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Training results | Train Loss | Validation Loss | Train Rougel | Epoch | |:----------:|:---------------:|:---------------------------------------------:|:-----:| | 2.3199 | 3.2826 | tf.Tensor(0.3922559, shape=(), dtype=float32) | 0 | | {
"text": " Training results | Train Loss | Validation Loss | Train Rougel | Epoch | |:----------:|:---------------:|:---------------------------------------------:|:-----:| | 2.3199 | 3.2826 | tf.Tensor(0.3922559, shape=(), dtype=float32) | 0 | "
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] | Snorkel | null | null | null | false | null | 0000d04a-8e39-4ddc-a9b4-84dd88965d8d | {
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Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2452 | 1.0 | 878 | 0.0709 | 0.9184 | 0.9206 | 0.9195 | 0.9803 | | 0.0501 | 2.0 |... | {
"text": " Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2452 | 1.0 | 878 | 0.0709 | 0.9184 | 0.9206 | 0.9195 | 0.9803 | | 0.0501 ... | [
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] | Snorkel | null | null | null | false | null | 0000e88d-fd73-4f83-af10-e09c794b1c8a | {
"split": "unlabelled"
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starbot-transformers This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.4079 | {
"text": " starbot-transformers This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.4079 "
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"split": "unlabelled"
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distilbert-base-uncased-finetuned-paws This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the pawsx dataset. It achieves the following results on the evaluation set: - Loss: 0.3850 - Accuracy: 0.8355 - F1: 0.8362 | {
"text": " distilbert-base-uncased-finetuned-paws This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the pawsx dataset. It achieves the following results on the evaluation set: - Loss: 0.3850 - Accuracy: 0.8355 - F1: 0.8362 "
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] | Snorkel | null | null | null | false | null | 00033e9c-49a6-4480-b741-d8749b116cc2 | {
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Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0065 | 5.03 | 3000 | 0.6425 | 35.1077 | | {
"text": " Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0065 | 5.03 | 3000 | 0.6425 | 35.1077 | "
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] | Snorkel | null | null | null | false | null | 00036b60-002e-474c-b0a7-b589aceeb8ee | {
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mt5-small-finetuned-18jan-3 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6115 - Rouge1: 7.259 - Rouge2: 0.3667 - Rougel: 7.1595 - Rougelsum: 7.156 | {
"text": " mt5-small-finetuned-18jan-3 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6115 - Rouge1: 7.259 - Rouge2: 0.3667 - Rougel: 7.1595 - Rougelsum: 7.156 "
} | [
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] | Snorkel | null | null | null | false | null | 00040eee-1877-421c-8043-749aac151325 | {
"split": "unlabelled"
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deberta-base-mnli-finetuned-cola This model is a fine-tuned version of [microsoft/deberta-base-mnli](https://huggingface.co/microsoft/deberta-base-mnli) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8205 - Matthews Correlation: 0.6282 | {
"text": " deberta-base-mnli-finetuned-cola This model is a fine-tuned version of [microsoft/deberta-base-mnli](https://huggingface.co/microsoft/deberta-base-mnli) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8205 - Matthews Correlation: 0.6282 "
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] | Snorkel | null | null | null | false | null | 00068de5-8094-4434-9c89-47e8ff3ed6e8 | {
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Long-Form Transcription The Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking algorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers [`pipeline`](https://huggingface.co/docs/transfor... | {
"text": " Long-Form Transcription The Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking algorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers [`pipeline`](https://huggingface.co/... | [
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] | Snorkel | null | null | null | false | null | 00073259-8a7f-47ae-8d56-40d7483ef72e | {
"split": "unlabelled"
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bert-finetuned-race This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3863 - Accuracy: 0.2982 | {
"text": " bert-finetuned-race This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3863 - Accuracy: 0.2982 "
} | [
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] | Snorkel | null | null | null | false | null | 000cf5c3-0684-49f5-b3a7-22fc4cc4142a | {
"split": "unlabelled"
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Training The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_ctc/speech_to_text_ctc.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/citr... | {
"text": " Training The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_ctc/speech_to_text_ctc.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/... | [
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] | Snorkel | null | null | null | false | null | 000d88b1-3767-4188-afa4-2a0423c50158 | {
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Authors <b>IndoBART</b> was trained and evaluated by Samuel Cahyawijaya*, Genta Indra Winata*, Bryan Wilie*, Karissa Vincentio*, Xiaohong Li*, Adhiguna Kuncoro*, Sebastian Ruder, Zhi Yuan Lim, Syafri Bahar, Masayu Leylia Khodra, Ayu Purwarianti, Pascale Fung | {
"text": " Authors <b>IndoBART</b> was trained and evaluated by Samuel Cahyawijaya*, Genta Indra Winata*, Bryan Wilie*, Karissa Vincentio*, Xiaohong Li*, Adhiguna Kuncoro*, Sebastian Ruder, Zhi Yuan Lim, Syafri Bahar, Masayu Leylia Khodra, Ayu Purwarianti, Pascale Fung "
} | [
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] | Snorkel | null | null | null | false | null | 000f3d53-a41d-4b22-b2c3-1e73bcb59624 | {
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opus-mt-de-ha * source languages: de * target languages: ha * OPUS readme: [de-ha](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-ha/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://... | {
"text": " opus-mt-de-ha * source languages: de * target languages: ha * OPUS readme: [de-ha](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-ha/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.... | [
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Citations Please, cite this model using the following citation. ``` @inproceedings{dan2022electra-base-irony, title={北見工業大学 テキスト情報処理研究室 ELECTRA Base 皮肉検出モデル (Megagon Labs ver.)}, author={団 俊輔 and プタシンスキ ミハウ and ジェプカ ラファウ and 桝井 文人}, publisher={HuggingFace}, year={2022}, url = "https://huggingface.co/kit... | {
"text": " Citations Please, cite this model using the following citation. ``` @inproceedings{dan2022electra-base-irony, title={北見工業大学 テキスト情報処理研究室 ELECTRA Base 皮肉検出モデル (Megagon Labs ver.)}, author={団 俊輔 and プタシンスキ ミハウ and ジェプカ ラファウ and 桝井 文人}, publisher={HuggingFace}, year={2022}, url = \"https://hugg... | [
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] | Snorkel | null | null | null | false | null | 001956fd-8554-4511-83fa-6f4698fd27c7 | {
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S2T-SMALL-COVOST2-EN-ET-ST `s2t-small-covost2-en-et-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST). The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/spe... | {
"text": " S2T-SMALL-COVOST2-EN-ET-ST `s2t-small-covost2-en-et-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST). The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in [this repository](https://github.com/pytorch/fairseq/tree/master... | [
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] | Snorkel | null | null | null | false | null | 001d9d90-e547-484c-89e9-bd2f913dd0e0 | {
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xlm-roberta-large-xnli-finetuned-mnli-SJP This model is a fine-tuned version of [joeddav/xlm-roberta-large-xnli](https://huggingface.co/joeddav/xlm-roberta-large-xnli) on the swiss_judgment_prediction dataset. It achieves the following results on the evaluation set: - Loss: 1.3456 - Accuracy: 0.7957 | {
"text": " xlm-roberta-large-xnli-finetuned-mnli-SJP This model is a fine-tuned version of [joeddav/xlm-roberta-large-xnli](https://huggingface.co/joeddav/xlm-roberta-large-xnli) on the swiss_judgment_prediction dataset. It achieves the following results on the evaluation set: - Loss: 1.3456 - Accuracy: 0.7957 "
} | [
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] | Snorkel | null | null | null | false | null | 001e2fa3-ca8e-447d-8bd6-a0b23d49c479 | {
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Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 459 | 0.3655 | 0.8578 | 0.8990 | | 0.524 | 2.0 | 918 | 0.6061 | 0.8260 | 0.8823 | | 0.2971 |... | {
"text": " Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 459 | 0.3655 | 0.8578 | 0.8990 | | 0.524 | 2.0 | 918 | 0.6061 | 0.8260 | 0.8823 | | 0.... | [
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] | Snorkel | null | null | null | false | null | 001f7317-3eb8-4785-9701-a185159a667b | {
"split": "unlabelled"
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Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | {
"text": " Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: lin... | [
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] | Snorkel | null | null | null | false | null | 001f8ab1-c896-4d08-928d-85650fd793c3 | {
"split": "unlabelled"
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How does this make training possible with 1 minute of training data? The model has been trained on 168 datasets, ~20 hours of data, or ~19.8 thousand audio files. This is smaller than LJ speech but it has way more variety in voices, which LJ speech doesn't have. this variety allows the model to learn speech in differ... | {
"text": " How does this make training possible with 1 minute of training data? The model has been trained on 168 datasets, ~20 hours of data, or ~19.8 thousand audio files. This is smaller than LJ speech but it has way more variety in voices, which LJ speech doesn't have. this variety allows the model to learn spe... | [
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] | Snorkel | null | null | null | false | null | 00203ec4-f000-49c8-a04a-f342739afb51 | {
"split": "unlabelled"
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Nalisten-Likeness-1 Dreambooth model trained by nalisten1 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fas... | {
"text": " Nalisten-Likeness-1 Dreambooth model trained by nalisten1 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/T... | [
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] | Snorkel | null | null | null | false | null | 0022dc88-7014-49a9-b874-86c949516535 | {
"split": "unlabelled"
} | Default | {
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Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 296 | 0.6001 | 0.6435 | 0.6344 | 0.5087 | 0.4156 | | 0.6011 | 2.0 |... | {
"text": " Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 296 | 0.6001 | 0.6435 | 0.6344 | 0.5087 | 0.4156 | | 0.6011 ... | [
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] | Snorkel | null | null | null | false | null | 0023423a-6a07-426d-9375-d96c06dd09c0 | {
"split": "unlabelled"
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"text_length": 665
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Training results | Training Loss | Epoch | Step | Validation Loss | Gen Len | P | R | F1 | Score | Bleu-precisions | Bleu-bp | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:------:|:------:|:------------------... | {
"text": " Training results | Training Loss | Epoch | Step | Validation Loss | Gen Len | P | R | F1 | Score | Bleu-precisions | Bleu-bp | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:------:|:------:|:-----... | [
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] | Snorkel | null | null | null | false | null | 00237127-b6f9-4480-bdd6-e689a87df245 | {
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} | Default | {
"text_length": 579
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Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 1 | nan | 0.0114 | 3.3338 | | No log | 2.0 | 2 | nan | 0.0114 | 3.3338 | | No log | 3.0... | {
"text": " Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 1 | nan | 0.0114 | 3.3338 | | No log | 2.0 | 2 | nan | 0.0114 | 3.3338 | | No log... | [
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] | Snorkel | null | null | null | false | null | 00241479-1600-488f-880e-997b93cea609 | {
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wav2vec2-large-xls-r-300m-en-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the librispeech_asr dataset. It achieves the following results on the evaluation set: - Loss: 2.7541 - Wer: 1.0 - Cer: 0.9877 | {
"text": " wav2vec2-large-xls-r-300m-en-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the librispeech_asr dataset. It achieves the following results on the evaluation set: - Loss: 2.7541 - Wer: 1.0 - Cer: 0.9877 "
} | [
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This repository hosts the TFLite version of `diffusion model` part of [KerasCV Stable Diffusion](https://github.com/keras-team/keras-cv/tree/master/keras_cv/models/stable_diffusion). Stable Diffusion consists of `text encoder`, `diffusion model`, `decoder`, and some glue codes to handl inputs and outputs of each par... | {
"text": " This repository hosts the TFLite version of `diffusion model` part of [KerasCV Stable Diffusion](https://github.com/keras-team/keras-cv/tree/master/keras_cv/models/stable_diffusion). Stable Diffusion consists of `text encoder`, `diffusion model`, `decoder`, and some glue codes to handl inputs and output... | [
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] | Snorkel | null | null | null | false | null | 00257cf0-e62d-4caa-8d47-f319abd50432 | {
"split": "unlabelled"
} | Default | {
"text_length": 996
} | 0dataset_mention |
test-mlm This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6481 | {
"text": " test-mlm This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6481 "
} | [
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"label": "no_dataset_mention",
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] | Snorkel | null | null | null | false | null | 0026aaea-d2b7-4984-bb0d-84723d410e5e | {
"split": "unlabelled"
} | Default | {
"text_length": 229
} | 0dataset_mention |
exp_w2v2r_es_xls-r_accent_surpeninsular-10_nortepeninsular-0_s61 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this ... | {
"text": " exp_w2v2r_es_xls-r_accent_surpeninsular-10_nortepeninsular-0_s61 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). Whe... | [
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] | Snorkel | null | null | null | false | null | 0026c458-a7b9-4862-a40c-e8530a533d6a | {
"split": "unlabelled"
} | Default | {
"text_length": 493
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distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2248 - Accuracy: 0.9235 - F1: 0.9234 | {
"text": " distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2248 - Accuracy: 0.9235 - F1: 0.9234 "
} | [
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{
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] | Snorkel | null | null | null | false | null | 00276a3c-83c8-422b-bea5-ce5b4ca7bd4c | {
"split": "unlabelled"
} | Default | {
"text_length": 285
} | 0dataset_mention |
distilroberta-clickbait This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on a dataset of headlines. It achieves the following results on the evaluation set: - Loss: 0.0268 - Acc: 0.9963 | {
"text": " distilroberta-clickbait This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on a dataset of headlines. It achieves the following results on the evaluation set: - Loss: 0.0268 - Acc: 0.9963 "
} | [
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{
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] | Snorkel | null | null | null | false | null | 002fa958-fcb8-44d6-acef-8eb65b86999a | {
"split": "unlabelled"
} | Default | {
"text_length": 242
} | 0dataset_mention |
distilbert_sa_GLUE_Experiment_logit_kd_qnli_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3931 - Accuracy: 0.5870 | {
"text": " distilbert_sa_GLUE_Experiment_logit_kd_qnli_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3931 - Accuracy: 0.5870 "
} | [
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"score": 4.0600428406159373e-7
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] | Snorkel | null | null | null | false | null | 0031efcc-4300-4de5-9bd9-172927556516 | {
"split": "unlabelled"
} | Default | {
"text_length": 280
} | 0dataset_mention |
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | {
"text": " Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: line... | [
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] | Snorkel | null | null | null | false | null | 00336152-36d4-4b0e-84ab-9d4967b8f820 | {
"split": "unlabelled"
} | Default | {
"text_length": 406
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Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8185 | 1.0 | 70 | 0.3369 | 0.7449 | | 0.2899 | 2.0 | 140 | 0.2740 | 0.7919 | | {
"text": " Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8185 | 1.0 | 70 | 0.3369 | 0.7449 | | 0.2899 | 2.0 | 140 | 0.2740 | 0.7919 | "
} | [
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] | Snorkel | null | null | null | false | null | 00341ff0-df54-43b1-96d2-fed4cd42182d | {
"split": "unlabelled"
} | Default | {
"text_length": 261
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Model description The PAN model proposes a a lightweight convolutional neural network for image super resolution. Pixel attention (PA) is similar to channel attention and spatial attention in formulation. PA however produces 3D attention maps instead of a 1D attention vector or a 2D map. This attention scheme introduc... | {
"text": " Model description The PAN model proposes a a lightweight convolutional neural network for image super resolution. Pixel attention (PA) is similar to channel attention and spatial attention in formulation. PA however produces 3D attention maps instead of a 1D attention vector or a 2D map. This attention sc... | [
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] | Snorkel | null | null | null | false | null | 0039a2d9-932e-4028-8924-9df88a02e520 | {
"split": "unlabelled"
} | Default | {
"text_length": 473
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model prediction questions = model.generate_q(list_context="Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.", list_answer="Dopo il 1971") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-small-itquad-qg"... | {
"text": " model prediction questions = model.generate_q(list_context=\"Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.\", list_answer=\"Dopo il 1971\") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline(\"text2text-generation\", \"lmqg/... | [
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] | Snorkel | null | null | null | false | null | 003c8d21-3b19-4851-9d68-c06b4fa59df5 | {
"split": "unlabelled"
} | Default | {
"text_length": 445
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`kan-bayashi/vctk_tts_train_gst_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4036266/ This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | {
"text": " `kan-bayashi/vctk_tts_train_gst_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4036266/ This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/). "
} | [
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] | Snorkel | null | null | null | false | null | 003f0e39-a04c-49ce-9ec1-2de92621e02f | {
"split": "unlabelled"
} | Default | {
"text_length": 256
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Dreamy Painting on Stable Diffusion This is the `<dreamy-painting>` 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. Yo... | {
"text": " Dreamy Painting on Stable Diffusion This is the `<dreamy-painting>` 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)... | [
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] | Snorkel | null | null | null | false | null | 00401ada-92c8-4da9-967d-4667769228da | {
"split": "unlabelled"
} | Default | {
"text_length": 1572
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exp_w2v2t_th_xlsr-53_s218 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition on Thai using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your spe... | {
"text": " exp_w2v2t_th_xlsr-53_s218 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition on Thai using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure ... | [
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] | Snorkel | null | null | null | false | null | 0042bdfb-2c8e-440e-b4de-afc1c041ff40 | {
"split": "unlabelled"
} | Default | {
"text_length": 463
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Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.7272339744854407e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | {
"text": " Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.7272339744854407e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 "
} | [
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{
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] | Snorkel | null | null | null | false | null | 0043e17d-92f8-4a29-827c-2b48c3b8f3f0 | {
"split": "unlabelled"
} | Default | {
"text_length": 284
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Please Note! This model is NOT the 19.2M images Characters Model on TrinArt, but an improved version of the original Trin-sama Twitter bot model. This model is intended to retain the original SD's aesthetics as much as possible while nudging the model to anime/manga style. | {
"text": " Please Note! This model is NOT the 19.2M images Characters Model on TrinArt, but an improved version of the original Trin-sama Twitter bot model. This model is intended to retain the original SD's aesthetics as much as possible while nudging the model to anime/manga style."
} | [
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] | Snorkel | null | null | null | false | null | 00468179-c2f1-430e-a19d-ade3c5d678f0 | {
"split": "unlabelled"
} | Default | {
"text_length": 274
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Training procedure
The training was run on an NVIDIA DGX Station with 4XTesla V100 GPUs.
Training code is available at https://github.com/source-data/soda-roberta
- Model fine-tuned: EMBL/bio-lm
- Tokenizer vocab size: 50265
- Training data: EMBO/sd-nlp
- Dataset configuration: GENEPROD_ROLES
- Training w... | {
"text": " Training procedure\r \r The training was run on an NVIDIA DGX Station with 4XTesla V100 GPUs.\r \r Training code is available at https://github.com/source-data/soda-roberta\r \r - Model fine-tuned: EMBL/bio-lm\r - Tokenizer vocab size: 50265\r - Training data: EMBO/sd-nlp\r - Dataset configuration: GENEPR... | [
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] | Snorkel | null | null | null | false | null | 004773d8-f8d7-4ede-96f9-0efc932dc959 | {
"split": "unlabelled"
} | Default | {
"text_length": 705
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Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2885 | 1.0 | 522 | 1.2005 | | 1.2209 | 2.0 | 1044 | 1.1594 | | 1.1871 | 3.0 | 1566 | 1.1263 | | 1.1455 | 4.0 | 2088 | 1.1098 ... | {
"text": " Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2885 | 1.0 | 522 | 1.2005 | | 1.2209 | 2.0 | 1044 | 1.1594 | | 1.1871 | 3.0 | 1566 | 1.1263 | | 1.1455 | 4.0 | 20... | [
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] | Snorkel | null | null | null | false | null | 0048443c-7120-46dd-a41d-1050a079a076 | {
"split": "unlabelled"
} | Default | {
"text_length": 1165
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TODO: fill in a link to your training script here. If you trained your model in a colab, simply fill in the link here. If you trained the model locally, it would be great if you could upload the training script on github and paste the link here. --> | {
"text": " TODO: fill in a link to your training script here. If you trained your model in a colab, simply fill in the link here. If you trained the model locally, it would be great if you could upload the training script on github and paste the link here. --> "
} | [
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] | Snorkel | null | null | null | false | null | 004a2226-a758-4d2c-b6ad-2bb1b849abf0 | {
"split": "unlabelled"
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"text_length": 251
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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 sen... | {
"text": " 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 ... | [
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] | Snorkel | null | null | null | false | null | 004ef0a9-aab8-41d9-9df5-98bf718a6abd | {
"split": "unlabelled"
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lilt-en-funsd This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset. It achieves the following results on the evaluation set: - Loss: 1.7699 - Answer: {'precision': 0.8906439854191981, 'recall': 0.89718482252... | {
"text": " lilt-en-funsd This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset. It achieves the following results on the evaluation set: - Loss: 1.7699 - Answer: {'precision': 0.8906439854191981, 'recall': ... | [
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"split": "unlabelled"
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"text_length": 702
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Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 33276, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay'... | {
"text": " Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 33276, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': No... | [
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] | Snorkel | null | null | null | false | null | 00547293-cafe-493a-b496-6564934857f4 | {
"split": "unlabelled"
} | Default | {
"text_length": 459
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esm2_t12_35M_UR50D-finetuned-ARG-classification This model is a fine-tuned version of [facebook/esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) on an unknown dataset. It achieves the following results on the evaluation set: | {
"text": " esm2_t12_35M_UR50D-finetuned-ARG-classification This model is a fine-tuned version of [facebook/esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) on an unknown dataset. It achieves the following results on the evaluation set: "
} | [
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] | Snorkel | null | null | null | false | null | 0058a1bc-699f-45a9-b101-7d4526d3a859 | {
"split": "unlabelled"
} | Default | {
"text_length": 252
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berttest2 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0674 - Precision: 0.9138 - Recall: 0.9325 - F1: 0.9230 - Accuracy: 0.9823 | {
"text": " berttest2 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0674 - Precision: 0.9138 - Recall: 0.9325 - F1: 0.9230 - Accuracy: 0.9823 "
} | [
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] | Snorkel | null | null | null | false | null | 005a6c9d-78a5-4ae8-ab10-ac0bbb6aad97 | {
"split": "unlabelled"
} | Default | {
"text_length": 276
} | 0dataset_mention |
Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.5993 | 0.5 | 100 | 0.6257 | 37.9294 | | 0.352 | 1.35 | 200 | 0.5881 | 44.2387 | | {
"text": " Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.5993 | 0.5 | 100 | 0.6257 | 37.9294 | | 0.352 | 1.35 | 200 | 0.5881 | 44.2387 | "
} | [
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] | Snorkel | null | null | null | false | null | 005a743c-7224-47d8-8a3f-af026f6256c5 | {
"split": "unlabelled"
} | Default | {
"text_length": 265
} | 1no_dataset_mention |
Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 11.0967 | 1.0 | 118 | 4.6437 | 1.0 | | 3.4973 | 2.0 | 236 | 3.2588 | 1.0 | | 3.1305 | 3.0 | 354 | 2.6566 | 1.0 | |... | {
"text": " Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 11.0967 | 1.0 | 118 | 4.6437 | 1.0 | | 3.4973 | 2.0 | 236 | 3.2588 | 1.0 | | 3.1305 | 3.0 | 354 | 2.6566 ... | [
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] | Snorkel | null | null | null | false | null | 005c1785-a543-4b7b-a608-49fe20b47b19 | {
"split": "unlabelled"
} | Default | {
"text_length": 1941
} | 1no_dataset_mention |
distilbart-cnn-arxiv-pubmed-pubmed-v3-e8 This model is a fine-tuned version of [theojolliffe/distilbart-cnn-arxiv-pubmed-pubmed](https://huggingface.co/theojolliffe/distilbart-cnn-arxiv-pubmed-pubmed) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8422 - Rouge1: 54.9328 - Ro... | {
"text": " distilbart-cnn-arxiv-pubmed-pubmed-v3-e8 This model is a fine-tuned version of [theojolliffe/distilbart-cnn-arxiv-pubmed-pubmed](https://huggingface.co/theojolliffe/distilbart-cnn-arxiv-pubmed-pubmed) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8422 - Rouge1:... | [
{
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{
"label": "no_dataset_mention",
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] | Snorkel | null | null | null | false | null | 005cb28f-0845-42f0-9dd0-9d6cfb096778 | {
"split": "unlabelled"
} | Default | {
"text_length": 391
} | 0dataset_mention |
finetuned_token_itr0_3e-05_all_16_02_2022-20_12_04 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1620 - Precision: 0... | {
"text": " finetuned_token_itr0_3e-05_all_16_02_2022-20_12_04 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1620 -... | [
{
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{
"label": "no_dataset_mention",
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] | Snorkel | null | null | null | false | null | 00604cae-27a0-49bc-8b0e-788c01e2f829 | {
"split": "unlabelled"
} | Default | {
"text_length": 376
} | 0dataset_mention |
funnel-transformer-xlarge_cls_CR This model is a fine-tuned version of [funnel-transformer/xlarge](https://huggingface.co/funnel-transformer/xlarge) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2563 - Accuracy: 0.9388 | {
"text": " funnel-transformer-xlarge_cls_CR This model is a fine-tuned version of [funnel-transformer/xlarge](https://huggingface.co/funnel-transformer/xlarge) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2563 - Accuracy: 0.9388 "
} | [
{
"label": "dataset_mention",
"score": 0.9976600967163771
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{
"label": "no_dataset_mention",
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] | Snorkel | null | null | null | false | null | 0064f624-7b0d-4aba-85c6-4eedbed33b3a | {
"split": "unlabelled"
} | Default | {
"text_length": 266
} | 0dataset_mention |
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.8087 | 1.0 | 157 | 0.7144 | | 0.7182 | 2.0 | 314 | 0.6918 | | 0.7041 | 3.0 | 471 | 0.6918 | | {
"text": " Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.8087 | 1.0 | 157 | 0.7144 | | 0.7182 | 2.0 | 314 | 0.6918 | | 0.7041 | 3.0 | 471 | 0.6918 | "
} | [
{
"label": "no_dataset_mention",
"score": 0.9487513515880792
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{
"label": "dataset_mention",
"score": 0.051248648411920804
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] | Snorkel | null | null | null | false | null | 0069f3b9-ac6b-44c5-9459-71631669e0ee | {
"split": "unlabelled"
} | Default | {
"text_length": 276
} | 1no_dataset_mention |
Danish BERT fine-tuned for Sentiment Analysis with `senda` This model detects polarity ('positive', 'neutral', 'negative') of Danish texts. It is trained and tested on Tweets annotated by [Alexandra Institute](https://github.com/alexandrainst). The model is trained with the [`senda`](https://github.com/ebanalyse/se... | {
"text": " Danish BERT fine-tuned for Sentiment Analysis with `senda` This model detects polarity ('positive', 'neutral', 'negative') of Danish texts. It is trained and tested on Tweets annotated by [Alexandra Institute](https://github.com/alexandrainst). The model is trained with the [`senda`](https://github.com... | [
{
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{
"label": "no_dataset_mention",
"score": 0.22646879185573326
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] | Snorkel | null | null | null | false | null | 006cbf85-88c2-4bf1-b0ff-7247e8121134 | {
"split": "unlabelled"
} | Default | {
"text_length": 694
} | 0dataset_mention |
distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2235 - Accuracy: 0.9265 - F1: 0.9268 | {
"text": " distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2235 - Accuracy: 0.9265 - F1: 0.9268 "
} | [
{
"label": "dataset_mention",
"score": 0.9976600967163771
},
{
"label": "no_dataset_mention",
"score": 0.0023399032836228136
}
] | Snorkel | null | null | null | false | null | 006ed878-7e4a-4edb-9fc4-0ea6ad435a8d | {
"split": "unlabelled"
} | Default | {
"text_length": 285
} | 0dataset_mention |
model by no3 This your the Stable Diffusion model fine-tuned the azura-sd-1.4-beta3 concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **sks_azura** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.g... | {
"text": " model by no3 This your the Stable Diffusion model fine-tuned the azura-sd-1.4-beta3 concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **sks_azura** You can also train your own concepts and upload them to the library by using [this notebook](https://col... | [
{
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},
{
"label": "no_dataset_mention",
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] | Snorkel | null | null | null | false | null | 0072383d-19a6-4827-903b-5504fac9aeae | {
"split": "unlabelled"
} | Default | {
"text_length": 1444
} | 0dataset_mention |
wav2vec2-base-20sec-timit-and-dementiabank This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4338 - Wer: 0.2313 | {
"text": " wav2vec2-base-20sec-timit-and-dementiabank This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4338 - Wer: 0.2313 "
} | [
{
"label": "dataset_mention",
"score": 0.9976600967163771
},
{
"label": "no_dataset_mention",
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}
] | Snorkel | null | null | null | false | null | 0072895f-57e7-4ff9-9d06-4483abcf3cbb | {
"split": "unlabelled"
} | Default | {
"text_length": 263
} | 0dataset_mention |
Details model architecture This model checkpoint - **t5-efficient-small-el8-dl2** - is of model type **Small** with the following variations: - **el** is **8** - **dl** is **2** It has **50.03** million parameters and thus requires *ca.* **200.11 MB** of memory in full precision (*fp32*) or **100.05 MB** of memory... | {
"text": " Details model architecture This model checkpoint - **t5-efficient-small-el8-dl2** - is of model type **Small** with the following variations: - **el** is **8** - **dl** is **2** It has **50.03** million parameters and thus requires *ca.* **200.11 MB** of memory in full precision (*fp32*) or **100.05 M... | [
{
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},
{
"label": "no_dataset_mention",
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}
] | Snorkel | null | null | null | false | null | 0072c779-6d01-4a98-a68f-ae0a52ffc168 | {
"split": "unlabelled"
} | Default | {
"text_length": 473
} | 0dataset_mention |
mini-mlm-tweet-target-imdb This model is a fine-tuned version of [muhtasham/mini-mlm-tweet](https://huggingface.co/muhtasham/mini-mlm-tweet) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.4742 - Accuracy: 0.8324 - F1: 0.9085 | {
"text": " mini-mlm-tweet-target-imdb This model is a fine-tuned version of [muhtasham/mini-mlm-tweet](https://huggingface.co/muhtasham/mini-mlm-tweet) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.4742 - Accuracy: 0.8324 - F1: 0.9085 "
} | [
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"score": 0.9976600967163771
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{
"label": "no_dataset_mention",
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] | Snorkel | null | null | null | false | null | 00761bf3-e16e-48e9-bcee-4a432c9b91c0 | {
"split": "unlabelled"
} | Default | {
"text_length": 269
} | 0dataset_mention |
Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2.380655430044305e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2.3... | {
"text": " Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2.380655430044305e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learni... | [
{
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},
{
"label": "dataset_mention",
"score": 0.051248648411920804
}
] | Snorkel | null | null | null | false | null | 0076ceca-5efc-4185-9ba5-b92711652c3a | {
"split": "unlabelled"
} | Default | {
"text_length": 666
} | 1no_dataset_mention |
模型介绍 模型分成四部分： * Text Encoder：把中文文本输入转化成 Embedding 向量 * Latent Diffusion Model：在 Latent 空间中根据文本输入处理随机生成的噪声 * Auto Encoder：将 Latent 空间中的张量还原为图片 * Super Resolution：提升图片分辨率 我们使用中文模型CLIP-ViT-L作为 Text Encoder，使用 [latent-diffusion](https://github.com/CompVis/latent-diffusion) 中的 Auto Encoder，使用 [ESRGAN](https://github.co... | {
"text": " 模型介绍 模型分成四部分： * Text Encoder：把中文文本输入转化成 Embedding 向量 * Latent Diffusion Model：在 Latent 空间中根据文本输入处理随机生成的噪声 * Auto Encoder：将 Latent 空间中的张量还原为图片 * Super Resolution：提升图片分辨率 我们使用中文模型CLIP-ViT-L作为 Text Encoder，使用 [latent-diffusion](https://github.com/CompVis/latent-diffusion) 中的 Auto Encoder，使用 [ESRGAN](http... | [
{
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{
"label": "no_dataset_mention",
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] | Snorkel | null | null | null | false | null | 0078a37a-3034-4dcf-9754-e43b19737409 | {
"split": "unlabelled"
} | Default | {
"text_length": 571
} | 0dataset_mention |
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 5 - eval_batch_size: 5 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 | {
"text": " Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 5 - eval_batch_size: 5 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 "
} | [
{
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{
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}
] | Snorkel | null | null | null | false | null | 007c38eb-6042-4403-8e23-693e1e16ed3f | {
"split": "unlabelled"
} | Default | {
"text_length": 265
} | 1no_dataset_mention |
Whisper Large Nepali - Drishti Sharma This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2551 - Wer: 18.8467 | {
"text": " Whisper Large Nepali - Drishti Sharma This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2551 - Wer: 18.8467 "
} | [
{
"label": "dataset_mention",
"score": 0.9997774755092824
},
{
"label": "no_dataset_mention",
"score": 0.00022252449071764603
}
] | Snorkel | null | null | null | false | null | 0082113b-9207-4ed2-8cdb-e1aaf2dc5fd7 | {
"split": "unlabelled"
} | Default | {
"text_length": 268
} | 0dataset_mention |
kobart_16_5.6e-5_datav2_min30_lp5.0_temperature1.0 This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.7174 - Rouge1: 35.7621 - Rouge2: 12.8914 - Rougel: 23.6695 - Bleu1: 2... | {
"text": " kobart_16_5.6e-5_datav2_min30_lp5.0_temperature1.0 This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.7174 - Rouge1: 35.7621 - Rouge2: 12.8914 - Rougel: 23.66... | [
{
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{
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] | Snorkel | null | null | null | false | null | 00878d6e-e7d1-4849-8a00-6105e036dc21 | {
"split": "unlabelled"
} | Default | {
"text_length": 395
} | 0dataset_mention |
opus-mt-es-en-finetuned-es-to-en This model is a fine-tuned version of [Helsinki-NLP/opus-mt-es-en](https://huggingface.co/Helsinki-NLP/opus-mt-es-en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5851 - Bleu: 71.1382 - Gen Len: 10.3225 | {
"text": " opus-mt-es-en-finetuned-es-to-en This model is a fine-tuned version of [Helsinki-NLP/opus-mt-es-en](https://huggingface.co/Helsinki-NLP/opus-mt-es-en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5851 - Bleu: 71.1382 - Gen Len: 10.3225 "
} | [
{
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"score": 0.9976600967163771
},
{
"label": "no_dataset_mention",
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}
] | Snorkel | null | null | null | false | null | 0087b79a-b8fa-4952-9d4b-6c73ff528e3f | {
"split": "unlabelled"
} | Default | {
"text_length": 282
} | 0dataset_mention |
fathyshalab/domain_transfer_general-massive_general-roberta-large-v1-5-95 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://... | {
"text": " fathyshalab/domain_transfer_general-massive_general-roberta-large-v1-5-95 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transfor... | [
{
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{
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] | Snorkel | null | null | null | false | null | 00887d92-d0b8-4dfc-8178-b22debc313f4 | {
"split": "unlabelled"
} | Default | {
"text_length": 453
} | 0dataset_mention |
wav2vec2-base-ft-keyword-spotting This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset. It achieves the following results on the evaluation set: - Loss: 0.0824 - Accuracy: 0.9826 | {
"text": " wav2vec2-base-ft-keyword-spotting This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset. It achieves the following results on the evaluation set: - Loss: 0.0824 - Accuracy: 0.9826 "
} | [
{
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"score": 0.9976600967163771
},
{
"label": "no_dataset_mention",
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] | Snorkel | null | null | null | false | null | 0088c808-76d0-49f2-9dc5-8279986ad1c5 | {
"split": "unlabelled"
} | Default | {
"text_length": 261
} | 0dataset_mention |
BibTeX Entry and Citation Info ``` @article{wang2021you, title={You Only Learn One Representation: Unified Network for Multiple Tasks}, author={Wang, Chien-Yao and Yeh, I-Hau and Liao, Hong-Yuan Mark}, journal={arXiv preprint arXiv:2105.04206}, year={2021} } ``` | {
"text": " BibTeX Entry and Citation Info ``` @article{wang2021you, title={You Only Learn One Representation: Unified Network for Multiple Tasks}, author={Wang, Chien-Yao and Yeh, I-Hau and Liao, Hong-Yuan Mark}, journal={arXiv preprint arXiv:2105.04206}, year={2021} } ```"
} | [
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{
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] | Snorkel | null | null | null | false | null | 008ec3d2-1f30-41ab-a7dc-984391a6af80 | {
"split": "unlabelled"
} | Default | {
"text_length": 272
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T5-Efficient-LARGE-NL2 (Deep-Narrow version) T5-Efficient-LARGE-NL2 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint an... | {
"text": " T5-Efficient-LARGE-NL2 (Deep-Narrow version) T5-Efficient-LARGE-NL2 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* ... | [
{
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"score": 0.7735312081442667
},
{
"label": "no_dataset_mention",
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}
] | Snorkel | null | null | null | false | null | 0092aa08-9697-4b0c-ab73-91acf2cf8b72 | {
"split": "unlabelled"
} | Default | {
"text_length": 2073
} | 0dataset_mention |
T5-Efficient-SMALL-DM2000 (Deep-Narrow version) T5-Efficient-SMALL-DM2000 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpo... | {
"text": " T5-Efficient-SMALL-DM2000 (Deep-Narrow version) T5-Efficient-SMALL-DM2000 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-... | [
{
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},
{
"label": "no_dataset_mention",
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}
] | Snorkel | null | null | null | false | null | 0099b49d-0d2e-41b6-8235-4d68592caf1e | {
"split": "unlabelled"
} | Default | {
"text_length": 2079
} | 0dataset_mention |
Available Models - **unimo-text-1.0**, *12 layer, 12 heads, 768 hidden size, pretrained model* - **unimo-text-1.0-large**, *24 layer, 16 heads, 1024 hidden size, pretrained model* - **unimo-text-1.0-lcsts-new**, *12 layer, 12 heads, 768 hidden size, finetuned on the lcsts-new Chinese summarization dataset* - **unimo-... | {
"text": " Available Models - **unimo-text-1.0**, *12 layer, 12 heads, 768 hidden size, pretrained model* - **unimo-text-1.0-large**, *24 layer, 16 heads, 1024 hidden size, pretrained model* - **unimo-text-1.0-lcsts-new**, *12 layer, 12 heads, 768 hidden size, finetuned on the lcsts-new Chinese summarization datase... | [
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{
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] | Snorkel | null | null | null | false | null | 009c8e37-c1cd-4698-a2f3-afcdb89fbb8e | {
"split": "unlabelled"
} | Default | {
"text_length": 441
} | 0dataset_mention |
exp_w2v2t_it_no-pretraining_s615 Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (it)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has bee... | {
"text": " exp_w2v2t_it_no-pretraining_s615 Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (it)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This ... | [
{
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"score": 0.9729673904845365
},
{
"label": "no_dataset_mention",
"score": 0.027032609515463546
}
] | Snorkel | null | null | null | false | null | 009f8052-1c14-442a-9af0-cfd73a2b34e0 | {
"split": "unlabelled"
} | Default | {
"text_length": 413
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also support OFA checkpoints. e.g. "OFA-Sys/ofa-large" if torch.cuda.is_available(): model.cuda() prompt = "please describe this image according to the given question: what piece of clothing is this boy putting on?" image = "glove_boy.jpeg" print(model.caption(prompt, image)) ``` To try generic captioning, just ... | {
"text": " also support OFA checkpoints. e.g. \"OFA-Sys/ofa-large\" if torch.cuda.is_available(): model.cuda() prompt = \"please describe this image according to the given question: what piece of clothing is this boy putting on?\" image = \"glove_boy.jpeg\" print(model.caption(prompt, image)) ``` To try generi... | [
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] | Snorkel | null | null | null | false | null | 00a0a159-4677-4adc-bf3b-4a95004e4fa8 | {
"split": "unlabelled"
} | Default | {
"text_length": 670
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Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 4.4466 | 1.0 | 2067 | 4.1217 | 0.3847 | | 3.9191 | 2.0 | 4134 | 3.6562 | 0.4298 | | 3.6397 | 3.0 | 6201 | 3.4417 ... | {
"text": " Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 4.4466 | 1.0 | 2067 | 4.1217 | 0.3847 | | 3.9191 | 2.0 | 4134 | 3.6562 | 0.4298 | | 3.6397 | 3.0 | 6201 | 3.... | [
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] | Snorkel | null | null | null | false | null | 00a0d3bb-b90a-4074-8cb1-de7c5f023697 | {
"split": "unlabelled"
} | Default | {
"text_length": 462
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Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 272 | 1.9605 | 9.0786 | 17.3148 | | 2.3992 | 2.0 | 544 | 1.8884 | 10.1443 | 17.3301 | | 2.3992 |... | {
"text": " Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 272 | 1.9605 | 9.0786 | 17.3148 | | 2.3992 | 2.0 | 544 | 1.8884 | 10.1443 | 17.3301 | | 2.... | [
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] | Snorkel | null | null | null | false | null | 00a168df-bbcc-4820-8760-f73ebedeac21 | {
"split": "unlabelled"
} | Default | {
"text_length": 873
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DialoGPT Trained on the Speech of a Game Character This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a game character, Joshua from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game script d... | {
"text": " DialoGPT Trained on the Speech of a Game Character This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a game character, Joshua from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle ... | [
{
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] | Snorkel | null | null | null | false | null | 00a2159b-7f8e-4f60-af73-cb68f19e6f62 | {
"split": "unlabelled"
} | Default | {
"text_length": 784
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Bicleaner AI full model for en-sq Bicleaner AI is a tool that aims at detecting noisy sentence pairs in a parallel corpus. It indicates the likelihood of a pair of sentences being mutual translations (with a value near to 1) or not (with a value near to 0). Sentence pairs considered very noisy are scored with 0. Find... | {
"text": " Bicleaner AI full model for en-sq Bicleaner AI is a tool that aims at detecting noisy sentence pairs in a parallel corpus. It indicates the likelihood of a pair of sentences being mutual translations (with a value near to 1) or not (with a value near to 0). Sentence pairs considered very noisy are scored ... | [
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] | Snorkel | null | null | null | false | null | 00a4e766-4631-44d9-955f-d0785a066201 | {
"split": "unlabelled"
} | Default | {
"text_length": 427
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Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.5361 | 2.3102 | 0 | | 1.9179 | 1.8637 | 1 | | 1.6133 | 1.8637 | 2 | | {
"text": " Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.5361 | 2.3102 | 0 | | 1.9179 | 1.8637 | 1 | | 1.6133 | 1.8637 | 2 | "
} | [
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{
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] | Snorkel | null | null | null | false | null | 00aa52f2-b52d-4378-bab9-b738ea7f851e | {
"split": "unlabelled"
} | Default | {
"text_length": 226
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Intended uses & limitations You can directly use this model as a language detector, i.e. for sequence classification tasks. Currently, it supports the following 41 languages, modern and medieval: Modern: Bulgarian (bg), Croatian (hr), Czech (cs), Danish (da), Dutch (nl), English (en), Estonian (et), Finnish (fi), Fre... | {
"text": " Intended uses & limitations You can directly use this model as a language detector, i.e. for sequence classification tasks. Currently, it supports the following 41 languages, modern and medieval: Modern: Bulgarian (bg), Croatian (hr), Czech (cs), Danish (da), Dutch (nl), English (en), Estonian (et), Finn... | [
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] | Snorkel | null | null | null | false | null | 00ad7ee4-1981-483e-b1f8-e0caea27a6cd | {
"split": "unlabelled"
} | Default | {
"text_length": 883
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Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6744 | 0.12 | 50 | 0.6094 | 0.66 | | 0.4942 | 0.23 | 100 | 0.3772 | 0.8667 | | 0.3857 | 0.35 | 150 | 0.3256 | 0.... | {
"text": " Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6744 | 0.12 | 50 | 0.6094 | 0.66 | | 0.4942 | 0.23 | 100 | 0.3772 | 0.8667 | | 0.3857 | 0.35 | 150 | 0.3256 ... | [
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] | Snorkel | null | null | null | false | null | 00ae3d4b-82a2-48ab-977b-c0f5937b86a7 | {
"split": "unlabelled"
} | Default | {
"text_length": 2811
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Overview  We propose DiffCSE, an unsupervised contrastive learning framework for learning sentence embeddings. DiffCSE learns sentence embeddings that are sensitive to the difference between the original sentence and an edited sentence, where the ed... | {
"text": " Overview  We propose DiffCSE, an unsupervised contrastive learning framework for learning sentence embeddings. DiffCSE learns sentence embeddings that are sensitive to the difference between the original sentence and an edited sentence,... | [
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] | Snorkel | null | null | null | false | null | 00af7821-c19f-4511-8398-8d08c6e0a0bb | {
"split": "unlabelled"
} | Default | {
"text_length": 984
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PIXEL (Pixel-based Encoder of Language) PIXEL is a language model trained to reconstruct masked image patches that contain rendered text. PIXEL was pretrained on the *English* Wikipedia and Bookcorpus (in total around 3.2B words) but can theoretically be finetuned on data in any written language that can be typeset o... | {
"text": " PIXEL (Pixel-based Encoder of Language) PIXEL is a language model trained to reconstruct masked image patches that contain rendered text. PIXEL was pretrained on the *English* Wikipedia and Bookcorpus (in total around 3.2B words) but can theoretically be finetuned on data in any written language that can... | [
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{
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"score": 0.020599810543146933
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] | Snorkel | null | null | null | false | null | 00afba6c-d709-4b30-9219-6f5de8b1b4ce | {
"split": "unlabelled"
} | Default | {
"text_length": 661
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Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.3356 | 1.0 | 1033 | 0.2558 | 0.3761 | | 0.2588 | 2.0 | 2066 | 0.2352 | 0.5246 | | 0.2252 | 3.0 | 3099 | 0.2292 | 0.599... | {
"text": " Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.3356 | 1.0 | 1033 | 0.2558 | 0.3761 | | 0.2588 | 2.0 | 2066 | 0.2352 | 0.5246 | | 0.2252 | 3.0 | 3099 | 0.2292 ... | [
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] | Snorkel | null | null | null | false | null | 00b399ab-d7ec-4890-98a5-61332e116c74 | {
"split": "unlabelled"
} | Default | {
"text_length": 753
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MODEL BY ShadoWxShinigamI Use Token - Rangoli mdjrny-rngli at the beginning of your prompt Training - 2240 steps, v1-5 Base, 28 images, 640x640 Prompt engineering is not required. In case something doesn't work, use Weighted prompts. Examples:- ![clown.png](https://s3.amazonaws.com/moonup/production/uploads/1669208... | {
"text": "MODEL BY ShadoWxShinigamI Use Token - Rangoli mdjrny-rngli at the beginning of your prompt Training - 2240 steps, v1-5 Base, 28 images, 640x640 Prompt engineering is not required. In case something doesn't work, use Weighted prompts. Examples:- ![clown.png](https://s3.amazonaws.com/moonup/production/up... | [
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] | Snorkel | null | null | null | false | null | 00b4e9a8-8ef4-44bb-a6d4-8dab19c00e86 | {
"split": "unlabelled"
} | Default | {
"text_length": 901
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Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 24 - eval_batch_size: 48 - seed: 2022 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.15 - num_epochs: 50 - mixed_precisio... | {
"text": " Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 24 - eval_batch_size: 48 - seed: 2022 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.15 - num_epochs: 50 - m... | [
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{
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] | Snorkel | null | null | null | false | null | 00b68cfa-064b-4c81-a61d-0efe19bfd097 | {
"split": "unlabelled"
} | Default | {
"text_length": 344
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Model and Samples - [`speecht5_vc.pt`](./speecht5_vc.pt) are reimplemented Voice Conversion fine-tuning on the released manifest **but with a smaller batch size or max updates** (Ensure the manifest is ok). - `samples` are created by the released fine-tuned model and vocoder. | {
"text": " Model and Samples - [`speecht5_vc.pt`](./speecht5_vc.pt) are reimplemented Voice Conversion fine-tuning on the released manifest **but with a smaller batch size or max updates** (Ensure the manifest is ok). - `samples` are created by the released fine-tuned model and vocoder. "
} | [
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] | Snorkel | null | null | null | false | null | 00b76191-51e2-4c6d-a742-2ed61c29c89d | {
"split": "unlabelled"
} | Default | {
"text_length": 280
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requirement packages !pip install git+https://github.com/huggingface/datasets.git !pip install git+https://github.com/huggingface/transformers.git !pip install torchaudio !pip install librosa !pip install jiwer ``` **Normalizer** ```bash !wget -O normalizer.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-lith... | {
"text": " requirement packages !pip install git+https://github.com/huggingface/datasets.git !pip install git+https://github.com/huggingface/transformers.git !pip install torchaudio !pip install librosa !pip install jiwer ``` **Normalizer** ```bash !wget -O normalizer.py https://huggingface.co/m3hrdadfi/wav2vec2-la... | [
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] | Snorkel | null | null | null | false | null | 00b9bcf8-e228-47e9-8c66-50f964bf6bc2 | {
"split": "unlabelled"
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sick Dreambooth model trained by Z3R069 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion... | {
"text": " sick Dreambooth model trained by Z3R069 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-sta... | [
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] | Snorkel | null | null | null | false | null | 00bb67a5-d968-4591-a198-16fa5bdb2d8d | {
"split": "unlabelled"
} | Default | {
"text_length": 602
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Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4126 | 0.1 | 500 | 2.2797 | 127.2639 | | 0.2099 | 0.1 | 1000 | 0.1774 | 28.2494 | | 0.1736 | 0.2 | 1500 | 0.1565 | 27... | {
"text": " Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4126 | 0.1 | 500 | 2.2797 | 127.2639 | | 0.2099 | 0.1 | 1000 | 0.1774 | 28.2494 | | 0.1736 | 0.2 | 1500 | 0.1565 ... | [
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] | Snorkel | null | null | null | false | null | 00bdacb8-acb3-4cbf-8fa1-dae260bc5e17 | {
"split": "unlabelled"
} | Default | {
"text_length": 765
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monogptari-6.7b This model is a fine-tuned version of [facebook/opt-6.7b](https://huggingface.co/facebook/opt-6.7b) on an english monogatari (物語) dataset. It achieves the following results on the evaluation set: - Loss: 0.7030 - Accuracy: 0.8436 | {
"text": " monogptari-6.7b This model is a fine-tuned version of [facebook/opt-6.7b](https://huggingface.co/facebook/opt-6.7b) on an english monogatari (物語) dataset. It achieves the following results on the evaluation set: - Loss: 0.7030 - Accuracy: 0.8436 "
} | [
{
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{
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"score": 0.0023399032836228136
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] | Snorkel | null | null | null | false | null | 00be849d-db9a-47b4-a56d-8fc5579b2d46 | {
"split": "unlabelled"
} | Default | {
"text_length": 249
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Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 5.0001 | 1.0 | 935 | 3.1102 | 0.0 | 0.0 | 0.0 | 0.0 | | 3.4066 | 2.0 | 1870 ... | {
"text": " Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 5.0001 | 1.0 | 935 | 3.1102 | 0.0 | 0.0 | 0.0 | 0.0 | | 3.4066 | ... | [
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{
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] | Snorkel | null | null | null | false | null | 00c0a362-41dc-46dc-8a56-85e12c6ad3e7 | {
"split": "unlabelled"
} | Default | {
"text_length": 561
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recklessrecursion/2008_Sichuan_earthquake-clustered This model is a fine-tuned version of [nandysoham16/12-clustered_aug](https://huggingface.co/nandysoham16/12-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5049 - Train End Logits Accuracy: 0.8507 - Tra... | {
"text": " recklessrecursion/2008_Sichuan_earthquake-clustered This model is a fine-tuned version of [nandysoham16/12-clustered_aug](https://huggingface.co/nandysoham16/12-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5049 - Train End Logits Accuracy:... | [
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] | Snorkel | null | null | null | false | null | 00c19819-671b-4fb8-a4e7-7873d90598de | {
"split": "unlabelled"
} | Default | {
"text_length": 475
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Setup Create two Habana instances ([AWS EC2 DL1](https://aws.amazon.com/ec2/instance-types/dl1/)) using [Habana® Deep Learning Base AMI (Ubuntu 20.04)](https://aws.amazon.com/marketplace/pp/prodview-fw46rwuxrtfse) Create the PyTorch docker container running: ```bash docker run --name pytorch -td --runtime=habana -... | {
"text": " Setup Create two Habana instances ([AWS EC2 DL1](https://aws.amazon.com/ec2/instance-types/dl1/)) using [Habana® Deep Learning Base AMI (Ubuntu 20.04)](https://aws.amazon.com/marketplace/pp/prodview-fw46rwuxrtfse) Create the PyTorch docker container running: ```bash docker run --name pytorch -td --run... | [
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{
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] | Snorkel | null | null | null | false | null | 00c2dafa-d8d7-4de5-9280-f9e490dc6f38 | {
"split": "unlabelled"
} | Default | {
"text_length": 617
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Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3.75e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 100.0 - mixed_pre... | {
"text": " Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3.75e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 100.... | [
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{
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] | Snorkel | null | null | null | false | null | 00c2edce-2307-43d1-baf3-b44f58aa7ba8 | {
"split": "unlabelled"
} | Default | {
"text_length": 349
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Training data We trained different variants T0 with different mixtures of datasets. |Model|Training datasets| |--|--| |FLIPPED-11B|- Multiple-Choice QA: CommonsenseQA, DREAM, QUAIL, QuaRTz, Social IQA, WiQA, Cosmos, QASC, Quarel, SciQ<br>- Sentiment: Amazon, App Reviews, IMDB, Rotten Tomatoes, Yelp<br>- Topic Classif... | {
"text": " Training data We trained different variants T0 with different mixtures of datasets. |Model|Training datasets| |--|--| |FLIPPED-11B|- Multiple-Choice QA: CommonsenseQA, DREAM, QUAIL, QuaRTz, Social IQA, WiQA, Cosmos, QASC, Quarel, SciQ<br>- Sentiment: Amazon, App Reviews, IMDB, Rotten Tomatoes, Yelp<br>- ... | [
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{
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"score": 2.513507231577376e-7
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] | Snorkel | null | null | null | false | null | 00c4ddcc-5984-4ada-99fc-8f321cbd318f | {
"split": "unlabelled"
} | Default | {
"text_length": 525
} | 0dataset_mention |
Evaluation The model can be evaluated as follows on the Estonian test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "et", split="test") we... | {
"text": " Evaluation The model can be evaluated as follows on the Estonian test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset(\"common_voice\", \"et\",... | [
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] | Snorkel | null | null | null | false | null | 00c55438-c247-4e89-935f-7df5e0adc903 | {
"split": "unlabelled"
} | Default | {
"text_length": 591
} | 0dataset_mention |
opus-mt-fi-ZH * source languages: fi * target languages: cmn,cn,yue,ze_zh,zh_cn,zh_CN,zh_HK,zh_tw,zh_TW,zh_yue,zhs,zht,zh * OPUS readme: [fi-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+z... | {
"text": " opus-mt-fi-ZH * source languages: fi * target languages: cmn,cn,yue,ze_zh,zh_cn,zh_CN,zh_HK,zh_tw,zh_TW,zh_yue,zhs,zht,zh * OPUS readme: [fi-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-cmn+cn+yue+ze_zh+zh_cn+zh_CN+... | [
{
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{
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"score": 0.0023399032836228136
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] | Snorkel | null | null | null | false | null | 00c5d0e8-b1c1-4d6a-b317-d28588056498 | {
"split": "unlabelled"
} | Default | {
"text_length": 1107
} | 0dataset_mention |
How to use Now we are ready to try out how the model works as a chatting partner! ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch mode_name = 'liam168/chat-DialoGPT-small-zh' tokenizer = AutoTokenizer.from_pretrained(mode_name) model = AutoModelForCausalLM.from_pretrained(mode_na... | {
"text": " How to use Now we are ready to try out how the model works as a chatting partner! ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch mode_name = 'liam168/chat-DialoGPT-small-zh' tokenizer = AutoTokenizer.from_pretrained(mode_name) model = AutoModelForCausalLM.from_pretr... | [
{
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] | Snorkel | null | null | null | false | null | 00c6214d-eead-4277-93e0-76cdf3857e6e | {
"split": "unlabelled"
} | Default | {
"text_length": 325
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Results The model achieves a 80.1 zero-shot top-1 accuracy on ImageNet-1k. An initial round of benchmarks have been performed on a wider range of datasets, and will soon be visible at https://github.com/LAION-AI/CLIP_benchmark/blob/main/benchmark/results.ipynb **TODO** - create table for just this model's metrics. ... | {
"text": " Results The model achieves a 80.1 zero-shot top-1 accuracy on ImageNet-1k. An initial round of benchmarks have been performed on a wider range of datasets, and will soon be visible at https://github.com/LAION-AI/CLIP_benchmark/blob/main/benchmark/results.ipynb **TODO** - create table for just this mode... | [
{
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{
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] | Snorkel | null | null | null | false | null | 00c641eb-5b9e-44b4-8e3a-84f32f2fe5e6 | {
"split": "unlabelled"
} | Default | {
"text_length": 321
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Example of usage: ```python from transformers import AlbertTokenizer, GPT2LMHeadModel tokenizer = AlbertTokenizer.from_pretrained("kyryl0s/gpt2-uk-zno-edition") model = GPT2LMHeadModel.from_pretrained("kyryl0s/gpt2-uk-zno-edition") input_ids = tokenizer.encode("ZNOTITLE: За яку працю треба більше поважати людину - за ... | {
"text": " Example of usage: ```python from transformers import AlbertTokenizer, GPT2LMHeadModel tokenizer = AlbertTokenizer.from_pretrained(\"kyryl0s/gpt2-uk-zno-edition\") model = GPT2LMHeadModel.from_pretrained(\"kyryl0s/gpt2-uk-zno-edition\") input_ids = tokenizer.encode(\"ZNOTITLE: За яку працю треба більше пов... | [
{
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{
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"score": 0.22646879185573326
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] | Snorkel | null | null | null | false | null | 00c87102-21a5-43ff-ac23-13999e0335f7 | {
"split": "unlabelled"
} | Default | {
"text_length": 603
} | 0dataset_mention |
Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 42 | 1.0675 | 51.743 | 31.3774 | 34.1939 | 48.7234 | 14... | {
"text": " Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 42 | 1.0675 | 51.743 | 31.3774 | 34.1939 | 4... | [
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{
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] | Snorkel | null | null | null | false | null | 00cc1010-e80c-48c2-9a80-2dedcbcbcfc9 | {
"split": "unlabelled"
} | Default | {
"text_length": 639
} | 1no_dataset_mention |
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