hann skrifaði fjölda skáldsagna , sumar í nokkrum hlutum , leikrit , kvæði , smásagnasöfn og endurminningabækur og gaf auk þess út mörg greinasöfn og ritgerðir . Bækurnar eru fjölbreyttar en eiga það sameiginlegt að vera skrifaðar af einstakri stílgáfu , djúpum mannskilningi og víðtækri þekkingu á sögu og samfélagi . Þar birtast oft afgerandi skoðanir á þjóðfélagsmálum og sögupersónur eru margar einkar eftirminnilegar ; tilsvör þeirra og lunderni hafa orðið samofin þjóðarsálinni . Þekktustu verk Halldórs eru eflaust skáldsögurnar stóru og rismiklu , s.s. Salka Valka , Sjálfstætt fólk , Heimsljós , Íslandsklukkan og Gerpla , og raunar mætti telja upp mun fleiri ; Kvæðabók hans er í uppáhaldi hjá mörgum sem og minningabækurnar sem hann skrifaði á efri árum um æskuár sín ; af þekktum greinasöfnum og ritgerðum má nefna Alþýðubókina og Skáldatíma . Mikið hefur verið skrifað um verk og ævi skáldsins , en hér skal aðeins bent á ítarlega frásögn og greiningu Halldórs Guðmundssonar í bókinni Halldór Laxness – ævisaga ." --- # TExAS-SQuAD-is This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the TExAS-SQuAD-is dataset. It achieves the following results on the evaluation set: - Exact match: 56.91% - F1-score: 59.93% ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.1458 | 1.0 | 4219 | 1.8892 | | 1.9202 | 2.0 | 8438 | 1.8566 | | 1.7377 | 3.0 | 12657 | 1.8688 | ### Framework versions - Transformers 4.12.2 - Pytorch 1.8.1+cu101 - Datasets 1.12.1 - Tokenizers 0.10.3
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saburbutt/albert_xxlarge_tweetqa
7150327493a59e9385599ba52d5c5e718f3bc925
2021-04-13T22:33:28.000Z
[ "pytorch", "albert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
saburbutt
null
saburbutt/albert_xxlarge_tweetqa
0
null
transformers
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saburbutt/roberta_large_tweetqa
5271225a49afb4db53c4eb3f62660bf93a80fc4a
2021-05-20T20:01:21.000Z
[ "pytorch", "jax", "roberta", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
saburbutt
null
saburbutt/roberta_large_tweetqa
0
null
transformers
Entry not found
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saburbutt/xlmroberta_large_tweetqa
6bf8fdbed87bc11df8f0540a6152c4648bc72ed6
2020-11-16T01:21:38.000Z
[ "pytorch", "xlm-roberta", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
saburbutt
null
saburbutt/xlmroberta_large_tweetqa
0
null
transformers
Entry not found
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safik/dummy-model
b0fa95425973cbb4c56b4a80b054b948c736f75a
2022-02-12T15:48:48.000Z
[ "pytorch", "camembert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
safik
null
safik/dummy-model
0
null
transformers
Entry not found
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saibo/blank_bert_uncased_L-2_H-128_A-2
70fcee235e0ca2aa79649a534e9c04b7f3b8e948
2021-12-13T09:26:11.000Z
[ "pytorch", "bert", "feature-extraction", "transformers" ]
feature-extraction
false
saibo
null
saibo/blank_bert_uncased_L-2_H-128_A-2
0
null
transformers
The weight of this model is randomly initiated and this can be particularly useful when we aim to train a language model from scratch or benchmark the effect of pretraining. It's important to note that tokenizer of this random model is the same as the original pretrained model because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight. A debatable advantage of pulling this model from Huggingface is to avoid using random seed in order to obtain the same randomness at each time. The code to obtain such a random model: ```python #!/usr/bin/env python3 # -*- coding: utf-8 -*- # @Filename : random_model # @Date : 2021-12-13-10-08 import os from transformers import AutoModel, AutoTokenizer def auto_name_blank_model(model_url:str): original_model_name:str = os.path.basename(model_url) return "blank_"+original_model_name pretrained_model_url="google/bert_uncased_L-2_H-128_A-2" tokenizer = AutoTokenizer.from_pretrained(pretrained_model_url) model = AutoModel.from_pretrained(pretrained_model_url) model.init_weights() new_repo_name:str = auto_name_blank_model(pretrained_model_url) model.push_to_hub(new_repo_name) tokenizer.push_to_hub(new_repo_name) # uploading files to an existing repo will overwrite the files without prompt. ```
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sail/poolformer_s24
ff108f65a6511d2bacead81d14792d4a954b1999
2022-04-08T07:48:50.000Z
[ "pytorch", "poolformer", "image-classification", "dataset:imagenet", "arxiv:2111.11418", "transformers", "vision", "license:apache-2.0" ]
image-classification
false
sail
null
sail/poolformer_s24
0
null
transformers
--- license: apache-2.0 tags: - image-classification - vision datasets: - imagenet --- # PoolFormer (S24 model) PoolFormer model trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) by Yu et al. and first released in [this repository](https://github.com/sail-sg/poolformer). ## Model description PoolFormer is a model that replaces attention token mixer in transfomrers with extremely simple operator, pooling. Transformers have shown great potential in computer vision tasks. A common belief is their attention-based token mixer module contributes most to their competence. However, recent works show the attention-based module in transformers can be replaced by spatial MLPs and the resulted models still perform quite well. Based on this observation, we hypothesize that the general architecture of the transformers, instead of the specific token mixer module, is more essential to the model's performance. To verify this, we deliberately replace the attention module in transformers with an embarrassingly simple spatial pooling operator to conduct only the most basic token mixing. Surprisingly, we observe that the derived model, termed as PoolFormer, achieves competitive performance on multiple computer vision tasks. For example, on ImageNet-1K, PoolFormer achieves 82.1% top-1 accuracy, surpassing well-tuned vision transformer/MLP-like baselines DeiT-B/ResMLP-B24 by 0.3%/1.1% accuracy with 35%/52% fewer parameters and 48%/60% fewer MACs. The effectiveness of PoolFormer verifies our hypothesis and urges us to initiate the concept of "MetaFormer", a general architecture abstracted from transformers without specifying the token mixer. Based on the extensive experiments, we argue that MetaFormer is the key player in achieving superior results for recent transformer and MLP-like models on vision tasks. This work calls for more future research dedicated to improving MetaFormer instead of focusing on the token mixer modules. Additionally, our proposed PoolFormer could serve as a starting baseline for future MetaFormer architecture design. ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=sail/poolformer) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import PoolFormerFeatureExtractor, PoolFormerForImageClassification from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = PoolFormerFeatureExtractor.from_pretrained('sail/poolformer_s24') model = PoolFormerForImageClassification.from_pretrained('sail/poolformer_s24') inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` Currently, both the feature extractor and model support PyTorch. ## Training data The poolformer model was trained on [ImageNet-1k](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes. ## Training procedure ### Preprocessing The exact details of preprocessing of images during training/validation can be found [here](https://github.com/sail-sg/poolformer/blob/main/train.py#L529-L572). ### Pretraining The model was trained on TPU-v3s. Training resolution is 224. For all hyperparameters (such as batch size and learning rate), please refer to the original paper. ## Evaluation results | Model | ImageNet top-1 accuracy | # params | URL | |---------------------------------------|-------------------------|----------|------------------------------------------------------------------| | PoolFormer-S12 | 77.2 | 12M | https://huggingface.co/sail/poolformer_s12 | | **PoolFormer-S24** | **80.3** | **21M** | **https://huggingface.co/sail/poolformer_s24** | | PoolFormer-S36 | 81.4 | 31M | https://huggingface.co/sail/poolformer_s36 | | PoolFormer-M36 | 82.1 | 56M | https://huggingface.co/sail/poolformer_m36 | | PoolFormer-M48 | 82.5 | 73M | https://huggingface.co/sail/poolformer_m48 | ### BibTeX entry and citation info ```bibtex @article{yu2021metaformer, title={MetaFormer is Actually What You Need for Vision}, author={Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng}, journal={arXiv preprint arXiv:2111.11418}, year={2021} } ```
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sakai026/Chizuru
ef4de0e394bd0eac4ffd7a46adaa3578b1cf7169
2022-02-07T21:40:22.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
sakai026
null
sakai026/Chizuru
0
null
transformers
--- tags: - conversational --- # Chizuru Ichinose GPT-Model
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salti/wav2vec2-large-xlsr-arabic-common_voice-10_epochs
fb12feb890239acd0e048473966221f6cc26be88
2021-05-19T13:38:15.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers", "model-index" ]
automatic-speech-recognition
false
salti
null
salti/wav2vec2-large-xlsr-arabic-common_voice-10_epochs
0
null
transformers
--- model-index: - name: wav2vec2-large-xlsr-arabic-common_voice-10_epochs --- <!-- 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-arabic-common_voice-10_epochs This model was trained from scratch on an unkown dataset. It achieves the following results on the evaluation set: - Loss: 0.3581 - Wer: 0.4555 ## 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_ratio: 0.1 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.1701 | 0.9 | 400 | 3.1599 | 1.0 | | 0.8933 | 1.8 | 800 | 0.7198 | 0.7877 | | 0.5849 | 2.7 | 1200 | 0.5046 | 0.6253 | | 0.3858 | 3.6 | 1600 | 0.4247 | 0.5561 | | 0.3083 | 4.49 | 2000 | 0.4026 | 0.5251 | | 0.2556 | 5.39 | 2400 | 0.4010 | 0.5051 | | 0.2221 | 6.29 | 2800 | 0.3765 | 0.4861 | | 0.2026 | 7.19 | 3200 | 0.3652 | 0.4794 | | 0.1996 | 8.09 | 3600 | 0.3627 | 0.4660 | | 0.1755 | 8.99 | 4000 | 0.3582 | 0.4619 | | 0.1697 | 9.89 | 4400 | 0.3581 | 0.4555 | ### Framework versions - Transformers 4.6.0 - Pytorch 1.8.1+cu102 - Datasets 1.6.2 - Tokenizers 0.10.2
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samantharhay/wav2vec2-base-libir-zenodo
6a7698b36780aa901ad510f0790ee45d71cbc8ba
2021-11-22T19:29:29.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
samantharhay
null
samantharhay/wav2vec2-base-libir-zenodo
0
null
transformers
--- license: apache-2.0 tags: - generated_from_trainer model-index: name: wav2vec2-base-libir-zenodo --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-libir-zenodo This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4238 - Wer: 0.4336 ## 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: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.053 | 1.0 | 31 | 3.1494 | 0.7345 | | 2.9742 | 2.0 | 62 | 3.0527 | 0.7257 | | 2.9139 | 3.0 | 93 | 2.8808 | 0.7257 | | 2.6586 | 4.0 | 124 | 2.6648 | 0.6726 | | 2.7117 | 5.0 | 155 | 2.4695 | 0.6372 | | 2.5173 | 6.0 | 186 | 2.3087 | 0.6195 | | 2.3665 | 7.0 | 217 | 2.2745 | 0.6018 | | 2.1276 | 8.0 | 248 | 2.2180 | 0.5752 | | 2.1624 | 9.0 | 279 | 2.1311 | 0.5752 | | 2.0312 | 10.0 | 310 | 2.0358 | 0.5575 | | 2.0652 | 11.0 | 341 | 1.9146 | 0.5310 | | 1.7963 | 12.0 | 372 | 1.8346 | 0.5221 | | 1.6811 | 13.0 | 403 | 1.8351 | 0.5398 | | 1.5929 | 14.0 | 434 | 1.8256 | 0.4779 | | 1.6644 | 15.0 | 465 | 1.7572 | 0.4779 | | 1.5411 | 16.0 | 496 | 1.8740 | 0.4779 | | 1.4027 | 17.0 | 527 | 1.5143 | 0.4779 | | 1.2634 | 18.0 | 558 | 1.3864 | 0.4867 | | 1.1053 | 19.0 | 589 | 1.3192 | 0.4425 | | 1.0517 | 20.0 | 620 | 1.4705 | 0.4602 | | 1.1033 | 21.0 | 651 | 1.6006 | 0.4956 | | 0.9992 | 22.0 | 682 | 1.4748 | 0.5044 | | 0.8987 | 23.0 | 713 | 1.3544 | 0.4867 | | 0.9656 | 24.0 | 744 | 1.2673 | 0.4336 | | 0.952 | 25.0 | 775 | 1.3955 | 0.4071 | | 0.8507 | 26.0 | 806 | 1.3520 | 0.4425 | | 0.8269 | 27.0 | 837 | 1.8992 | 0.4336 | | 0.7255 | 28.0 | 868 | 1.9850 | 0.4425 | | 0.8269 | 29.0 | 899 | 3.0089 | 0.4425 | | 0.6178 | 30.0 | 930 | 1.4238 | 0.4336 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
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samantharhay/wav2vec2-base-timit-demo-colab
4dee1538d2c0dbf7fd0ad4a5c174eba1e93e3b4c
2021-11-18T03:30:31.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
samantharhay
null
samantharhay/wav2vec2-base-timit-demo-colab
0
null
transformers
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2368 - Wer: 0.8655 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 10.7111 | 4.0 | 500 | 6.1429 | 1.0 | | 4.8905 | 8.0 | 1000 | 6.0597 | 1.0 | | 3.4516 | 12.0 | 1500 | 3.0125 | 1.0 | | 2.9895 | 16.0 | 2000 | 2.9629 | 1.0 | | 2.9155 | 20.0 | 2500 | 2.4479 | 1.0 | | 2.3186 | 24.0 | 3000 | 1.5888 | 0.9565 | | 1.8469 | 28.0 | 3500 | 1.2368 | 0.8655 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
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samantharhay/wav2vec2-base-zenodo-test
09c7dd10bedffc38e9c5eb8bade8d165190f7982
2021-11-22T19:11:23.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
samantharhay
null
samantharhay/wav2vec2-base-zenodo-test
0
null
transformers
Entry not found
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samitizerxu/wav2vec2-xls-r-300m-es
5eaf5c719dc8735e416ff55d972bb002c8ef00ae
2022-03-24T11:56:03.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "es", "dataset:common_voice", "transformers", "common_voice", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
samitizerxu
null
samitizerxu/wav2vec2-xls-r-300m-es
0
null
transformers
--- language: - es license: apache-2.0 tags: - automatic-speech-recognition - common_voice - generated_from_trainer - es - robust-speech-event - hf-asr-leaderboard datasets: - common_voice model-index: - name: wav2vec2-cls-r-300m-es results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 7 type: mozilla-foundation/common_voice_7_0 args: es metrics: - name: Test WER type: wer value: 37.37 - name: Test CER type: cer value: 7.11 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Dev Data type: speech-recognition-community-v2/dev_data args: es metrics: - name: Test WER type: wer value: 55.69 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Test Data type: speech-recognition-community-v2/eval_data args: es metrics: - name: Test WER type: wer value: 57.28 --- <!-- 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-cls-r-300m-es This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - ES dataset. It achieves the following results on the evaluation set: - Loss: 0.5160 - Wer: 0.4016 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.1277 | 1.14 | 500 | 2.0259 | 0.9999 | | 1.4111 | 2.28 | 1000 | 1.1251 | 0.8894 | | 0.8461 | 3.42 | 1500 | 0.8205 | 0.7244 | | 0.5042 | 4.57 | 2000 | 0.6116 | 0.5463 | | 0.3072 | 5.71 | 2500 | 0.5507 | 0.4506 | | 0.2181 | 6.85 | 3000 | 0.5213 | 0.4177 | | 0.1608 | 7.99 | 3500 | 0.5161 | 0.4019 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0 #### Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test` ```bash python eval.py --model_id samitizerxu/wav2vec2-xls-r-300m-es --dataset mozilla-foundation/common_voice_7_0 --config es --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash python eval.py --model_id samitizerxu/wav2vec2-xls-r-300m-es --dataset speech-recognition-community-v2/dev_data --config es --split validation --chunk_length_s 5.0 --stride_length_s 1.0 ```
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sammy786/wav2vec2-large-xlsr-mongolian
80c0a7cc621ef4879ef6c39c8177cb61f92988d0
2021-04-02T11:36:53.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "mn", "dataset:common_voice", "transformers", "audio", "speech", "xlsr-fine-tuning-week", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
sammy786
null
sammy786/wav2vec2-large-xlsr-mongolian
0
null
transformers
--- language: mn datasets: - common_voice tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Mongolian by Salim Shaikh results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice mn type: common_voice args: {mn} metrics: - name: Test WER type: wer value: 38.14 --- # Wav2Vec2-Large-XLSR-53-Mongolian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Mongolian using the [Common Voice](https://huggingface.co/datasets/common_voice) When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torchaudio from datasets import load_dataset, load_metric from transformers import ( Wav2Vec2ForCTC, Wav2Vec2Processor, ) import torch import re import sys model_name = "sammy786/wav2vec2-large-xlsr-mongolian" device = "cuda" chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�\\)\\(\\*)]' model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) processor = Wav2Vec2Processor.from_pretrained(model_name) ds = load_dataset("common_voice", "mn", split="test", data_dir="./cv-corpus-6.1-2020-12-11") resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000) def map_to_array(batch): speech, _ = torchaudio.load(batch["path"]) batch["speech"] = resampler.forward(speech.squeeze(0)).numpy() batch["sampling_rate"] = resampler.new_freq batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " " return batch ds = ds.map(map_to_array) def map_to_pred(batch): features = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt") input_values = features.input_values.to(device) attention_mask = features.attention_mask.to(device) with torch.no_grad(): logits = model(input_values, attention_mask=attention_mask).logits pred_ids = torch.argmax(logits, dim=-1) batch["predicted"] = processor.batch_decode(pred_ids) batch["target"] = batch["sentence"] return batch result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys())) wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` **Test Result**: 38.14 %
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sammy786/wav2vec2-xlsr-bashkir
9503ad11ced5cc0d71db9238a0b94886cb0d63bb
2022-03-23T18:35:07.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "ba", "dataset:mozilla-foundation/common_voice_8_0", "transformers", "generated_from_trainer", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
sammy786
null
sammy786/wav2vec2-xlsr-bashkir
0
null
transformers
--- language: - ba license: apache-2.0 tags: - automatic-speech-recognition - ba - generated_from_trainer - hf-asr-leaderboard - model_for_talk - mozilla-foundation/common_voice_8_0 - robust-speech-event datasets: - mozilla-foundation/common_voice_8_0 model-index: - name: sammy786/wav2vec2-xlsr-bashkir results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 8 type: mozilla-foundation/common_voice_8_0 args: ba metrics: - name: Test WER type: wer value: 11.32 - name: Test CER type: cer value: 2.34 --- # sammy786/wav2vec2-xlsr-bashkir This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - ba dataset. It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets): - Loss: - Wer: ## Model description "facebook/wav2vec2-xls-r-1b" was finetuned. ## Intended uses & limitations More information needed ## Training and evaluation data Training data - Common voice Finnish train.tsv, dev.tsv and other.tsv ## Training procedure For creating the train dataset, all possible datasets were appended and 90-10 split was used. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000045637994662983496 - train_batch_size: 16 - eval_batch_size: 16 - seed: 13 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Step | Training Loss | Validation Loss | Wer | |:----:|:-------------:|:---------------:|:--------:| | 200 | 5.387100 | 1.982867 | 1.000000 | | 400 | 1.269800 | 0.369958 | 0.545755 | | 600 | 0.903600 | 0.287705 | 0.465594 | | 800 | 0.787300 | 0.235142 | 0.417091 | | 1000 | 0.816300 | 0.206325 | 0.390534 | | 1200 | 0.700500 | 0.197106 | 0.383987 | | 1400 | 0.707100 | 0.179855 | 0.381368 | | 1600 | 0.657800 | 0.181605 | 0.370593 | | 1800 | 0.647800 | 0.168626 | 0.358767 | | 2000 | 0.650700 | 0.164833 | 0.351483 | | 2200 | 0.490900 | 0.168133 | 0.363309 | | 2400 | 0.431000 | 0.161201 | 0.344350 | | 2600 | 0.372100 | 0.160254 | 0.338280 | | 2800 | 0.367500 | 0.150885 | 0.329687 | | 3000 | 0.351300 | 0.154112 | 0.331392 | | 3200 | 0.314800 | 0.147147 | 0.326700 | | 3400 | 0.316800 | 0.142681 | 0.325090 | | 3600 | 0.313000 | 0.138736 | 0.319553 | | 3800 | 0.291800 | 0.138166 | 0.315570 | | 4000 | 0.311300 | 0.135977 | 0.322894 | | 4200 | 0.304900 | 0.128820 | 0.308627 | | 4400 | 0.301600 | 0.129475 | 0.307440 | | 4600 | 0.281800 | 0.131863 | 0.305967 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.10.3 #### Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id sammy786/wav2vec2-xlsr-bashkir --dataset mozilla-foundation/common_voice_8_0 --config ba --split test ```
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sammy786/wav2vec2-xlsr-finnish
f692779778d4321cf95846c57be11e03b4c126aa
2022-03-23T18:34:11.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "fi", "dataset:mozilla-foundation/common_voice_8_0", "transformers", "generated_from_trainer", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
sammy786
null
sammy786/wav2vec2-xlsr-finnish
0
null
transformers
--- language: - fi license: apache-2.0 tags: - automatic-speech-recognition - fi - generated_from_trainer - hf-asr-leaderboard - model_for_talk - mozilla-foundation/common_voice_8_0 - robust-speech-event datasets: - mozilla-foundation/common_voice_8_0 model-index: - name: sammy786/wav2vec2-xlsr-finnish results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 8 type: mozilla-foundation/common_voice_8_0 args: fi metrics: - name: Test WER type: wer value: 13.72 - name: Test CER type: cer value: 2.35 --- # sammy786/wav2vec2-xlsr-finnish This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - fi dataset. It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets): - Loss: 8.7555 - Wer: 23.0231 ## Model description "facebook/wav2vec2-xls-r-1b" was finetuned. ## Intended uses & limitations More information needed ## Training and evaluation data Training data - Common voice Finnish train.tsv, dev.tsv, invalidated.tsv and other.tsv ## Training procedure For creating the train dataset, all possible datasets were appended and 90-10 split was used. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000045637994662983496 - train_batch_size: 8 - eval_batch_size: 16 - seed: 13 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Step | Training Loss | Validation Loss | Wer | |:----:|:-------------:|:---------------:|:--------:| | 200 | 4.253700 | 0.881733 | 0.967007 | | 400 | 0.864800 | 0.226977 | 0.420836 | | 600 | 0.607000 | 0.157473 | 0.343375 | | 800 | 0.380200 | 0.145640 | 0.302672 | | 1000 | 0.318400 | 0.128028 | 0.293886 | | 1200 | 0.261100 | 0.121414 | 0.289941 | | 1400 | 0.232300 | 0.113451 | 0.279182 | | 1600 | 0.216600 | 0.113649 | 0.282948 | | 1800 | 0.202500 | 0.112375 | 0.276134 | | 2000 | 0.190000 | 0.105725 | 0.273803 | | 2200 | 0.171000 | 0.109715 | 0.270755 | | 2400 | 0.156500 | 0.105042 | 0.264300 | | 2600 | 0.155600 | 0.108337 | 0.260714 | | 2800 | 0.149100 | 0.112435 | 0.263583 | | 3000 | 0.145100 | 0.106193 | 0.261969 | | 3200 | 0.131700 | 0.102860 | 0.251210 | | 3400 | 0.129100 | 0.096058 | 0.246907 | | 3600 | 0.121600 | 0.099932 | 0.246369 | | 3800 | 0.112000 | 0.099041 | 0.244397 | | 4000 | 0.114100 | 0.101566 | 0.242604 | | 4200 | 0.111500 | 0.089498 | 0.239197 | | 4400 | 0.099800 | 0.092835 | 0.240990 | | 4600 | 0.095300 | 0.093518 | 0.238121 | | 4800 | 0.094300 | 0.090783 | 0.240631 | | 5000 | 0.089000 | 0.094046 | 0.238479 | | 5200 | 0.088000 | 0.089342 | 0.235252 | | 5400 | 0.083600 | 0.087770 | 0.234535 | | 5600 | 0.083600 | 0.088804 | 0.234355 | | 5800 | 0.080300 | 0.090168 | 0.231307 | | 6000 | 0.078100 | 0.090163 | 0.230949 | | 6200 | 0.075600 | 0.088876 | 0.232383 | | 6400 | 0.078700 | 0.087235 | 0.232024 | | 6600 | 0.074800 | 0.086825 | 0.231486 | | 6800 | 0.076400 | 0.087308 | 0.231845 | | 7000 | 0.070700 | 0.087695 | 0.230769 | | 7200 | 0.075500 | 0.087555 | 0.230231 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.10.3 #### Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id sammy786/wav2vec2-xlsr-finnish --dataset mozilla-foundation/common_voice_8_0 --config fi --split test ```
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sammy786/wav2vec2-xlsr-mongolian
b8d154569f158febe9e878743fb4016d6805f569
2022-03-23T18:30:27.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "mn", "dataset:mozilla-foundation/common_voice_8_0", "transformers", "generated_from_trainer", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
sammy786
null
sammy786/wav2vec2-xlsr-mongolian
0
null
transformers
--- language: - mn license: apache-2.0 tags: - automatic-speech-recognition - generated_from_trainer - hf-asr-leaderboard - mn - model_for_talk - mozilla-foundation/common_voice_8_0 - robust-speech-event datasets: - mozilla-foundation/common_voice_8_0 model-index: - name: sammy786/wav2vec2-xlsr-mongolian results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 8 type: mozilla-foundation/common_voice_8_0 args: mn metrics: - name: Test WER type: wer value: 32.63 - name: Test CER type: cer value: 9.26 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Dev Data type: speech-recognition-community-v2/dev_data args: mn metrics: - name: Test WER type: wer value: 91.26 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Test Data type: speech-recognition-community-v2/eval_data args: mn metrics: - name: Test WER type: wer value: 91.37 --- # sammy786/wav2vec2-xlsr-mongolian This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - mn dataset. It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets): - Loss: 31.52 - Wer: 34.1522 ## Model description "facebook/wav2vec2-xls-r-1b" was finetuned. ## Intended uses & limitations More information needed ## Training and evaluation data Training data - Common voice Finnish train.tsv, dev.tsv and other.tsv ## Training procedure For creating the train dataset, all possible datasets were appended and 90-10 split was used. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000045637994662983496 - train_batch_size: 16 - eval_batch_size: 16 - seed: 13 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Step | Training Loss | Validation Loss | Wer | |:----:|:-------------:|:---------------:|:--------:| | 200 | 4.906200 | 3.012986 | 1.000000 | | 400 | 1.734600 | 0.704821 | 0.750497 | | 600 | 1.132100 | 0.496223 | 0.531241 | | 800 | 0.929300 | 0.468937 | 0.469043 | | 1000 | 0.772300 | 0.425313 | 0.448168 | | 1200 | 0.623900 | 0.394633 | 0.414229 | | 1400 | 0.512400 | 0.369225 | 0.397614 | | 1600 | 0.439900 | 0.346033 | 0.391650 | | 1800 | 0.391300 | 0.358454 | 0.379296 | | 2000 | 0.377000 | 0.346822 | 0.359415 | | 2200 | 0.347500 | 0.325205 | 0.348481 | | 2400 | 0.343600 | 0.315233 | 0.344078 | | 2600 | 0.328000 | 0.308826 | 0.341522 | | 2800 | 0.358200 | 0.331786 | 0.343084 | | 3000 | 0.417200 | 0.370051 | 0.356433 | | 3200 | 0.685300 | 0.595438 | 0.407413 | | 3400 | 0.764100 | 0.643449 | 0.359983 | | 3600 | 0.717100 | 0.505033 | 0.371911 | | 3800 | 0.620900 | 0.464138 | 0.369071 | | 4000 | 0.590700 | 0.445417 | 0.363249 | | 4200 | 0.561000 | 0.440727 | 0.360267 | | 4400 | 0.550600 | 0.447122 | 0.360267 | | 4600 | 0.562100 | 0.457020 | 0.359841 | | 4800 | 0.578800 | 0.470477 | 0.360551 | | 5000 | 0.580400 | 0.481413 | 0.362539 | | 5200 | 0.605500 | 0.485240 | 0.362823 | | 5400 | 0.582900 | 0.486654 | 0.362965 | | 5600 | 0.593900 | 0.486715 | 0.363107 | | 5800 | 0.590900 | 0.486716 | 0.363107 | | 6000 | 0.587200 | 0.486716 | 0.363107 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.10.3 #### Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id sammy786/wav2vec2-xlsr-mongolian --dataset mozilla-foundation/common_voice_8_0 --config mn --split test ```
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samuelssonm/DialoGPT-small-rick
9d59ae2d6842bfd8665252d2002728ea37491518
2021-11-29T23:30:34.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
samuelssonm
null
samuelssonm/DialoGPT-small-rick
0
null
transformers
--- tags: - conversational --- # Rick and Morty DialoGPT Model
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sangrimlee/mt5-small-ans-ext
a9f4593a709157e972e0507b9675e57de9b1b13d
2021-03-03T12:14:59.000Z
[ "pytorch", "mt5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
sangrimlee
null
sangrimlee/mt5-small-ans-ext
0
null
transformers
Entry not found
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sanjanareddy226/JakeBot
0fb02eb7114fa64cd4fce8dc8fd2ffe76eaffe03
2021-10-23T06:25:41.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
sanjanareddy226
null
sanjanareddy226/JakeBot
0
null
transformers
--- tags: - conversational --- # Jake Peralta bot
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sankalpjha1/mr.bot_haary
9222616ce458968c6de7907f6b667d89d7ae16cf
2021-10-21T07:21:19.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
sankalpjha1
null
sankalpjha1/mr.bot_haary
0
null
transformers
--- tags: - conversational --- # Mr.bot_haary
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santhoshkolloju/ques_gen
80da3324ff407638124b2a87885da597a8339a13
2020-07-07T10:36:21.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
santhoshkolloju
null
santhoshkolloju/ques_gen
0
null
transformers
Entry not found
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saraks/cuad-distil-document_name-cased-08-31-v1
93ad9639e25e7856474702744bd01c3b9bda7fe0
2021-08-31T16:19:23.000Z
[ "pytorch", "distilbert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
saraks
null
saraks/cuad-distil-document_name-cased-08-31-v1
0
null
transformers
Entry not found
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sarim/myModel
47edacd4d65b1d76c6375e626b74533b035f2cc9
2021-03-20T12:53:37.000Z
[ "pytorch", "distilbert", "feature-extraction", "transformers" ]
feature-extraction
false
sarim
null
sarim/myModel
0
null
transformers
first commit
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sarnikowski/convbert-small-da-cased
6086e125654b7bba33019045a4a0a5d919e1101e
2021-03-01T22:15:15.000Z
[ "pytorch", "tf", "convbert", "da", "arxiv:2008.02496", "transformers", "license:cc-by-4.0" ]
null
false
sarnikowski
null
sarnikowski/convbert-small-da-cased
0
null
transformers
--- language: da license: cc-by-4.0 --- # Danish ConvBERT small (cased) [ConvBERT](https://arxiv.org/abs/2008.02496) model pretrained on a custom Danish corpus (~17.5gb). For details regarding data sources and training procedure, along with benchmarks on downstream tasks, go to: https://github.com/sarnikowski/danish_transformers ## Usage ```python from transformers import ConvBertTokenizer, ConvBertModel tokenizer = ConvBertTokenizer.from_pretrained("sarnikowski/convbert-small-da-cased") model = ConvBertModel.from_pretrained("sarnikowski/convbert-small-da-cased") ``` ## Questions? If you have any questions feel free to open an issue on the [danish_transformers](https://github.com/sarnikowski/danish_transformers) repository, or send an email to [email protected]
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sberbank-ai/RuDOLPH-350M
568d1ea5af6e110507d9a38bac8c6979755d4727
2022-02-04T16:54:03.000Z
[ "pytorch" ]
null
false
sberbank-ai
null
sberbank-ai/RuDOLPH-350M
0
9
null
# RuDOLPH-350M (Medium) RuDOLPH: One Hyper-Modal Transformer can be creative as DALL-E and smart as CLIP <img src="https://raw.githubusercontent.com/sberbank-ai/ru-dolph/master/pics/rudolph-generated.png" height="60" border="2"/> Model was trained by [Sber AI](https://github.com/sberbank-ai) and [SberDevices](https://sberdevices.ru/) teams. * Task: `text2image generation`; `self reranking`; `text ranking`; `image ranking`; `image2text generation`; `zero-shot image classification`, `text2text generation`; * Language: `Russian` * Type: `encoder-decoder` * Num Parameters: `350M` * Training Data Volume: `156 million text-image pairs` # Model Description **Ru**ssian **D**iffusion **O**n **L**anguage **P**icture **H**yper-modality (RuDOLPH) 350M is a fast and light text-image-text transformer (350M GPT-3) designed for a quick and easy fine-tuning setup for the solution of various tasks: from generating images by text description and image classification to visual question answering and more. This model demonstrates the power of Hyper-modality Transformers. *(!!!) Hyper-modality means generalized multi-modal, e.g., model that consists of two multi-modal parts: text-2-image and image-2-text becomes text and image hyper-modality model* # Sparse Attention Mask The primary proposed method is to modify the sparse transformer's attention mask to better control multi-modalities and up to the next level with "hyper-modality". It allows us to calculate the transitions of modalities in both directions, unlike another similar work DALL-E Transformer, which used only one direction, "text to image". The proposed "image to right text" direction is achieved by extension sparse attention mask to the right for auto-repressively text generation with image condition without attention to left text. <img src="https://raw.githubusercontent.com/sberbank-ai/ru-dolph/master/pics/attention_masks.png" height="40" border="2"/> # Authors + Alex Shonenkov: [Github](https://github.com/shonenkov), [Kaggle GM](https://www.kaggle.com/shonenkov) + Michael Konstantinov: [Mishin Learning](https://t.me/mishin_learning), [Transformer Community](https://transformer.community/)
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sberbank-ai/rudalle-Emojich
0b1701f27f3b0f9f7912a75b4b45eea9c6e92afe
2021-12-02T11:06:48.000Z
[ "pytorch" ]
null
false
sberbank-ai
null
sberbank-ai/rudalle-Emojich
0
7
null
# Emojich ![](./pics/emojich_rgba_100.png) ### generate emojis from text Model was trained by [Sber AI](https://github.com/sberbank-ai) * Task: `text2image generation` * Num Parameters: `1.3 B` * Training Data Volume: `120 million text-image pairs` & [`2749 text-emoji pairs`](https://www.kaggle.com/shonenkov/russian-emoji) [![Telegram](https://img.shields.io/badge/Telegram-Stickers-blue?style=for-the-badge&logo=data:image/svg%2bxml;base64,PHN2ZyBlbmFibGUtYmFja2dyb3VuZD0ibmV3IDAgMCAyNCAyNCIgaGVpZ2h0PSI1MTIiIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjUxMiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj48cGF0aCBkPSJtOS40MTcgMTUuMTgxLS4zOTcgNS41ODRjLjU2OCAwIC44MTQtLjI0NCAxLjEwOS0uNTM3bDIuNjYzLTIuNTQ1IDUuNTE4IDQuMDQxYzEuMDEyLjU2NCAxLjcyNS4yNjcgMS45OTgtLjkzMWwzLjYyMi0xNi45NzIuMDAxLS4wMDFjLjMyMS0xLjQ5Ni0uNTQxLTIuMDgxLTEuNTI3LTEuNzE0bC0yMS4yOSA4LjE1MWMtMS40NTMuNTY0LTEuNDMxIDEuMzc0LS4yNDcgMS43NDFsNS40NDMgMS42OTMgMTIuNjQzLTcuOTExYy41OTUtLjM5NCAxLjEzNi0uMTc2LjY5MS4yMTh6IiBmaWxsPSIjMDM5YmU1Ii8+PC9zdmc+)](https://telegram.me/addstickers/SberAI_ruDALLE) ### Model Description 😋 Emojich is a 1.3 billion params model from the family GPT3-like, it generates emoji-style images with the brain of ◾ Malevich. ### Fine-tuning stage: The main goal of fine-tuning is trying to keep the generalization of [ruDALL-E Malevich (XL)](https://huggingface.co/sberbank-ai/rudalle-Malevich) model on text to emoji tasks. ruDALL-E Malevich is a multi-modality big pretrained transformer, that uses images and texts. The idea with freezing feedforward and self-attention layers in pretrained transformer is demonstrated high performance in changing different modalities. Also, the model has a good chance for over-fitting text modality and lost generalization. To deal with this problem is increased coefficient 10^3 in weighted cross-entropy loss for image codebooks part. Full version of training code is available on Kaggle: [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://www.kaggle.com/shonenkov/emojich-rudall-e) ### Examples of generated emojis All examples are generated automatically (without manual cherry-picking) with hyper-parameters: seed 42, batch size 16, top-k 2048, top-p 0.995, temperature 1.0, GPU A100. For making better generative emojis should use more attempts (~512) and select the best one manually. *Remember, the great art makers became "great" after creating just only one masterpiece.* ![](./pics/examples.png)
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sberbank-ai/rudalle-Malevich
87ef2bc10f02da71b61003aeed15cc6bbc0557cf
2022-01-11T02:20:10.000Z
[ "pytorch", "ru", "en", "PyTorch", "Transformers", "text-to-image" ]
text-to-image
false
sberbank-ai
null
sberbank-ai/rudalle-Malevich
0
24
null
--- language: - ru - en pipeline_tag: text-to-image tags: - PyTorch - Transformers thumbnail: "https://github.com/sberbank-ai/ru-dalle" --- # ruDALL-E Malevich (XL) ## Generate images from text <img style="text-align:center; display:block;" src="https://huggingface.co/sberbank-ai/rudalle-Malevich/resolve/main/dalle-malevich.jpg" width="200"> "Avocado painting in the style of Malevich" * [Technical Report (Russian)](https://habr.com/ru/company/sberbank/blog/586926) * [Demo](https://rudalle.ru) Model was trained by [Sber AI](https://github.com/sberbank-ai) and [SberDevices](https://sberdevices.ru/) teams. * Task: `text2image generation` * Type: `encoder-decoder` * Num Parameters: `1.3 B` * Training Data Volume: `120 million text-image pairs` ### Model Description This is a 1.3 billion parameter model for Russian, recreating OpenAI's [DALL·E](https://openai.com/blog/dall-e/), a model capable of generating arbitrary images from a text prompt that describes the desired result. The generation pipeline includes ruDALL-E, ruCLIP for ranging results, and a superresolution model. You can use automatic translation into Russian to create desired images with ruDALL-E. ### How to Use The easiest way to get familiar with the code and the models is to follow the inference notebook we provide in our [github repo](https://github.com/sberbank-ai/ru-dalle). ## Motivation One might say that “investigate, master, and train” is our engineering motto. Well, we caught the scent, and today we can say that we created from scratch a complete pipeline for generating images from descriptive textual input written in Russian. Teams at SberAI, SberDevices, Samara University, AIRI and SberCloud all actively contributed. We trained two versions of the model, each a different size, and named them after Russia’s great abstractionists: Vasily Kandinsky and Kazimir Malevich. * ruDALL-E Kandinsky (XXL), with 12 billion parameters * ruDALL-E Malevich (XL), having 1.3 billion parameters Some of our models are already freely available: * ruDALL-E Malevich (XL) [[GitHub](https://github.com/sberbank-ai/ru-dalle), [HuggingFace](https://huggingface.co/sberbank-ai/rudalle-Malevich)] * Sber VQ-GAN [[GitHub](https://github.com/sberbank-ai/sber-vq-gan), [HuggingFace](https://huggingface.co/sberbank-ai/Sber-VQGAN)] * ruCLIP Small [[GitHub](https://github.com/sberbank-ai/ru-clip), [HuggingFace](https://huggingface.co/sberbank-ai/ru-clip)] * Super Resolution (Real ESRGAN) [[GitHub](https://github.com/sberbank-ai/Real-ESRGAN), [HuggingFace](https://huggingface.co/sberbank-ai/Real-ESRGAN)] The latter two models are included in the pipeline for generating images from text (as you’ll see later on). The models ruDALL-E Malevich (XL), ruDALL-E Kandinsky (XXL), ruCLIP Small, ruCLIP Large, and Super Resolution (Real ESRGAN) will also soon be available on [DataHub](https://mlspace.aicloud.sbercloud.ru/mlspace/datahub). Training the ruDALL-E neural networks on the Christofari cluster has become the largest calculation task in Russia: * ruDALL-E Kandinsky (XXL) was trained for 37 days on the 512 GPU TESLA V100, and then also for 11 more days on the 128 GPU TESLA V100, for a total of 20,352 GPU-days; * ruDALL-E Malevich (XL) was trained for 8 days on the 128 GPU TESLA V100, and then also for 15 more days on the 192 GPU TESLA V100, for a total of 3,904 GPU-days. Accordingly, training for both models totalled 24,256 GPU-days. ## Model capabilities The long term goal of this research is the creation of multimodal neural networks. They will be able to pull on concepts from a variety of mediums---from text and visuals at first---in order to better understand the world as a whole. Image generation might seem like the wrong rabbit hole in our century of big data and search engines. But it actually addresses two important requirements that search is currently unable to cope with: 1. Being able to describe in writing exactly what you’re looking for and getting a completely new image created personally for you. 2. Being able to create at any time as many license-free illustrations as you could possibly want "Grand Canyon" <img style="text-align:center; display:block;" src="https://habrastorage.org/webt/kb/sv/ih/kbsvihfsmz3fx5mvitii0seimi0.jpeg" width="800"> "Salvador Dali picture" <img style="text-align:center; display:block;" src="https://habrastorage.org/webt/r8/nl/oi/r8nloiq-l8j2ckg6pzh2pufsklm.jpeg" width="800"> "An eagle sits in a tree, looking to the side" <img style="text-align:center; display:block;" src="https://habrastorage.org/r/w1560/getpro/habr/upload_files/10a/19c/fa2/10a19cfa2cc84aa7c8b99820890e908d.png" width="800"> "Elegant living room with green stuffed chairs" <img style="text-align:center; display:block;" src="https://habrastorage.org/r/w1560/getpro/habr/upload_files/6fe/e69/d7c/6fee69d7c392239d587725799e0e41e4.png" width="800"> “Raccoon with a gun” <img style="text-align:center; display:block;" src="https://habrastorage.org/r/w1560/getpro/habr/upload_files/3bb/1b8/7c4/3bb1b87c45bf9305cd342ae9900ac245.png" width="800"> “Pretty lake at sunset” <img style="text-align:center; display:block;" src="https://habrastorage.org/r/w1560/getpro/habr/upload_files/241/781/fe9/241781fe99da510d4d5fea03af635e88.png" width="800">
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seduerr/pai_comma
3ba41e36bfbbbce0c1acf9c25b683cbd07f16ac0
2021-05-05T14:38:46.000Z
[ "pytorch" ]
null
false
seduerr
null
seduerr/pai_comma
0
null
null
Entry not found
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seduerr/pai_con
a76d539afcf9da08138fd7de4354d6e2b5ad7114
2021-06-23T14:12:35.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
seduerr
null
seduerr/pai_con
0
null
transformers
‘contrast: ‘
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