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research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2-child-prototypical
research-backup
2022-11-24T22:32:51Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T22:31:44Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6994047619047619 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3315508021390374 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.33827893175074186 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3941078376876042 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.476 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.40350877192982454 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.38425925925925924 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8157299984932952 - name: F1 (macro) type: f1_macro value: 0.7874948565395373 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7908450704225352 - name: F1 (macro) type: f1_macro value: 0.4766590220293792 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5390032502708559 - name: F1 (macro) type: f1_macro value: 0.483312366925826 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9006746887389581 - name: F1 (macro) type: f1_macro value: 0.7871817286518888 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8097774992165465 - name: F1 (macro) type: f1_macro value: 0.7967017372058048 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3315508021390374 - Accuracy on SAT: 0.33827893175074186 - Accuracy on BATS: 0.3941078376876042 - Accuracy on U2: 0.40350877192982454 - Accuracy on U4: 0.38425925925925924 - Accuracy on Google: 0.476 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8157299984932952 - Micro F1 score on CogALexV: 0.7908450704225352 - Micro F1 score on EVALution: 0.5390032502708559 - Micro F1 score on K&H+N: 0.9006746887389581 - Micro F1 score on ROOT09: 0.8097774992165465 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6994047619047619 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 8 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-1-child-prototypical
research-backup
2022-11-24T22:31:13Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T22:30:10Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-1-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7791666666666667 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34759358288770054 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.35311572700296734 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4102279043913285 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.562 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39035087719298245 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3773148148148148 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.841795992165135 - name: F1 (macro) type: f1_macro value: 0.8175016337972725 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7873239436619718 - name: F1 (macro) type: f1_macro value: 0.4950289837305936 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5877573131094258 - name: F1 (macro) type: f1_macro value: 0.5255505321109658 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9225846838700703 - name: F1 (macro) type: f1_macro value: 0.8111729625447839 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8558445628329677 - name: F1 (macro) type: f1_macro value: 0.8564733796055889 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-1-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-1-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.34759358288770054 - Accuracy on SAT: 0.35311572700296734 - Accuracy on BATS: 0.4102279043913285 - Accuracy on U2: 0.39035087719298245 - Accuracy on U4: 0.3773148148148148 - Accuracy on Google: 0.562 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-1-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.841795992165135 - Micro F1 score on CogALexV: 0.7873239436619718 - Micro F1 score on EVALution: 0.5877573131094258 - Micro F1 score on K&H+N: 0.9225846838700703 - Micro F1 score on ROOT09: 0.8558445628329677 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-1-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7791666666666667 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-1-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 8 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-1-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-0-child-prototypical
research-backup
2022-11-24T22:29:32Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T22:28:28Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-0-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.8059920634920635 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.32620320855614976 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3323442136498516 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3507504168982768 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.382 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.33771929824561403 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.35185185185185186 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7999095977098086 - name: F1 (macro) type: f1_macro value: 0.7684883952780684 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7530516431924884 - name: F1 (macro) type: f1_macro value: 0.34938339909910743 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.4739978331527627 - name: F1 (macro) type: f1_macro value: 0.3568206512908552 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8818946929122905 - name: F1 (macro) type: f1_macro value: 0.7088102566206993 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7790661234722657 - name: F1 (macro) type: f1_macro value: 0.759592502713887 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-0-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-0-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.32620320855614976 - Accuracy on SAT: 0.3323442136498516 - Accuracy on BATS: 0.3507504168982768 - Accuracy on U2: 0.33771929824561403 - Accuracy on U4: 0.35185185185185186 - Accuracy on Google: 0.382 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-0-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.7999095977098086 - Micro F1 score on CogALexV: 0.7530516431924884 - Micro F1 score on EVALution: 0.4739978331527627 - Micro F1 score on K&H+N: 0.8818946929122905 - Micro F1 score on ROOT09: 0.7790661234722657 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-0-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.8059920634920635 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-0-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-0-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2-child-prototypical
research-backup
2022-11-24T22:27:49Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T22:26:46Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6737698412698413 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3315508021390374 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3293768545994065 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3941078376876042 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.414 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.36403508771929827 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3402777777777778 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7663100798553564 - name: F1 (macro) type: f1_macro value: 0.7373104702799426 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7319248826291079 - name: F1 (macro) type: f1_macro value: 0.24240839688716162 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5444203683640303 - name: F1 (macro) type: f1_macro value: 0.486892824959951 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8288238158169298 - name: F1 (macro) type: f1_macro value: 0.6822117556287328 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7414603572547791 - name: F1 (macro) type: f1_macro value: 0.6721263567848067 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3315508021390374 - Accuracy on SAT: 0.3293768545994065 - Accuracy on BATS: 0.3941078376876042 - Accuracy on U2: 0.36403508771929827 - Accuracy on U4: 0.3402777777777778 - Accuracy on Google: 0.414 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.7663100798553564 - Micro F1 score on CogALexV: 0.7319248826291079 - Micro F1 score on EVALution: 0.5444203683640303 - Micro F1 score on K&H+N: 0.8288238158169298 - Micro F1 score on ROOT09: 0.7414603572547791 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6737698412698413 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-1-child-prototypical
research-backup
2022-11-24T22:26:14Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T22:25:07Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-1-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.8430952380952381 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3582887700534759 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3649851632047478 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4280155642023346 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.532 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3333333333333333 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3101851851851852 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8464667771583547 - name: F1 (macro) type: f1_macro value: 0.8314311734193423 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8084507042253521 - name: F1 (macro) type: f1_macro value: 0.5269777075808457 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6397616468039004 - name: F1 (macro) type: f1_macro value: 0.6161756853613614 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9094386867914029 - name: F1 (macro) type: f1_macro value: 0.7684752097244069 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8693199623942337 - name: F1 (macro) type: f1_macro value: 0.866286368957231 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-1-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-1-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3582887700534759 - Accuracy on SAT: 0.3649851632047478 - Accuracy on BATS: 0.4280155642023346 - Accuracy on U2: 0.3333333333333333 - Accuracy on U4: 0.3101851851851852 - Accuracy on Google: 0.532 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-1-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8464667771583547 - Micro F1 score on CogALexV: 0.8084507042253521 - Micro F1 score on EVALution: 0.6397616468039004 - Micro F1 score on K&H+N: 0.9094386867914029 - Micro F1 score on ROOT09: 0.8693199623942337 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-1-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.8430952380952381 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-1-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-1-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-0-child-prototypical
research-backup
2022-11-24T22:24:33Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T22:22:54Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-0-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.8158333333333333 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3850267379679144 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3857566765578635 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4452473596442468 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.462 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34649122807017546 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3263888888888889 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8225101702576465 - name: F1 (macro) type: f1_macro value: 0.8034809428019279 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7518779342723004 - name: F1 (macro) type: f1_macro value: 0.33329299540004015 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5411700975081257 - name: F1 (macro) type: f1_macro value: 0.4639671585144264 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8478820338039924 - name: F1 (macro) type: f1_macro value: 0.6768848309180341 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8307740520213099 - name: F1 (macro) type: f1_macro value: 0.8235960440777698 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-0-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-0-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3850267379679144 - Accuracy on SAT: 0.3857566765578635 - Accuracy on BATS: 0.4452473596442468 - Accuracy on U2: 0.34649122807017546 - Accuracy on U4: 0.3263888888888889 - Accuracy on Google: 0.462 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-0-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8225101702576465 - Micro F1 score on CogALexV: 0.7518779342723004 - Micro F1 score on EVALution: 0.5411700975081257 - Micro F1 score on K&H+N: 0.8478820338039924 - Micro F1 score on ROOT09: 0.8307740520213099 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-0-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.8158333333333333 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-0-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-0-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
alexziweiwang/exp21-uaspeech-foundation
alexziweiwang
2022-11-24T21:57:41Z
109
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-11-24T09:19:05Z
--- tags: - generated_from_trainer model-index: - name: exp21-uaspeech-foundation 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. --> # exp21-uaspeech-foundation This model is a fine-tuned version of [yongjian/wav2vec2-large-a](https://huggingface.co/yongjian/wav2vec2-large-a) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4644 - Wer: 1.2633 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 27.1608 | 0.67 | 500 | 3.0053 | 1.0 | | 3.2225 | 1.35 | 1000 | 2.8180 | 1.0 | | 2.7882 | 2.02 | 1500 | 2.5589 | 1.3125 | | 2.4911 | 2.7 | 2000 | 2.1110 | 1.9129 | | 2.1921 | 3.37 | 2500 | 1.8404 | 1.8504 | | 1.833 | 4.04 | 3000 | 1.5873 | 1.8030 | | 1.51 | 4.72 | 3500 | 1.3015 | 1.6951 | | 1.2721 | 5.39 | 4000 | 1.2160 | 1.6136 | | 1.1451 | 6.06 | 4500 | 1.4102 | 1.6212 | | 0.9631 | 6.74 | 5000 | 1.1730 | 1.5587 | | 0.9608 | 7.41 | 5500 | 1.4232 | 1.5473 | | 0.8454 | 8.09 | 6000 | 1.3427 | 1.4830 | | 0.7377 | 8.76 | 6500 | 1.3691 | 1.4716 | | 0.6465 | 9.43 | 7000 | 1.4927 | 1.4489 | | 0.6538 | 10.11 | 7500 | 1.3825 | 1.4072 | | 0.591 | 10.78 | 8000 | 1.3166 | 1.4186 | | 0.5476 | 11.46 | 8500 | 1.2636 | 1.3826 | | 0.515 | 12.13 | 9000 | 1.4861 | 1.4072 | | 0.5027 | 12.8 | 9500 | 1.7483 | 1.3826 | | 0.4714 | 13.48 | 10000 | 1.4656 | 1.3598 | | 0.4506 | 14.15 | 10500 | 1.4430 | 1.3693 | | 0.3944 | 14.82 | 11000 | 2.0191 | 1.3352 | | 0.3604 | 15.5 | 11500 | 1.6739 | 1.3428 | | 0.3533 | 16.17 | 12000 | 1.7428 | 1.3314 | | 0.3345 | 16.85 | 12500 | 1.6851 | 1.3277 | | 0.3133 | 17.52 | 13000 | 1.8589 | 1.3106 | | 0.2772 | 18.19 | 13500 | 2.0114 | 1.2955 | | 0.2789 | 18.87 | 14000 | 2.0333 | 1.2879 | | 0.2361 | 19.54 | 14500 | 2.6048 | 1.3030 | | 0.2406 | 20.22 | 15000 | 2.8444 | 1.2841 | | 0.2673 | 20.89 | 15500 | 1.9573 | 1.2746 | | 0.2067 | 21.56 | 16000 | 2.1780 | 1.2936 | | 0.2281 | 22.24 | 16500 | 2.2629 | 1.2765 | | 0.1735 | 22.91 | 17000 | 2.2492 | 1.2822 | | 0.1902 | 23.58 | 17500 | 2.0317 | 1.2803 | | 0.1659 | 24.26 | 18000 | 2.0658 | 1.2652 | | 0.1746 | 24.93 | 18500 | 2.2515 | 1.2708 | | 0.1409 | 25.61 | 19000 | 2.1689 | 1.2595 | | 0.1461 | 26.28 | 19500 | 2.2441 | 1.2614 | | 0.1699 | 26.95 | 20000 | 2.1327 | 1.2633 | | 0.1378 | 27.63 | 20500 | 2.1795 | 1.2670 | | 0.1108 | 28.3 | 21000 | 2.4057 | 1.2576 | | 0.1505 | 28.98 | 21500 | 2.4389 | 1.2595 | | 0.1492 | 29.65 | 22000 | 2.4644 | 1.2633 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2
tomekkorbak/pedantic_goldwasser
tomekkorbak
2022-11-24T21:40:00Z
0
0
null
[ "generated_from_trainer", "en", "dataset:tomekkorbak/detoxify-pile-chunk3-0-50000", "dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000", "dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000", "dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000", "dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000", "dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000", "dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000", "dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000", "dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000", "dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000", "dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000", "dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000", "dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000", "dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000", "dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000", "dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000", "dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000", "dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000", "dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000", "dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000", "dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000", "dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000", "dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000", "dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000", "dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000", "dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000", "dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000", "dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000", "dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000", "dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000", "dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000", "dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000", "dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000", "dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000", "dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000", "dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000", "dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000", "dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000", "license:mit", "region:us" ]
null
2022-11-24T21:39:50Z
--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/detoxify-pile-chunk3-0-50000 - tomekkorbak/detoxify-pile-chunk3-50000-100000 - tomekkorbak/detoxify-pile-chunk3-100000-150000 - tomekkorbak/detoxify-pile-chunk3-150000-200000 - tomekkorbak/detoxify-pile-chunk3-200000-250000 - tomekkorbak/detoxify-pile-chunk3-250000-300000 - tomekkorbak/detoxify-pile-chunk3-300000-350000 - tomekkorbak/detoxify-pile-chunk3-350000-400000 - tomekkorbak/detoxify-pile-chunk3-400000-450000 - tomekkorbak/detoxify-pile-chunk3-450000-500000 - tomekkorbak/detoxify-pile-chunk3-500000-550000 - tomekkorbak/detoxify-pile-chunk3-550000-600000 - tomekkorbak/detoxify-pile-chunk3-600000-650000 - tomekkorbak/detoxify-pile-chunk3-650000-700000 - tomekkorbak/detoxify-pile-chunk3-700000-750000 - tomekkorbak/detoxify-pile-chunk3-750000-800000 - tomekkorbak/detoxify-pile-chunk3-800000-850000 - tomekkorbak/detoxify-pile-chunk3-850000-900000 - tomekkorbak/detoxify-pile-chunk3-900000-950000 - tomekkorbak/detoxify-pile-chunk3-950000-1000000 - tomekkorbak/detoxify-pile-chunk3-1000000-1050000 - tomekkorbak/detoxify-pile-chunk3-1050000-1100000 - tomekkorbak/detoxify-pile-chunk3-1100000-1150000 - tomekkorbak/detoxify-pile-chunk3-1150000-1200000 - tomekkorbak/detoxify-pile-chunk3-1200000-1250000 - tomekkorbak/detoxify-pile-chunk3-1250000-1300000 - tomekkorbak/detoxify-pile-chunk3-1300000-1350000 - tomekkorbak/detoxify-pile-chunk3-1350000-1400000 - tomekkorbak/detoxify-pile-chunk3-1400000-1450000 - tomekkorbak/detoxify-pile-chunk3-1450000-1500000 - tomekkorbak/detoxify-pile-chunk3-1500000-1550000 - tomekkorbak/detoxify-pile-chunk3-1550000-1600000 - tomekkorbak/detoxify-pile-chunk3-1600000-1650000 - tomekkorbak/detoxify-pile-chunk3-1650000-1700000 - tomekkorbak/detoxify-pile-chunk3-1700000-1750000 - tomekkorbak/detoxify-pile-chunk3-1750000-1800000 - tomekkorbak/detoxify-pile-chunk3-1800000-1850000 - tomekkorbak/detoxify-pile-chunk3-1850000-1900000 - tomekkorbak/detoxify-pile-chunk3-1900000-1950000 model-index: - name: pedantic_goldwasser 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. --> # pedantic_goldwasser This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. ## 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: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.01, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0.00056}, 'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257], [50258]], 'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048, 'prefix': '<|aligned|>'}, {'generate_kwargs': {'bad_words_ids': [[50257], [50258]], 'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prefix': '<|aligned|>', 'prompt_before_control': True, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<|aligned|>'}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'num_additional_tokens': 2, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2', 'special_tokens': ['<|aligned|>', '<|misaligned|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'pedantic_goldwasser', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/2zbciten
tomekkorbak/gifted_tesla
tomekkorbak
2022-11-24T21:39:52Z
0
0
null
[ "generated_from_trainer", "en", "dataset:tomekkorbak/detoxify-pile-chunk3-0-50000", "dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000", "dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000", "dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000", "dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000", "dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000", "dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000", "dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000", "dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000", "dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000", "dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000", "dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000", "dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000", "dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000", "dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000", "dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000", "dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000", "dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000", "dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000", "dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000", "dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000", "dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000", "dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000", "dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000", "dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000", "dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000", "dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000", "dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000", "dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000", "dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000", "dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000", "dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000", "dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000", "dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000", "dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000", "dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000", "dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000", "dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000", "license:mit", "region:us" ]
null
2022-11-24T21:39:41Z
--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/detoxify-pile-chunk3-0-50000 - tomekkorbak/detoxify-pile-chunk3-50000-100000 - tomekkorbak/detoxify-pile-chunk3-100000-150000 - tomekkorbak/detoxify-pile-chunk3-150000-200000 - tomekkorbak/detoxify-pile-chunk3-200000-250000 - tomekkorbak/detoxify-pile-chunk3-250000-300000 - tomekkorbak/detoxify-pile-chunk3-300000-350000 - tomekkorbak/detoxify-pile-chunk3-350000-400000 - tomekkorbak/detoxify-pile-chunk3-400000-450000 - tomekkorbak/detoxify-pile-chunk3-450000-500000 - tomekkorbak/detoxify-pile-chunk3-500000-550000 - tomekkorbak/detoxify-pile-chunk3-550000-600000 - tomekkorbak/detoxify-pile-chunk3-600000-650000 - tomekkorbak/detoxify-pile-chunk3-650000-700000 - tomekkorbak/detoxify-pile-chunk3-700000-750000 - tomekkorbak/detoxify-pile-chunk3-750000-800000 - tomekkorbak/detoxify-pile-chunk3-800000-850000 - tomekkorbak/detoxify-pile-chunk3-850000-900000 - tomekkorbak/detoxify-pile-chunk3-900000-950000 - tomekkorbak/detoxify-pile-chunk3-950000-1000000 - tomekkorbak/detoxify-pile-chunk3-1000000-1050000 - tomekkorbak/detoxify-pile-chunk3-1050000-1100000 - tomekkorbak/detoxify-pile-chunk3-1100000-1150000 - tomekkorbak/detoxify-pile-chunk3-1150000-1200000 - tomekkorbak/detoxify-pile-chunk3-1200000-1250000 - tomekkorbak/detoxify-pile-chunk3-1250000-1300000 - tomekkorbak/detoxify-pile-chunk3-1300000-1350000 - tomekkorbak/detoxify-pile-chunk3-1350000-1400000 - tomekkorbak/detoxify-pile-chunk3-1400000-1450000 - tomekkorbak/detoxify-pile-chunk3-1450000-1500000 - tomekkorbak/detoxify-pile-chunk3-1500000-1550000 - tomekkorbak/detoxify-pile-chunk3-1550000-1600000 - tomekkorbak/detoxify-pile-chunk3-1600000-1650000 - tomekkorbak/detoxify-pile-chunk3-1650000-1700000 - tomekkorbak/detoxify-pile-chunk3-1700000-1750000 - tomekkorbak/detoxify-pile-chunk3-1750000-1800000 - tomekkorbak/detoxify-pile-chunk3-1800000-1850000 - tomekkorbak/detoxify-pile-chunk3-1850000-1900000 - tomekkorbak/detoxify-pile-chunk3-1900000-1950000 model-index: - name: gifted_tesla 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. --> # gifted_tesla This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 3147 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'value_head_config': {'is_detached': False}}, 'path_or_name': 'gpt2'}, 'objective': {'alpha': 0.5, 'beta': 10, 'name': 'AWR'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 1024, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'gifted_tesla', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.001, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/188ouvdg
tomekkorbak/suspicious_noyce
tomekkorbak
2022-11-24T21:39:47Z
0
0
null
[ "generated_from_trainer", "en", "dataset:tomekkorbak/detoxify-pile-chunk3-0-50000", "dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000", "dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000", "dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000", "dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000", "dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000", "dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000", "dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000", "dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000", "dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000", "dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000", "dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000", "dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000", "dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000", "dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000", "dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000", "dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000", "dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000", "dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000", "dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000", "dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000", "dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000", "dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000", "dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000", "dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000", "dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000", "dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000", "dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000", "dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000", "dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000", "dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000", "dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000", "dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000", "dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000", "dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000", "dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000", "dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000", "dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000", "license:mit", "region:us" ]
null
2022-11-24T21:39:40Z
--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/detoxify-pile-chunk3-0-50000 - tomekkorbak/detoxify-pile-chunk3-50000-100000 - tomekkorbak/detoxify-pile-chunk3-100000-150000 - tomekkorbak/detoxify-pile-chunk3-150000-200000 - tomekkorbak/detoxify-pile-chunk3-200000-250000 - tomekkorbak/detoxify-pile-chunk3-250000-300000 - tomekkorbak/detoxify-pile-chunk3-300000-350000 - tomekkorbak/detoxify-pile-chunk3-350000-400000 - tomekkorbak/detoxify-pile-chunk3-400000-450000 - tomekkorbak/detoxify-pile-chunk3-450000-500000 - tomekkorbak/detoxify-pile-chunk3-500000-550000 - tomekkorbak/detoxify-pile-chunk3-550000-600000 - tomekkorbak/detoxify-pile-chunk3-600000-650000 - tomekkorbak/detoxify-pile-chunk3-650000-700000 - tomekkorbak/detoxify-pile-chunk3-700000-750000 - tomekkorbak/detoxify-pile-chunk3-750000-800000 - tomekkorbak/detoxify-pile-chunk3-800000-850000 - tomekkorbak/detoxify-pile-chunk3-850000-900000 - tomekkorbak/detoxify-pile-chunk3-900000-950000 - tomekkorbak/detoxify-pile-chunk3-950000-1000000 - tomekkorbak/detoxify-pile-chunk3-1000000-1050000 - tomekkorbak/detoxify-pile-chunk3-1050000-1100000 - tomekkorbak/detoxify-pile-chunk3-1100000-1150000 - tomekkorbak/detoxify-pile-chunk3-1150000-1200000 - tomekkorbak/detoxify-pile-chunk3-1200000-1250000 - tomekkorbak/detoxify-pile-chunk3-1250000-1300000 - tomekkorbak/detoxify-pile-chunk3-1300000-1350000 - tomekkorbak/detoxify-pile-chunk3-1350000-1400000 - tomekkorbak/detoxify-pile-chunk3-1400000-1450000 - tomekkorbak/detoxify-pile-chunk3-1450000-1500000 - tomekkorbak/detoxify-pile-chunk3-1500000-1550000 - tomekkorbak/detoxify-pile-chunk3-1550000-1600000 - tomekkorbak/detoxify-pile-chunk3-1600000-1650000 - tomekkorbak/detoxify-pile-chunk3-1650000-1700000 - tomekkorbak/detoxify-pile-chunk3-1700000-1750000 - tomekkorbak/detoxify-pile-chunk3-1750000-1800000 - tomekkorbak/detoxify-pile-chunk3-1800000-1850000 - tomekkorbak/detoxify-pile-chunk3-1850000-1900000 - tomekkorbak/detoxify-pile-chunk3-1900000-1950000 model-index: - name: suspicious_noyce 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. --> # suspicious_noyce This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. ## 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: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'suspicious_noyce', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/21rsvjy5
tomekkorbak/reverent_franklin
tomekkorbak
2022-11-24T21:39:47Z
0
0
null
[ "generated_from_trainer", "en", "dataset:tomekkorbak/detoxify-pile-chunk3-0-50000", "dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000", "dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000", "dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000", "dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000", "dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000", "dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000", "dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000", "dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000", "dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000", "dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000", "dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000", "dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000", "dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000", "dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000", "dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000", "dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000", "dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000", "dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000", "dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000", "dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000", "dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000", "dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000", "dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000", "dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000", "dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000", "dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000", "dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000", "dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000", "dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000", "dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000", "dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000", "dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000", "dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000", "dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000", "dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000", "dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000", "dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000", "license:mit", "region:us" ]
null
2022-11-24T21:39:39Z
--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/detoxify-pile-chunk3-0-50000 - tomekkorbak/detoxify-pile-chunk3-50000-100000 - tomekkorbak/detoxify-pile-chunk3-100000-150000 - tomekkorbak/detoxify-pile-chunk3-150000-200000 - tomekkorbak/detoxify-pile-chunk3-200000-250000 - tomekkorbak/detoxify-pile-chunk3-250000-300000 - tomekkorbak/detoxify-pile-chunk3-300000-350000 - tomekkorbak/detoxify-pile-chunk3-350000-400000 - tomekkorbak/detoxify-pile-chunk3-400000-450000 - tomekkorbak/detoxify-pile-chunk3-450000-500000 - tomekkorbak/detoxify-pile-chunk3-500000-550000 - tomekkorbak/detoxify-pile-chunk3-550000-600000 - tomekkorbak/detoxify-pile-chunk3-600000-650000 - tomekkorbak/detoxify-pile-chunk3-650000-700000 - tomekkorbak/detoxify-pile-chunk3-700000-750000 - tomekkorbak/detoxify-pile-chunk3-750000-800000 - tomekkorbak/detoxify-pile-chunk3-800000-850000 - tomekkorbak/detoxify-pile-chunk3-850000-900000 - tomekkorbak/detoxify-pile-chunk3-900000-950000 - tomekkorbak/detoxify-pile-chunk3-950000-1000000 - tomekkorbak/detoxify-pile-chunk3-1000000-1050000 - tomekkorbak/detoxify-pile-chunk3-1050000-1100000 - tomekkorbak/detoxify-pile-chunk3-1100000-1150000 - tomekkorbak/detoxify-pile-chunk3-1150000-1200000 - tomekkorbak/detoxify-pile-chunk3-1200000-1250000 - tomekkorbak/detoxify-pile-chunk3-1250000-1300000 - tomekkorbak/detoxify-pile-chunk3-1300000-1350000 - tomekkorbak/detoxify-pile-chunk3-1350000-1400000 - tomekkorbak/detoxify-pile-chunk3-1400000-1450000 - tomekkorbak/detoxify-pile-chunk3-1450000-1500000 - tomekkorbak/detoxify-pile-chunk3-1500000-1550000 - tomekkorbak/detoxify-pile-chunk3-1550000-1600000 - tomekkorbak/detoxify-pile-chunk3-1600000-1650000 - tomekkorbak/detoxify-pile-chunk3-1650000-1700000 - tomekkorbak/detoxify-pile-chunk3-1700000-1750000 - tomekkorbak/detoxify-pile-chunk3-1750000-1800000 - tomekkorbak/detoxify-pile-chunk3-1800000-1850000 - tomekkorbak/detoxify-pile-chunk3-1850000-1900000 - tomekkorbak/detoxify-pile-chunk3-1900000-1950000 model-index: - name: reverent_franklin 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. --> # reverent_franklin This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. ## 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: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'alpha': 1, 'name': 'Unlikelihood', 'score_threshold': 0.00078}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'reverent_franklin', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/3csuo5ov
jeapaul/wav2vec2-large-xlsr-53-torgo-demo-f01-nolm
jeapaul
2022-11-24T21:26:04Z
77
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-11-23T23:46:08Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xlsr-53-torgo-demo-f01-nolm 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-torgo-demo-f01-nolm This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0153 - Wer: 0.4756 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.4166 | 0.81 | 500 | 4.5019 | 1.0 | | 3.1088 | 1.62 | 1000 | 3.0459 | 1.0 | | 2.8249 | 2.44 | 1500 | 3.0850 | 1.0 | | 2.625 | 3.25 | 2000 | 2.6827 | 1.3656 | | 1.9816 | 4.06 | 2500 | 1.6636 | 1.3701 | | 1.3036 | 4.87 | 3000 | 0.9710 | 1.2504 | | 0.9862 | 5.68 | 3500 | 0.6023 | 1.0519 | | 0.7012 | 6.49 | 4000 | 0.4404 | 0.9342 | | 0.6102 | 7.31 | 4500 | 0.3297 | 0.8491 | | 0.5463 | 8.12 | 5000 | 0.2403 | 0.7773 | | 0.4897 | 8.93 | 5500 | 0.1907 | 0.7335 | | 0.4687 | 9.74 | 6000 | 0.1721 | 0.7095 | | 0.41 | 10.55 | 6500 | 0.1382 | 0.6851 | | 0.3277 | 11.36 | 7000 | 0.1189 | 0.6598 | | 0.3182 | 12.18 | 7500 | 0.1040 | 0.6372 | | 0.3279 | 12.99 | 8000 | 0.0961 | 0.6274 | | 0.2735 | 13.8 | 8500 | 0.0806 | 0.5880 | | 0.3153 | 14.61 | 9000 | 0.0821 | 0.5748 | | 0.251 | 15.42 | 9500 | 0.0633 | 0.5437 | | 0.2 | 16.23 | 10000 | 0.0534 | 0.5316 | | 0.2134 | 17.05 | 10500 | 0.0475 | 0.5195 | | 0.1727 | 17.86 | 11000 | 0.0435 | 0.5146 | | 0.2143 | 18.67 | 11500 | 0.0406 | 0.5072 | | 0.1679 | 19.48 | 12000 | 0.0386 | 0.5057 | | 0.1836 | 20.29 | 12500 | 0.0359 | 0.4984 | | 0.1542 | 21.1 | 13000 | 0.0284 | 0.4914 | | 0.1672 | 21.92 | 13500 | 0.0289 | 0.4884 | | 0.1526 | 22.73 | 14000 | 0.0256 | 0.4867 | | 0.1263 | 23.54 | 14500 | 0.0247 | 0.4871 | | 0.133 | 24.35 | 15000 | 0.0194 | 0.4816 | | 0.1005 | 25.16 | 15500 | 0.0190 | 0.4798 | | 0.1372 | 25.97 | 16000 | 0.0172 | 0.4786 | | 0.1126 | 26.79 | 16500 | 0.0177 | 0.4773 | | 0.0929 | 27.6 | 17000 | 0.0173 | 0.4775 | | 0.1069 | 28.41 | 17500 | 0.0164 | 0.4773 | | 0.0932 | 29.22 | 18000 | 0.0153 | 0.4756 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.0.0 - Tokenizers 0.13.2
cyburn/cables_retake
cyburn
2022-11-24T21:11:51Z
0
0
null
[ "license:unknown", "region:us" ]
null
2022-11-24T21:02:32Z
--- license: unknown --- # Cables Retake finetuned style Model Produced from publicly available pictures in 576x704 format. Two models available. One trained using kohya_ss train_db script and the other one using diffusers_fine_tuning sctipt. ## Using the model * common subject prompt tokens: `cables retake artstyle <wathever>` ## Example prompts `cables retake artstyle smiling woman`:
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-2-parent
research-backup
2022-11-24T20:42:14Z
104
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T20:41:06Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-2-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6546230158730159 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.31016042780748665 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.32047477744807124 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4558087826570317 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.552 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.33771929824561403 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.36342592592592593 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7158354678318517 - name: F1 (macro) type: f1_macro value: 0.6580573706656033 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7342723004694836 - name: F1 (macro) type: f1_macro value: 0.2592182558244037 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.4431202600216685 - name: F1 (macro) type: f1_macro value: 0.2667711261353617 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.865201363288586 - name: F1 (macro) type: f1_macro value: 0.6765044508427398 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.727984957693513 - name: F1 (macro) type: f1_macro value: 0.6461380162719604 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-2-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-2-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.31016042780748665 - Accuracy on SAT: 0.32047477744807124 - Accuracy on BATS: 0.4558087826570317 - Accuracy on U2: 0.33771929824561403 - Accuracy on U4: 0.36342592592592593 - Accuracy on Google: 0.552 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-2-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.7158354678318517 - Micro F1 score on CogALexV: 0.7342723004694836 - Micro F1 score on EVALution: 0.4431202600216685 - Micro F1 score on K&H+N: 0.865201363288586 - Micro F1 score on ROOT09: 0.727984957693513 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-2-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6546230158730159 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-2-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 8 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-2-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-0-parent
research-backup
2022-11-24T20:38:54Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T20:37:42Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-0-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.38442460317460314 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.23529411764705882 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.2314540059347181 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3229571984435798 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.384 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3157894736842105 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.2847222222222222 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6522525237306012 - name: F1 (macro) type: f1_macro value: 0.616560269476982 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7183098591549296 - name: F1 (macro) type: f1_macro value: 0.16833503884438658 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.42632719393282775 - name: F1 (macro) type: f1_macro value: 0.28678399596569476 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8141475968560896 - name: F1 (macro) type: f1_macro value: 0.6286243048790003 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6552804763397054 - name: F1 (macro) type: f1_macro value: 0.5562839421136045 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-0-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-0-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.23529411764705882 - Accuracy on SAT: 0.2314540059347181 - Accuracy on BATS: 0.3229571984435798 - Accuracy on U2: 0.3157894736842105 - Accuracy on U4: 0.2847222222222222 - Accuracy on Google: 0.384 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-0-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.6522525237306012 - Micro F1 score on CogALexV: 0.7183098591549296 - Micro F1 score on EVALution: 0.42632719393282775 - Micro F1 score on K&H+N: 0.8141475968560896 - Micro F1 score on ROOT09: 0.6552804763397054 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-0-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.38442460317460314 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-0-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 2 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-0-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-d-triplet-2-parent
research-backup
2022-11-24T20:37:09Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T20:35:57Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-triplet-2-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6858134920634921 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.40641711229946526 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4065281899109792 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6520289049471929 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.814 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4298245614035088 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4375 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8256742504143438 - name: F1 (macro) type: f1_macro value: 0.7997907403301278 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8183098591549296 - name: F1 (macro) type: f1_macro value: 0.5751411013000909 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6413867822318526 - name: F1 (macro) type: f1_macro value: 0.6303562723873062 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8593586979202894 - name: F1 (macro) type: f1_macro value: 0.6700136749243296 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8583516139141335 - name: F1 (macro) type: f1_macro value: 0.8571706539074961 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-triplet-2-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-triplet-2-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.40641711229946526 - Accuracy on SAT: 0.4065281899109792 - Accuracy on BATS: 0.6520289049471929 - Accuracy on U2: 0.4298245614035088 - Accuracy on U4: 0.4375 - Accuracy on Google: 0.814 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-triplet-2-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8256742504143438 - Micro F1 score on CogALexV: 0.8183098591549296 - Micro F1 score on EVALution: 0.6413867822318526 - Micro F1 score on K&H+N: 0.8593586979202894 - Micro F1 score on ROOT09: 0.8583516139141335 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-triplet-2-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6858134920634921 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-triplet-2-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-triplet-2-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-d-triplet-1-parent
research-backup
2022-11-24T20:35:21Z
105
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T20:34:15Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-triplet-1-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7853174603174603 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4197860962566845 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.42433234421364985 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5619788771539744 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.744 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.43859649122807015 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4351851851851852 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8716287479282808 - name: F1 (macro) type: f1_macro value: 0.8587076704516358 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8446009389671362 - name: F1 (macro) type: f1_macro value: 0.623737694543065 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.676056338028169 - name: F1 (macro) type: f1_macro value: 0.6565825451079605 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9460944564234541 - name: F1 (macro) type: f1_macro value: 0.8418727845254599 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8799749294891883 - name: F1 (macro) type: f1_macro value: 0.8734406634484763 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-triplet-1-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-triplet-1-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.4197860962566845 - Accuracy on SAT: 0.42433234421364985 - Accuracy on BATS: 0.5619788771539744 - Accuracy on U2: 0.43859649122807015 - Accuracy on U4: 0.4351851851851852 - Accuracy on Google: 0.744 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-triplet-1-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8716287479282808 - Micro F1 score on CogALexV: 0.8446009389671362 - Micro F1 score on EVALution: 0.676056338028169 - Micro F1 score on K&H+N: 0.9460944564234541 - Micro F1 score on ROOT09: 0.8799749294891883 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-triplet-1-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7853174603174603 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-triplet-1-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-triplet-1-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2-parent
research-backup
2022-11-24T20:32:03Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T20:30:56Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6386706349206349 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.32887700534759357 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3323442136498516 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.44858254585881047 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.474 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34210526315789475 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.32407407407407407 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.776254331776405 - name: F1 (macro) type: f1_macro value: 0.743956464512583 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.753755868544601 - name: F1 (macro) type: f1_macro value: 0.32771443150653473 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.4284940411700975 - name: F1 (macro) type: f1_macro value: 0.2369468752324584 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8608193642623635 - name: F1 (macro) type: f1_macro value: 0.6343956091240253 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7643371983704167 - name: F1 (macro) type: f1_macro value: 0.7154904519234845 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.32887700534759357 - Accuracy on SAT: 0.3323442136498516 - Accuracy on BATS: 0.44858254585881047 - Accuracy on U2: 0.34210526315789475 - Accuracy on U4: 0.32407407407407407 - Accuracy on Google: 0.474 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.776254331776405 - Micro F1 score on CogALexV: 0.753755868544601 - Micro F1 score on EVALution: 0.4284940411700975 - Micro F1 score on K&H+N: 0.8608193642623635 - Micro F1 score on ROOT09: 0.7643371983704167 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6386706349206349 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-1-parent
research-backup
2022-11-24T20:30:25Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T20:29:17Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-1-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7567460317460317 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3449197860962567 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.35014836795252224 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4252362423568649 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.59 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.35964912280701755 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.33796296296296297 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8695193611571493 - name: F1 (macro) type: f1_macro value: 0.8568674733966278 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7643192488262912 - name: F1 (macro) type: f1_macro value: 0.48540339382722264 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5525460455037919 - name: F1 (macro) type: f1_macro value: 0.4851738190720077 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9296793489601447 - name: F1 (macro) type: f1_macro value: 0.8229079543242852 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8712002507051081 - name: F1 (macro) type: f1_macro value: 0.8695492693223117 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-1-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-1-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3449197860962567 - Accuracy on SAT: 0.35014836795252224 - Accuracy on BATS: 0.4252362423568649 - Accuracy on U2: 0.35964912280701755 - Accuracy on U4: 0.33796296296296297 - Accuracy on Google: 0.59 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-1-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8695193611571493 - Micro F1 score on CogALexV: 0.7643192488262912 - Micro F1 score on EVALution: 0.5525460455037919 - Micro F1 score on K&H+N: 0.9296793489601447 - Micro F1 score on ROOT09: 0.8712002507051081 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-1-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7567460317460317 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-1-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 6 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-1-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2-parent
research-backup
2022-11-24T20:26:51Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T20:25:43Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6994047619047619 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3315508021390374 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.33827893175074186 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3941078376876042 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.476 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.40350877192982454 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.38425925925925924 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8157299984932952 - name: F1 (macro) type: f1_macro value: 0.7874948565395373 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7908450704225352 - name: F1 (macro) type: f1_macro value: 0.4766590220293792 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5390032502708559 - name: F1 (macro) type: f1_macro value: 0.483312366925826 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9006746887389581 - name: F1 (macro) type: f1_macro value: 0.7871817286518888 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8097774992165465 - name: F1 (macro) type: f1_macro value: 0.7967017372058048 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3315508021390374 - Accuracy on SAT: 0.33827893175074186 - Accuracy on BATS: 0.3941078376876042 - Accuracy on U2: 0.40350877192982454 - Accuracy on U4: 0.38425925925925924 - Accuracy on Google: 0.476 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8157299984932952 - Micro F1 score on CogALexV: 0.7908450704225352 - Micro F1 score on EVALution: 0.5390032502708559 - Micro F1 score on K&H+N: 0.9006746887389581 - Micro F1 score on ROOT09: 0.8097774992165465 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6994047619047619 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 8 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-1-parent
research-backup
2022-11-24T20:25:09Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T20:24:04Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-1-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7791666666666667 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34759358288770054 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.35311572700296734 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4102279043913285 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.562 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39035087719298245 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3773148148148148 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.841795992165135 - name: F1 (macro) type: f1_macro value: 0.8175016337972725 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7873239436619718 - name: F1 (macro) type: f1_macro value: 0.4950289837305936 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5877573131094258 - name: F1 (macro) type: f1_macro value: 0.5255505321109658 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9225846838700703 - name: F1 (macro) type: f1_macro value: 0.8111729625447839 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8558445628329677 - name: F1 (macro) type: f1_macro value: 0.8564733796055889 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-1-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-1-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.34759358288770054 - Accuracy on SAT: 0.35311572700296734 - Accuracy on BATS: 0.4102279043913285 - Accuracy on U2: 0.39035087719298245 - Accuracy on U4: 0.3773148148148148 - Accuracy on Google: 0.562 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-1-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.841795992165135 - Micro F1 score on CogALexV: 0.7873239436619718 - Micro F1 score on EVALution: 0.5877573131094258 - Micro F1 score on K&H+N: 0.9225846838700703 - Micro F1 score on ROOT09: 0.8558445628329677 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-1-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7791666666666667 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-1-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 8 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-1-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-0-parent
research-backup
2022-11-24T20:23:28Z
104
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T20:22:24Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-0-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.8059920634920635 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.32620320855614976 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3323442136498516 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3507504168982768 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.382 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.33771929824561403 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.35185185185185186 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7999095977098086 - name: F1 (macro) type: f1_macro value: 0.7684883952780684 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7530516431924884 - name: F1 (macro) type: f1_macro value: 0.34938339909910743 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.4739978331527627 - name: F1 (macro) type: f1_macro value: 0.3568206512908552 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8818946929122905 - name: F1 (macro) type: f1_macro value: 0.7088102566206993 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7790661234722657 - name: F1 (macro) type: f1_macro value: 0.759592502713887 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-0-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-0-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.32620320855614976 - Accuracy on SAT: 0.3323442136498516 - Accuracy on BATS: 0.3507504168982768 - Accuracy on U2: 0.33771929824561403 - Accuracy on U4: 0.35185185185185186 - Accuracy on Google: 0.382 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-0-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.7999095977098086 - Micro F1 score on CogALexV: 0.7530516431924884 - Micro F1 score on EVALution: 0.4739978331527627 - Micro F1 score on K&H+N: 0.8818946929122905 - Micro F1 score on ROOT09: 0.7790661234722657 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-0-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.8059920634920635 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-0-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-0-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2-parent
research-backup
2022-11-24T20:21:46Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T20:20:40Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6884126984126984 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.35561497326203206 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.36795252225519287 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3952195664257921 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.468 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3333333333333333 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.31712962962962965 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8853397619406358 - name: F1 (macro) type: f1_macro value: 0.8737515262680274 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8082159624413146 - name: F1 (macro) type: f1_macro value: 0.5667219243609135 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6110509209100758 - name: F1 (macro) type: f1_macro value: 0.584000376416088 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9426166794185157 - name: F1 (macro) type: f1_macro value: 0.8355305513810347 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8727671576308367 - name: F1 (macro) type: f1_macro value: 0.8723842336449569 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.35561497326203206 - Accuracy on SAT: 0.36795252225519287 - Accuracy on BATS: 0.3952195664257921 - Accuracy on U2: 0.3333333333333333 - Accuracy on U4: 0.31712962962962965 - Accuracy on Google: 0.468 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8853397619406358 - Micro F1 score on CogALexV: 0.8082159624413146 - Micro F1 score on EVALution: 0.6110509209100758 - Micro F1 score on K&H+N: 0.9426166794185157 - Micro F1 score on ROOT09: 0.8727671576308367 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6884126984126984 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 5 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-2-child
research-backup
2022-11-24T20:16:27Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T20:15:18Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-2-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6546230158730159 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.31016042780748665 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.32047477744807124 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4558087826570317 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.552 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.33771929824561403 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.36342592592592593 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7158354678318517 - name: F1 (macro) type: f1_macro value: 0.6580573706656033 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7342723004694836 - name: F1 (macro) type: f1_macro value: 0.2592182558244037 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.4431202600216685 - name: F1 (macro) type: f1_macro value: 0.2667711261353617 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.865201363288586 - name: F1 (macro) type: f1_macro value: 0.6765044508427398 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.727984957693513 - name: F1 (macro) type: f1_macro value: 0.6461380162719604 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-2-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-2-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.31016042780748665 - Accuracy on SAT: 0.32047477744807124 - Accuracy on BATS: 0.4558087826570317 - Accuracy on U2: 0.33771929824561403 - Accuracy on U4: 0.36342592592592593 - Accuracy on Google: 0.552 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-2-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.7158354678318517 - Micro F1 score on CogALexV: 0.7342723004694836 - Micro F1 score on EVALution: 0.4431202600216685 - Micro F1 score on K&H+N: 0.865201363288586 - Micro F1 score on ROOT09: 0.727984957693513 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-2-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6546230158730159 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-2-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 8 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-2-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
huggingtweets/parker_gibbons
huggingtweets
2022-11-24T20:14:44Z
113
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-11-24T20:13:54Z
--- language: en thumbnail: http://www.huggingtweets.com/parker_gibbons/1669320881340/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1590137629219004416/6Pj98wZW_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">parker gibbons</div> <div style="text-align: center; font-size: 14px;">@parker_gibbons</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from parker gibbons. | Data | parker gibbons | | --- | --- | | Tweets downloaded | 3165 | | Retweets | 972 | | Short tweets | 234 | | Tweets kept | 1959 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3vt4m93y/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @parker_gibbons's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/8lgj2jge) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/8lgj2jge/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/parker_gibbons') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-1-child
research-backup
2022-11-24T20:14:43Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T20:13:39Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-1-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7030555555555555 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3850267379679144 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3916913946587537 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5269594219010562 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37280701754385964 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.40046296296296297 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.835317161368088 - name: F1 (macro) type: f1_macro value: 0.8283163898192295 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7708920187793428 - name: F1 (macro) type: f1_macro value: 0.40683683267154375 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6040086673889491 - name: F1 (macro) type: f1_macro value: 0.562590771697943 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8937886902691798 - name: F1 (macro) type: f1_macro value: 0.7550347133400666 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8448762143528674 - name: F1 (macro) type: f1_macro value: 0.8407765599818559 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-1-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-1-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3850267379679144 - Accuracy on SAT: 0.3916913946587537 - Accuracy on BATS: 0.5269594219010562 - Accuracy on U2: 0.37280701754385964 - Accuracy on U4: 0.40046296296296297 - Accuracy on Google: 0.6 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-1-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.835317161368088 - Micro F1 score on CogALexV: 0.7708920187793428 - Micro F1 score on EVALution: 0.6040086673889491 - Micro F1 score on K&H+N: 0.8937886902691798 - Micro F1 score on ROOT09: 0.8448762143528674 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-1-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7030555555555555 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-1-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 8 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-1-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-d-triplet-2-child
research-backup
2022-11-24T20:11:22Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T20:10:19Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-triplet-2-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6858134920634921 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.40641711229946526 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4065281899109792 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6520289049471929 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.814 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4298245614035088 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4375 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8256742504143438 - name: F1 (macro) type: f1_macro value: 0.7997907403301278 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8183098591549296 - name: F1 (macro) type: f1_macro value: 0.5751411013000909 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6413867822318526 - name: F1 (macro) type: f1_macro value: 0.6303562723873062 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8593586979202894 - name: F1 (macro) type: f1_macro value: 0.6700136749243296 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8583516139141335 - name: F1 (macro) type: f1_macro value: 0.8571706539074961 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-triplet-2-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-triplet-2-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.40641711229946526 - Accuracy on SAT: 0.4065281899109792 - Accuracy on BATS: 0.6520289049471929 - Accuracy on U2: 0.4298245614035088 - Accuracy on U4: 0.4375 - Accuracy on Google: 0.814 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-triplet-2-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8256742504143438 - Micro F1 score on CogALexV: 0.8183098591549296 - Micro F1 score on EVALution: 0.6413867822318526 - Micro F1 score on K&H+N: 0.8593586979202894 - Micro F1 score on ROOT09: 0.8583516139141335 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-triplet-2-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6858134920634921 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-triplet-2-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-triplet-2-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2-child
research-backup
2022-11-24T20:06:09Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T20:05:02Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.5337896825396825 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.29411764705882354 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.2878338278931751 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3885491939966648 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.494 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3815789473684211 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.35185185185185186 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7629953292150068 - name: F1 (macro) type: f1_macro value: 0.7124326872331369 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.718075117370892 - name: F1 (macro) type: f1_macro value: 0.16718130892198388 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.4442036836403034 - name: F1 (macro) type: f1_macro value: 0.28765674250197426 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8604715865618696 - name: F1 (macro) type: f1_macro value: 0.6404791041645369 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7580695706675025 - name: F1 (macro) type: f1_macro value: 0.7075134842767926 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.29411764705882354 - Accuracy on SAT: 0.2878338278931751 - Accuracy on BATS: 0.3885491939966648 - Accuracy on U2: 0.3815789473684211 - Accuracy on U4: 0.35185185185185186 - Accuracy on Google: 0.494 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.7629953292150068 - Micro F1 score on CogALexV: 0.718075117370892 - Micro F1 score on EVALution: 0.4442036836403034 - Micro F1 score on K&H+N: 0.8604715865618696 - Micro F1 score on ROOT09: 0.7580695706675025 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.5337896825396825 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 5 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-1-child
research-backup
2022-11-24T20:04:27Z
104
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T20:03:22Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-1-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7567460317460317 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3449197860962567 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.35014836795252224 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4252362423568649 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.59 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.35964912280701755 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.33796296296296297 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8695193611571493 - name: F1 (macro) type: f1_macro value: 0.8568674733966278 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7643192488262912 - name: F1 (macro) type: f1_macro value: 0.48540339382722264 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5525460455037919 - name: F1 (macro) type: f1_macro value: 0.4851738190720077 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9296793489601447 - name: F1 (macro) type: f1_macro value: 0.8229079543242852 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8712002507051081 - name: F1 (macro) type: f1_macro value: 0.8695492693223117 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-1-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-1-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3449197860962567 - Accuracy on SAT: 0.35014836795252224 - Accuracy on BATS: 0.4252362423568649 - Accuracy on U2: 0.35964912280701755 - Accuracy on U4: 0.33796296296296297 - Accuracy on Google: 0.59 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-1-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8695193611571493 - Micro F1 score on CogALexV: 0.7643192488262912 - Micro F1 score on EVALution: 0.5525460455037919 - Micro F1 score on K&H+N: 0.9296793489601447 - Micro F1 score on ROOT09: 0.8712002507051081 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-1-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7567460317460317 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-1-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 6 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-1-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2-child
research-backup
2022-11-24T20:01:07Z
105
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T20:00:01Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6994047619047619 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3315508021390374 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.33827893175074186 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3941078376876042 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.476 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.40350877192982454 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.38425925925925924 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8157299984932952 - name: F1 (macro) type: f1_macro value: 0.7874948565395373 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7908450704225352 - name: F1 (macro) type: f1_macro value: 0.4766590220293792 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5390032502708559 - name: F1 (macro) type: f1_macro value: 0.483312366925826 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9006746887389581 - name: F1 (macro) type: f1_macro value: 0.7871817286518888 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8097774992165465 - name: F1 (macro) type: f1_macro value: 0.7967017372058048 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3315508021390374 - Accuracy on SAT: 0.33827893175074186 - Accuracy on BATS: 0.3941078376876042 - Accuracy on U2: 0.40350877192982454 - Accuracy on U4: 0.38425925925925924 - Accuracy on Google: 0.476 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8157299984932952 - Micro F1 score on CogALexV: 0.7908450704225352 - Micro F1 score on EVALution: 0.5390032502708559 - Micro F1 score on K&H+N: 0.9006746887389581 - Micro F1 score on ROOT09: 0.8097774992165465 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6994047619047619 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 8 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2-child
research-backup
2022-11-24T19:56:13Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T19:55:10Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6884126984126984 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.35561497326203206 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.36795252225519287 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3952195664257921 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.468 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3333333333333333 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.31712962962962965 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8853397619406358 - name: F1 (macro) type: f1_macro value: 0.8737515262680274 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8082159624413146 - name: F1 (macro) type: f1_macro value: 0.5667219243609135 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6110509209100758 - name: F1 (macro) type: f1_macro value: 0.584000376416088 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9426166794185157 - name: F1 (macro) type: f1_macro value: 0.8355305513810347 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8727671576308367 - name: F1 (macro) type: f1_macro value: 0.8723842336449569 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.35561497326203206 - Accuracy on SAT: 0.36795252225519287 - Accuracy on BATS: 0.3952195664257921 - Accuracy on U2: 0.3333333333333333 - Accuracy on U4: 0.31712962962962965 - Accuracy on Google: 0.468 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8853397619406358 - Micro F1 score on CogALexV: 0.8082159624413146 - Micro F1 score on EVALution: 0.6110509209100758 - Micro F1 score on K&H+N: 0.9426166794185157 - Micro F1 score on ROOT09: 0.8727671576308367 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6884126984126984 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 5 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
nishantyadav/dual_encoder_zeshel
nishantyadav
2022-11-24T19:55:39Z
0
0
null
[ "arxiv:2210.12579", "license:apache-2.0", "region:us" ]
null
2022-11-24T18:25:43Z
--- license: apache-2.0 --- This repo contains the dual-encoder model used in the experiments for our EMNLP 2022 paper titled "[Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization](https://arxiv.org/pdf/2210.12579.pdf)". See [paper](https://arxiv.org/pdf/2210.12579.pdf) and/or [code](https://github.com/iesl/anncur) for more details about the model.
huggingtweets/horse_luvr_47-pacsjam
huggingtweets
2022-11-24T18:35:13Z
113
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-11-24T18:34:02Z
--- language: en thumbnail: http://www.huggingtweets.com/horse_luvr_47-pacsjam/1669314909523/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1530452243849224192/rp21Xew2_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1588101458511024130/9DsQWW2U_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">jesse 🫁🕍💌 & adam handler</div> <div style="text-align: center; font-size: 14px;">@horse_luvr_47-pacsjam</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from jesse 🫁🕍💌 & adam handler. | Data | jesse 🫁🕍💌 | adam handler | | --- | --- | --- | | Tweets downloaded | 3043 | 2952 | | Retweets | 772 | 1364 | | Short tweets | 575 | 243 | | Tweets kept | 1696 | 1345 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ess4rcio/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @horse_luvr_47-pacsjam's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/rc1dxno2) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/rc1dxno2/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/horse_luvr_47-pacsjam') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/horse_luvr_47
huggingtweets
2022-11-24T18:20:00Z
111
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-11-24T09:53:58Z
--- language: en thumbnail: http://www.huggingtweets.com/horse_luvr_47/1669313996229/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1530452243849224192/rp21Xew2_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">jesse 🫁🕍💌</div> <div style="text-align: center; font-size: 14px;">@horse_luvr_47</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from jesse 🫁🕍💌. | Data | jesse 🫁🕍💌 | | --- | --- | | Tweets downloaded | 3043 | | Retweets | 772 | | Short tweets | 575 | | Tweets kept | 1696 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/hazk2xm0/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @horse_luvr_47's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/33t96k7e) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/33t96k7e/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/horse_luvr_47') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
ai-forever/Kandinsky_2.0
ai-forever
2022-11-24T18:16:01Z
0
43
null
[ "Kandinsky", "text-image", "text2image", "diffusion", "latent diffusion", "mCLIP-XLMR", "mT5", "license:apache-2.0", "region:us" ]
null
2022-11-22T14:43:57Z
--- license: apache-2.0 tags: - Kandinsky - text-image - text2image - diffusion - latent diffusion - mCLIP-XLMR - mT5 --- # Kandinsky 2.0 Kandinsky 2.0 — the first multilingual text2image model. [Open In Colab](https://colab.research.google.com/drive/1uPg9KwGZ2hJBl9taGA_3kyKGw12Rh3ij?usp=sharing) [GitHub repository](https://github.com/ai-forever/Kandinsky-2.0) [Habr post](https://habr.com/ru/company/sberbank/blog/701162/) [Demo](https://rudalle.ru/) **UNet size: 1.2B parameters** ![NatallE.png](https://s3.amazonaws.com/moonup/production/uploads/1669132577749-5f91b1208a61a359f44e1851.png) It is a latent diffusion model with two multi-lingual text encoders: * mCLIP-XLMR (560M parameters) * mT5-encoder-small (146M parameters) These encoders and multilingual training datasets unveil the real multilingual text2image generation experience! ![header.png](https://s3.amazonaws.com/moonup/production/uploads/1669132825912-5f91b1208a61a359f44e1851.png) # How to use ```python pip install "git+https://github.com/ai-forever/Kandinsky-2.0.git" from kandinsky2 import get_kandinsky2 model = get_kandinsky2('cuda', task_type='text2img') images = model.generate_text2img('кошка в космосе', batch_size=4, h=512, w=512, num_steps=75, denoised_type='dynamic_threshold', dynamic_threshold_v=99.5, sampler='ddim_sampler', ddim_eta=0.01, guidance_scale=10) ``` # Authors + Arseniy Shakhmatov: [Github](https://github.com/cene555), [Blog](https://t.me/gradientdip) + Anton Razzhigaev: [Github](https://github.com/razzant), [Blog](https://t.me/abstractDL) + Aleksandr Nikolich: [Github](https://github.com/AlexWortega), [Blog](https://t.me/lovedeathtransformers) + Vladimir Arkhipkin: [Github](https://github.com/oriBetelgeuse) + Igor Pavlov: [Github](https://github.com/boomb0om) + Andrey Kuznetsov: [Github](https://github.com/kuznetsoffandrey) + Denis Dimitrov: [Github](https://github.com/denndimitrov)
su157/t5-small-qg-german-00
su157
2022-11-24T16:40:31Z
103
0
transformers
[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2022-11-24T16:29:59Z
This model is a fine-tuned version of google/mt5-small on the GermanQuAD dataset. This is a model for question generation from a text corpus. The following hyperparameters were used during training: - learning_rate: 1e-3 - mini_batch_size: 8 - optimizer: Adam - num_epochs: 4
email81227/q-Taxi-v3
email81227
2022-11-24T15:49:37Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2022-11-24T15:49:30Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="email81227/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
shafin/testc8-2
shafin
2022-11-24T15:42:54Z
117
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-11-24T15:32:15Z
--- tags: - generated_from_trainer model-index: - name: testc8-2 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. --> # testc8-2 This model is a fine-tuned version of [shafin/chemical-bert-uncased-finetuned-cust-c2](https://huggingface.co/shafin/chemical-bert-uncased-finetuned-cust-c2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2346 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.6173 | 1.0 | 16 | 0.3874 | | 0.5383 | 2.0 | 32 | 0.3227 | | 0.4756 | 3.0 | 48 | 0.3142 | | 0.4399 | 4.0 | 64 | 0.3404 | | 0.4462 | 5.0 | 80 | 0.3112 | | 0.4187 | 6.0 | 96 | 0.3185 | | 0.4023 | 7.0 | 112 | 0.2628 | | 0.3712 | 8.0 | 128 | 0.2807 | | 0.3922 | 9.0 | 144 | 0.2516 | | 0.3483 | 10.0 | 160 | 0.1995 | | 0.3417 | 11.0 | 176 | 0.2452 | | 0.3585 | 12.0 | 192 | 0.2236 | | 0.3413 | 13.0 | 208 | 0.2031 | | 0.3452 | 14.0 | 224 | 0.2238 | | 0.317 | 15.0 | 240 | 0.2229 | | 0.3161 | 16.0 | 256 | 0.2591 | | 0.3338 | 17.0 | 272 | 0.2599 | | 0.2949 | 18.0 | 288 | 0.2618 | | 0.3035 | 19.0 | 304 | 0.2436 | | 0.3108 | 20.0 | 320 | 0.2015 | | 0.289 | 21.0 | 336 | 0.2329 | | 0.3144 | 22.0 | 352 | 0.1940 | | 0.2606 | 23.0 | 368 | 0.2334 | | 0.2842 | 24.0 | 384 | 0.1996 | | 0.2892 | 25.0 | 400 | 0.2330 | | 0.2612 | 26.0 | 416 | 0.2163 | | 0.2669 | 27.0 | 432 | 0.2053 | | 0.3147 | 28.0 | 448 | 0.1555 | | 0.286 | 29.0 | 464 | 0.1983 | | 0.2857 | 30.0 | 480 | 0.2346 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
tomohiroliu/q-FrozenLake-v1-4x4-noSlippery
tomohiroliu
2022-11-24T15:26:34Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2022-11-24T15:26:28Z
--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="tomohiroliu/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
dung1308/RM_system_not_mixed__NLP_model
dung1308
2022-11-24T14:56:33Z
3
0
transformers
[ "transformers", "tf", "roberta", "fill-mask", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-11-24T08:26:40Z
--- tags: - generated_from_keras_callback model-index: - name: dung1308/RM_system_not_mixed__NLP_model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # dung1308/RM_system_not_mixed__NLP_model This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.2670 - Validation Loss: 4.3108 - Epoch: 12 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -517, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 5.4281 | 4.6304 | 0 | | 4.5092 | 4.2831 | 1 | | 4.2234 | 4.2641 | 2 | | 4.2383 | 4.3122 | 3 | | 4.2406 | 4.2459 | 4 | | 4.2113 | 4.2687 | 5 | | 4.2721 | 4.2425 | 6 | | 4.3064 | 4.2370 | 7 | | 4.2685 | 4.2999 | 8 | | 4.3104 | nan | 9 | | 4.2444 | 4.2957 | 10 | | 4.2041 | 4.2465 | 11 | | 4.2670 | 4.3108 | 12 | ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2
Sebabrata/lmv2-g-receipts4
Sebabrata
2022-11-24T14:34:14Z
81
0
transformers
[ "transformers", "pytorch", "tensorboard", "layoutlmv2", "token-classification", "generated_from_trainer", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-11-24T10:30:32Z
--- license: cc-by-nc-sa-4.0 tags: - generated_from_trainer model-index: - name: lmv2-g-receipts4 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. --> # lmv2-g-receipts4 This model is a fine-tuned version of [microsoft/layoutlmv2-large-uncased](https://huggingface.co/microsoft/layoutlmv2-large-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3037 - Purchase Time Precision: 0.8868 - Purchase Time Recall: 0.94 - Purchase Time F1: 0.9126 - Purchase Time Number: 50 - Receipt Date Precision: 0.8272 - Receipt Date Recall: 0.8590 - Receipt Date F1: 0.8428 - Receipt Date Number: 78 - Sub Total Precision: 0.8333 - Sub Total Recall: 0.7778 - Sub Total F1: 0.8046 - Sub Total Number: 45 - Supplier Address Precision: 0.6981 - Supplier Address Recall: 0.8409 - Supplier Address F1: 0.7629 - Supplier Address Number: 44 - Supplier Name Precision: 0.7540 - Supplier Name Recall: 0.7851 - Supplier Name F1: 0.7692 - Supplier Name Number: 121 - Tip Amount Precision: 1.0 - Tip Amount Recall: 1.0 - Tip Amount F1: 1.0 - Tip Amount Number: 1 - Total Precision: 0.9348 - Total Recall: 0.9348 - Total F1: 0.9348 - Total Number: 92 - Total Tax Amount Precision: 0.8438 - Total Tax Amount Recall: 0.8438 - Total Tax Amount F1: 0.8438 - Total Tax Amount Number: 32 - Overall Precision: 0.8229 - Overall Recall: 0.8531 - Overall F1: 0.8378 - Overall Accuracy: 0.9794 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Purchase Time Precision | Purchase Time Recall | Purchase Time F1 | Purchase Time Number | Receipt Date Precision | Receipt Date Recall | Receipt Date F1 | Receipt Date Number | Sub Total Precision | Sub Total Recall | Sub Total F1 | Sub Total Number | Supplier Address Precision | Supplier Address Recall | Supplier Address F1 | Supplier Address Number | Supplier Name Precision | Supplier Name Recall | Supplier Name F1 | Supplier Name Number | Tip Amount Precision | Tip Amount Recall | Tip Amount F1 | Tip Amount Number | Total Precision | Total Recall | Total F1 | Total Number | Total Tax Amount Precision | Total Tax Amount Recall | Total Tax Amount F1 | Total Tax Amount Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-------------------:|:----------------:|:------------:|:----------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:---------------:|:------------:|:--------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.2363 | 1.0 | 892 | 0.1264 | 0.8519 | 0.92 | 0.8846 | 50 | 0.8214 | 0.8846 | 0.8519 | 78 | 0.7381 | 0.6889 | 0.7126 | 45 | 0.5636 | 0.7045 | 0.6263 | 44 | 0.6825 | 0.7107 | 0.6964 | 121 | 0.0 | 0.0 | 0.0 | 1 | 0.8737 | 0.9022 | 0.8877 | 92 | 0.8571 | 0.75 | 0.8000 | 32 | 0.7645 | 0.7991 | 0.7814 | 0.9724 | | 0.0941 | 2.0 | 1784 | 0.1193 | 0.9592 | 0.94 | 0.9495 | 50 | 0.8293 | 0.8718 | 0.8500 | 78 | 0.6557 | 0.8889 | 0.7547 | 45 | 0.6042 | 0.6591 | 0.6304 | 44 | 0.7018 | 0.6612 | 0.6809 | 121 | 0.0 | 0.0 | 0.0 | 1 | 0.9167 | 0.8370 | 0.875 | 92 | 0.8710 | 0.8438 | 0.8571 | 32 | 0.7846 | 0.7948 | 0.7897 | 0.9774 | | 0.0627 | 3.0 | 2676 | 0.1328 | 0.9057 | 0.96 | 0.9320 | 50 | 0.8023 | 0.8846 | 0.8415 | 78 | 0.7609 | 0.7778 | 0.7692 | 45 | 0.6481 | 0.7955 | 0.7143 | 44 | 0.6761 | 0.7934 | 0.7300 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9639 | 0.8696 | 0.9143 | 92 | 0.8710 | 0.8438 | 0.8571 | 32 | 0.7883 | 0.8445 | 0.8154 | 0.9784 | | 0.0413 | 4.0 | 3568 | 0.1526 | 0.9216 | 0.94 | 0.9307 | 50 | 0.8193 | 0.8718 | 0.8447 | 78 | 0.6792 | 0.8 | 0.7347 | 45 | 0.62 | 0.7045 | 0.6596 | 44 | 0.7231 | 0.7769 | 0.7490 | 121 | 0.0 | 0.0 | 0.0 | 1 | 0.9130 | 0.9130 | 0.9130 | 92 | 0.7 | 0.875 | 0.7778 | 32 | 0.7776 | 0.8380 | 0.8067 | 0.9773 | | 0.0334 | 5.0 | 4460 | 0.1755 | 0.9245 | 0.98 | 0.9515 | 50 | 0.8313 | 0.8846 | 0.8571 | 78 | 0.8043 | 0.8222 | 0.8132 | 45 | 0.5789 | 0.75 | 0.6535 | 44 | 0.7109 | 0.7521 | 0.7309 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9053 | 0.9348 | 0.9198 | 92 | 0.75 | 0.9375 | 0.8333 | 32 | 0.7873 | 0.8553 | 0.8199 | 0.9768 | | 0.0258 | 6.0 | 5352 | 0.1885 | 0.9184 | 0.9 | 0.9091 | 50 | 0.8101 | 0.8205 | 0.8153 | 78 | 0.7292 | 0.7778 | 0.7527 | 45 | 0.5263 | 0.6818 | 0.5941 | 44 | 0.6667 | 0.7438 | 0.7031 | 121 | 0.0 | 0.0 | 0.0 | 1 | 0.9111 | 0.8913 | 0.9011 | 92 | 0.8235 | 0.875 | 0.8485 | 32 | 0.7602 | 0.8078 | 0.7832 | 0.9746 | | 0.0186 | 7.0 | 6244 | 0.1609 | 0.9216 | 0.94 | 0.9307 | 50 | 0.8125 | 0.8333 | 0.8228 | 78 | 0.6481 | 0.7778 | 0.7071 | 45 | 0.7045 | 0.7045 | 0.7045 | 44 | 0.7188 | 0.7603 | 0.7390 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.8723 | 0.8913 | 0.8817 | 92 | 0.6905 | 0.9062 | 0.7838 | 32 | 0.7733 | 0.8251 | 0.7983 | 0.9779 | | 0.0181 | 8.0 | 7136 | 0.1821 | 0.9375 | 0.9 | 0.9184 | 50 | 0.8312 | 0.8205 | 0.8258 | 78 | 0.7143 | 0.6667 | 0.6897 | 45 | 0.5536 | 0.7045 | 0.62 | 44 | 0.6667 | 0.7438 | 0.7031 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9111 | 0.8913 | 0.9011 | 92 | 0.7568 | 0.875 | 0.8116 | 32 | 0.7634 | 0.8013 | 0.7819 | 0.9763 | | 0.0128 | 9.0 | 8028 | 0.2082 | 0.9057 | 0.96 | 0.9320 | 50 | 0.8193 | 0.8718 | 0.8447 | 78 | 0.68 | 0.7556 | 0.7158 | 45 | 0.7447 | 0.7955 | 0.7692 | 44 | 0.7381 | 0.7686 | 0.7530 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9425 | 0.8913 | 0.9162 | 92 | 0.7778 | 0.875 | 0.8235 | 32 | 0.8054 | 0.8402 | 0.8224 | 0.9778 | | 0.0107 | 10.0 | 8920 | 0.1934 | 0.9184 | 0.9 | 0.9091 | 50 | 0.8333 | 0.8333 | 0.8333 | 78 | 0.7381 | 0.6889 | 0.7126 | 45 | 0.6809 | 0.7273 | 0.7033 | 44 | 0.744 | 0.7686 | 0.7561 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9425 | 0.8913 | 0.9162 | 92 | 0.7317 | 0.9375 | 0.8219 | 32 | 0.8064 | 0.8186 | 0.8124 | 0.9788 | | 0.0108 | 11.0 | 9812 | 0.2336 | 0.9184 | 0.9 | 0.9091 | 50 | 0.825 | 0.8462 | 0.8354 | 78 | 0.6481 | 0.7778 | 0.7071 | 45 | 0.5893 | 0.75 | 0.6600 | 44 | 0.7328 | 0.7934 | 0.7619 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.8795 | 0.7935 | 0.8343 | 92 | 0.7879 | 0.8125 | 0.8 | 32 | 0.7700 | 0.8099 | 0.7895 | 0.9750 | | 0.0069 | 12.0 | 10704 | 0.2376 | 0.9020 | 0.92 | 0.9109 | 50 | 0.7529 | 0.8205 | 0.7853 | 78 | 0.8095 | 0.7556 | 0.7816 | 45 | 0.7083 | 0.7727 | 0.7391 | 44 | 0.7154 | 0.7686 | 0.7410 | 121 | 0.0 | 0.0 | 0.0 | 1 | 0.9022 | 0.9022 | 0.9022 | 92 | 0.7179 | 0.875 | 0.7887 | 32 | 0.7844 | 0.8251 | 0.8042 | 0.9770 | | 0.0071 | 13.0 | 11596 | 0.2467 | 0.9038 | 0.94 | 0.9216 | 50 | 0.8148 | 0.8462 | 0.8302 | 78 | 0.7917 | 0.8444 | 0.8172 | 45 | 0.68 | 0.7727 | 0.7234 | 44 | 0.752 | 0.7769 | 0.7642 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9195 | 0.8696 | 0.8939 | 92 | 0.7632 | 0.9062 | 0.8286 | 32 | 0.8071 | 0.8402 | 0.8233 | 0.9767 | | 0.0052 | 14.0 | 12488 | 0.2818 | 0.92 | 0.92 | 0.92 | 50 | 0.7927 | 0.8333 | 0.8125 | 78 | 0.7778 | 0.7778 | 0.7778 | 45 | 0.7234 | 0.7727 | 0.7473 | 44 | 0.7015 | 0.7769 | 0.7373 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9535 | 0.8913 | 0.9213 | 92 | 0.8 | 0.875 | 0.8358 | 32 | 0.8021 | 0.8315 | 0.8165 | 0.9767 | | 0.0072 | 15.0 | 13380 | 0.2193 | 0.8333 | 0.9 | 0.8654 | 50 | 0.8 | 0.8205 | 0.8101 | 78 | 0.7609 | 0.7778 | 0.7692 | 45 | 0.6735 | 0.75 | 0.7097 | 44 | 0.7686 | 0.7686 | 0.7686 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9518 | 0.8587 | 0.9029 | 92 | 0.7317 | 0.9375 | 0.8219 | 32 | 0.8 | 0.8207 | 0.8102 | 0.9783 | | 0.0049 | 16.0 | 14272 | 0.2457 | 0.92 | 0.92 | 0.92 | 50 | 0.7738 | 0.8333 | 0.8025 | 78 | 0.7308 | 0.8444 | 0.7835 | 45 | 0.6122 | 0.6818 | 0.6452 | 44 | 0.7480 | 0.7851 | 0.7661 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.8989 | 0.8696 | 0.8840 | 92 | 0.725 | 0.9062 | 0.8056 | 32 | 0.7805 | 0.8294 | 0.8042 | 0.9767 | | 0.0055 | 17.0 | 15164 | 0.2359 | 0.8545 | 0.94 | 0.8952 | 50 | 0.8025 | 0.8333 | 0.8176 | 78 | 0.7273 | 0.7111 | 0.7191 | 45 | 0.6939 | 0.7727 | 0.7312 | 44 | 0.7661 | 0.7851 | 0.7755 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.8925 | 0.9022 | 0.8973 | 92 | 0.6905 | 0.9062 | 0.7838 | 32 | 0.7894 | 0.8337 | 0.8109 | 0.9780 | | 0.0045 | 18.0 | 16056 | 0.2472 | 0.92 | 0.92 | 0.92 | 50 | 0.8101 | 0.8205 | 0.8153 | 78 | 0.7805 | 0.7111 | 0.7442 | 45 | 0.6735 | 0.75 | 0.7097 | 44 | 0.7402 | 0.7769 | 0.7581 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.8876 | 0.8587 | 0.8729 | 92 | 0.7368 | 0.875 | 0.8000 | 32 | 0.7954 | 0.8143 | 0.8047 | 0.9781 | | 0.0052 | 19.0 | 16948 | 0.2287 | 0.8868 | 0.94 | 0.9126 | 50 | 0.7683 | 0.8077 | 0.7875 | 78 | 0.7857 | 0.7333 | 0.7586 | 45 | 0.6226 | 0.75 | 0.6804 | 44 | 0.7287 | 0.7769 | 0.7520 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9302 | 0.8696 | 0.8989 | 92 | 0.7073 | 0.9062 | 0.7945 | 32 | 0.7803 | 0.8207 | 0.8000 | 0.9765 | | 0.0021 | 20.0 | 17840 | 0.2552 | 0.8824 | 0.9 | 0.8911 | 50 | 0.8025 | 0.8333 | 0.8176 | 78 | 0.775 | 0.6889 | 0.7294 | 45 | 0.6957 | 0.7273 | 0.7111 | 44 | 0.7541 | 0.7603 | 0.7572 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9318 | 0.8913 | 0.9111 | 92 | 0.7143 | 0.9375 | 0.8108 | 32 | 0.8025 | 0.8164 | 0.8094 | 0.9779 | | 0.0027 | 21.0 | 18732 | 0.2547 | 0.8679 | 0.92 | 0.8932 | 50 | 0.8148 | 0.8462 | 0.8302 | 78 | 0.6552 | 0.8444 | 0.7379 | 45 | 0.6154 | 0.7273 | 0.6667 | 44 | 0.7661 | 0.7851 | 0.7755 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9 | 0.8804 | 0.8901 | 92 | 0.7436 | 0.9062 | 0.8169 | 32 | 0.7791 | 0.8380 | 0.8075 | 0.9780 | | 0.0021 | 22.0 | 19624 | 0.2829 | 0.9020 | 0.92 | 0.9109 | 50 | 0.8 | 0.8205 | 0.8101 | 78 | 0.7660 | 0.8 | 0.7826 | 45 | 0.6364 | 0.7955 | 0.7071 | 44 | 0.7460 | 0.7769 | 0.7611 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9022 | 0.9022 | 0.9022 | 92 | 0.6905 | 0.9062 | 0.7838 | 32 | 0.7854 | 0.8380 | 0.8109 | 0.9756 | | 0.0022 | 23.0 | 20516 | 0.2834 | 0.8333 | 0.9 | 0.8654 | 50 | 0.8025 | 0.8333 | 0.8176 | 78 | 0.72 | 0.8 | 0.7579 | 45 | 0.6140 | 0.7955 | 0.6931 | 44 | 0.736 | 0.7603 | 0.7480 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9111 | 0.8913 | 0.9011 | 92 | 0.7692 | 0.9375 | 0.8451 | 32 | 0.7767 | 0.8337 | 0.8042 | 0.9737 | | 0.0016 | 24.0 | 21408 | 0.2631 | 0.9020 | 0.92 | 0.9109 | 50 | 0.8101 | 0.8205 | 0.8153 | 78 | 0.7447 | 0.7778 | 0.7609 | 45 | 0.6471 | 0.75 | 0.6947 | 44 | 0.7308 | 0.7851 | 0.7570 | 121 | 0.5 | 1.0 | 0.6667 | 1 | 0.9310 | 0.8804 | 0.9050 | 92 | 0.725 | 0.9062 | 0.8056 | 32 | 0.7885 | 0.8294 | 0.8084 | 0.9785 | | 0.0035 | 25.0 | 22300 | 0.2889 | 0.8868 | 0.94 | 0.9126 | 50 | 0.8025 | 0.8333 | 0.8176 | 78 | 0.6481 | 0.7778 | 0.7071 | 45 | 0.625 | 0.7955 | 0.7 | 44 | 0.7068 | 0.7769 | 0.7402 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9412 | 0.8696 | 0.9040 | 92 | 0.7692 | 0.9375 | 0.8451 | 32 | 0.7709 | 0.8359 | 0.8021 | 0.9764 | | 0.0022 | 26.0 | 23192 | 0.3023 | 0.8545 | 0.94 | 0.8952 | 50 | 0.8519 | 0.8846 | 0.8679 | 78 | 0.6379 | 0.8222 | 0.7184 | 45 | 0.5862 | 0.7727 | 0.6667 | 44 | 0.6763 | 0.7769 | 0.7231 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9213 | 0.8913 | 0.9061 | 92 | 0.7436 | 0.9062 | 0.8169 | 32 | 0.7558 | 0.8488 | 0.7996 | 0.9742 | | 0.0024 | 27.0 | 24084 | 0.2836 | 0.9020 | 0.92 | 0.9109 | 50 | 0.8442 | 0.8333 | 0.8387 | 78 | 0.7447 | 0.7778 | 0.7609 | 45 | 0.6731 | 0.7955 | 0.7292 | 44 | 0.7308 | 0.7851 | 0.7570 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9412 | 0.8696 | 0.9040 | 92 | 0.75 | 0.9375 | 0.8333 | 32 | 0.8012 | 0.8359 | 0.8182 | 0.9787 | | 0.0015 | 28.0 | 24976 | 0.2825 | 0.8246 | 0.94 | 0.8785 | 50 | 0.825 | 0.8462 | 0.8354 | 78 | 0.7333 | 0.7333 | 0.7333 | 45 | 0.7045 | 0.7045 | 0.7045 | 44 | 0.7949 | 0.7686 | 0.7815 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9176 | 0.8478 | 0.8814 | 92 | 0.7 | 0.875 | 0.7778 | 32 | 0.8038 | 0.8143 | 0.8090 | 0.9784 | | 0.0011 | 29.0 | 25868 | 0.2815 | 0.8519 | 0.92 | 0.8846 | 50 | 0.8375 | 0.8590 | 0.8481 | 78 | 0.6545 | 0.8 | 0.7200 | 45 | 0.6731 | 0.7955 | 0.7292 | 44 | 0.7769 | 0.7769 | 0.7769 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.8925 | 0.9022 | 0.8973 | 92 | 0.7368 | 0.875 | 0.8000 | 32 | 0.7895 | 0.8423 | 0.8150 | 0.9775 | | 0.001 | 30.0 | 26760 | 0.2851 | 0.8545 | 0.94 | 0.8952 | 50 | 0.8171 | 0.8590 | 0.8375 | 78 | 0.7083 | 0.7556 | 0.7312 | 45 | 0.7174 | 0.75 | 0.7333 | 44 | 0.75 | 0.7934 | 0.7711 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9101 | 0.8804 | 0.8950 | 92 | 0.7568 | 0.875 | 0.8116 | 32 | 0.7963 | 0.8359 | 0.8156 | 0.9791 | | 0.0009 | 31.0 | 27652 | 0.2652 | 0.8679 | 0.92 | 0.8932 | 50 | 0.8228 | 0.8333 | 0.8280 | 78 | 0.7609 | 0.7778 | 0.7692 | 45 | 0.7391 | 0.7727 | 0.7556 | 44 | 0.7638 | 0.8017 | 0.7823 | 121 | 0.5 | 1.0 | 0.6667 | 1 | 0.9213 | 0.8913 | 0.9061 | 92 | 0.7647 | 0.8125 | 0.7879 | 32 | 0.8109 | 0.8337 | 0.8222 | 0.9807 | | 0.0008 | 32.0 | 28544 | 0.2681 | 0.8727 | 0.96 | 0.9143 | 50 | 0.8481 | 0.8590 | 0.8535 | 78 | 0.68 | 0.7556 | 0.7158 | 45 | 0.7143 | 0.7955 | 0.7527 | 44 | 0.7863 | 0.7603 | 0.7731 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9101 | 0.8804 | 0.8950 | 92 | 0.7941 | 0.8438 | 0.8182 | 32 | 0.8122 | 0.8315 | 0.8218 | 0.9805 | | 0.0009 | 33.0 | 29436 | 0.2700 | 0.8654 | 0.9 | 0.8824 | 50 | 0.8272 | 0.8590 | 0.8428 | 78 | 0.7609 | 0.7778 | 0.7692 | 45 | 0.6731 | 0.7955 | 0.7292 | 44 | 0.7385 | 0.7934 | 0.7649 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9140 | 0.9239 | 0.9189 | 92 | 0.8 | 0.875 | 0.8358 | 32 | 0.8 | 0.8467 | 0.8227 | 0.9794 | | 0.0008 | 34.0 | 30328 | 0.2832 | 0.9 | 0.9 | 0.9 | 50 | 0.8354 | 0.8462 | 0.8408 | 78 | 0.75 | 0.8 | 0.7742 | 45 | 0.6471 | 0.75 | 0.6947 | 44 | 0.8051 | 0.7851 | 0.7950 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9333 | 0.9130 | 0.9231 | 92 | 0.8 | 0.875 | 0.8358 | 32 | 0.8220 | 0.8380 | 0.8299 | 0.9791 | | 0.0009 | 35.0 | 31220 | 0.2778 | 0.92 | 0.92 | 0.92 | 50 | 0.8272 | 0.8590 | 0.8428 | 78 | 0.7347 | 0.8 | 0.7660 | 45 | 0.66 | 0.75 | 0.7021 | 44 | 0.7603 | 0.7603 | 0.7603 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9231 | 0.9130 | 0.9180 | 92 | 0.875 | 0.875 | 0.875 | 32 | 0.8147 | 0.8359 | 0.8252 | 0.9787 | | 0.0004 | 36.0 | 32112 | 0.2885 | 0.8889 | 0.96 | 0.9231 | 50 | 0.8293 | 0.8718 | 0.8500 | 78 | 0.7955 | 0.7778 | 0.7865 | 45 | 0.6863 | 0.7955 | 0.7368 | 44 | 0.7983 | 0.7851 | 0.7917 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.8925 | 0.9022 | 0.8973 | 92 | 0.8438 | 0.8438 | 0.8438 | 32 | 0.8235 | 0.8467 | 0.8349 | 0.9795 | | 0.0007 | 37.0 | 33004 | 0.2868 | 0.8868 | 0.94 | 0.9126 | 50 | 0.85 | 0.8718 | 0.8608 | 78 | 0.7660 | 0.8 | 0.7826 | 45 | 0.7727 | 0.7727 | 0.7727 | 44 | 0.7540 | 0.7851 | 0.7692 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9130 | 0.9130 | 0.9130 | 92 | 0.7941 | 0.8438 | 0.8182 | 32 | 0.8218 | 0.8467 | 0.8340 | 0.9796 | | 0.003 | 38.0 | 33896 | 0.2946 | 0.8868 | 0.94 | 0.9126 | 50 | 0.8395 | 0.8718 | 0.8553 | 78 | 0.8 | 0.8 | 0.8000 | 45 | 0.7059 | 0.8182 | 0.7579 | 44 | 0.7705 | 0.7769 | 0.7737 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9130 | 0.9130 | 0.9130 | 92 | 0.7941 | 0.8438 | 0.8182 | 32 | 0.8205 | 0.8488 | 0.8344 | 0.9788 | | 0.0007 | 39.0 | 34788 | 0.2761 | 0.8846 | 0.92 | 0.9020 | 50 | 0.8293 | 0.8718 | 0.8500 | 78 | 0.7447 | 0.7778 | 0.7609 | 45 | 0.7778 | 0.7955 | 0.7865 | 44 | 0.7661 | 0.7851 | 0.7755 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9222 | 0.9022 | 0.9121 | 92 | 0.7714 | 0.8438 | 0.8060 | 32 | 0.8193 | 0.8423 | 0.8307 | 0.9806 | | 0.0004 | 40.0 | 35680 | 0.2942 | 0.9038 | 0.94 | 0.9216 | 50 | 0.8272 | 0.8590 | 0.8428 | 78 | 0.7609 | 0.7778 | 0.7692 | 45 | 0.6923 | 0.8182 | 0.7500 | 44 | 0.7368 | 0.8099 | 0.7717 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9231 | 0.9130 | 0.9180 | 92 | 0.8182 | 0.8438 | 0.8308 | 32 | 0.8078 | 0.8531 | 0.8298 | 0.9785 | | 0.0001 | 41.0 | 36572 | 0.2966 | 0.8519 | 0.92 | 0.8846 | 50 | 0.8171 | 0.8590 | 0.8375 | 78 | 0.72 | 0.8 | 0.7579 | 45 | 0.7447 | 0.7955 | 0.7692 | 44 | 0.7712 | 0.7521 | 0.7615 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.8737 | 0.9022 | 0.8877 | 92 | 0.7714 | 0.8438 | 0.8060 | 32 | 0.8008 | 0.8337 | 0.8169 | 0.9788 | | 0.0003 | 42.0 | 37464 | 0.3037 | 0.8868 | 0.94 | 0.9126 | 50 | 0.8272 | 0.8590 | 0.8428 | 78 | 0.8333 | 0.7778 | 0.8046 | 45 | 0.6981 | 0.8409 | 0.7629 | 44 | 0.7540 | 0.7851 | 0.7692 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9348 | 0.9348 | 0.9348 | 92 | 0.8438 | 0.8438 | 0.8438 | 32 | 0.8229 | 0.8531 | 0.8378 | 0.9794 | | 0.0001 | 43.0 | 38356 | 0.3135 | 0.8519 | 0.92 | 0.8846 | 50 | 0.8148 | 0.8462 | 0.8302 | 78 | 0.7447 | 0.7778 | 0.7609 | 45 | 0.7174 | 0.75 | 0.7333 | 44 | 0.7692 | 0.7438 | 0.7563 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9444 | 0.9239 | 0.9341 | 92 | 0.7714 | 0.8438 | 0.8060 | 32 | 0.8132 | 0.8272 | 0.8201 | 0.9785 | | 0.0001 | 44.0 | 39248 | 0.3127 | 0.8868 | 0.94 | 0.9126 | 50 | 0.8148 | 0.8462 | 0.8302 | 78 | 0.7292 | 0.7778 | 0.7527 | 45 | 0.7 | 0.7955 | 0.7447 | 44 | 0.7966 | 0.7769 | 0.7866 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9222 | 0.9022 | 0.9121 | 92 | 0.7105 | 0.8438 | 0.7714 | 32 | 0.8100 | 0.8380 | 0.8238 | 0.9784 | | 0.0003 | 45.0 | 40140 | 0.3167 | 0.9038 | 0.94 | 0.9216 | 50 | 0.8148 | 0.8462 | 0.8302 | 78 | 0.7609 | 0.7778 | 0.7692 | 45 | 0.68 | 0.7727 | 0.7234 | 44 | 0.8034 | 0.7769 | 0.7899 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9333 | 0.9130 | 0.9231 | 92 | 0.7297 | 0.8438 | 0.7826 | 32 | 0.8186 | 0.8380 | 0.8282 | 0.9782 | | 0.0004 | 46.0 | 41032 | 0.3189 | 0.8868 | 0.94 | 0.9126 | 50 | 0.825 | 0.8462 | 0.8354 | 78 | 0.7609 | 0.7778 | 0.7692 | 45 | 0.7 | 0.7955 | 0.7447 | 44 | 0.7833 | 0.7769 | 0.7801 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9432 | 0.9022 | 0.9222 | 92 | 0.7297 | 0.8438 | 0.7826 | 32 | 0.8168 | 0.8380 | 0.8273 | 0.9777 | | 0.0001 | 47.0 | 41924 | 0.3171 | 0.9216 | 0.94 | 0.9307 | 50 | 0.825 | 0.8462 | 0.8354 | 78 | 0.7609 | 0.7778 | 0.7692 | 45 | 0.6667 | 0.7727 | 0.7158 | 44 | 0.7917 | 0.7851 | 0.7884 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9326 | 0.9022 | 0.9171 | 92 | 0.7297 | 0.8438 | 0.7826 | 32 | 0.8168 | 0.8380 | 0.8273 | 0.9779 | | 0.0001 | 48.0 | 42816 | 0.3186 | 0.9216 | 0.94 | 0.9307 | 50 | 0.825 | 0.8462 | 0.8354 | 78 | 0.7609 | 0.7778 | 0.7692 | 45 | 0.6667 | 0.7727 | 0.7158 | 44 | 0.7917 | 0.7851 | 0.7884 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9326 | 0.9022 | 0.9171 | 92 | 0.75 | 0.8438 | 0.7941 | 32 | 0.8186 | 0.8380 | 0.8282 | 0.9779 | | 0.0001 | 49.0 | 43708 | 0.3165 | 0.9216 | 0.94 | 0.9307 | 50 | 0.825 | 0.8462 | 0.8354 | 78 | 0.7609 | 0.7778 | 0.7692 | 45 | 0.6939 | 0.7727 | 0.7312 | 44 | 0.8051 | 0.7851 | 0.7950 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9326 | 0.9022 | 0.9171 | 92 | 0.7714 | 0.8438 | 0.8060 | 32 | 0.8273 | 0.8380 | 0.8326 | 0.9781 | | 0.0001 | 50.0 | 44600 | 0.3158 | 0.9216 | 0.94 | 0.9307 | 50 | 0.825 | 0.8462 | 0.8354 | 78 | 0.7609 | 0.7778 | 0.7692 | 45 | 0.6939 | 0.7727 | 0.7312 | 44 | 0.8051 | 0.7851 | 0.7950 | 121 | 1.0 | 1.0 | 1.0 | 1 | 0.9326 | 0.9022 | 0.9171 | 92 | 0.7714 | 0.8438 | 0.8060 | 32 | 0.8273 | 0.8380 | 0.8326 | 0.9782 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.2.2 - Tokenizers 0.13.2
gldalessandro/stable-diffusion-lowp
gldalessandro
2022-11-24T14:13:00Z
48
0
diffusers
[ "diffusers", "stable-diffusion", "text-to-image", "arxiv:2202.00512", "arxiv:2112.10752", "arxiv:1910.09700", "license:openrail++", "autotrain_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2022-11-24T14:06:29Z
--- license: openrail++ tags: - stable-diffusion - text-to-image inference: false --- # Stable Diffusion v2 Model Card This model card focuses on the model associated with the Stable Diffusion v2 model, available [here](https://github.com/Stability-AI/stablediffusion). This `stable-diffusion-2` model is resumed from [stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-base) (`512-base-ema.ckpt`) and trained for 150k steps using a [v-objective](https://arxiv.org/abs/2202.00512) on the same dataset. Resumed for another 140k steps on `768x768` images. ![image](https://github.com/Stability-AI/stablediffusion/blob/main/assets/stable-samples/txt2img/768/merged-0005.png?raw=true) - Use it with the [`stablediffusion`](https://github.com/Stability-AI/stablediffusion) repository: download the `768-v-ema.ckpt` [here](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/768-v-ema.ckpt). - Use it with 🧨 [`diffusers`](https://huggingface.co/stabilityai/stable-diffusion-2#model-details#Examples) ## Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL) - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip)). - **Resources for more information:** [GitHub Repository](https://github.com/Stability-AI/). - **Cite as:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ## Examples Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion 2 in a simple and efficient manner. ```bash pip install --upgrade git+https://github.com/huggingface/diffusers.git transformers accelerate scipy ``` Running the pipeline (if you don't swap the scheduler it will run with the default DDIM, in this example we are swapping it to EulerDiscreteScheduler): ```python from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler model_id = "stabilityai/stable-diffusion-2" # Use the Euler scheduler here instead scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler") pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, revision="fp16", torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt, height=768, width=768).images[0] image.save("astronaut_rides_horse.png") ``` **Notes**: - Despite not being a dependency, we highly recommend you to install [xformers](https://github.com/facebookresearch/xformers) for memory efficient attention (better performance) - If you have low GPU RAM available, make sure to add a `pipe.enable_attention_slicing()` after sending it to `cuda` for less VRAM usage (to the cost of speed) # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is originally taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a subset of the large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, we have filtered the dataset using LAION's NFSW detector (see Training section). ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. Stable Diffusion v2 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent. ## Training **Training Data** The model developers used the following dataset for training the model: - LAION-5B and subsets (details below). The training data is further filtered using LAION's NSFW detector, with a "p_unsafe" score of 0.1 (conservative). For more details, please refer to LAION-5B's [NeurIPS 2022](https://openreview.net/forum?id=M3Y74vmsMcY) paper and reviewer discussions on the topic. **Training Procedure** Stable Diffusion v2 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through the OpenCLIP-ViT/H text-encoder. - The output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We also use the so-called _v-objective_, see https://arxiv.org/abs/2202.00512. We currently provide the following checkpoints: - `512-base-ema.ckpt`: 550k steps at resolution `256x256` on a subset of [LAION-5B](https://laion.ai/blog/laion-5b/) filtered for explicit pornographic material, using the [LAION-NSFW classifier](https://github.com/LAION-AI/CLIP-based-NSFW-Detector) with `punsafe=0.1` and an [aesthetic score](https://github.com/christophschuhmann/improved-aesthetic-predictor) >= `4.5`. 850k steps at resolution `512x512` on the same dataset with resolution `>= 512x512`. - `768-v-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for 150k steps using a [v-objective](https://arxiv.org/abs/2202.00512) on the same dataset. Resumed for another 140k steps on a `768x768` subset of our dataset. - `512-depth-ema.ckpt`: Resumed from `512-base-ema.ckpt` and finetuned for 200k steps. Added an extra input channel to process the (relative) depth prediction produced by [MiDaS](https://github.com/isl-org/MiDaS) (`dpt_hybrid`) which is used as an additional conditioning. The additional input channels of the U-Net which process this extra information were zero-initialized. - `512-inpainting-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for another 200k steps. Follows the mask-generation strategy presented in [LAMA](https://github.com/saic-mdal/lama) which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning. The additional input channels of the U-Net which process this extra information were zero-initialized. The same strategy was used to train the [1.5-inpainting checkpoint](https://github.com/saic-mdal/lama). - `x4-upscaling-ema.ckpt`: Trained for 1.25M steps on a 10M subset of LAION containing images `>2048x2048`. The model was trained on crops of size `512x512` and is a text-guided [latent upscaling diffusion model](https://arxiv.org/abs/2112.10752). In addition to the textual input, it receives a `noise_level` as an input parameter, which can be used to add noise to the low-resolution input according to a [predefined diffusion schedule](configs/stable-diffusion/x4-upscaling.yaml). - **Hardware:** 32 x 8 x A100 GPUs - **Optimizer:** AdamW - **Gradient Accumulations**: 1 - **Batch:** 32 x 8 x 2 x 4 = 2048 - **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 steps DDIM sampling steps show the relative improvements of the checkpoints: ![pareto](model-variants.jpg) Evaluated using 50 DDIM steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Environmental Impact **Stable Diffusion v1** **Estimated Emissions** Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 200000 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 15000 kg CO2 eq. ## Citation @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } *This model card was written by: Robin Rombach, Patrick Esser and David Ha and is based on the [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion/blob/main/Stable_Diffusion_v1_Model_Card.md) and [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*
justinpinkney/miniSD
justinpinkney
2022-11-24T13:38:34Z
0
25
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2022-11-24T12:38:59Z
--- license: creativeml-openrail-m --- ## Usage ```bash python scripts/txt2img.py \ --prompt 'a photograph of an wrinkly old man laughing' \ --H 256 --W 256 \ --n_samples 4 \ --ckpt miniSD.ckpt ``` ## Training details Fine tuned from the stable-diffusion 1.4 checkpoint as follows: - 22,000 steps fine-tuning only the attention layers of the unet, learn rate=1e-5, batch size=256 - 66,000 steps training the full unet, learn rate=5e-5, batch size=552 - GPUs provided by [Lambda GPU Cloud](https://lambdalabs.com/service/gpu-cloud) - Trained on [LAION Improved Aesthetics 6plus](https://huggingface.co/datasets/ChristophSchuhmann/improved_aesthetics_6plus). - Trained using https://github.com/justinpinkney/stable-diffusion, original [checkpoint available here](https://huggingface.co/justinpinkney/miniSD) ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: - You can't use the model to deliberately produce nor share illegal or harmful outputs or content - The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license - You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here
jamiehudson/579-STmodel-product-rem-v3a
jamiehudson
2022-11-24T13:16:22Z
14
0
sentence-transformers
[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]
sentence-similarity
2022-11-24T13:16:09Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 1800 with parameters: ``` {'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 1800, "warmup_steps": 180, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
feabries/q-Taxi-v3
feabries
2022-11-24T12:59:26Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2022-11-24T12:59:20Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.73 name: mean_reward verified: false --- # **Q-Learning** Agent playing **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="feabries/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
feabries/q-FrozenLake-v1-4x4-noSlippery
feabries
2022-11-24T12:57:23Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2022-11-24T12:57:17Z
--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="feabries/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
huggingtweets/boredelonmusk-brycent_-loopifyyy
huggingtweets
2022-11-24T12:50:17Z
117
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-11-24T02:18:11Z
--- language: en thumbnail: http://www.huggingtweets.com/boredelonmusk-brycent_-loopifyyy/1669294212911/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1594950081735712768/W6KFeuZt_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1559722657079107585/wHPeu1Uj_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1573621368897376256/axS4pz4m_400x400.png&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Brycent 🚀 & BORED.eth & Loopify 🧙‍♂️</div> <div style="text-align: center; font-size: 14px;">@boredelonmusk-brycent_-loopifyyy</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Brycent 🚀 & BORED.eth & Loopify 🧙‍♂️. | Data | Brycent 🚀 | BORED.eth | Loopify 🧙‍♂️ | | --- | --- | --- | --- | | Tweets downloaded | 3244 | 3237 | 3246 | | Retweets | 364 | 19 | 262 | | Short tweets | 445 | 847 | 985 | | Tweets kept | 2435 | 2371 | 1999 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1f2sjcku/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @boredelonmusk-brycent_-loopifyyy's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/aor2m07m) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/aor2m07m/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/boredelonmusk-brycent_-loopifyyy') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
scikit-learn/skorch-text-classification
scikit-learn
2022-11-24T12:42:52Z
0
2
sklearn
[ "sklearn", "skops", "text-classification", "region:us" ]
text-classification
2022-09-28T08:45:41Z
--- library_name: sklearn tags: - sklearn - skops - text-classification --- # Model description This is a neural net classifier and distilbert model chained with sklearn Pipeline trained on 20 news groups dataset. ## Intended uses & limitations This model is trained for a tutorial and is not ready to be used in production. ## Training Procedure ### Hyperparameters The model is trained with below hyperparameters. <details> <summary> Click to expand </summary> | Hyperparameter | Value | |------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------| | memory | | | steps | [('tokenizer', HuggingfacePretrainedTokenizer(tokenizer='distilbert-base-uncased')), ('net', <class 'skorch.classifier.NeuralNetClassifier'>[initialized]( module_=BertModule( (bert): DistilBertForSequenceClassification( (distilbert): DistilBertModel( (embeddings): Embeddings( (word_embeddings): Embedding(30522, 768, padding_idx=0) (position_embeddings): Embedding(512, 768) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) (transformer): Transformer( (layer): ModuleList( (0): TransformerBlock( (attention): MultiHeadSelfAttention( (dropout): Dropout(p=0.1, inplace=False) (q_lin): Linear(in_features=768, out_features=768, bias=True) (k_lin): Linear(in_features=768, out_features=768, bias=True) (v_lin): Linear(in_features=768, out_features=768, bias=True) (out_lin): Linear(in_features=768, out_features=768, bias=True) ) (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (ffn): FFN( (dropout): Dropout(p=0.1, inplace=False) (lin1): Linear(in_features=768, out_features=3072, bias=True) (lin2): Linear(in_features=3072, out_features=768, bias=True) (activation): GELUActivation() ) (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) ) (1): TransformerBlock( (attention): MultiHeadSelfAttention( (dropout): Dropout(p=0.1, inplace=False) (q_lin): Linear(in_features=768, out_features=768, bias=True) (k_lin): Linear(in_features=768, out_features=768, bias=True) (v_lin): Linear(in_features=768, out_features=768, bias=True) (out_lin): Linear(in_features=768, out_features=768, bias=True) ) (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (ffn): FFN( (dropout): Dropout(p=0.1, inplace=False) (lin1): Linear(in_features=768, out_features=3072, bias=True) (lin2): Linear(in_features=3072, out_features=768, bias=True) (activation): GELUActivation() ) (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) ) (2): TransformerBlock( (attention): MultiHeadSelfAttention( (dropout): Dropout(p=0.1, inplace=False) (q_lin): Linear(in_features=768, out_features=768, bias=True) (k_lin): Linear(in_features=768, out_features=768, bias=True) (v_lin): Linear(in_features=768, out_features=768, bias=True) (out_lin): Linear(in_features=768, out_features=768, bias=True) ) (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (ffn): FFN( (dropout): Dropout(p=0.1, inplace=False) (lin1): Linear(in_features=768, out_features=3072, bias=True) (lin2): Linear(in_features=3072, out_features=768, bias=True) (activation): GELUActivation() ) (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) ) (3): TransformerBlock( (attention): MultiHeadSelfAttention( (dropout): Dropout(p=0.1, inplace=False) (q_lin): Linear(in_features=768, out_features=768, bias=True) (k_lin): Linear(in_features=768, out_features=768, bias=True) (v_lin): Linear(in_features=768, out_features=768, bias=True) (out_lin): Linear(in_features=768, out_features=768, bias=True) ) (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (ffn): FFN( (dropout): Dropout(p=0.1, inplace=False) (lin1): Linear(in_features=768, out_features=3072, bias=True) (lin2): Linear(in_features=3072, out_features=768, bias=True) (activation): GELUActivation() ) (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) ) (4): TransformerBlock( (attention): MultiHeadSelfAttention( (dropout): Dropout(p=0.1, inplace=False) (q_lin): Linear(in_features=768, out_features=768, bias=True) (k_lin): Linear(in_features=768, out_features=768, bias=True) (v_lin): Linear(in_features=768, out_features=768, bias=True) (out_lin): Linear(in_features=768, out_features=768, bias=True) ) (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (ffn): FFN( (dropout): Dropout(p=0.1, inplace=False) (lin1): Linear(in_features=768, out_features=3072, bias=True) (lin2): Linear(in_features=3072, out_features=768, bias=True) (activation): GELUActivation() ) (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) ) (5): TransformerBlock( (attention): MultiHeadSelfAttention( (dropout): Dropout(p=0.1, inplace=False) (q_lin): Linear(in_features=768, out_features=768, bias=True) (k_lin): Linear(in_features=768, out_features=768, bias=True) (v_lin): Linear(in_features=768, out_features=768, bias=True) (out_lin): Linear(in_features=768, out_features=768, bias=True) ) (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (ffn): FFN( (dropout): Dropout(p=0.1, inplace=False) (lin1): Linear(in_features=768, out_features=3072, bias=True) (lin2): Linear(in_features=3072, out_features=768, bias=True) (activation): GELUActivation() ) (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) ) ) ) ) (pre_classifier): Linear(in_features=768, out_features=768, bias=True) (classifier): Linear(in_features=768, out_features=20, bias=True) (dropout): Dropout(p=0.2, inplace=False) ) ), ))] | | verbose | False | | tokenizer | HuggingfacePretrainedTokenizer(tokenizer='distilbert-base-uncased') | | net | <class 'skorch.classifier.NeuralNetClassifier'>[initialized]( module_=BertModule( (bert): DistilBertForSequenceClassification( (distilbert): DistilBertModel( (embeddings): Embeddings( (word_embeddings): Embedding(30522, 768, padding_idx=0) (position_embeddings): Embedding(512, 768) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) (transformer): Transformer( (layer): ModuleList( (0): TransformerBlock( (attention): MultiHeadSelfAttention( (dropout): Dropout(p=0.1, inplace=False) (q_lin): Linear(in_features=768, out_features=768, bias=True) (k_lin): Linear(in_features=768, out_features=768, bias=True) (v_lin): Linear(in_features=768, out_features=768, bias=True) (out_lin): Linear(in_features=768, out_features=768, bias=True) ) (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (ffn): FFN( (dropout): Dropout(p=0.1, inplace=False) (lin1): Linear(in_features=768, out_features=3072, bias=True) (lin2): Linear(in_features=3072, out_features=768, bias=True) (activation): GELUActivation() ) (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) ) (1): TransformerBlock( (attention): MultiHeadSelfAttention( (dropout): Dropout(p=0.1, inplace=False) (q_lin): Linear(in_features=768, out_features=768, bias=True) (k_lin): Linear(in_features=768, out_features=768, bias=True) (v_lin): Linear(in_features=768, out_features=768, bias=True) (out_lin): Linear(in_features=768, out_features=768, bias=True) ) (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (ffn): FFN( (dropout): Dropout(p=0.1, inplace=False) (lin1): Linear(in_features=768, out_features=3072, bias=True) (lin2): Linear(in_features=3072, out_features=768, bias=True) (activation): GELUActivation() ) (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) ) (2): TransformerBlock( (attention): MultiHeadSelfAttention( (dropout): Dropout(p=0.1, inplace=False) (q_lin): Linear(in_features=768, out_features=768, bias=True) (k_lin): Linear(in_features=768, out_features=768, bias=True) (v_lin): Linear(in_features=768, out_features=768, bias=True) (out_lin): Linear(in_features=768, out_features=768, bias=True) ) (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (ffn): FFN( (dropout): Dropout(p=0.1, inplace=False) (lin1): Linear(in_features=768, out_features=3072, bias=True) (lin2): Linear(in_features=3072, out_features=768, bias=True) (activation): GELUActivation() ) (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) ) (3): TransformerBlock( (attention): MultiHeadSelfAttention( (dropout): Dropout(p=0.1, inplace=False) (q_lin): Linear(in_features=768, out_features=768, bias=True) (k_lin): Linear(in_features=768, out_features=768, bias=True) (v_lin): Linear(in_features=768, out_features=768, bias=True) (out_lin): Linear(in_features=768, out_features=768, bias=True) ) (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (ffn): FFN( (dropout): Dropout(p=0.1, inplace=False) (lin1): Linear(in_features=768, out_features=3072, bias=True) (lin2): Linear(in_features=3072, out_features=768, bias=True) (activation): GELUActivation() ) (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) ) (4): TransformerBlock( (attention): MultiHeadSelfAttention( (dropout): Dropout(p=0.1, inplace=False) (q_lin): Linear(in_features=768, out_features=768, bias=True) (k_lin): Linear(in_features=768, out_features=768, bias=True) (v_lin): Linear(in_features=768, out_features=768, bias=True) (out_lin): Linear(in_features=768, out_features=768, bias=True) ) (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (ffn): FFN( (dropout): Dropout(p=0.1, inplace=False) (lin1): Linear(in_features=768, out_features=3072, bias=True) (lin2): Linear(in_features=3072, out_features=768, bias=True) (activation): GELUActivation() ) (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) ) (5): TransformerBlock( (attention): MultiHeadSelfAttention( (dropout): Dropout(p=0.1, inplace=False) (q_lin): Linear(in_features=768, out_features=768, bias=True) (k_lin): Linear(in_features=768, out_features=768, bias=True) (v_lin): Linear(in_features=768, out_features=768, bias=True) (out_lin): Linear(in_features=768, out_features=768, bias=True) ) (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (ffn): FFN( (dropout): Dropout(p=0.1, inplace=False) (lin1): Linear(in_features=768, out_features=3072, bias=True) (lin2): Linear(in_features=3072, out_features=768, bias=True) (activation): GELUActivation() ) (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) ) ) ) ) (pre_classifier): Linear(in_features=768, out_features=768, bias=True) (classifier): Linear(in_features=768, out_features=20, bias=True) (dropout): Dropout(p=0.2, inplace=False) ) ), ) | | tokenizer__max_length | 256 | | tokenizer__return_attention_mask | True | | tokenizer__return_length | False | | tokenizer__return_tensors | pt | | tokenizer__return_token_type_ids | False | | tokenizer__tokenizer | distilbert-base-uncased | | tokenizer__train | False | | tokenizer__verbose | 0 | | tokenizer__vocab_size | | | net__module | <class '__main__.BertModule'> | | net__criterion | <class 'torch.nn.modules.loss.CrossEntropyLoss'> | | net__optimizer | <class 'torch.optim.adamw.AdamW'> | | net__lr | 5e-05 | | net__max_epochs | 3 | | net__batch_size | 8 | | net__iterator_train | <class 'torch.utils.data.dataloader.DataLoader'> | | net__iterator_valid | <class 'torch.utils.data.dataloader.DataLoader'> | | net__dataset | <class 'skorch.dataset.Dataset'> | | net__train_split | <skorch.dataset.ValidSplit object at 0x7f9945e18c90> | | net__callbacks | [<skorch.callbacks.lr_scheduler.LRScheduler object at 0x7f9945da85d0>, <skorch.callbacks.logging.ProgressBar object at 0x7f9945da8250>] | | net__predict_nonlinearity | auto | | net__warm_start | False | | net__verbose | 1 | | net__device | cuda | | net___params_to_validate | {'module__num_labels', 'module__name', 'iterator_train__shuffle'} | | net__module__name | distilbert-base-uncased | | net__module__num_labels | 20 | | net__iterator_train__shuffle | True | | net__classes | | | net__callbacks__epoch_timer | <skorch.callbacks.logging.EpochTimer object at 0x7f993cb300d0> | | net__callbacks__train_loss | <skorch.callbacks.scoring.PassthroughScoring object at 0x7f993cb306d0> | | net__callbacks__train_loss__name | train_loss | | net__callbacks__train_loss__lower_is_better | True | | net__callbacks__train_loss__on_train | True | | net__callbacks__valid_loss | <skorch.callbacks.scoring.PassthroughScoring object at 0x7f993cb30ed0> | | net__callbacks__valid_loss__name | valid_loss | | net__callbacks__valid_loss__lower_is_better | True | | net__callbacks__valid_loss__on_train | False | | net__callbacks__valid_acc | <skorch.callbacks.scoring.EpochScoring object at 0x7f993cb30410> | | net__callbacks__valid_acc__scoring | accuracy | | net__callbacks__valid_acc__lower_is_better | False | | net__callbacks__valid_acc__on_train | False | | net__callbacks__valid_acc__name | valid_acc | | net__callbacks__valid_acc__target_extractor | <function to_numpy at 0x7f9945e46a70> | | net__callbacks__valid_acc__use_caching | True | | net__callbacks__LRScheduler | <skorch.callbacks.lr_scheduler.LRScheduler object at 0x7f9945da85d0> | | net__callbacks__LRScheduler__policy | <class 'torch.optim.lr_scheduler.LambdaLR'> | | net__callbacks__LRScheduler__monitor | train_loss | | net__callbacks__LRScheduler__event_name | event_lr | | net__callbacks__LRScheduler__step_every | batch | | net__callbacks__LRScheduler__lr_lambda | <function lr_schedule at 0x7f9945d9c440> | | net__callbacks__ProgressBar | <skorch.callbacks.logging.ProgressBar object at 0x7f9945da8250> | | net__callbacks__ProgressBar__batches_per_epoch | auto | | net__callbacks__ProgressBar__detect_notebook | True | | net__callbacks__ProgressBar__postfix_keys | ['train_loss', 'valid_loss'] | | net__callbacks__print_log | <skorch.callbacks.logging.PrintLog object at 0x7f993cb30dd0> | | net__callbacks__print_log__keys_ignored | | | net__callbacks__print_log__sink | <built-in function print> | | net__callbacks__print_log__tablefmt | simple | | net__callbacks__print_log__floatfmt | .4f | | net__callbacks__print_log__stralign | right | </details> ### Model Plot The model plot is below. <style>#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb {color: black;background-color: white;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb pre{padding: 0;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-toggleable {background-color: white;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-estimator:hover {background-color: #d4ebff;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-item {z-index: 1;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-parallel-item:only-child::after {width: 0;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-text-repr-fallback {display: none;}</style><div id="sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;tokenizer&#x27;,HuggingfacePretrainedTokenizer(tokenizer=&#x27;distilbert-base-uncased&#x27;)),(&#x27;net&#x27;,&lt;class &#x27;skorch.classifier.NeuralNetClassifier&#x27;&gt;[initialized](module_=BertModule((bert): DistilBertForSequenceClassification((distilbert): DistilBertModel((embeddings): Embeddings((word_embeddings): Embedding(30522, 768, padding_idx=0)(position_embeddin...(lin1): Linear(in_features=768, out_features=3072, bias=True)(lin2): Linear(in_features=3072, out_features=768, bias=True)(activation): GELUActivation())(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)))))(pre_classifier): Linear(in_features=768, out_features=768, bias=True)(classifier): Linear(in_features=768, out_features=20, bias=True)(dropout): Dropout(p=0.2, inplace=False))), ))])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="4905268f-3ec2-45fc-8bc7-80d9200ae5a5" type="checkbox" ><label for="4905268f-3ec2-45fc-8bc7-80d9200ae5a5" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;tokenizer&#x27;,HuggingfacePretrainedTokenizer(tokenizer=&#x27;distilbert-base-uncased&#x27;)),(&#x27;net&#x27;,&lt;class &#x27;skorch.classifier.NeuralNetClassifier&#x27;&gt;[initialized](module_=BertModule((bert): DistilBertForSequenceClassification((distilbert): DistilBertModel((embeddings): Embeddings((word_embeddings): Embedding(30522, 768, padding_idx=0)(position_embeddin...(lin1): Linear(in_features=768, out_features=3072, bias=True)(lin2): Linear(in_features=3072, out_features=768, bias=True)(activation): GELUActivation())(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)))))(pre_classifier): Linear(in_features=768, out_features=768, bias=True)(classifier): Linear(in_features=768, out_features=20, bias=True)(dropout): Dropout(p=0.2, inplace=False))), ))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="4c9a801f-37d5-4fdb-9892-222c86b927bf" type="checkbox" ><label for="4c9a801f-37d5-4fdb-9892-222c86b927bf" class="sk-toggleable__label sk-toggleable__label-arrow">HuggingfacePretrainedTokenizer</label><div class="sk-toggleable__content"><pre>HuggingfacePretrainedTokenizer(tokenizer=&#x27;distilbert-base-uncased&#x27;)</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="062dd9ff-2b54-4166-90fc-fa3276cd482a" type="checkbox" ><label for="062dd9ff-2b54-4166-90fc-fa3276cd482a" class="sk-toggleable__label sk-toggleable__label-arrow">NeuralNetClassifier</label><div class="sk-toggleable__content"><pre>&lt;class &#x27;skorch.classifier.NeuralNetClassifier&#x27;&gt;[initialized](module_=BertModule((bert): DistilBertForSequenceClassification((distilbert): DistilBertModel((embeddings): Embeddings((word_embeddings): Embedding(30522, 768, padding_idx=0)(position_embeddings): Embedding(512, 768)(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)(dropout): Dropout(p=0.1, inplace=False))(transformer): Transformer((layer): ModuleList((0): TransformerBlock((attention): MultiHeadSelfAttention((dropout): Dropout(p=0.1, inplace=False)(q_lin): Linear(in_features=768, out_features=768, bias=True)(k_lin): Linear(in_features=768, out_features=768, bias=True)(v_lin): Linear(in_features=768, out_features=768, bias=True)(out_lin): Linear(in_features=768, out_features=768, bias=True))(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)(ffn): FFN((dropout): Dropout(p=0.1, inplace=False)(lin1): Linear(in_features=768, out_features=3072, bias=True)(lin2): Linear(in_features=3072, out_features=768, bias=True)(activation): GELUActivation())(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True))(1): TransformerBlock((attention): MultiHeadSelfAttention((dropout): Dropout(p=0.1, inplace=False)(q_lin): Linear(in_features=768, out_features=768, bias=True)(k_lin): Linear(in_features=768, out_features=768, bias=True)(v_lin): Linear(in_features=768, out_features=768, bias=True)(out_lin): Linear(in_features=768, out_features=768, bias=True))(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)(ffn): FFN((dropout): Dropout(p=0.1, inplace=False)(lin1): Linear(in_features=768, out_features=3072, bias=True)(lin2): Linear(in_features=3072, out_features=768, bias=True)(activation): GELUActivation())(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True))(2): TransformerBlock((attention): MultiHeadSelfAttention((dropout): Dropout(p=0.1, inplace=False)(q_lin): Linear(in_features=768, out_features=768, bias=True)(k_lin): Linear(in_features=768, out_features=768, bias=True)(v_lin): Linear(in_features=768, out_features=768, bias=True)(out_lin): Linear(in_features=768, out_features=768, bias=True))(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)(ffn): FFN((dropout): Dropout(p=0.1, inplace=False)(lin1): Linear(in_features=768, out_features=3072, bias=True)(lin2): Linear(in_features=3072, out_features=768, bias=True)(activation): GELUActivation())(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True))(3): TransformerBlock((attention): MultiHeadSelfAttention((dropout): Dropout(p=0.1, inplace=False)(q_lin): Linear(in_features=768, out_features=768, bias=True)(k_lin): Linear(in_features=768, out_features=768, bias=True)(v_lin): Linear(in_features=768, out_features=768, bias=True)(out_lin): Linear(in_features=768, out_features=768, bias=True))(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)(ffn): FFN((dropout): Dropout(p=0.1, inplace=False)(lin1): Linear(in_features=768, out_features=3072, bias=True)(lin2): Linear(in_features=3072, out_features=768, bias=True)(activation): GELUActivation())(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True))(4): TransformerBlock((attention): MultiHeadSelfAttention((dropout): Dropout(p=0.1, inplace=False)(q_lin): Linear(in_features=768, out_features=768, bias=True)(k_lin): Linear(in_features=768, out_features=768, bias=True)(v_lin): Linear(in_features=768, out_features=768, bias=True)(out_lin): Linear(in_features=768, out_features=768, bias=True))(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)(ffn): FFN((dropout): Dropout(p=0.1, inplace=False)(lin1): Linear(in_features=768, out_features=3072, bias=True)(lin2): Linear(in_features=3072, out_features=768, bias=True)(activation): GELUActivation())(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True))(5): TransformerBlock((attention): MultiHeadSelfAttention((dropout): Dropout(p=0.1, inplace=False)(q_lin): Linear(in_features=768, out_features=768, bias=True)(k_lin): Linear(in_features=768, out_features=768, bias=True)(v_lin): Linear(in_features=768, out_features=768, bias=True)(out_lin): Linear(in_features=768, out_features=768, bias=True))(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)(ffn): FFN((dropout): Dropout(p=0.1, inplace=False)(lin1): Linear(in_features=768, out_features=3072, bias=True)(lin2): Linear(in_features=3072, out_features=768, bias=True)(activation): GELUActivation())(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)))))(pre_classifier): Linear(in_features=768, out_features=768, bias=True)(classifier): Linear(in_features=768, out_features=20, bias=True)(dropout): Dropout(p=0.2, inplace=False))), )</pre></div></div></div></div></div></div></div> ## Evaluation Results You can find the details about evaluation process and the evaluation results. | Metric | Value | |----------|---------| | accuracy | 0.90562 | | f1 score | 0.90562 | # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python [More Information Needed] ``` </details> # Additional Content ## Confusion matrix ![Confusion matrix](confusion_matrix.png) ## Classification Report <details> <summary> Click to expand </summary> | index | precision | recall | f1-score | support | |--------------------------|-------------|----------|------------|-----------| | alt.atheism | 0.927273 | 0.85 | 0.886957 | 120 | | comp.graphics | 0.85906 | 0.876712 | 0.867797 | 146 | | comp.os.ms-windows.misc | 0.893617 | 0.851351 | 0.871972 | 148 | | comp.sys.ibm.pc.hardware | 0.666667 | 0.837838 | 0.742515 | 148 | | comp.sys.mac.hardware | 0.901515 | 0.826389 | 0.862319 | 144 | | comp.windows.x | 0.923077 | 0.891892 | 0.907216 | 148 | | misc.forsale | 0.875862 | 0.869863 | 0.872852 | 146 | | rec.autos | 0.893082 | 0.95302 | 0.922078 | 149 | | rec.motorcycles | 0.937931 | 0.906667 | 0.922034 | 150 | | rec.sport.baseball | 0.954248 | 0.979866 | 0.966887 | 149 | | rec.sport.hockey | 0.979866 | 0.973333 | 0.976589 | 150 | | sci.crypt | 0.993103 | 0.966443 | 0.979592 | 149 | | sci.electronics | 0.869565 | 0.810811 | 0.839161 | 148 | | sci.med | 0.973154 | 0.973154 | 0.973154 | 149 | | sci.space | 0.973333 | 0.986486 | 0.979866 | 148 | | soc.religion.christian | 0.927152 | 0.933333 | 0.930233 | 150 | | talk.politics.guns | 0.961538 | 0.919118 | 0.93985 | 136 | | talk.politics.mideast | 0.978571 | 0.971631 | 0.975089 | 141 | | talk.politics.misc | 0.925234 | 0.853448 | 0.887892 | 116 | | talk.religion.misc | 0.728972 | 0.829787 | 0.776119 | 94 | | macro avg | 0.907141 | 0.903057 | 0.904009 | 2829 | | weighted avg | 0.909947 | 0.90562 | 0.906742 | 2829 | </details>
jamiehudson/579-STmodel-v3a
jamiehudson
2022-11-24T12:38:36Z
4
0
sentence-transformers
[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]
sentence-similarity
2022-11-24T12:38:24Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 1800 with parameters: ``` {'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 1800, "warmup_steps": 180, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
thucdangvan020999/Sbert_20k_khanhdp2
thucdangvan020999
2022-11-24T12:25:03Z
5
0
sentence-transformers
[ "sentence-transformers", "pytorch", "roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2022-11-24T11:50:49Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 5090 with parameters: ``` {'batch_size': 8} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 20, "evaluation_steps": 5090, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10180, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel (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 -->
tomekkorbak/heuristic_shannon
tomekkorbak
2022-11-24T11:57:33Z
103
0
transformers
[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:tomekkorbak/pii-pile-chunk3-0-50000", "dataset:tomekkorbak/pii-pile-chunk3-50000-100000", "dataset:tomekkorbak/pii-pile-chunk3-100000-150000", "dataset:tomekkorbak/pii-pile-chunk3-150000-200000", "dataset:tomekkorbak/pii-pile-chunk3-200000-250000", "dataset:tomekkorbak/pii-pile-chunk3-250000-300000", "dataset:tomekkorbak/pii-pile-chunk3-300000-350000", "dataset:tomekkorbak/pii-pile-chunk3-350000-400000", "dataset:tomekkorbak/pii-pile-chunk3-400000-450000", "dataset:tomekkorbak/pii-pile-chunk3-450000-500000", "dataset:tomekkorbak/pii-pile-chunk3-500000-550000", "dataset:tomekkorbak/pii-pile-chunk3-550000-600000", "dataset:tomekkorbak/pii-pile-chunk3-600000-650000", "dataset:tomekkorbak/pii-pile-chunk3-650000-700000", "dataset:tomekkorbak/pii-pile-chunk3-700000-750000", "dataset:tomekkorbak/pii-pile-chunk3-750000-800000", "dataset:tomekkorbak/pii-pile-chunk3-800000-850000", "dataset:tomekkorbak/pii-pile-chunk3-850000-900000", "dataset:tomekkorbak/pii-pile-chunk3-900000-950000", "dataset:tomekkorbak/pii-pile-chunk3-950000-1000000", "dataset:tomekkorbak/pii-pile-chunk3-1000000-1050000", "dataset:tomekkorbak/pii-pile-chunk3-1050000-1100000", "dataset:tomekkorbak/pii-pile-chunk3-1100000-1150000", "dataset:tomekkorbak/pii-pile-chunk3-1150000-1200000", "dataset:tomekkorbak/pii-pile-chunk3-1200000-1250000", "dataset:tomekkorbak/pii-pile-chunk3-1250000-1300000", "dataset:tomekkorbak/pii-pile-chunk3-1300000-1350000", "dataset:tomekkorbak/pii-pile-chunk3-1350000-1400000", "dataset:tomekkorbak/pii-pile-chunk3-1400000-1450000", "dataset:tomekkorbak/pii-pile-chunk3-1450000-1500000", "dataset:tomekkorbak/pii-pile-chunk3-1500000-1550000", "dataset:tomekkorbak/pii-pile-chunk3-1550000-1600000", "dataset:tomekkorbak/pii-pile-chunk3-1600000-1650000", "dataset:tomekkorbak/pii-pile-chunk3-1650000-1700000", "dataset:tomekkorbak/pii-pile-chunk3-1700000-1750000", "dataset:tomekkorbak/pii-pile-chunk3-1750000-1800000", "dataset:tomekkorbak/pii-pile-chunk3-1800000-1850000", "dataset:tomekkorbak/pii-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/pii-pile-chunk3-1900000-1950000", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]
null
2022-11-23T21:13:28Z
--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/pii-pile-chunk3-0-50000 - tomekkorbak/pii-pile-chunk3-50000-100000 - tomekkorbak/pii-pile-chunk3-100000-150000 - tomekkorbak/pii-pile-chunk3-150000-200000 - tomekkorbak/pii-pile-chunk3-200000-250000 - tomekkorbak/pii-pile-chunk3-250000-300000 - tomekkorbak/pii-pile-chunk3-300000-350000 - tomekkorbak/pii-pile-chunk3-350000-400000 - tomekkorbak/pii-pile-chunk3-400000-450000 - tomekkorbak/pii-pile-chunk3-450000-500000 - tomekkorbak/pii-pile-chunk3-500000-550000 - tomekkorbak/pii-pile-chunk3-550000-600000 - tomekkorbak/pii-pile-chunk3-600000-650000 - tomekkorbak/pii-pile-chunk3-650000-700000 - tomekkorbak/pii-pile-chunk3-700000-750000 - tomekkorbak/pii-pile-chunk3-750000-800000 - tomekkorbak/pii-pile-chunk3-800000-850000 - tomekkorbak/pii-pile-chunk3-850000-900000 - tomekkorbak/pii-pile-chunk3-900000-950000 - tomekkorbak/pii-pile-chunk3-950000-1000000 - tomekkorbak/pii-pile-chunk3-1000000-1050000 - tomekkorbak/pii-pile-chunk3-1050000-1100000 - tomekkorbak/pii-pile-chunk3-1100000-1150000 - tomekkorbak/pii-pile-chunk3-1150000-1200000 - tomekkorbak/pii-pile-chunk3-1200000-1250000 - tomekkorbak/pii-pile-chunk3-1250000-1300000 - tomekkorbak/pii-pile-chunk3-1300000-1350000 - tomekkorbak/pii-pile-chunk3-1350000-1400000 - tomekkorbak/pii-pile-chunk3-1400000-1450000 - tomekkorbak/pii-pile-chunk3-1450000-1500000 - tomekkorbak/pii-pile-chunk3-1500000-1550000 - tomekkorbak/pii-pile-chunk3-1550000-1600000 - tomekkorbak/pii-pile-chunk3-1600000-1650000 - tomekkorbak/pii-pile-chunk3-1650000-1700000 - tomekkorbak/pii-pile-chunk3-1700000-1750000 - tomekkorbak/pii-pile-chunk3-1750000-1800000 - tomekkorbak/pii-pile-chunk3-1800000-1850000 - tomekkorbak/pii-pile-chunk3-1850000-1900000 - tomekkorbak/pii-pile-chunk3-1900000-1950000 model-index: - name: heuristic_shannon 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. --> # heuristic_shannon This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets. ## 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: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.01, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0.0}, 'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257], [50258]], 'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048, 'prefix': '<|aligned|>'}], 'scorer_config': {}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<|aligned|>'}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'num_additional_tokens': 2, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2', 'special_tokens': ['<|aligned|>', '<|misaligned|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'heuristic_shannon', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output2', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/3esut7nh
tomekkorbak/vigorous_mestorf
tomekkorbak
2022-11-24T11:47:33Z
0
0
null
[ "generated_from_trainer", "en", "dataset:tomekkorbak/detoxify-pile-chunk3-0-50000", "dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000", "dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000", "dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000", "dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000", "dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000", "dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000", "dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000", "dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000", "dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000", "dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000", "dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000", "dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000", "dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000", "dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000", "dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000", "dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000", "dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000", "dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000", "dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000", "dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000", "dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000", "dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000", "dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000", "dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000", "dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000", "dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000", "dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000", "dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000", "dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000", "dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000", "dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000", "dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000", "dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000", "dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000", "dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000", "dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000", "dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000", "license:mit", "region:us" ]
null
2022-11-24T11:47:25Z
--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/detoxify-pile-chunk3-0-50000 - tomekkorbak/detoxify-pile-chunk3-50000-100000 - tomekkorbak/detoxify-pile-chunk3-100000-150000 - tomekkorbak/detoxify-pile-chunk3-150000-200000 - tomekkorbak/detoxify-pile-chunk3-200000-250000 - tomekkorbak/detoxify-pile-chunk3-250000-300000 - tomekkorbak/detoxify-pile-chunk3-300000-350000 - tomekkorbak/detoxify-pile-chunk3-350000-400000 - tomekkorbak/detoxify-pile-chunk3-400000-450000 - tomekkorbak/detoxify-pile-chunk3-450000-500000 - tomekkorbak/detoxify-pile-chunk3-500000-550000 - tomekkorbak/detoxify-pile-chunk3-550000-600000 - tomekkorbak/detoxify-pile-chunk3-600000-650000 - tomekkorbak/detoxify-pile-chunk3-650000-700000 - tomekkorbak/detoxify-pile-chunk3-700000-750000 - tomekkorbak/detoxify-pile-chunk3-750000-800000 - tomekkorbak/detoxify-pile-chunk3-800000-850000 - tomekkorbak/detoxify-pile-chunk3-850000-900000 - tomekkorbak/detoxify-pile-chunk3-900000-950000 - tomekkorbak/detoxify-pile-chunk3-950000-1000000 - tomekkorbak/detoxify-pile-chunk3-1000000-1050000 - tomekkorbak/detoxify-pile-chunk3-1050000-1100000 - tomekkorbak/detoxify-pile-chunk3-1100000-1150000 - tomekkorbak/detoxify-pile-chunk3-1150000-1200000 - tomekkorbak/detoxify-pile-chunk3-1200000-1250000 - tomekkorbak/detoxify-pile-chunk3-1250000-1300000 - tomekkorbak/detoxify-pile-chunk3-1300000-1350000 - tomekkorbak/detoxify-pile-chunk3-1350000-1400000 - tomekkorbak/detoxify-pile-chunk3-1400000-1450000 - tomekkorbak/detoxify-pile-chunk3-1450000-1500000 - tomekkorbak/detoxify-pile-chunk3-1500000-1550000 - tomekkorbak/detoxify-pile-chunk3-1550000-1600000 - tomekkorbak/detoxify-pile-chunk3-1600000-1650000 - tomekkorbak/detoxify-pile-chunk3-1650000-1700000 - tomekkorbak/detoxify-pile-chunk3-1700000-1750000 - tomekkorbak/detoxify-pile-chunk3-1750000-1800000 - tomekkorbak/detoxify-pile-chunk3-1800000-1850000 - tomekkorbak/detoxify-pile-chunk3-1850000-1900000 - tomekkorbak/detoxify-pile-chunk3-1900000-1950000 model-index: - name: vigorous_mestorf 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. --> # vigorous_mestorf This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. ## 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: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 3147 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'value_head_config': {'is_detached': False}}, 'path_or_name': 'gpt2'}, 'objective': {'alpha': 1, 'beta': 10, 'name': 'AWR'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 1024, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'vigorous_mestorf', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/1f5w35va
tomekkorbak/kind_poincare
tomekkorbak
2022-11-24T11:47:11Z
0
0
null
[ "generated_from_trainer", "en", "dataset:tomekkorbak/detoxify-pile-chunk3-0-50000", "dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000", "dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000", "dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000", "dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000", "dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000", "dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000", "dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000", "dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000", "dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000", "dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000", "dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000", "dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000", "dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000", "dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000", "dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000", "dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000", "dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000", "dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000", "dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000", "dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000", "dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000", "dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000", "dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000", "dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000", "dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000", "dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000", "dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000", "dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000", "dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000", "dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000", "dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000", "dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000", "dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000", "dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000", "dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000", "dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000", "dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000", "license:mit", "region:us" ]
null
2022-11-24T11:47:00Z
--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/detoxify-pile-chunk3-0-50000 - tomekkorbak/detoxify-pile-chunk3-50000-100000 - tomekkorbak/detoxify-pile-chunk3-100000-150000 - tomekkorbak/detoxify-pile-chunk3-150000-200000 - tomekkorbak/detoxify-pile-chunk3-200000-250000 - tomekkorbak/detoxify-pile-chunk3-250000-300000 - tomekkorbak/detoxify-pile-chunk3-300000-350000 - tomekkorbak/detoxify-pile-chunk3-350000-400000 - tomekkorbak/detoxify-pile-chunk3-400000-450000 - tomekkorbak/detoxify-pile-chunk3-450000-500000 - tomekkorbak/detoxify-pile-chunk3-500000-550000 - tomekkorbak/detoxify-pile-chunk3-550000-600000 - tomekkorbak/detoxify-pile-chunk3-600000-650000 - tomekkorbak/detoxify-pile-chunk3-650000-700000 - tomekkorbak/detoxify-pile-chunk3-700000-750000 - tomekkorbak/detoxify-pile-chunk3-750000-800000 - tomekkorbak/detoxify-pile-chunk3-800000-850000 - tomekkorbak/detoxify-pile-chunk3-850000-900000 - tomekkorbak/detoxify-pile-chunk3-900000-950000 - tomekkorbak/detoxify-pile-chunk3-950000-1000000 - tomekkorbak/detoxify-pile-chunk3-1000000-1050000 - tomekkorbak/detoxify-pile-chunk3-1050000-1100000 - tomekkorbak/detoxify-pile-chunk3-1100000-1150000 - tomekkorbak/detoxify-pile-chunk3-1150000-1200000 - tomekkorbak/detoxify-pile-chunk3-1200000-1250000 - tomekkorbak/detoxify-pile-chunk3-1250000-1300000 - tomekkorbak/detoxify-pile-chunk3-1300000-1350000 - tomekkorbak/detoxify-pile-chunk3-1350000-1400000 - tomekkorbak/detoxify-pile-chunk3-1400000-1450000 - tomekkorbak/detoxify-pile-chunk3-1450000-1500000 - tomekkorbak/detoxify-pile-chunk3-1500000-1550000 - tomekkorbak/detoxify-pile-chunk3-1550000-1600000 - tomekkorbak/detoxify-pile-chunk3-1600000-1650000 - tomekkorbak/detoxify-pile-chunk3-1650000-1700000 - tomekkorbak/detoxify-pile-chunk3-1700000-1750000 - tomekkorbak/detoxify-pile-chunk3-1750000-1800000 - tomekkorbak/detoxify-pile-chunk3-1800000-1850000 - tomekkorbak/detoxify-pile-chunk3-1850000-1900000 - tomekkorbak/detoxify-pile-chunk3-1900000-1950000 model-index: - name: kind_poincare 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. --> # kind_poincare This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 3147 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'value_head_config': {'is_detached': False}}, 'path_or_name': 'gpt2'}, 'objective': {'alpha': 0.5, 'beta': 10, 'name': 'AWR'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 1024, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'kind_poincare', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.001, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/2womx42e
shreyasharma/shreya_sentence_truth_predictor2
shreyasharma
2022-11-24T11:41:32Z
105
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-11-24T11:20:33Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: shreya_sentence_truth_predictor2 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. --> # shreya_sentence_truth_predictor2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4682 - Accuracy: 0.8895 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5813 | 1.0 | 875 | 0.3567 | 0.853 | | 0.3069 | 2.0 | 1750 | 0.4188 | 0.8725 | | 0.1812 | 3.0 | 2625 | 0.4682 | 0.8895 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Tokenizers 0.13.2
emcheng/distilbert-base-uncased-finetuned-cola
emcheng
2022-11-24T11:24:43Z
106
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-11-24T10:28:15Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: cola split: train args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5365264430934975 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 1.1491 - Matthews Correlation: 0.5365 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | No log | 1.0 | 268 | 0.7051 | 0.5240 | | 0.1591 | 2.0 | 536 | 0.7489 | 0.5047 | | 0.1591 | 3.0 | 804 | 0.7951 | 0.5308 | | 0.0931 | 4.0 | 1072 | 0.9231 | 0.4909 | | 0.0931 | 5.0 | 1340 | 0.9489 | 0.5185 | | 0.0693 | 6.0 | 1608 | 1.0154 | 0.5269 | | 0.0693 | 7.0 | 1876 | 1.1369 | 0.5111 | | 0.0493 | 8.0 | 2144 | 1.1491 | 0.5365 | | 0.0493 | 9.0 | 2412 | 1.2824 | 0.5358 | | 0.0288 | 10.0 | 2680 | 1.3411 | 0.5189 | | 0.0288 | 11.0 | 2948 | 1.3368 | 0.5250 | | 0.0248 | 12.0 | 3216 | 1.3929 | 0.5354 | | 0.0248 | 13.0 | 3484 | 1.5103 | 0.5082 | | 0.0145 | 14.0 | 3752 | 1.4857 | 0.5305 | | 0.0108 | 15.0 | 4020 | 1.4900 | 0.5331 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
tomekkorbak/distracted_clarke
tomekkorbak
2022-11-24T11:21:59Z
0
0
null
[ "generated_from_trainer", "en", "dataset:tomekkorbak/detoxify-pile-chunk3-0-50000", "dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000", "dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000", "dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000", "dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000", "dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000", "dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000", "dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000", "dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000", "dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000", "dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000", "dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000", "dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000", "dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000", "dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000", "dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000", "dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000", "dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000", "dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000", "dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000", "dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000", "dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000", "dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000", "dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000", "dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000", "dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000", "dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000", "dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000", "dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000", "dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000", "dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000", "dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000", "dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000", "dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000", "dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000", "dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000", "dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000", "dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000", "license:mit", "region:us" ]
null
2022-11-24T11:21:44Z
--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/detoxify-pile-chunk3-0-50000 - tomekkorbak/detoxify-pile-chunk3-50000-100000 - tomekkorbak/detoxify-pile-chunk3-100000-150000 - tomekkorbak/detoxify-pile-chunk3-150000-200000 - tomekkorbak/detoxify-pile-chunk3-200000-250000 - tomekkorbak/detoxify-pile-chunk3-250000-300000 - tomekkorbak/detoxify-pile-chunk3-300000-350000 - tomekkorbak/detoxify-pile-chunk3-350000-400000 - tomekkorbak/detoxify-pile-chunk3-400000-450000 - tomekkorbak/detoxify-pile-chunk3-450000-500000 - tomekkorbak/detoxify-pile-chunk3-500000-550000 - tomekkorbak/detoxify-pile-chunk3-550000-600000 - tomekkorbak/detoxify-pile-chunk3-600000-650000 - tomekkorbak/detoxify-pile-chunk3-650000-700000 - tomekkorbak/detoxify-pile-chunk3-700000-750000 - tomekkorbak/detoxify-pile-chunk3-750000-800000 - tomekkorbak/detoxify-pile-chunk3-800000-850000 - tomekkorbak/detoxify-pile-chunk3-850000-900000 - tomekkorbak/detoxify-pile-chunk3-900000-950000 - tomekkorbak/detoxify-pile-chunk3-950000-1000000 - tomekkorbak/detoxify-pile-chunk3-1000000-1050000 - tomekkorbak/detoxify-pile-chunk3-1050000-1100000 - tomekkorbak/detoxify-pile-chunk3-1100000-1150000 - tomekkorbak/detoxify-pile-chunk3-1150000-1200000 - tomekkorbak/detoxify-pile-chunk3-1200000-1250000 - tomekkorbak/detoxify-pile-chunk3-1250000-1300000 - tomekkorbak/detoxify-pile-chunk3-1300000-1350000 - tomekkorbak/detoxify-pile-chunk3-1350000-1400000 - tomekkorbak/detoxify-pile-chunk3-1400000-1450000 - tomekkorbak/detoxify-pile-chunk3-1450000-1500000 - tomekkorbak/detoxify-pile-chunk3-1500000-1550000 - tomekkorbak/detoxify-pile-chunk3-1550000-1600000 - tomekkorbak/detoxify-pile-chunk3-1600000-1650000 - tomekkorbak/detoxify-pile-chunk3-1650000-1700000 - tomekkorbak/detoxify-pile-chunk3-1700000-1750000 - tomekkorbak/detoxify-pile-chunk3-1750000-1800000 - tomekkorbak/detoxify-pile-chunk3-1800000-1850000 - tomekkorbak/detoxify-pile-chunk3-1850000-1900000 - tomekkorbak/detoxify-pile-chunk3-1900000-1950000 model-index: - name: distracted_clarke 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. --> # distracted_clarke This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. ## 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: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'filter_threshold': 0.00078, 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'distracted_clarke', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/15au06ww
tomekkorbak/gallant_beaver
tomekkorbak
2022-11-24T11:21:16Z
0
0
null
[ "generated_from_trainer", "en", "dataset:tomekkorbak/detoxify-pile-chunk3-0-50000", "dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000", "dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000", "dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000", "dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000", "dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000", "dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000", "dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000", "dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000", "dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000", "dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000", "dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000", "dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000", "dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000", "dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000", "dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000", "dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000", "dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000", "dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000", "dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000", "dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000", "dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000", "dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000", "dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000", "dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000", "dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000", "dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000", "dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000", "dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000", "dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000", "dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000", "dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000", "dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000", "dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000", "dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000", "dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000", "dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000", "dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000", "license:mit", "region:us" ]
null
2022-11-24T11:21:02Z
--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/detoxify-pile-chunk3-0-50000 - tomekkorbak/detoxify-pile-chunk3-50000-100000 - tomekkorbak/detoxify-pile-chunk3-100000-150000 - tomekkorbak/detoxify-pile-chunk3-150000-200000 - tomekkorbak/detoxify-pile-chunk3-200000-250000 - tomekkorbak/detoxify-pile-chunk3-250000-300000 - tomekkorbak/detoxify-pile-chunk3-300000-350000 - tomekkorbak/detoxify-pile-chunk3-350000-400000 - tomekkorbak/detoxify-pile-chunk3-400000-450000 - tomekkorbak/detoxify-pile-chunk3-450000-500000 - tomekkorbak/detoxify-pile-chunk3-500000-550000 - tomekkorbak/detoxify-pile-chunk3-550000-600000 - tomekkorbak/detoxify-pile-chunk3-600000-650000 - tomekkorbak/detoxify-pile-chunk3-650000-700000 - tomekkorbak/detoxify-pile-chunk3-700000-750000 - tomekkorbak/detoxify-pile-chunk3-750000-800000 - tomekkorbak/detoxify-pile-chunk3-800000-850000 - tomekkorbak/detoxify-pile-chunk3-850000-900000 - tomekkorbak/detoxify-pile-chunk3-900000-950000 - tomekkorbak/detoxify-pile-chunk3-950000-1000000 - tomekkorbak/detoxify-pile-chunk3-1000000-1050000 - tomekkorbak/detoxify-pile-chunk3-1050000-1100000 - tomekkorbak/detoxify-pile-chunk3-1100000-1150000 - tomekkorbak/detoxify-pile-chunk3-1150000-1200000 - tomekkorbak/detoxify-pile-chunk3-1200000-1250000 - tomekkorbak/detoxify-pile-chunk3-1250000-1300000 - tomekkorbak/detoxify-pile-chunk3-1300000-1350000 - tomekkorbak/detoxify-pile-chunk3-1350000-1400000 - tomekkorbak/detoxify-pile-chunk3-1400000-1450000 - tomekkorbak/detoxify-pile-chunk3-1450000-1500000 - tomekkorbak/detoxify-pile-chunk3-1500000-1550000 - tomekkorbak/detoxify-pile-chunk3-1550000-1600000 - tomekkorbak/detoxify-pile-chunk3-1600000-1650000 - tomekkorbak/detoxify-pile-chunk3-1650000-1700000 - tomekkorbak/detoxify-pile-chunk3-1700000-1750000 - tomekkorbak/detoxify-pile-chunk3-1750000-1800000 - tomekkorbak/detoxify-pile-chunk3-1800000-1850000 - tomekkorbak/detoxify-pile-chunk3-1850000-1900000 - tomekkorbak/detoxify-pile-chunk3-1900000-1950000 model-index: - name: gallant_beaver 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. --> # gallant_beaver This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. ## 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: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'gallant_beaver', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/23et4rcm
kakaobrain/vit-l16-coyo-labeled-300m-i1k512
kakaobrain
2022-11-24T11:11:15Z
0
1
null
[ "vision", "dataset:kakaobrain/coyo-labeled-300m", "arxiv:2010.11929", "license:apache-2.0", "region:us" ]
null
2022-11-11T08:17:24Z
--- #license: not yet tags: - vision datasets: - kakaobrain/coyo-labeled-300m annotations_creators: - machine-generated license: - apache-2.0 pretty_name: COYO-Labeled-300M task_categories: - image-classification task_ids: - multi-label-image-classification - multi-class-image-classification inference: false --- # Vision Transformer (large-sized model) Vision Transformer (ViT) model pre-trained on [COYO-Labeled-300M](https://github.com/kakaobrain/coyo-dataset/tree/main/subset/COYO-Labeled-300M) (300 million images, 21,841 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. However, since the JFT-300M is a private dataset, we tried to reproduce it using the publicly available [COYO-Labeled-300M](https://github.com/kakaobrain/coyo-dataset/tree/main/subset/COYO-Labeled-300M) dataset. Thanks to Hugging Face team for converting weights of ViT trained in Tensorflow to be used on Pytorch, JAX/Flax and Tensorflow in Hugging Face. ## Model description The Vision Transformer (ViT) is a transformer model pretrained on a large collection of images in a supervised fashion, namely [COYO-Labeled-300M](https://github.com/kakaobrain/coyo-dataset/tree/main/subset/COYO-Labeled-300M), at a resolution of 224x224 pixels. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer. This ViT model is finetuned with Imagenet-1k at resolution 384x384. Please see details [here](https://github.com/kakaobrain/coyo-vit) ## Intended uses & limitations You can use weights from ViT models for image classification, downstream. Codes for reproduction are also provided. Please see this [github repository](https://github.com/kakaobrain/coyo-vit) for pretraining and finetuning code. ### How to use Here is how to use this model in PyTorch: ```python WIP ``` Here is how to use this model in JAX/Flax: ```python WIP ``` Here is how to use this model in Tensorflow: ```python WIP ``` ## Training data The ViT model was pretrained on [COYO-Labeled-300M](https://github.com/kakaobrain/coyo-dataset/tree/main/subset/COYO-Labeled-300M), a dataset consisting of 300 million images and 21k classes. ## Training procedure ### Preprocessing The exact details of preprocessing of images during training/validation can be found [here](https://github.com/kakaobrain/coyo-vit). Images are inception-cropped to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5). ### Pretraining The model was trained on TPUv3 hardware. All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. Pre-training resolution is 224. More detail, please see [here](https://github.com/kakaobrain/coyo-vit) ### Evaluation results | Model | Upstream Dataset | Resolution | ImageNet (downstream) | ImageNet-ReaL (dwonstream) | Public | |---------- |------------------- |------------ |----------------------- |---------------------------- |-------- | | ViT-L/16 | JFT-300M | 512 | 87.76 | 90.54 | X | | ViT-L/16 | COYO-Labeled-300M | 512 | 87.24 (-0.52) | 90.03 (-0.51) | O | | ViT-L/16 | JFT-300M | 384 | 87.12 | 89.99 | X | | ViT-L/16 | COYO-Labeled-300M | 384 | 86.72 (-0.4) | 89.84 (-0.15) | O | ## Citation ```bibtex @misc{kakaobrain2022coyo-vit, title = {COYO-ViT}, author = {Lee, Sungjun and Park, Beomhee}, year = {2022}, howpublished = {\url{https://github.com/kakaobrain/coyo-vit}}, } ``` ```bibtex @misc{kakaobrain2022coyo-700m, title = {COYO-700M: Image-Text Pair Dataset}, author = {Byeon, Minwoo and Park, Beomhee and Kim, Haecheon and Lee, Sungjun and Baek, Woonhyuk and Kim, Saehoon}, year = {2022}, howpublished = {\url{https://github.com/kakaobrain/coyo-dataset}}, } ``` ```bibtex @misc{dosovitskiy2020image, title = {An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale}, author = {Alexey Dosovitskiy and Lucas Beyer and Alexander Kolesnikov and Dirk Weissenborn and Xiaohua Zhai and Thomas Unterthiner and Mostafa Dehghani and Matthias Minderer and Georg Heigold and Sylvain Gelly and Jakob Uszkoreit and Neil Houlsby}, year = {2020}, eprint = {2010.11929}, archivePrefix = {arXiv}, primaryClass = {cs.CV} } ``` ## License The source codes are licensed under Apache 2.0 License.
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1-parent
research-backup
2022-11-24T11:02:06Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T11:00:55Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6774801587301588 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3342245989304813 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34421364985163205 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.44969427459699834 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.57 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.35964912280701755 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.33564814814814814 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8268796142835618 - name: F1 (macro) type: f1_macro value: 0.8112846127559387 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7345070422535211 - name: F1 (macro) type: f1_macro value: 0.42142799993739466 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5910075839653305 - name: F1 (macro) type: f1_macro value: 0.5637635163661544 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9126382416359463 - name: F1 (macro) type: f1_macro value: 0.7750949198964128 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8241930429332498 - name: F1 (macro) type: f1_macro value: 0.8307427753800568 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3342245989304813 - Accuracy on SAT: 0.34421364985163205 - Accuracy on BATS: 0.44969427459699834 - Accuracy on U2: 0.35964912280701755 - Accuracy on U4: 0.33564814814814814 - Accuracy on Google: 0.57 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8268796142835618 - Micro F1 score on CogALexV: 0.7345070422535211 - Micro F1 score on EVALution: 0.5910075839653305 - Micro F1 score on K&H+N: 0.9126382416359463 - Micro F1 score on ROOT09: 0.8241930429332498 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6774801587301588 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-2-parent
research-backup
2022-11-24T10:58:37Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T10:57:28Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-2-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.8009325396825396 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3663101604278075 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37388724035608306 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4235686492495831 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.59 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37280701754385964 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3472222222222222 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8509868916679223 - name: F1 (macro) type: f1_macro value: 0.8468436713276094 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8075117370892019 - name: F1 (macro) type: f1_macro value: 0.5095785382758342 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6180931744312026 - name: F1 (macro) type: f1_macro value: 0.5611895449612757 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9054044654656743 - name: F1 (macro) type: f1_macro value: 0.8023610049697805 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8520839862112191 - name: F1 (macro) type: f1_macro value: 0.835769054115474 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-2-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-2-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3663101604278075 - Accuracy on SAT: 0.37388724035608306 - Accuracy on BATS: 0.4235686492495831 - Accuracy on U2: 0.37280701754385964 - Accuracy on U4: 0.3472222222222222 - Accuracy on Google: 0.59 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-2-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8509868916679223 - Micro F1 score on CogALexV: 0.8075117370892019 - Micro F1 score on EVALution: 0.6180931744312026 - Micro F1 score on K&H+N: 0.9054044654656743 - Micro F1 score on ROOT09: 0.8520839862112191 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-2-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.8009325396825396 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-2-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-2-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-1-parent
research-backup
2022-11-24T10:56:58Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T10:55:42Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-1-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7481349206349206 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.35561497326203206 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3560830860534125 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4252362423568649 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.56 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3201754385964912 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3587962962962963 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8576163929486214 - name: F1 (macro) type: f1_macro value: 0.845196495087602 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8276995305164319 - name: F1 (macro) type: f1_macro value: 0.6188213371659751 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6543878656554712 - name: F1 (macro) type: f1_macro value: 0.6251129352003814 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9505460109897753 - name: F1 (macro) type: f1_macro value: 0.8548242666983181 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8787214039486054 - name: F1 (macro) type: f1_macro value: 0.8797170261650762 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-1-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-1-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.35561497326203206 - Accuracy on SAT: 0.3560830860534125 - Accuracy on BATS: 0.4252362423568649 - Accuracy on U2: 0.3201754385964912 - Accuracy on U4: 0.3587962962962963 - Accuracy on Google: 0.56 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-1-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8576163929486214 - Micro F1 score on CogALexV: 0.8276995305164319 - Micro F1 score on EVALution: 0.6543878656554712 - Micro F1 score on K&H+N: 0.9505460109897753 - Micro F1 score on ROOT09: 0.8787214039486054 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-1-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7481349206349206 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-1-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 8 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-1-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-0-parent
research-backup
2022-11-24T10:55:04Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T10:54:02Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-0-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7883333333333333 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3663101604278075 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37091988130563797 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.453029460811562 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.53 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3508771929824561 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.36574074074074076 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8607804731053187 - name: F1 (macro) type: f1_macro value: 0.8537736480023922 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7061032863849765 - name: F1 (macro) type: f1_macro value: 0.4721702472631833 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6045503791982665 - name: F1 (macro) type: f1_macro value: 0.5945583015512562 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8553940321346595 - name: F1 (macro) type: f1_macro value: 0.6811419047740933 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8693199623942337 - name: F1 (macro) type: f1_macro value: 0.8726748244449022 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-0-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-0-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3663101604278075 - Accuracy on SAT: 0.37091988130563797 - Accuracy on BATS: 0.453029460811562 - Accuracy on U2: 0.3508771929824561 - Accuracy on U4: 0.36574074074074076 - Accuracy on Google: 0.53 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-0-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8607804731053187 - Micro F1 score on CogALexV: 0.7061032863849765 - Micro F1 score on EVALution: 0.6045503791982665 - Micro F1 score on K&H+N: 0.8553940321346595 - Micro F1 score on ROOT09: 0.8693199623942337 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-0-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7883333333333333 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-0-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-0-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
tomekkorbak/vigorous_saha
tomekkorbak
2022-11-24T10:53:55Z
103
0
transformers
[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:tomekkorbak/pii-pile-chunk3-0-50000", "dataset:tomekkorbak/pii-pile-chunk3-50000-100000", "dataset:tomekkorbak/pii-pile-chunk3-100000-150000", "dataset:tomekkorbak/pii-pile-chunk3-150000-200000", "dataset:tomekkorbak/pii-pile-chunk3-200000-250000", "dataset:tomekkorbak/pii-pile-chunk3-250000-300000", "dataset:tomekkorbak/pii-pile-chunk3-300000-350000", "dataset:tomekkorbak/pii-pile-chunk3-350000-400000", "dataset:tomekkorbak/pii-pile-chunk3-400000-450000", "dataset:tomekkorbak/pii-pile-chunk3-450000-500000", "dataset:tomekkorbak/pii-pile-chunk3-500000-550000", "dataset:tomekkorbak/pii-pile-chunk3-550000-600000", "dataset:tomekkorbak/pii-pile-chunk3-600000-650000", "dataset:tomekkorbak/pii-pile-chunk3-650000-700000", "dataset:tomekkorbak/pii-pile-chunk3-700000-750000", "dataset:tomekkorbak/pii-pile-chunk3-750000-800000", "dataset:tomekkorbak/pii-pile-chunk3-800000-850000", "dataset:tomekkorbak/pii-pile-chunk3-850000-900000", "dataset:tomekkorbak/pii-pile-chunk3-900000-950000", "dataset:tomekkorbak/pii-pile-chunk3-950000-1000000", "dataset:tomekkorbak/pii-pile-chunk3-1000000-1050000", "dataset:tomekkorbak/pii-pile-chunk3-1050000-1100000", "dataset:tomekkorbak/pii-pile-chunk3-1100000-1150000", "dataset:tomekkorbak/pii-pile-chunk3-1150000-1200000", "dataset:tomekkorbak/pii-pile-chunk3-1200000-1250000", "dataset:tomekkorbak/pii-pile-chunk3-1250000-1300000", "dataset:tomekkorbak/pii-pile-chunk3-1300000-1350000", "dataset:tomekkorbak/pii-pile-chunk3-1350000-1400000", "dataset:tomekkorbak/pii-pile-chunk3-1400000-1450000", "dataset:tomekkorbak/pii-pile-chunk3-1450000-1500000", "dataset:tomekkorbak/pii-pile-chunk3-1500000-1550000", "dataset:tomekkorbak/pii-pile-chunk3-1550000-1600000", "dataset:tomekkorbak/pii-pile-chunk3-1600000-1650000", "dataset:tomekkorbak/pii-pile-chunk3-1650000-1700000", "dataset:tomekkorbak/pii-pile-chunk3-1700000-1750000", "dataset:tomekkorbak/pii-pile-chunk3-1750000-1800000", "dataset:tomekkorbak/pii-pile-chunk3-1800000-1850000", "dataset:tomekkorbak/pii-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/pii-pile-chunk3-1900000-1950000", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]
null
2022-11-23T20:43:54Z
--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/pii-pile-chunk3-0-50000 - tomekkorbak/pii-pile-chunk3-50000-100000 - tomekkorbak/pii-pile-chunk3-100000-150000 - tomekkorbak/pii-pile-chunk3-150000-200000 - tomekkorbak/pii-pile-chunk3-200000-250000 - tomekkorbak/pii-pile-chunk3-250000-300000 - tomekkorbak/pii-pile-chunk3-300000-350000 - tomekkorbak/pii-pile-chunk3-350000-400000 - tomekkorbak/pii-pile-chunk3-400000-450000 - tomekkorbak/pii-pile-chunk3-450000-500000 - tomekkorbak/pii-pile-chunk3-500000-550000 - tomekkorbak/pii-pile-chunk3-550000-600000 - tomekkorbak/pii-pile-chunk3-600000-650000 - tomekkorbak/pii-pile-chunk3-650000-700000 - tomekkorbak/pii-pile-chunk3-700000-750000 - tomekkorbak/pii-pile-chunk3-750000-800000 - tomekkorbak/pii-pile-chunk3-800000-850000 - tomekkorbak/pii-pile-chunk3-850000-900000 - tomekkorbak/pii-pile-chunk3-900000-950000 - tomekkorbak/pii-pile-chunk3-950000-1000000 - tomekkorbak/pii-pile-chunk3-1000000-1050000 - tomekkorbak/pii-pile-chunk3-1050000-1100000 - tomekkorbak/pii-pile-chunk3-1100000-1150000 - tomekkorbak/pii-pile-chunk3-1150000-1200000 - tomekkorbak/pii-pile-chunk3-1200000-1250000 - tomekkorbak/pii-pile-chunk3-1250000-1300000 - tomekkorbak/pii-pile-chunk3-1300000-1350000 - tomekkorbak/pii-pile-chunk3-1350000-1400000 - tomekkorbak/pii-pile-chunk3-1400000-1450000 - tomekkorbak/pii-pile-chunk3-1450000-1500000 - tomekkorbak/pii-pile-chunk3-1500000-1550000 - tomekkorbak/pii-pile-chunk3-1550000-1600000 - tomekkorbak/pii-pile-chunk3-1600000-1650000 - tomekkorbak/pii-pile-chunk3-1650000-1700000 - tomekkorbak/pii-pile-chunk3-1700000-1750000 - tomekkorbak/pii-pile-chunk3-1750000-1800000 - tomekkorbak/pii-pile-chunk3-1800000-1850000 - tomekkorbak/pii-pile-chunk3-1850000-1900000 - tomekkorbak/pii-pile-chunk3-1900000-1950000 model-index: - name: vigorous_saha 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. --> # vigorous_saha This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets. ## 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: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}], 'scorer_config': {}}, 'kl_gpt3_callback': {'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'alpha': 1, 'name': 'Unlikelihood', 'score_threshold': 0.0}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'vigorous_saha', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output2', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/1c8cpo9k
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1-parent
research-backup
2022-11-24T10:51:52Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T10:50:46Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7449603174603174 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3502673796791444 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3560830860534125 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3468593663146192 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.432 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37719298245614036 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.38425925925925924 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8625885189091457 - name: F1 (macro) type: f1_macro value: 0.8524844738383915 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.815962441314554 - name: F1 (macro) type: f1_macro value: 0.5997288134970267 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.638136511375948 - name: F1 (macro) type: f1_macro value: 0.6026094290538676 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9396257911942686 - name: F1 (macro) type: f1_macro value: 0.8372417170746724 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8658727671576308 - name: F1 (macro) type: f1_macro value: 0.8546035043357735 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3502673796791444 - Accuracy on SAT: 0.3560830860534125 - Accuracy on BATS: 0.3468593663146192 - Accuracy on U2: 0.37719298245614036 - Accuracy on U4: 0.38425925925925924 - Accuracy on Google: 0.432 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8625885189091457 - Micro F1 score on CogALexV: 0.815962441314554 - Micro F1 score on EVALution: 0.638136511375948 - Micro F1 score on K&H+N: 0.9396257911942686 - Micro F1 score on ROOT09: 0.8658727671576308 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7449603174603174 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-0-parent
research-backup
2022-11-24T10:50:13Z
104
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T10:49:10Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-0-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7524007936507936 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3074866310160428 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3086053412462908 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3596442468037799 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.42 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34210526315789475 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3449074074074074 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8558083471447944 - name: F1 (macro) type: f1_macro value: 0.8411936200629997 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7896713615023474 - name: F1 (macro) type: f1_macro value: 0.5579408818659266 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6121343445287107 - name: F1 (macro) type: f1_macro value: 0.5771775585052653 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9407386798358489 - name: F1 (macro) type: f1_macro value: 0.8356897329372952 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8445628329677217 - name: F1 (macro) type: f1_macro value: 0.8285132890039661 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-0-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-0-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3074866310160428 - Accuracy on SAT: 0.3086053412462908 - Accuracy on BATS: 0.3596442468037799 - Accuracy on U2: 0.34210526315789475 - Accuracy on U4: 0.3449074074074074 - Accuracy on Google: 0.42 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-0-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8558083471447944 - Micro F1 score on CogALexV: 0.7896713615023474 - Micro F1 score on EVALution: 0.6121343445287107 - Micro F1 score on K&H+N: 0.9407386798358489 - Micro F1 score on ROOT09: 0.8445628329677217 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-0-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7524007936507936 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-0-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 5 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-0-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-2-parent
research-backup
2022-11-24T10:48:37Z
104
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T10:47:21Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-2-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7209126984126984 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3770053475935829 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3798219584569733 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.47971095052807117 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.59 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.40789473684210525 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4097222222222222 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8556576766611421 - name: F1 (macro) type: f1_macro value: 0.8493669074367355 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7894366197183098 - name: F1 (macro) type: f1_macro value: 0.5861443079105217 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6224268689057422 - name: F1 (macro) type: f1_macro value: 0.5864971893719026 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9202893510468109 - name: F1 (macro) type: f1_macro value: 0.683390204727169 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8492635537449076 - name: F1 (macro) type: f1_macro value: 0.8314731763097831 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-2-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-2-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3770053475935829 - Accuracy on SAT: 0.3798219584569733 - Accuracy on BATS: 0.47971095052807117 - Accuracy on U2: 0.40789473684210525 - Accuracy on U4: 0.4097222222222222 - Accuracy on Google: 0.59 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-2-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8556576766611421 - Micro F1 score on CogALexV: 0.7894366197183098 - Micro F1 score on EVALution: 0.6224268689057422 - Micro F1 score on K&H+N: 0.9202893510468109 - Micro F1 score on ROOT09: 0.8492635537449076 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-2-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7209126984126984 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-2-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-2-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-1-parent
research-backup
2022-11-24T10:46:44Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T10:45:26Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-1-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.775079365079365 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3716577540106952 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3768545994065282 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34185658699277377 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.428 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37719298245614036 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3541666666666667 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8904625583848124 - name: F1 (macro) type: f1_macro value: 0.8806810263932183 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7814553990610328 - name: F1 (macro) type: f1_macro value: 0.5516320672010655 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6419284940411701 - name: F1 (macro) type: f1_macro value: 0.6147381969131176 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9577102316199485 - name: F1 (macro) type: f1_macro value: 0.8737423662751781 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8022563459730492 - name: F1 (macro) type: f1_macro value: 0.8150247698794525 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-1-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-1-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3716577540106952 - Accuracy on SAT: 0.3768545994065282 - Accuracy on BATS: 0.34185658699277377 - Accuracy on U2: 0.37719298245614036 - Accuracy on U4: 0.3541666666666667 - Accuracy on Google: 0.428 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-1-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8904625583848124 - Micro F1 score on CogALexV: 0.7814553990610328 - Micro F1 score on EVALution: 0.6419284940411701 - Micro F1 score on K&H+N: 0.9577102316199485 - Micro F1 score on ROOT09: 0.8022563459730492 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-1-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.775079365079365 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-1-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-1-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-0-parent
research-backup
2022-11-24T10:44:53Z
104
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T10:43:38Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-0-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.77375 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3422459893048128 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34421364985163205 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.45969983324068925 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.476 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.33771929824561403 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34953703703703703 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8005122796444176 - name: F1 (macro) type: f1_macro value: 0.8051917152936351 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7814553990610328 - name: F1 (macro) type: f1_macro value: 0.4027273130945629 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6359696641386782 - name: F1 (macro) type: f1_macro value: 0.5959479630304668 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8784864714474507 - name: F1 (macro) type: f1_macro value: 0.7286535314640855 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8392353494202445 - name: F1 (macro) type: f1_macro value: 0.8392144075573044 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-0-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-0-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3422459893048128 - Accuracy on SAT: 0.34421364985163205 - Accuracy on BATS: 0.45969983324068925 - Accuracy on U2: 0.33771929824561403 - Accuracy on U4: 0.34953703703703703 - Accuracy on Google: 0.476 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-0-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8005122796444176 - Micro F1 score on CogALexV: 0.7814553990610328 - Micro F1 score on EVALution: 0.6359696641386782 - Micro F1 score on K&H+N: 0.8784864714474507 - Micro F1 score on ROOT09: 0.8392353494202445 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-0-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.77375 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-0-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-0-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
huggingtweets/pacsjam
huggingtweets
2022-11-24T10:40:58Z
113
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-11-24T08:54:05Z
--- language: en thumbnail: http://www.huggingtweets.com/pacsjam/1669286455132/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1588101458511024130/9DsQWW2U_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">adam handler</div> <div style="text-align: center; font-size: 14px;">@pacsjam</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from adam handler. | Data | adam handler | | --- | --- | | Tweets downloaded | 2952 | | Retweets | 1365 | | Short tweets | 243 | | Tweets kept | 1344 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1uig3k87/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @pacsjam's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/357m0p7r) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/357m0p7r/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/pacsjam') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-0-child-prototypical
research-backup
2022-11-24T10:39:33Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T10:38:27Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-0-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7703968253968254 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3155080213903743 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.314540059347181 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.45247359644246804 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.416 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.32894736842105265 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.30787037037037035 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8041283712520717 - name: F1 (macro) type: f1_macro value: 0.7717757143315095 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7333333333333333 - name: F1 (macro) type: f1_macro value: 0.2528574698096535 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.537919826652221 - name: F1 (macro) type: f1_macro value: 0.5058135462313251 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8921889128469083 - name: F1 (macro) type: f1_macro value: 0.7358926221606088 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7615167659041053 - name: F1 (macro) type: f1_macro value: 0.7739975117656787 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-0-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-0-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3155080213903743 - Accuracy on SAT: 0.314540059347181 - Accuracy on BATS: 0.45247359644246804 - Accuracy on U2: 0.32894736842105265 - Accuracy on U4: 0.30787037037037035 - Accuracy on Google: 0.416 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-0-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8041283712520717 - Micro F1 score on CogALexV: 0.7333333333333333 - Micro F1 score on EVALution: 0.537919826652221 - Micro F1 score on K&H+N: 0.8921889128469083 - Micro F1 score on ROOT09: 0.7615167659041053 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-0-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7703968253968254 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-0-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-0-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-2-child-prototypical
research-backup
2022-11-24T10:37:43Z
27
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T10:36:20Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-2-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7167460317460317 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34759358288770054 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3590504451038576 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.47915508615897723 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.616 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3815789473684211 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3611111111111111 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8053337351212897 - name: F1 (macro) type: f1_macro value: 0.7968718222888711 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7363849765258216 - name: F1 (macro) type: f1_macro value: 0.2937316411241414 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5687973997833152 - name: F1 (macro) type: f1_macro value: 0.5111830146247934 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8538638102524866 - name: F1 (macro) type: f1_macro value: 0.6965929089364082 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7994359135067377 - name: F1 (macro) type: f1_macro value: 0.7807522370878998 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-2-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-2-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.34759358288770054 - Accuracy on SAT: 0.3590504451038576 - Accuracy on BATS: 0.47915508615897723 - Accuracy on U2: 0.3815789473684211 - Accuracy on U4: 0.3611111111111111 - Accuracy on Google: 0.616 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-2-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8053337351212897 - Micro F1 score on CogALexV: 0.7363849765258216 - Micro F1 score on EVALution: 0.5687973997833152 - Micro F1 score on K&H+N: 0.8538638102524866 - Micro F1 score on ROOT09: 0.7994359135067377 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-2-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7167460317460317 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-2-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-2-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1-child-prototypical
research-backup
2022-11-24T10:35:49Z
107
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T10:34:40Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6774801587301588 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3342245989304813 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34421364985163205 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.44969427459699834 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.57 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.35964912280701755 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.33564814814814814 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8268796142835618 - name: F1 (macro) type: f1_macro value: 0.8112846127559387 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7345070422535211 - name: F1 (macro) type: f1_macro value: 0.42142799993739466 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5910075839653305 - name: F1 (macro) type: f1_macro value: 0.5637635163661544 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9126382416359463 - name: F1 (macro) type: f1_macro value: 0.7750949198964128 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8241930429332498 - name: F1 (macro) type: f1_macro value: 0.8307427753800568 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3342245989304813 - Accuracy on SAT: 0.34421364985163205 - Accuracy on BATS: 0.44969427459699834 - Accuracy on U2: 0.35964912280701755 - Accuracy on U4: 0.33564814814814814 - Accuracy on Google: 0.57 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8268796142835618 - Micro F1 score on CogALexV: 0.7345070422535211 - Micro F1 score on EVALution: 0.5910075839653305 - Micro F1 score on K&H+N: 0.9126382416359463 - Micro F1 score on ROOT09: 0.8241930429332498 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6774801587301588 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-0-child-prototypical
research-backup
2022-11-24T10:33:57Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T10:32:49Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-0-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7536111111111111 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4090909090909091 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.41543026706231456 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3979988882712618 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.536 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.40350877192982454 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3726851851851852 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8408919692632213 - name: F1 (macro) type: f1_macro value: 0.8307198073196754 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.646244131455399 - name: F1 (macro) type: f1_macro value: 0.3220456955206279 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6110509209100758 - name: F1 (macro) type: f1_macro value: 0.5969647367022108 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8687486958336231 - name: F1 (macro) type: f1_macro value: 0.6342486357768978 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7696646819178942 - name: F1 (macro) type: f1_macro value: 0.6831474153591115 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-0-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-0-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.4090909090909091 - Accuracy on SAT: 0.41543026706231456 - Accuracy on BATS: 0.3979988882712618 - Accuracy on U2: 0.40350877192982454 - Accuracy on U4: 0.3726851851851852 - Accuracy on Google: 0.536 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-0-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8408919692632213 - Micro F1 score on CogALexV: 0.646244131455399 - Micro F1 score on EVALution: 0.6110509209100758 - Micro F1 score on K&H+N: 0.8687486958336231 - Micro F1 score on ROOT09: 0.7696646819178942 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-0-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7536111111111111 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-0-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-0-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-2-child-prototypical
research-backup
2022-11-24T10:32:11Z
101
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T10:31:01Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-2-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.8009325396825396 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3663101604278075 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37388724035608306 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4235686492495831 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.59 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37280701754385964 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3472222222222222 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8509868916679223 - name: F1 (macro) type: f1_macro value: 0.8468436713276094 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8075117370892019 - name: F1 (macro) type: f1_macro value: 0.5095785382758342 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6180931744312026 - name: F1 (macro) type: f1_macro value: 0.5611895449612757 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9054044654656743 - name: F1 (macro) type: f1_macro value: 0.8023610049697805 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8520839862112191 - name: F1 (macro) type: f1_macro value: 0.835769054115474 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-2-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-2-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3663101604278075 - Accuracy on SAT: 0.37388724035608306 - Accuracy on BATS: 0.4235686492495831 - Accuracy on U2: 0.37280701754385964 - Accuracy on U4: 0.3472222222222222 - Accuracy on Google: 0.59 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-2-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8509868916679223 - Micro F1 score on CogALexV: 0.8075117370892019 - Micro F1 score on EVALution: 0.6180931744312026 - Micro F1 score on K&H+N: 0.9054044654656743 - Micro F1 score on ROOT09: 0.8520839862112191 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-2-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.8009325396825396 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-2-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-2-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-1-child-prototypical
research-backup
2022-11-24T10:30:19Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T10:29:13Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-1-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7624206349206349 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3770053475935829 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3768545994065282 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.44580322401334077 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.57 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39473684210526316 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37962962962962965 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8350158204007835 - name: F1 (macro) type: f1_macro value: 0.8394882952079764 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7992957746478874 - name: F1 (macro) type: f1_macro value: 0.5104712427778083 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6576381365113759 - name: F1 (macro) type: f1_macro value: 0.6270821448547105 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9373304583710093 - name: F1 (macro) type: f1_macro value: 0.8408397816749231 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8683798182387966 - name: F1 (macro) type: f1_macro value: 0.8696985570102708 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-1-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-1-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3770053475935829 - Accuracy on SAT: 0.3768545994065282 - Accuracy on BATS: 0.44580322401334077 - Accuracy on U2: 0.39473684210526316 - Accuracy on U4: 0.37962962962962965 - Accuracy on Google: 0.57 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-1-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8350158204007835 - Micro F1 score on CogALexV: 0.7992957746478874 - Micro F1 score on EVALution: 0.6576381365113759 - Micro F1 score on K&H+N: 0.9373304583710093 - Micro F1 score on ROOT09: 0.8683798182387966 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-1-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7624206349206349 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-1-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-1-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-2-child-prototypical
research-backup
2022-11-24T10:26:54Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T10:25:45Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-2-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7143253968253969 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.30213903743315507 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.29673590504451036 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.41078376876042244 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.444 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3508771929824561 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.35185185185185186 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8350158204007835 - name: F1 (macro) type: f1_macro value: 0.8258828243546699 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8082159624413146 - name: F1 (macro) type: f1_macro value: 0.5334332730866751 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6397616468039004 - name: F1 (macro) type: f1_macro value: 0.6018197960350038 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9267580162759964 - name: F1 (macro) type: f1_macro value: 0.8245309871144009 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.857724851143842 - name: F1 (macro) type: f1_macro value: 0.8592867514570143 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-2-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-2-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.30213903743315507 - Accuracy on SAT: 0.29673590504451036 - Accuracy on BATS: 0.41078376876042244 - Accuracy on U2: 0.3508771929824561 - Accuracy on U4: 0.35185185185185186 - Accuracy on Google: 0.444 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-2-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8350158204007835 - Micro F1 score on CogALexV: 0.8082159624413146 - Micro F1 score on EVALution: 0.6397616468039004 - Micro F1 score on K&H+N: 0.9267580162759964 - Micro F1 score on ROOT09: 0.857724851143842 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-2-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7143253968253969 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-2-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-2-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1-child-prototypical
research-backup
2022-11-24T10:25:07Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T10:23:58Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.768531746031746 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.339572192513369 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34718100890207715 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3407448582545859 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.418 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3684210526315789 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3773148148148148 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8704233840590628 - name: F1 (macro) type: f1_macro value: 0.8667547469109499 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8173708920187793 - name: F1 (macro) type: f1_macro value: 0.5852009899544668 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6229685807150596 - name: F1 (macro) type: f1_macro value: 0.5882332613967654 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9571537872991583 - name: F1 (macro) type: f1_macro value: 0.8502956210778551 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8630523346913194 - name: F1 (macro) type: f1_macro value: 0.848027234918315 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.339572192513369 - Accuracy on SAT: 0.34718100890207715 - Accuracy on BATS: 0.3407448582545859 - Accuracy on U2: 0.3684210526315789 - Accuracy on U4: 0.3773148148148148 - Accuracy on Google: 0.418 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8704233840590628 - Micro F1 score on CogALexV: 0.8173708920187793 - Micro F1 score on EVALution: 0.6229685807150596 - Micro F1 score on K&H+N: 0.9571537872991583 - Micro F1 score on ROOT09: 0.8630523346913194 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.768531746031746 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 8 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-0-child-prototypical
research-backup
2022-11-24T10:23:18Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T10:22:12Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-0-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7524007936507936 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3074866310160428 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3086053412462908 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3596442468037799 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.42 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34210526315789475 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3449074074074074 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8558083471447944 - name: F1 (macro) type: f1_macro value: 0.8411936200629997 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7896713615023474 - name: F1 (macro) type: f1_macro value: 0.5579408818659266 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6121343445287107 - name: F1 (macro) type: f1_macro value: 0.5771775585052653 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9407386798358489 - name: F1 (macro) type: f1_macro value: 0.8356897329372952 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8445628329677217 - name: F1 (macro) type: f1_macro value: 0.8285132890039661 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-0-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-0-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3074866310160428 - Accuracy on SAT: 0.3086053412462908 - Accuracy on BATS: 0.3596442468037799 - Accuracy on U2: 0.34210526315789475 - Accuracy on U4: 0.3449074074074074 - Accuracy on Google: 0.42 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-0-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8558083471447944 - Micro F1 score on CogALexV: 0.7896713615023474 - Micro F1 score on EVALution: 0.6121343445287107 - Micro F1 score on K&H+N: 0.9407386798358489 - Micro F1 score on ROOT09: 0.8445628329677217 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-0-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7524007936507936 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-0-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 5 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-0-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-0-child-prototypical
research-backup
2022-11-24T10:18:09Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T10:16:59Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-0-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7728968253968254 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.30213903743315507 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3026706231454006 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.471372984991662 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.464 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3333333333333333 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3402777777777778 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.843152026518005 - name: F1 (macro) type: f1_macro value: 0.8361849616930509 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7950704225352113 - name: F1 (macro) type: f1_macro value: 0.4837034332216315 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5882990249187432 - name: F1 (macro) type: f1_macro value: 0.567743951813541 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9092300201711067 - name: F1 (macro) type: f1_macro value: 0.7832602190691849 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8495769351300533 - name: F1 (macro) type: f1_macro value: 0.835545478650368 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-0-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-0-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.30213903743315507 - Accuracy on SAT: 0.3026706231454006 - Accuracy on BATS: 0.471372984991662 - Accuracy on U2: 0.3333333333333333 - Accuracy on U4: 0.3402777777777778 - Accuracy on Google: 0.464 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-0-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.843152026518005 - Micro F1 score on CogALexV: 0.7950704225352113 - Micro F1 score on EVALution: 0.5882990249187432 - Micro F1 score on K&H+N: 0.9092300201711067 - Micro F1 score on ROOT09: 0.8495769351300533 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-0-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7728968253968254 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-0-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-0-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-2-child
research-backup
2022-11-24T10:16:20Z
104
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T10:15:12Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-2-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7171428571428572 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.32887700534759357 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34421364985163205 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4952751528627015 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.632 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.41228070175438597 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.42824074074074076 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8559590176284467 - name: F1 (macro) type: f1_macro value: 0.8248318748581803 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7924882629107982 - name: F1 (macro) type: f1_macro value: 0.4853007429083929 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6175514626218852 - name: F1 (macro) type: f1_macro value: 0.5680442993124223 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9162551297210823 - name: F1 (macro) type: f1_macro value: 0.776158186402129 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7966154810404262 - name: F1 (macro) type: f1_macro value: 0.7470489921619655 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-2-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-2-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.32887700534759357 - Accuracy on SAT: 0.34421364985163205 - Accuracy on BATS: 0.4952751528627015 - Accuracy on U2: 0.41228070175438597 - Accuracy on U4: 0.42824074074074076 - Accuracy on Google: 0.632 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-2-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8559590176284467 - Micro F1 score on CogALexV: 0.7924882629107982 - Micro F1 score on EVALution: 0.6175514626218852 - Micro F1 score on K&H+N: 0.9162551297210823 - Micro F1 score on ROOT09: 0.7966154810404262 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-2-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7171428571428572 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-2-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-2-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
Livingwithmachines/erwt-year-st
Livingwithmachines
2022-11-24T10:12:57Z
107
0
transformers
[ "transformers", "pytorch", "distilbert", "fill-mask", "newspapers", "library", "historic", "glam", "mdma", "en", "arxiv:2211.10086", "arxiv:1910.14659", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-11-16T14:52:01Z
--- language: en tags: - newspapers - library - historic - glam - mdma license: mit metrics: - pseudo-perplexity widget: - text: "[1820] [SEP] We received a letter from [MASK] Majesty." - text: "[1850] [SEP] We received a letter from [MASK] Majesty." - text: "[MASK] [SEP] The Franco-Prussian war is a matter of great concern." - text: "[MASK] [SEP] The Schleswig war is a matter of great concern." --- **MODEL CARD UNDER CONSTRUCTION, ETA END OF NOVEMBER** <img src="https://upload.wikimedia.org/wikipedia/commons/5/5b/NCI_peas_in_pod.jpg" alt="erwt" width="200" > # ERWT-year-st 🌺ERWT\* a language model that (🤭 maybe 🤫) knows more about history than you...🌺 ERWT is a fine-tuned [`distilbert-base-uncased`](https://huggingface.co/distilbert-base-uncased) model trained on historical newspapers from the [Heritage Made Digital collection](https://huggingface.co/datasets/davanstrien/hmd-erwt-training). We trained a model based on a combination of text and **temporal metadata** (i.e. year information). ERWT performs [**time-sensitive masked language modelling**](#historical-language-change-herhis-majesty-%F0%9F%91%91) or [**date prediction**]((#date-prediction-pub-quiz-with-lms-%F0%9F%8D%BB)). This model is served by [Kaspar von Beelen](https://huggingface.co/Kaspar) and [Daniel van Strien](https://huggingface.co/davanstrien), *"Improving AI, one pea at a time"*. If these models happen to be useful, please cite our working paper. ``` @misc{https://doi.org/10.48550/arxiv.2211.10086, doi = {10.48550/ARXIV.2211.10086}, url = {https://arxiv.org/abs/2211.10086}, author = {Beelen, Kaspar and van Strien, Daniel}, keywords = {Computation and Language (cs.CL), Digital Libraries (cs.DL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Metadata Might Make Language Models Better}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International}} ``` \*ERWT is dutch for PEA. # Overview - [Introduction: Repent Now 😇](#introductory-note-repent-now-%F0%9F%98%87) - [Background: MDMA to the rescue 🙂](#background-mdma-to-the-rescue-%F0%9F%99%82) - [Intended Use: LMs as History Machines 🚂](#intended-use-lms-as-history-machines) - [Historical Language Change: Her/His Majesty? 👑](#historical-language-change-herhis-majesty-%F0%9F%91%91) - [Date Prediction: Pub Quiz with LMs 🍻](#date-prediction-pub-quiz-with-lms-%F0%9F%8D%BB) - [Limitations: Not all is well 😮](#limitations-not-all-is-well-%F0%9F%98%AE) - [Training Data](#training-data) - [Training Routine](#training-routine) - [Data Description](#data-description) - [Evaluation: 🤓 In case you care to count 🤓](#evaluation-%F0%9F%A4%93-in-case-you-care-to-count-%F0%9F%A4%93) ## Introductory Note: Repent Now. 😇 The ERWT models are trained for **experimental purposes**. Please consult the [**limitations**](#limitations-not-all-is-well-%F0%9F%98%AE) section (seriously before using the models. Seriously, read this section, **we don't repent in public just for fun**). If you can't get enough of these neural peas and crave some more. In that case, you can consult our working paper ["Metadata Might Make Language Models Better"](https://arxiv.org/abs/2211.10086) for more background information and nerdy evaluation stuff (work in progress, handle with care and kindness). ## Background: MDMA to the rescue. 🙂 ERWT was created using a **M**eta**D**ata **M**asking **A**pproach (or **MDMA** 💊), a scenario in which we train a Masked Language Model (MLM) on text and metadata simultaneously. Our intuition was that incorporating metadata (information that *describes* a text but and is not part of the content) may make language models "better", or at least make them more **sensitive** to historical, political and geographical aspects of language use. We mainly use temporal, political and geographical metadata. ERWT is a [`distilbert-base-uncased`](https://huggingface.co/distilbert-base-uncased) model, fine-tuned on a random subsample taken from the [Heritage Made Digital newspaper collection]((https://huggingface.co/datasets/davanstrien/hmd-erwt-training)). The training data comprises around half a billion words. To unleash the power of MDMA, we adapted to the training routine mainly by fidgeting with the input data. When preprocessing the text, we prepended each segment of hundred words with a time stamp (year of publication) and a special `[DATE]` token. The snippet below, taken from the [Londonderry Sentinel]:(https://www.britishnewspaperarchive.co.uk/viewer/bl/0001480/18700722/014/0002)... ``` Every scrap of intelligence relative to the war between France and Prussia is now read with interest. ``` ... would be formatted as: ```python "1870 [DATE] Every scrap of intelligence relative to the war between France and Prussia is now read with interest." ``` These text chunks are then forwarded to the data collator and eventually the language model. Exposed to the tokens and (temporal) metadata, the model learns a relation between text and time. When a text token is hidden, the prepended `year` field influences the prediction of the masked words. Vice versa, when the prepended metadata is hidden, the model predicts the year of publication based on the content. ## Intended Use: LMs as History Machines. Exposing the model to temporal metadata allows us to investigate **historical language change** and perform **date prediction**. ### Historical Language Change: Her/His Majesty? 👑 Let's show how ERWT works with a very concrete example. The ERWT models are trained on a handful of British newspapers published between 1800 and 1870. It can be used to monitor historical change in this specific context. Imagine you are confronted with the following snippet: "We received a letter from [MASK] Majesty" and want to predict the correct pronoun for the masked token (again assuming a British context). 👩‍🏫 **History Intermezzo** Please remember, for most of the nineteenth century, Queen Victoria ruled Britain, from 1837 to 1901 to be precise. Her nineteenth-century predecessors (George III, George IV and William IV) were all male. While a standard language model will provide you with one a general prediction—based on what it has observed during training–ERWT allows you to manipulate to prediction, by anchoring the text in a specific year. Doing this requires just a few lines of code: ```python from transformers import pipeline mask_filler = pipeline("fill-mask", model='Livingwithmachines/erwt-year-st') mask_filler(f"1820 [DATE] We received a letter from [MASK] Majesty.") ``` This returns "his" as the most likely filler: ```python {'score': 0.8527863025665283, 'token': 2010, 'token_str': 'his', 'sequence': '1820 we received a letter from his majesty.'} ``` However, if we change the date at the start of the sentence to 1850: ```python mask_filler(f"1850 [DATE] We received a letter from [MASK] Majesty.") ``` ERWT puts most of the probability mass on the token "her" and only a little bit on "his". ```python {'score': 0.8168327212333679, 'token': 2014, 'token_str': 'her', 'sequence': '1850 we received a letter from her majesty.'} ``` You can repeat this experiment for yourself using the example sentences in the **Hosted inference API** at the top right. Okay, but why is this **interesting**? Firstly, eyeballing some toy examples (but also using more rigorous metrics such as [perplexity](#evaluation-%F0%9F%A4%93-in-case-you-care-to-count-%F0%9F%A4%93)) shows that MLMs yield more accurate predictions when they have access to temporal metadata. In other words, **ERWT models are better at capturing historical context.** Secondly, MDMA may **reduce biases** that arise from imbalanced training data (or at least give us more of a handle on this problem). Admittedly, we have to prove this more formally, but some experiments at least hint in this direction. ### Date Prediction: Pub Quiz with LMs 🍻 Another feature of ERWT is **date prediction**. Remember that during training the temporal metadata token is regularly masked. In this case, the model effectively learns to situate documents in time based on the tokens in a text. By masking the year token at the beginning of the text string, ERWT guesses the document's year of publication. 👩‍🏫 **History Intermezzo** To unite the German states (there used to be [plenty](https://www.britannica.com/topic/German-Confederation)!), Prussia fought a number of wars with its neighbours in the second half of the nineteenth century. It invaded Denmark in 1864 (the second of the Schleswig Wars) and France in 1870 (the Franco-Prussian war). Reusing to code above, we can time-stamp documents by masking the year. For example, the line of python code below: ```python mask_filler("[MASK] [DATE] The Schleswig war is a matter of great concern.") ``` Outputs as most likely filler: ```python {'score': 0.48822104930877686, 'token': 6717, 'token_str': '1864', 'sequence': '1864 the schleswig war is a matter of great concern.'} ``` The prediction "1864" makes sense; this was indeed the year of Prussian troops (with some help of their Austrian friends) crossed the border into Schleswig, then part of the Kingdom of Denmark. A few years later, in 1870, Prussia aimed its artillery and bayonets southwards and invaded France. ```python mask_filler("[MASK] [DATE] The Franco-Prussian war is a matter of great concern.") ``` ERWT clearly learned a lot about the history of German unification by ploughing through a plethora of nineteenth-century newspaper articles: it correctly returns "1870" as the predicted year for the Franco-Prussian war! Again, we have to ask: Who cares? Wikipedia can tell us pretty much the same. More importantly, don't we already have timestamps for newspaper data? In both cases, our answer sounds "yes, but...". ERWT's time-stamping powers have little instrumental use and won't make us rich (but donations are welcome of course 🤑). Nonetheless, we believe date prediction has value for research purposes. We can use ERWT for "fictitious" prediction, i.e. as a diagnostic tool. Firstly, we used date prediction for evaluation purposes, to measure which training routine produces models that best capture the year of publication from a set of tokens. Secondly, we could use date prediction as an analytical or research tool, and study, for example, temporal variation **within** text documents; or scrutinise which features drive the time prediction (it goes without saying that the same applies to other metadata fields, like political orientation). ## Limitations: Not all is well 😮. The ERWT series were trained for evaluation purposes and therefore carry some critical limitations. ### Training Data Many of the limitations are a direct result of the training data. ERWT models are trained on a rather small subsample of nineteenth-century **British newspapers**, and its predictions have to be understood in this context (remember, "Her Majesty?"). The corpus has a strong **Metropolitan and liberal bias** (see the section on Data Description for more information). The training data spans from **1800 to 1870**. If your research interest is outside of this period, it's unlikely that ERWT will be of much use. Don't ask the poor model to predict when the Second World War happened. ERWT can be smart (at times) but it doesn't have the power of fortune-telling. At least not yet... Furthermore, historical models tend to reflect past (and present?) stereotypes and prejudices. We strongly advise against using these models outside of a research context. The predictions are likely to exhibit harmful biases, they should be investigated critically and understood within the context of nineteenth-century British cultural history. One way of evaluating a model's bias is to gauge the impact of changing a prompt on the predicted [MASK] token. Often a comparison is made between the predictions given for 'The **man** worked as a [MASK]' to 'The **woman** worked as a [MASK]'. An example of the output for this model: ``` 1810 [DATE] The man worked as a [MASK]. ``` Produces the following three top predicted mask tokens ```python [ { "score": 0.17358914017677307, "token": 10533, "token_str": "carpenter", }, { "score": 0.08387620747089386, "token": 22701, "token_str": "tailor", }, { "score": 0.068501777946949, "token": 6243, "token_str": "baker", } ] ``` ``` 1810 [DATE] The woman worked as a [MASK]. ``` Produces the following three top predicted mask tokens ```python [ { "score": 0.148710235953331, "token": 7947, "token_str": "servant", }, { "score": 0.07184035331010818, "token": 6243, "token_str": "baker", }, { "score": 0.0675836056470871, "token": 6821, "token_str": "nurse", }, ] ``` Mostly, prompt evaluation is done to assess the bias in *contemporary* language models. In the case of historic language models, the bias exhibited by a model *may* be a valuable research tool in assessing (at scale) language use over time, and the stereotypes and prejudices encoded in text corpora. For this particular prompt, the 'bias' exhibited by the language model (and the underlying data) may be a relatively accurate reflection of employment patterns during the 19th century. A possible area of exploration is to see how these predictions change when the model is prompted with different dates. With a dataset covering a more extended time period, we may expect to see a decline in the [MASK] `servant` toward the end of the 19th Century and particularly following the start of the First World War when the number of domestic servants employed in the United Kingdom fell rapidly. ### Training Routine We created various ERWT models as part of a wider experiment that aimed to establish best practices and guidelines for training models with metadata. An overview of all the models is available on our [GitHub](https://github.com/Living-with-machines/ERWT/) page. To reduce training time, we based our experiments on a random subsample of the HMD corpus, consisting of half a billion tokens. Furthermore, we only trained the models for one epoch, which implies they are most likely **undertrained** at the moment. We were mainly interested in the **relative** performance of the different ERWT models. We did, however, compared ERWT with [`distilbert-base-uncased`](https://huggingface.co/distilbert-base-uncased) in our evaluation experiments. And, of course, our tiny LM peas did much better. 🎉🥳 Want to know the details—Oh, critical reader!—then consult and cite [our working paper](https://arxiv.org/abs/2211.10086)! ## Data Description The ERWT models are trained on an openly accessible newspaper corpus created by the [Heritage Made Digital (HMD) newspaper digitisation project](footnote{https://blogs.bl.uk/thenewsroom/2019/01/heritage-made-digital-the-newspapers.html). The HMD newspapers comprise around 2 billion words in total, but the bulk of the articles originate from the (then) liberal paper *The Sun*. Geographically, most papers are metropolitan (i.e. based in London). The inclusion of *The Northern Daily Times* and *Liverpool Standard*, adds some geographical diversity to this corpus. The political classification is based on historical newspaper press directories, please read [our paper](https://academic.oup.com/dsh/advance-article/doi/10.1093/llc/fqac037/6644524?searchresult=1) on bias in newspaper collections for more information. The table below contains a more detailed overview of the corpus. | | | | | | |------|--------------------------|--------------|-----------|---------------| | NLP | Title | Politics | Location | Tokens | | 2083 | The Northern Daily Times | NEUTRAL | LIVERPOOL | 14.094.212 | | 2084 | The Northern Daily Times | NEUTRAL | LIVERPOOL | 34.450.366 | | 2085 | The Northern Daily Times | NEUTRAL | LIVERPOOL | 16.166.627 | | 2088 | The Liverpool Standard | CONSERVATIVE | LIVERPOOL | 149.204.800 | | 2090 | The Liverpool Standard | CONSERVATIVE | LIVERPOOL | 6.417.320 | | 2194 | The Sun | LIBERAL | LONDON | 1.155.791.480 | | 2244 | Colored News | NONE | LONDON | 53.634 | | 2642 | The Express | LIBERAL | LONDON | 236.240.555 | | 2644 | National Register | CONSERVATIVE | LONDON | 23.409.733 | | 2645 | The Press | CONSERVATIVE | LONDON | 15.702.276 | | 2646 | The Star | NONE | LONDON | 163.072.742 | | 2647 | The Statesman | RADICAL | LONDON | 61.225.215 | Temporally, most of the articles date from the second half of the nineteenth century. The figure below gives an overview of the number of articles by year. ![number of article by year](https://github.com/Living-with-machines/ERWT/raw/main/articles_by_year.png) ## Evaluation: 🤓 In case you care to count 🤓 Our article ["Metadata Might Make Language Models Better"](https://arxiv.org/abs/2211.10086) comprises an extensive evaluation of all the MDMA-infused language models. The table below shows the [pseudo-perplexity](https://arxiv.org/abs/1910.14659) scores for different models based on text documents of 64 and 128 tokens. In general, [ERWT-year-masked-25](https://huggingface.co/Livingwithmachines/erwt-year-masked-25) turned out to yield the most competitive scores across different tasks and we generally recommend you use this model. | text length | 64 | | 128 | | |------------------|----------------|--------|----------------|--------| | model | mean | sd | mean | sd | | DistilBERT | 354.40 | 376.32 | 229.19 | 294.70 | | HMDistilBERT | 32.94 | 64.78 | 25.72 | 45.99 | | ERWT-year | 31.49 | 61.85 | 24.97 | 44.58 | | ERWT-st | 31.69 | 62.42 | 25.03 | 44.74 | | ERWT-year-masked-25 | **30.97** | 61.50 | **24.59** | 44.36 | | ERWT-year-masked-75 | 31.02 | 61.41 | 24.63 | 44.40 | | PEA | 31.63 | 62.09 | 25.58 | 44.99 | | PEA-st | 31.65 | 62.19 | 25.59 | 44.99 | ## Questions? Questions? Feedback? Please leave a message!
tomekkorbak/lucid_varahamihira
tomekkorbak
2022-11-24T10:12:42Z
104
0
transformers
[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:tomekkorbak/pii-pile-chunk3-0-50000", "dataset:tomekkorbak/pii-pile-chunk3-50000-100000", "dataset:tomekkorbak/pii-pile-chunk3-100000-150000", "dataset:tomekkorbak/pii-pile-chunk3-150000-200000", "dataset:tomekkorbak/pii-pile-chunk3-200000-250000", "dataset:tomekkorbak/pii-pile-chunk3-250000-300000", "dataset:tomekkorbak/pii-pile-chunk3-300000-350000", "dataset:tomekkorbak/pii-pile-chunk3-350000-400000", "dataset:tomekkorbak/pii-pile-chunk3-400000-450000", "dataset:tomekkorbak/pii-pile-chunk3-450000-500000", "dataset:tomekkorbak/pii-pile-chunk3-500000-550000", "dataset:tomekkorbak/pii-pile-chunk3-550000-600000", "dataset:tomekkorbak/pii-pile-chunk3-600000-650000", "dataset:tomekkorbak/pii-pile-chunk3-650000-700000", "dataset:tomekkorbak/pii-pile-chunk3-700000-750000", "dataset:tomekkorbak/pii-pile-chunk3-750000-800000", "dataset:tomekkorbak/pii-pile-chunk3-800000-850000", "dataset:tomekkorbak/pii-pile-chunk3-850000-900000", "dataset:tomekkorbak/pii-pile-chunk3-900000-950000", "dataset:tomekkorbak/pii-pile-chunk3-950000-1000000", "dataset:tomekkorbak/pii-pile-chunk3-1000000-1050000", "dataset:tomekkorbak/pii-pile-chunk3-1050000-1100000", "dataset:tomekkorbak/pii-pile-chunk3-1100000-1150000", "dataset:tomekkorbak/pii-pile-chunk3-1150000-1200000", "dataset:tomekkorbak/pii-pile-chunk3-1200000-1250000", "dataset:tomekkorbak/pii-pile-chunk3-1250000-1300000", "dataset:tomekkorbak/pii-pile-chunk3-1300000-1350000", "dataset:tomekkorbak/pii-pile-chunk3-1350000-1400000", "dataset:tomekkorbak/pii-pile-chunk3-1400000-1450000", "dataset:tomekkorbak/pii-pile-chunk3-1450000-1500000", "dataset:tomekkorbak/pii-pile-chunk3-1500000-1550000", "dataset:tomekkorbak/pii-pile-chunk3-1550000-1600000", "dataset:tomekkorbak/pii-pile-chunk3-1600000-1650000", "dataset:tomekkorbak/pii-pile-chunk3-1650000-1700000", "dataset:tomekkorbak/pii-pile-chunk3-1700000-1750000", "dataset:tomekkorbak/pii-pile-chunk3-1750000-1800000", "dataset:tomekkorbak/pii-pile-chunk3-1800000-1850000", "dataset:tomekkorbak/pii-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/pii-pile-chunk3-1900000-1950000", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]
null
2022-11-23T20:20:16Z
--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/pii-pile-chunk3-0-50000 - tomekkorbak/pii-pile-chunk3-50000-100000 - tomekkorbak/pii-pile-chunk3-100000-150000 - tomekkorbak/pii-pile-chunk3-150000-200000 - tomekkorbak/pii-pile-chunk3-200000-250000 - tomekkorbak/pii-pile-chunk3-250000-300000 - tomekkorbak/pii-pile-chunk3-300000-350000 - tomekkorbak/pii-pile-chunk3-350000-400000 - tomekkorbak/pii-pile-chunk3-400000-450000 - tomekkorbak/pii-pile-chunk3-450000-500000 - tomekkorbak/pii-pile-chunk3-500000-550000 - tomekkorbak/pii-pile-chunk3-550000-600000 - tomekkorbak/pii-pile-chunk3-600000-650000 - tomekkorbak/pii-pile-chunk3-650000-700000 - tomekkorbak/pii-pile-chunk3-700000-750000 - tomekkorbak/pii-pile-chunk3-750000-800000 - tomekkorbak/pii-pile-chunk3-800000-850000 - tomekkorbak/pii-pile-chunk3-850000-900000 - tomekkorbak/pii-pile-chunk3-900000-950000 - tomekkorbak/pii-pile-chunk3-950000-1000000 - tomekkorbak/pii-pile-chunk3-1000000-1050000 - tomekkorbak/pii-pile-chunk3-1050000-1100000 - tomekkorbak/pii-pile-chunk3-1100000-1150000 - tomekkorbak/pii-pile-chunk3-1150000-1200000 - tomekkorbak/pii-pile-chunk3-1200000-1250000 - tomekkorbak/pii-pile-chunk3-1250000-1300000 - tomekkorbak/pii-pile-chunk3-1300000-1350000 - tomekkorbak/pii-pile-chunk3-1350000-1400000 - tomekkorbak/pii-pile-chunk3-1400000-1450000 - tomekkorbak/pii-pile-chunk3-1450000-1500000 - tomekkorbak/pii-pile-chunk3-1500000-1550000 - tomekkorbak/pii-pile-chunk3-1550000-1600000 - tomekkorbak/pii-pile-chunk3-1600000-1650000 - tomekkorbak/pii-pile-chunk3-1650000-1700000 - tomekkorbak/pii-pile-chunk3-1700000-1750000 - tomekkorbak/pii-pile-chunk3-1750000-1800000 - tomekkorbak/pii-pile-chunk3-1800000-1850000 - tomekkorbak/pii-pile-chunk3-1850000-1900000 - tomekkorbak/pii-pile-chunk3-1900000-1950000 model-index: - name: lucid_varahamihira 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. --> # lucid_varahamihira This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets. ## 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: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}], 'scorer_config': {}}, 'kl_gpt3_callback': {'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'lucid_varahamihira', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output2', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/2nk79xf2
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-2-child
research-backup
2022-11-24T10:10:55Z
104
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T10:09:48Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-2-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7944444444444444 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.393048128342246 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39465875370919884 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5508615897720957 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.73 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4298245614035088 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.42592592592592593 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8234141931595599 - name: F1 (macro) type: f1_macro value: 0.7945925077963739 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7929577464788734 - name: F1 (macro) type: f1_macro value: 0.49797428852111364 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6473456121343445 - name: F1 (macro) type: f1_macro value: 0.6513952195653971 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8730611393197468 - name: F1 (macro) type: f1_macro value: 0.7089529940574342 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8354747727984958 - name: F1 (macro) type: f1_macro value: 0.8172740679911564 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-2-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-2-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.393048128342246 - Accuracy on SAT: 0.39465875370919884 - Accuracy on BATS: 0.5508615897720957 - Accuracy on U2: 0.4298245614035088 - Accuracy on U4: 0.42592592592592593 - Accuracy on Google: 0.73 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-2-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8234141931595599 - Micro F1 score on CogALexV: 0.7929577464788734 - Micro F1 score on EVALution: 0.6473456121343445 - Micro F1 score on K&H+N: 0.8730611393197468 - Micro F1 score on ROOT09: 0.8354747727984958 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-2-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7944444444444444 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-2-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 5 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-2-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1-child
research-backup
2022-11-24T10:09:10Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T10:08:01Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7711706349206349 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.42245989304812837 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.42729970326409494 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5369649805447471 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.746 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39035087719298245 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4027777777777778 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9005574807895134 - name: F1 (macro) type: f1_macro value: 0.8976339821573811 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8204225352112676 - name: F1 (macro) type: f1_macro value: 0.5879808328006281 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6646803900325027 - name: F1 (macro) type: f1_macro value: 0.6393949146990776 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9513111219308618 - name: F1 (macro) type: f1_macro value: 0.8439276141187293 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.88937637104356 - name: F1 (macro) type: f1_macro value: 0.8834315185124441 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.42245989304812837 - Accuracy on SAT: 0.42729970326409494 - Accuracy on BATS: 0.5369649805447471 - Accuracy on U2: 0.39035087719298245 - Accuracy on U4: 0.4027777777777778 - Accuracy on Google: 0.746 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.9005574807895134 - Micro F1 score on CogALexV: 0.8204225352112676 - Micro F1 score on EVALution: 0.6646803900325027 - Micro F1 score on K&H+N: 0.9513111219308618 - Micro F1 score on ROOT09: 0.88937637104356 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7711706349206349 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 5 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-0-child
research-backup
2022-11-24T10:07:27Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T10:06:20Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-0-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7536111111111111 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4090909090909091 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.41543026706231456 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3979988882712618 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.536 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.40350877192982454 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3726851851851852 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8408919692632213 - name: F1 (macro) type: f1_macro value: 0.8307198073196754 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.646244131455399 - name: F1 (macro) type: f1_macro value: 0.3220456955206279 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6110509209100758 - name: F1 (macro) type: f1_macro value: 0.5969647367022108 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8687486958336231 - name: F1 (macro) type: f1_macro value: 0.6342486357768978 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7696646819178942 - name: F1 (macro) type: f1_macro value: 0.6831474153591115 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-0-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-0-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.4090909090909091 - Accuracy on SAT: 0.41543026706231456 - Accuracy on BATS: 0.3979988882712618 - Accuracy on U2: 0.40350877192982454 - Accuracy on U4: 0.3726851851851852 - Accuracy on Google: 0.536 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-0-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8408919692632213 - Micro F1 score on CogALexV: 0.646244131455399 - Micro F1 score on EVALution: 0.6110509209100758 - Micro F1 score on K&H+N: 0.8687486958336231 - Micro F1 score on ROOT09: 0.7696646819178942 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-0-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7536111111111111 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-0-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-0-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-2-child
research-backup
2022-11-24T09:59:50Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T09:58:34Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-2-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7143253968253969 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.30213903743315507 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.29673590504451036 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.41078376876042244 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.444 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3508771929824561 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.35185185185185186 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8350158204007835 - name: F1 (macro) type: f1_macro value: 0.8258828243546699 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8082159624413146 - name: F1 (macro) type: f1_macro value: 0.5334332730866751 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6397616468039004 - name: F1 (macro) type: f1_macro value: 0.6018197960350038 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9267580162759964 - name: F1 (macro) type: f1_macro value: 0.8245309871144009 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.857724851143842 - name: F1 (macro) type: f1_macro value: 0.8592867514570143 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-2-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-2-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.30213903743315507 - Accuracy on SAT: 0.29673590504451036 - Accuracy on BATS: 0.41078376876042244 - Accuracy on U2: 0.3508771929824561 - Accuracy on U4: 0.35185185185185186 - Accuracy on Google: 0.444 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-2-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8350158204007835 - Micro F1 score on CogALexV: 0.8082159624413146 - Micro F1 score on EVALution: 0.6397616468039004 - Micro F1 score on K&H+N: 0.9267580162759964 - Micro F1 score on ROOT09: 0.857724851143842 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-2-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7143253968253969 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-2-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-2-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1-child
research-backup
2022-11-24T09:57:56Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T09:56:42Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7782142857142857 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.33689839572192515 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3353115727002967 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.44191217342968314 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.516 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.36403508771929827 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3333333333333333 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8916679222540305 - name: F1 (macro) type: f1_macro value: 0.8835691803582005 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7997652582159624 - name: F1 (macro) type: f1_macro value: 0.5506574094908683 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6153846153846154 - name: F1 (macro) type: f1_macro value: 0.6018830205550925 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9635528969882451 - name: F1 (macro) type: f1_macro value: 0.8828743289803266 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8806016922594797 - name: F1 (macro) type: f1_macro value: 0.8769283661697559 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.33689839572192515 - Accuracy on SAT: 0.3353115727002967 - Accuracy on BATS: 0.44191217342968314 - Accuracy on U2: 0.36403508771929827 - Accuracy on U4: 0.3333333333333333 - Accuracy on Google: 0.516 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8916679222540305 - Micro F1 score on CogALexV: 0.7997652582159624 - Micro F1 score on EVALution: 0.6153846153846154 - Micro F1 score on K&H+N: 0.9635528969882451 - Micro F1 score on ROOT09: 0.8806016922594797 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7782142857142857 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 5 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-1-child
research-backup
2022-11-24T09:52:27Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-11-24T09:51:13Z
--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-1-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.775079365079365 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3716577540106952 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3768545994065282 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34185658699277377 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.428 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37719298245614036 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3541666666666667 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8904625583848124 - name: F1 (macro) type: f1_macro value: 0.8806810263932183 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7814553990610328 - name: F1 (macro) type: f1_macro value: 0.5516320672010655 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6419284940411701 - name: F1 (macro) type: f1_macro value: 0.6147381969131176 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9577102316199485 - name: F1 (macro) type: f1_macro value: 0.8737423662751781 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8022563459730492 - name: F1 (macro) type: f1_macro value: 0.8150247698794525 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-1-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-1-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3716577540106952 - Accuracy on SAT: 0.3768545994065282 - Accuracy on BATS: 0.34185658699277377 - Accuracy on U2: 0.37719298245614036 - Accuracy on U4: 0.3541666666666667 - Accuracy on Google: 0.428 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-1-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8904625583848124 - Micro F1 score on CogALexV: 0.7814553990610328 - Micro F1 score on EVALution: 0.6419284940411701 - Micro F1 score on K&H+N: 0.9577102316199485 - Micro F1 score on ROOT09: 0.8022563459730492 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-1-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.775079365079365 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-1-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-1-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
Galeros/a2c-AntBulletEnv-v0
Galeros
2022-11-24T09:22:14Z
0
0
stable-baselines3
[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-11-24T09:21:08Z
--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1507.30 +/- 342.32 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
scostiniano/bert-tagalog-base-uncased-ner-v1
scostiniano
2022-11-24T07:37:36Z
111
0
transformers
[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-11-24T06:06:20Z
--- license: gpl-3.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-tagalog-base-uncased-ner-v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Description - The dataset consists of 148 Filipino storytelling books, 5,005 total sentences, 45,792 total tokens, and 5,646 unique tokens. - This NER model only supports the Filipino language and does not include proper nouns, verbs, adjectives, and adverbs as of the moment - The input must undergo preprocessing. Soon I will upload the code to GitHub for preprocessing the input - To replicate the preprocessed input use this example as a guide - Input: "May umaapoy na bahay " - Preprocessed Input: "apoy bahay" # bert-tagalog-base-uncased-ner-v1 This model is a fine-tuned version of [jcblaise/bert-tagalog-base-uncased](https://huggingface.co/jcblaise/bert-tagalog-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2824 - Precision: 0.9091 - Recall: 0.8988 - F1: 0.9039 - Accuracy: 0.9488 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 205 | 0.5311 | 0.6465 | 0.5458 | 0.5919 | 0.8387 | | No log | 2.0 | 410 | 0.3052 | 0.7736 | 0.7811 | 0.7774 | 0.9110 | | 0.4693 | 3.0 | 615 | 0.2531 | 0.8493 | 0.8363 | 0.8427 | 0.9319 | | 0.4693 | 4.0 | 820 | 0.2384 | 0.8755 | 0.8715 | 0.8735 | 0.9402 | | 0.064 | 5.0 | 1025 | 0.2671 | 0.8909 | 0.8823 | 0.8866 | 0.9435 | | 0.064 | 6.0 | 1230 | 0.2527 | 0.8864 | 0.8920 | 0.8892 | 0.9459 | | 0.064 | 7.0 | 1435 | 0.2708 | 0.9088 | 0.9011 | 0.9049 | 0.9491 | | 0.0111 | 8.0 | 1640 | 0.2733 | 0.8992 | 0.8977 | 0.8984 | 0.9490 | | 0.0111 | 9.0 | 1845 | 0.2765 | 0.8991 | 0.8965 | 0.8978 | 0.9485 | | 0.0037 | 10.0 | 2050 | 0.2824 | 0.9091 | 0.8988 | 0.9039 | 0.9488 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
Jianxin1101/mmdetection
Jianxin1101
2022-11-24T07:15:45Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2022-11-24T07:15:45Z
--- license: creativeml-openrail-m ---
sd-concepts-library/twitch-league-of-legends
sd-concepts-library
2022-11-24T07:11:00Z
0
0
null
[ "license:mit", "region:us" ]
null
2022-11-24T07:10:50Z
--- license: mit --- ### Twitch League Of Legends on Stable Diffusion This is the `<twitch-lol>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<twitch-lol> 0](https://huggingface.co/sd-concepts-library/twitch-league-of-legends/resolve/main/concept_images/1.jpeg) ![<twitch-lol> 1](https://huggingface.co/sd-concepts-library/twitch-league-of-legends/resolve/main/concept_images/7.jpeg) ![<twitch-lol> 2](https://huggingface.co/sd-concepts-library/twitch-league-of-legends/resolve/main/concept_images/10.jpeg) ![<twitch-lol> 3](https://huggingface.co/sd-concepts-library/twitch-league-of-legends/resolve/main/concept_images/0.jpeg) ![<twitch-lol> 4](https://huggingface.co/sd-concepts-library/twitch-league-of-legends/resolve/main/concept_images/5.jpeg) ![<twitch-lol> 5](https://huggingface.co/sd-concepts-library/twitch-league-of-legends/resolve/main/concept_images/4.jpeg) ![<twitch-lol> 6](https://huggingface.co/sd-concepts-library/twitch-league-of-legends/resolve/main/concept_images/3.jpeg) ![<twitch-lol> 7](https://huggingface.co/sd-concepts-library/twitch-league-of-legends/resolve/main/concept_images/8.jpeg) ![<twitch-lol> 8](https://huggingface.co/sd-concepts-library/twitch-league-of-legends/resolve/main/concept_images/2.jpeg) ![<twitch-lol> 9](https://huggingface.co/sd-concepts-library/twitch-league-of-legends/resolve/main/concept_images/11.jpeg) ![<twitch-lol> 10](https://huggingface.co/sd-concepts-library/twitch-league-of-legends/resolve/main/concept_images/9.jpeg) ![<twitch-lol> 11](https://huggingface.co/sd-concepts-library/twitch-league-of-legends/resolve/main/concept_images/6.jpeg)
alexziweiwang/exp20-M04-both
alexziweiwang
2022-11-24T07:10:37Z
105
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-11-24T02:39:25Z
--- tags: - generated_from_trainer model-index: - name: exp20-M04-both 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. --> # exp20-M04-both This model is a fine-tuned version of [yongjian/wav2vec2-large-a](https://huggingface.co/yongjian/wav2vec2-large-a) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.3358 - Wer: 1.1374 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 37.2423 | 0.34 | 500 | 3.2577 | 1.0 | | 3.1334 | 0.68 | 1000 | 3.0084 | 1.0 | | 2.9616 | 1.02 | 1500 | 2.9946 | 1.0 | | 2.8067 | 1.36 | 2000 | 2.6228 | 1.3130 | | 2.732 | 1.7 | 2500 | 2.5059 | 1.5013 | | 2.3673 | 2.04 | 3000 | 2.1828 | 1.4911 | | 2.1378 | 2.38 | 3500 | 2.2066 | 1.4911 | | 1.9853 | 2.72 | 4000 | 1.9877 | 1.4580 | | 1.8574 | 3.06 | 4500 | 1.8850 | 1.4656 | | 1.7085 | 3.4 | 5000 | 1.9121 | 1.4606 | | 1.6161 | 3.74 | 5500 | 2.1036 | 1.4326 | | 1.5304 | 4.08 | 6000 | 1.9807 | 1.4478 | | 1.3531 | 4.42 | 6500 | 2.0211 | 1.4656 | | 1.3269 | 4.77 | 7000 | 1.9231 | 1.3893 | | 1.2312 | 5.11 | 7500 | 2.2652 | 1.4097 | | 1.1161 | 5.45 | 8000 | 1.9543 | 1.4529 | | 1.0305 | 5.79 | 8500 | 2.1463 | 1.4071 | | 0.9403 | 6.13 | 9000 | 3.7872 | 1.4071 | | 0.8723 | 6.47 | 9500 | 2.8466 | 1.4326 | | 0.8752 | 6.81 | 10000 | 2.2215 | 1.3766 | | 0.7774 | 7.15 | 10500 | 2.0462 | 1.3257 | | 0.74 | 7.49 | 11000 | 2.1928 | 1.3333 | | 0.7371 | 7.83 | 11500 | 2.8058 | 1.3410 | | 0.7075 | 8.17 | 12000 | 2.3100 | 1.3308 | | 0.6746 | 8.51 | 12500 | 2.6284 | 1.2875 | | 0.6233 | 8.85 | 13000 | 2.2268 | 1.3003 | | 0.7172 | 9.19 | 13500 | 2.1980 | 1.2926 | | 0.5697 | 9.53 | 14000 | 2.1950 | 1.2468 | | 0.5691 | 9.87 | 14500 | 2.1819 | 1.2316 | | 0.5062 | 10.21 | 15000 | 2.1426 | 1.2621 | | 0.4818 | 10.55 | 15500 | 2.2259 | 1.2545 | | 0.5083 | 10.89 | 16000 | 2.1764 | 1.2214 | | 0.3901 | 11.23 | 16500 | 2.2412 | 1.2341 | | 0.4275 | 11.57 | 17000 | 2.3781 | 1.2290 | | 0.4225 | 11.91 | 17500 | 2.1578 | 1.2443 | | 0.4106 | 12.25 | 18000 | 2.5651 | 1.2341 | | 0.3933 | 12.59 | 18500 | 2.1819 | 1.2265 | | 0.3821 | 12.93 | 19000 | 2.0564 | 1.1934 | | 0.3584 | 13.27 | 19500 | 2.5475 | 1.2290 | | 0.3468 | 13.61 | 20000 | 2.5857 | 1.1781 | | 0.3984 | 13.96 | 20500 | 2.2383 | 1.2239 | | 0.308 | 14.3 | 21000 | 2.4947 | 1.2137 | | 0.3356 | 14.64 | 21500 | 2.6563 | 1.2163 | | 0.3406 | 14.98 | 22000 | 2.3337 | 1.2061 | | 0.3297 | 15.32 | 22500 | 2.2793 | 1.1908 | | 0.3028 | 15.66 | 23000 | 2.6462 | 1.1654 | | 0.3226 | 16.0 | 23500 | 2.3785 | 1.1705 | | 0.2605 | 16.34 | 24000 | 2.7212 | 1.1858 | | 0.2669 | 16.68 | 24500 | 3.0365 | 1.2087 | | 0.2967 | 17.02 | 25000 | 2.4898 | 1.1934 | | 0.2547 | 17.36 | 25500 | 2.4020 | 1.1832 | | 0.2779 | 17.7 | 26000 | 2.5558 | 1.1705 | | 0.2341 | 18.04 | 26500 | 2.9406 | 1.1934 | | 0.2304 | 18.38 | 27000 | 3.1528 | 1.1603 | | 0.226 | 18.72 | 27500 | 3.0001 | 1.2163 | | 0.2319 | 19.06 | 28000 | 3.0117 | 1.1603 | | 0.1836 | 19.4 | 28500 | 2.8332 | 1.1858 | | 0.2085 | 19.74 | 29000 | 2.8757 | 1.1603 | | 0.2383 | 20.08 | 29500 | 3.2235 | 1.1934 | | 0.2006 | 20.42 | 30000 | 3.0189 | 1.1603 | | 0.1722 | 20.76 | 30500 | 2.8001 | 1.1527 | | 0.1955 | 21.1 | 31000 | 3.0401 | 1.1578 | | 0.1839 | 21.44 | 31500 | 3.2621 | 1.1578 | | 0.1592 | 21.78 | 32000 | 3.1740 | 1.1552 | | 0.1835 | 22.12 | 32500 | 3.3974 | 1.1934 | | 0.197 | 22.46 | 33000 | 2.8283 | 1.1425 | | 0.1788 | 22.8 | 33500 | 3.1983 | 1.1705 | | 0.169 | 23.14 | 34000 | 3.1978 | 1.1425 | | 0.1649 | 23.49 | 34500 | 3.1829 | 1.1552 | | 0.1431 | 23.83 | 35000 | 3.0528 | 1.1272 | | 0.1384 | 24.17 | 35500 | 3.3792 | 1.1196 | | 0.1234 | 24.51 | 36000 | 3.3988 | 1.1425 | | 0.1552 | 24.85 | 36500 | 3.1008 | 1.1170 | | 0.124 | 25.19 | 37000 | 2.9486 | 1.1374 | | 0.1439 | 25.53 | 37500 | 3.1028 | 1.1323 | | 0.1612 | 25.87 | 38000 | 3.0209 | 1.1043 | | 0.1456 | 26.21 | 38500 | 2.9466 | 1.1323 | | 0.1333 | 26.55 | 39000 | 3.1298 | 1.1221 | | 0.1368 | 26.89 | 39500 | 3.1051 | 1.1272 | | 0.1263 | 27.23 | 40000 | 3.2888 | 1.1298 | | 0.1198 | 27.57 | 40500 | 3.0984 | 1.1298 | | 0.1202 | 27.91 | 41000 | 3.1653 | 1.1374 | | 0.1252 | 28.25 | 41500 | 3.3016 | 1.1552 | | 0.1177 | 28.59 | 42000 | 3.2566 | 1.1349 | | 0.1072 | 28.93 | 42500 | 3.3303 | 1.1425 | | 0.1497 | 29.27 | 43000 | 3.2549 | 1.1399 | | 0.1089 | 29.61 | 43500 | 3.3121 | 1.1374 | | 0.0936 | 29.95 | 44000 | 3.3358 | 1.1374 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2
minhhoque/swin-tiny-patch4-window7-224-finetuned-eurosat
minhhoque
2022-11-24T05:42:36Z
209
0
transformers
[ "transformers", "pytorch", "tensorboard", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2022-11-24T05:13:19Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-eurosat results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9729629629629629 --- <!-- 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. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0775 - Accuracy: 0.9730 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2658 | 1.0 | 190 | 0.1305 | 0.9615 | | 0.1591 | 2.0 | 380 | 0.0781 | 0.9726 | | 0.1364 | 3.0 | 570 | 0.0775 | 0.9730 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
morgansoftware/LunarLander-v2
morgansoftware
2022-11-24T05:14:15Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-11-24T05:01:55Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 222.08 +/- 24.62 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Yuan0613/distilbert-base-uncased-finetuned-imdb
Yuan0613
2022-11-24T04:13:12Z
161
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "fill-mask", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-11-24T03:54:27Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-base-uncased-finetuned-imdb 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-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4721 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7086 | 1.0 | 157 | 2.4898 | | 2.5796 | 2.0 | 314 | 2.4230 | | 2.5269 | 3.0 | 471 | 2.4354 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
gary109/ai-light-dance_drums_ft_pretrain_wav2vec2-base-new-v6
gary109
2022-11-24T04:07:00Z
133
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer", "dataset:ai_light_dance", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-11-24T00:41:03Z
--- tags: - automatic-speech-recognition - gary109/AI_Light_Dance - generated_from_trainer datasets: - ai_light_dance metrics: - wer model-index: - name: ai-light-dance_drums_ft_pretrain_wav2vec2-base-new-v6 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ai-light-dance_drums_ft_pretrain_wav2vec2-base-new-v6 This model is a fine-tuned version of [gary109/ai-light-dance_drums_pretrain_wav2vec2-base-new](https://huggingface.co/gary109/ai-light-dance_drums_pretrain_wav2vec2-base-new) on the GARY109/AI_LIGHT_DANCE - ONSET-IDMT-MDB-ENST dataset. It achieves the following results on the evaluation set: - Loss: 0.6823 - Wer: 0.3851 ## 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: 0.0004 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 30 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 17.274 | 0.99 | 35 | 2.9045 | 1.0 | | 1.8443 | 1.99 | 70 | 3.5065 | 1.0 | | 1.709 | 2.99 | 105 | 2.0072 | 1.0 | | 1.4981 | 3.99 | 140 | 1.9510 | 0.9688 | | 1.2977 | 4.99 | 175 | 1.8863 | 0.5534 | | 1.1257 | 5.99 | 210 | 1.9137 | 0.4833 | | 1.1218 | 6.99 | 245 | 1.9707 | 0.4960 | | 0.8878 | 7.99 | 280 | 1.4179 | 0.4774 | | 0.8562 | 8.99 | 315 | 1.5276 | 0.4635 | | 1.5769 | 9.99 | 350 | 1.1270 | 0.4509 | | 0.796 | 10.99 | 385 | 1.2706 | 0.4496 | | 0.8776 | 11.99 | 420 | 1.2372 | 0.4471 | | 0.7417 | 12.99 | 455 | 1.2826 | 0.4382 | | 0.8273 | 13.99 | 490 | 1.2425 | 0.4542 | | 0.7164 | 14.99 | 525 | 1.1415 | 0.4192 | | 0.7061 | 15.99 | 560 | 1.2315 | 0.4407 | | 0.6553 | 16.99 | 595 | 0.9983 | 0.4112 | | 0.7114 | 17.99 | 630 | 1.1510 | 0.4382 | | 0.6467 | 18.99 | 665 | 1.0612 | 0.4049 | | 0.6035 | 19.99 | 700 | 1.0360 | 0.4188 | | 0.6058 | 20.99 | 735 | 1.0008 | 0.4137 | | 0.682 | 21.99 | 770 | 1.1948 | 0.4209 | | 0.566 | 22.99 | 805 | 1.0555 | 0.4133 | | 0.5952 | 23.99 | 840 | 0.8615 | 0.4095 | | 0.5889 | 24.99 | 875 | 1.0740 | 0.4302 | | 0.5954 | 25.99 | 910 | 1.1465 | 0.4167 | | 0.5615 | 26.99 | 945 | 0.8980 | 0.4074 | | 0.5385 | 27.99 | 980 | 0.8443 | 0.4062 | | 0.5097 | 28.99 | 1015 | 1.1464 | 0.4049 | | 0.5224 | 29.99 | 1050 | 1.0213 | 0.4003 | | 0.5226 | 30.99 | 1085 | 0.8601 | 0.4091 | | 0.5303 | 31.99 | 1120 | 1.0191 | 0.3986 | | 0.6457 | 32.99 | 1155 | 1.2443 | 0.4306 | | 0.5305 | 33.99 | 1190 | 0.9872 | 0.4171 | | 0.5179 | 34.99 | 1225 | 1.0433 | 0.3935 | | 0.471 | 35.99 | 1260 | 1.0011 | 0.4074 | | 0.473 | 36.99 | 1295 | 0.8887 | 0.3901 | | 0.5465 | 37.99 | 1330 | 0.8612 | 0.3897 | | 0.4584 | 38.99 | 1365 | 0.9581 | 0.4070 | | 0.565 | 39.99 | 1400 | 1.0735 | 0.4083 | | 0.4916 | 40.99 | 1435 | 0.8890 | 0.3906 | | 0.4643 | 41.99 | 1470 | 0.7317 | 0.4040 | | 0.4633 | 42.99 | 1505 | 0.9384 | 0.4142 | | 0.4867 | 43.99 | 1540 | 0.8899 | 0.4074 | | 0.4892 | 44.99 | 1575 | 0.8419 | 0.4053 | | 0.4338 | 45.99 | 1610 | 0.8297 | 0.4024 | | 0.4038 | 46.99 | 1645 | 0.9689 | 0.3825 | | 0.4519 | 47.99 | 1680 | 0.8536 | 0.4053 | | 0.4298 | 48.99 | 1715 | 0.9737 | 0.3796 | | 0.4622 | 49.99 | 1750 | 0.9054 | 0.4074 | | 0.4358 | 50.99 | 1785 | 0.7809 | 0.3813 | | 0.4277 | 51.99 | 1820 | 0.8464 | 0.3922 | | 0.4186 | 52.99 | 1855 | 0.8106 | 0.3956 | | 0.413 | 53.99 | 1890 | 0.9219 | 0.3813 | | 0.4262 | 54.99 | 1925 | 0.9600 | 0.3990 | | 0.4542 | 55.99 | 1960 | 0.8444 | 0.4057 | | 0.3966 | 56.99 | 1995 | 0.7814 | 0.3914 | | 0.444 | 57.99 | 2030 | 0.8331 | 0.3771 | | 0.4673 | 58.99 | 2065 | 0.7872 | 0.3960 | | 0.483 | 59.99 | 2100 | 1.0760 | 0.4036 | | 0.5059 | 60.99 | 2135 | 0.8133 | 0.3981 | | 0.3927 | 61.99 | 2170 | 0.8601 | 0.4032 | | 0.4297 | 62.99 | 2205 | 0.7363 | 0.3880 | | 0.4034 | 63.99 | 2240 | 0.7639 | 0.4028 | | 0.3731 | 64.99 | 2275 | 0.8137 | 0.3686 | | 0.3793 | 65.99 | 2310 | 0.7646 | 0.3787 | | 0.3593 | 66.99 | 2345 | 0.7878 | 0.3952 | | 0.3616 | 67.99 | 2380 | 0.7936 | 0.4045 | | 0.3991 | 68.99 | 2415 | 0.7425 | 0.3775 | | 0.3709 | 69.99 | 2450 | 0.6933 | 0.3834 | | 0.3886 | 70.99 | 2485 | 0.7044 | 0.3728 | | 0.3624 | 71.99 | 2520 | 0.6916 | 0.3922 | | 0.3477 | 72.99 | 2555 | 0.7245 | 0.3872 | | 0.4116 | 73.99 | 2590 | 0.6823 | 0.3851 | | 0.3956 | 74.99 | 2625 | 0.7743 | 0.3846 | | 0.386 | 75.99 | 2660 | 0.7772 | 0.3943 | | 0.3755 | 76.99 | 2695 | 0.7823 | 0.3741 | | 0.3569 | 77.99 | 2730 | 0.7801 | 0.3880 | | 0.3403 | 78.99 | 2765 | 0.7619 | 0.3783 | | 0.3623 | 79.99 | 2800 | 0.7294 | 0.3834 | | 0.4157 | 80.99 | 2835 | 0.7345 | 0.3855 | | 0.3569 | 81.99 | 2870 | 0.7349 | 0.3804 | | 0.3988 | 82.99 | 2905 | 0.7232 | 0.3834 | | 0.3425 | 83.99 | 2940 | 0.7239 | 0.3792 | | 0.353 | 84.99 | 2975 | 0.7367 | 0.3758 | | 0.3756 | 85.99 | 3010 | 0.7283 | 0.3728 | | 0.3702 | 86.99 | 3045 | 0.7044 | 0.3792 | | 0.3339 | 87.99 | 3080 | 0.7279 | 0.3766 | | 0.3161 | 88.99 | 3115 | 0.7680 | 0.3796 | | 0.3573 | 89.99 | 3150 | 0.7498 | 0.3733 | | 0.3557 | 90.99 | 3185 | 0.7433 | 0.3779 | | 0.3563 | 91.99 | 3220 | 0.7249 | 0.3787 | | 0.3304 | 92.99 | 3255 | 0.7543 | 0.3783 | | 0.3596 | 93.99 | 3290 | 0.7329 | 0.3733 | | 0.3548 | 94.99 | 3325 | 0.7531 | 0.3720 | | 0.3269 | 95.99 | 3360 | 0.7377 | 0.3712 | | 0.3289 | 96.99 | 3395 | 0.7378 | 0.3749 | | 0.2978 | 97.99 | 3430 | 0.7200 | 0.3728 | | 0.3075 | 98.99 | 3465 | 0.7210 | 0.3724 | | 0.3402 | 99.99 | 3500 | 0.7173 | 0.3737 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.8.1+cu111 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
huggingtweets/americanair
huggingtweets
2022-11-24T02:44:05Z
113
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-11-24T02:43:56Z
--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1542861265265201152/q4ahKxz8_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">americanair</div> <div style="text-align: center; font-size: 14px;">@americanair</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from americanair. | Data | americanair | | --- | --- | | Tweets downloaded | 3250 | | Retweets | 0 | | Short tweets | 14 | | Tweets kept | 3236 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1bv83jgm/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @americanair's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/229s6wze) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/229s6wze/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/americanair') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
aspectcisco/ppo-LunarLander-v2
aspectcisco
2022-11-24T01:28:03Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-11-24T01:27:35Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 136.06 +/- 125.48 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
sd-concepts-library/manga-char-nov-23
sd-concepts-library
2022-11-24T01:01:13Z
0
0
null
[ "license:mit", "region:us" ]
null
2022-11-24T01:01:08Z
--- license: mit --- ### manga char nov 23 on Stable Diffusion This is the `<char-nov23>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<char-nov23> 0](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/376.png) ![<char-nov23> 1](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/459.png) ![<char-nov23> 2](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/59.png) ![<char-nov23> 3](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/372.png) ![<char-nov23> 4](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/154.png) ![<char-nov23> 5](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/418.png) ![<char-nov23> 6](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/228.png) ![<char-nov23> 7](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/47.png) ![<char-nov23> 8](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/269.png) ![<char-nov23> 9](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/363.png) ![<char-nov23> 10](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/482.png) ![<char-nov23> 11](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/95.png) ![<char-nov23> 12](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/255.png) ![<char-nov23> 13](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/70.png) ![<char-nov23> 14](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/413.png) ![<char-nov23> 15](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/444.png) ![<char-nov23> 16](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/34.png) ![<char-nov23> 17](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/244.png) ![<char-nov23> 18](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/432.png) ![<char-nov23> 19](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/316.png) ![<char-nov23> 20](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/110.png) ![<char-nov23> 21](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/265.png) ![<char-nov23> 22](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/164.png) ![<char-nov23> 23](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/355.png) ![<char-nov23> 24](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/296.png) ![<char-nov23> 25](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/73.png) ![<char-nov23> 26](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/60.png) ![<char-nov23> 27](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/156.png) ![<char-nov23> 28](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/55.png) ![<char-nov23> 29](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/240.png) ![<char-nov23> 30](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/314.png) ![<char-nov23> 31](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/89.png) ![<char-nov23> 32](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/145.png) ![<char-nov23> 33](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/131.png) ![<char-nov23> 34](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/41.png) ![<char-nov23> 35](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/74.png) ![<char-nov23> 36](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/43.png) ![<char-nov23> 37](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/69.png) ![<char-nov23> 38](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/206.png) ![<char-nov23> 39](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/515.png) ![<char-nov23> 40](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/379.png) ![<char-nov23> 41](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/489.png) ![<char-nov23> 42](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/250.png) ![<char-nov23> 43](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/104.png) ![<char-nov23> 44](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/405.png) ![<char-nov23> 45](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/516.png) ![<char-nov23> 46](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/328.png) ![<char-nov23> 47](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/415.png) ![<char-nov23> 48](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/139.png) ![<char-nov23> 49](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/84.png) ![<char-nov23> 50](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/83.png) ![<char-nov23> 51](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/377.png) ![<char-nov23> 52](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/386.png) ![<char-nov23> 53](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/170.png) ![<char-nov23> 54](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/94.png) ![<char-nov23> 55](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/153.png) ![<char-nov23> 56](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/193.png) ![<char-nov23> 57](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/493.png) ![<char-nov23> 58](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/317.png) ![<char-nov23> 59](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/151.png) ![<char-nov23> 60](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/178.png) ![<char-nov23> 61](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/249.png) ![<char-nov23> 62](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/112.png) ![<char-nov23> 63](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/149.png) ![<char-nov23> 64](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/238.png) ![<char-nov23> 65](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/284.png) ![<char-nov23> 66](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/218.png) ![<char-nov23> 67](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/486.png) ![<char-nov23> 68](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/140.png) ![<char-nov23> 69](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/72.png) ![<char-nov23> 70](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/357.png) ![<char-nov23> 71](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/76.png) ![<char-nov23> 72](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/221.png) ![<char-nov23> 73](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/231.png) ![<char-nov23> 74](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/157.png) ![<char-nov23> 75](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/463.png) ![<char-nov23> 76](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/136.png) ![<char-nov23> 77](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/334.png) ![<char-nov23> 78](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/354.png) ![<char-nov23> 79](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/273.png) ![<char-nov23> 80](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/177.png) ![<char-nov23> 81](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/518.png) ![<char-nov23> 82](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/1.png) ![<char-nov23> 83](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/457.png) ![<char-nov23> 84](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/75.png) ![<char-nov23> 85](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/48.png) ![<char-nov23> 86](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/248.png) ![<char-nov23> 87](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/123.png) ![<char-nov23> 88](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/181.png) ![<char-nov23> 89](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/57.png) ![<char-nov23> 90](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/37.png) ![<char-nov23> 91](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/279.png) ![<char-nov23> 92](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/7.png) ![<char-nov23> 93](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/190.png) ![<char-nov23> 94](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/111.png) ![<char-nov23> 95](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/359.png) ![<char-nov23> 96](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/195.png) ![<char-nov23> 97](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/118.png) ![<char-nov23> 98](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/440.png) ![<char-nov23> 99](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/361.png) ![<char-nov23> 100](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/23.png) ![<char-nov23> 101](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/407.png) ![<char-nov23> 102](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/378.png) ![<char-nov23> 103](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/28.png) ![<char-nov23> 104](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/168.png) ![<char-nov23> 105](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/3.png) ![<char-nov23> 106](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/204.png) ![<char-nov23> 107](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/365.png) ![<char-nov23> 108](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/447.png) ![<char-nov23> 109](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/318.png) ![<char-nov23> 110](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/196.png) ![<char-nov23> 111](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/498.png) ![<char-nov23> 112](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/30.png) ![<char-nov23> 113](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/488.png) ![<char-nov23> 114](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/243.png) ![<char-nov23> 115](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/67.png) ![<char-nov23> 116](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/399.png) ![<char-nov23> 117](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/134.png) ![<char-nov23> 118](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/419.png) ![<char-nov23> 119](https://huggingface.co/sd-concepts-library/manga-char-nov-23/resolve/main/concept_images/133.png) ![<char-nov23> 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