Datasets:
davanstrien
/
model_cards_with_readmes_sections

Dataset card Data Studio Files
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apache-2.0
['MRC', 'SQuAD 1.1', 'roberta-large']
false
Model description An RoBERTa reading comprehension model for [SQuAD 1.1](https://aclanthology.org/D16-1264/). The model is initialized with [roberta-large](https://huggingface.co/roberta-large/) and fine-tuned on the [SQuAD 1.1 train data](https://huggingface.co/datasets/squad).
de5f3db27e1b9622d686e9a4dc89f2d3
apache-2.0
['MRC', 'SQuAD 1.1', 'roberta-large']
false
Intended uses & limitations You can use the raw model for the reading comprehension task. Biases associated with the pre-existing language model, roberta-large, that we used may be present in our fine-tuned model, squad-v1-roberta-large.
30ab28e3f71862087b031f38b8590c5c
apache-2.0
['MRC', 'SQuAD 1.1', 'roberta-large']
false
Usage You can use this model directly with the [PrimeQA](https://github.com/primeqa/primeqa) pipeline for reading comprehension [squad.ipynb](https://github.com/primeqa/primeqa/blob/main/notebooks/mrc/squad.ipynb). ```bibtex @article{2016arXiv160605250R, author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev}, Konstantin and {Liang}, Percy}, title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}", journal = {arXiv e-prints}, year = 2016, eid = {arXiv:1606.05250}, pages = {arXiv:1606.05250}, archivePrefix = {arXiv}, eprint = {1606.05250}, } ``` ```bibtex @article{DBLP:journals/corr/abs-1907-11692, author = {Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov}, title = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach}, journal = {CoRR}, volume = {abs/1907.11692}, year = {2019}, url = {http://arxiv.org/abs/1907.11692}, archivePrefix = {arXiv}, eprint = {1907.11692}, timestamp = {Thu, 01 Aug 2019 08:59:33 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
3a98689a702dac8323166d027705b6d2
openrail
['generated_from_trainer']
false
gpt2-shikoto This model was trained on a dataset I obtained from an online novel site. **Please be aware that the stories (training data) might contain inappropriate content. This model is intended for research purposes only.** The base model can be found [here](https://huggingface.co/jed351/gpt2-base-zh-hk), which was obtained by patching a [GPT2 Chinese model](https://huggingface.co/ckiplab/gpt2-base-chinese) and its tokenizer with Cantonese characters. Refer to the base model for info on the patching process. Besides language modeling, another aim of this experiment was to test the accelerate library by offloading certain workloads to CPU as well as finding the optimal training iterations. The perplexity of this model is 16.12 after 400,000 steps. Comparing to the previous [attempt](https://huggingface.co/jed351/gpt2_tiny_zh-hk-shikoto) 27.02 after 400,000 steps. It took around the same time duration to train this model but I only used 1 GPU here.
f368e8a726f7bf8c1cdbe0e54d241283
openrail
['generated_from_trainer']
false
Training procedure Please refer to the [script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling) provided by Huggingface. The model was trained for 400,000 steps on 1 NVIDIA Quadro RTX6000 for around 30 hours at the Research Computing Services of Imperial College London.
531c73d2c336da639f8f64f4c93418dd
openrail
['generated_from_trainer']
false
How to use it? ``` from transformers import AutoTokenizer from transformers import TextGenerationPipeline, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jed351/gpt2-base-zh-hk") model = AutoModelForCausalLM.from_pretrained("jed351/gpt2_base_zh-hk-shikoto")
aae5f03d8def5d1fbd57b7f62078135b
openrail
['generated_from_trainer']
false
try messing around with the parameters generator = TextGenerationPipeline(model, tokenizer, max_new_tokens=200, no_repeat_ngram_size=3)
5d4266d0948388e29183854db27909d3
cc-by-4.0
['question generation', 'answer extraction']
false
Model Card of `lmqg/t5-base-squad-qg-ae` This model is fine-tuned version of [t5-base](https://huggingface.co/t5-base) for question generation and answer extraction jointly on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
09c5a79f8ed157539c0e311f3dfa3580
cc-by-4.0
['question generation', 'answer extraction']
false
Overview - **Language model:** [t5-base](https://huggingface.co/t5-base) - **Language:** en - **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
ad1c09736807c93cea10870364f4767a
cc-by-4.0
['question generation', 'answer extraction']
false
model prediction question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/t5-base-squad-qg-ae")
cb8a72a53d6f2a20f1598d549c73e759
cc-by-4.0
['question generation', 'answer extraction']
false
question generation question = pipe("extract answers: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress.") ```
844b9e63c6e3d96867fb04a5befaba32
cc-by-4.0
['question generation', 'answer extraction']
false
Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-base-squad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:---------------------------------------------------------------| | BERTScore | 90.58 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_1 | 58.59 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_2 | 42.6 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_3 | 32.91 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_4 | 26.01 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | METEOR | 27 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | MoverScore | 64.72 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | ROUGE_L | 53.4 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-base-squad-qg-ae/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:---------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 92.53 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedF1Score (MoverScore) | 64.23 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedPrecision (BERTScore) | 92.35 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedPrecision (MoverScore) | 64.33 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedRecall (BERTScore) | 92.74 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedRecall (MoverScore) | 64.23 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | - ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/t5-base-squad-qg-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:-----------------|--------:|:--------|:---------------------------------------------------------------| | AnswerExactMatch | 58.9 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | AnswerF1Score | 70.18 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | BERTScore | 91.57 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_1 | 56.96 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_2 | 52.57 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_3 | 48.21 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_4 | 44.33 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | METEOR | 43.94 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | MoverScore | 82.16 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | ROUGE_L | 69.62 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
59479c76886a148bedfed8573015b426
cc-by-4.0
['question generation', 'answer extraction']
false
Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: ['paragraph_answer', 'paragraph_sentence'] - output_types: ['question', 'answer'] - prefix_types: ['qg', 'ae'] - model: t5-base - max_length: 512 - max_length_output: 32 - epoch: 6 - batch: 32 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 4 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/t5-base-squad-qg-ae/raw/main/trainer_config.json).
eddbce3c35ea631193b3b5051d360298
apache-2.0
['generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard']
false
wav2vec2-large-xls-r-300m-guarani-small This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4964 - Wer: 0.5957
c3a8ea5cda0f2f94f517742252c99c62
apache-2.0
['generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 30 - mixed_precision_training: Native AMP
77257090192d192f3571ba4de7f31456
apache-2.0
['generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 6.65 | 100 | 1.1326 | 1.0 | | 1.6569 | 13.32 | 200 | 0.5264 | 0.6478 | | 1.6569 | 19.97 | 300 | 0.5370 | 0.6261 | | 0.2293 | 26.65 | 400 | 0.4964 | 0.5957 |
0b9c1a2d5ca5c4eb2b62ee7ba4373df9
mit
['exbert']
false
TOD-XLMR TOD-XLMR is a conversationally specialized multilingual version based on [XLM-RoBERTa](https://huggingface.co/xlm-roberta-base). It is pre-trained on English conversational corpora consisting of nine human-to-human multi-turn task-oriented dialog (TOD) datasets as proposed in the paper [TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue](https://aclanthology.org/2020.emnlp-main.66.pdf) by Wu et al. and first released in [this repository](https://huggingface.co/TODBERT). The model is jointly trained with two objectives as proposed in TOD-BERT, including masked language modeling (MLM) and response contrastive loss (RCL). Masked language modeling is a common pretraining strategy utilized for BERT-based architectures, where a random sample of tokens in the input sequence is replaced with the special token [MASK] for predicting the original masked tokens. To further encourage the model to capture dialogic structure (i.e., dialog sequential order), response contrastive loss is implemented by using in-batch negative training with contrastive learning.
f1fec47d29b9eb2507e38337771f5420
mit
['exbert']
false
How to use Here is how to use this model to get the features of a given text in PyTorch: ``` from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("umanlp/TOD-XLMR") model = AutoModelForMaskedLM.from_pretrained("umanlp/TOD-XLMR")
f44cc188b504f4eb97bfe2d418e83f13
mit
['exbert']
false
forward pass output = model(**encoded_input) ``` Or you can also use `AutoModel` to load the pretrained model and further apply to downstream tasks: ``` from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("umanlp/TOD-XLMR") model = AutoModel("umanlp/TOD-XLMR")
31696168a747d17323c22944946f7093
mit
['generated_from_trainer']
false
xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1608 - F1: 0.8593
7bfa7e2b501a23408544ffc80bb402fe
apache-2.0
['Axon', 'Elixir']
false
ResNet This ResNet50 model was translated from the ONNX ResNetv1 model found at https://github.com/onnx/models/tree/main/vision/classification/resnet into Axon using [AxonOnnx](https://github.com/elixir-nx/axon_onnx) The following description is copied from the relevant description at the ONNX repository.
f73492648bee0c39ea92e10d13c61205
apache-2.0
['Axon', 'Elixir']
false
Use cases These ResNet models perform image classification - they take images as input and classify the major object in the image into a set of pre-defined classes. They are trained on ImageNet dataset which contains images from 1000 classes. ResNet models provide very high accuracies with affordable model sizes. They are ideal for cases when high accuracy of classification is required. ImageNet trained models are often used as the base layers for a transfer learning approach to training a model in your domain. Transfer learning can significantly reduce the processing necessary to train an accurate model in your domain. This model was published here with the expectation that it would be useful to the Elixir community for transfer learning and other similar approaches.
3a0c9bc97f5e4c5be7d20afb73976ab7
apache-2.0
['Axon', 'Elixir']
false
Description Deeper neural networks are more difficult to train. Residual learning framework ease the training of networks that are substantially deeper. The research explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. It also provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset the residual nets were evaluated with a depth of up to 152 layers — 8× deeper than VGG nets but still having lower complexity.
9061d4d387c5388c5db63bd4c8b9f8b6
apache-2.0
['Axon', 'Elixir']
false
Model ResNet models consists of residual blocks and came up to counter the effect of deteriorating accuracies with more layers due to network not learning the initial layers. ResNet v1 uses post-activation for the residual blocks.
441838aa974a3f2846ccd1031424726f
apache-2.0
['Axon', 'Elixir']
false
Input All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (N x 3 x H x W), where N is the batch size, and H and W are expected to be at least 224. The inference was done using jpeg image.
c67d5461ffe927d07d093335436fe069
apache-2.0
['Axon', 'Elixir']
false
Preprocessing The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225]. The transformation should preferably happen at preprocessing.
91d9fe687d98972a8bee6672e91de2bf
apache-2.0
['Axon', 'Elixir']
false
Postprocessing The post-processing involves calculating the softmax probability scores for each class. You can also sort them to report the most probable classes. Check [imagenet_postprocess.py](../imagenet_postprocess.py) for code.
d0b25679b85aee53adb990e70424aee0
apache-2.0
['Axon', 'Elixir']
false
Dataset Dataset used for train and validation: [ImageNet (ILSVRC2012)](http://www.image-net.org/challenges/LSVRC/2012/). Check [imagenet_prep](../imagenet_prep.md) for guidelines on preparing the dataset.
d2b3ab59cf59ca796961d1d3f6960f7d
apache-2.0
['Axon', 'Elixir']
false
References * **ResNetv1** [Deep residual learning for image recognition](https://arxiv.org/abs/1512.03385) He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778. 2016. * **ONNX source model** [onnx/models vision/classification/resnet resnet50-v1-7.onnx](https://github.com/onnx/models/tree/main/vision/classification/resnet/README)
1a20790fc55b3abab5b3fafb96c3fc50
apache-2.0
['translation']
false
lit-epo * source group: Lithuanian * target group: Esperanto * OPUS readme: [lit-epo](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/lit-epo/README.md) * model: transformer-align * source language(s): lit * target language(s): epo * model: transformer-align * pre-processing: normalization + SentencePiece (spm4k,spm4k) * download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/lit-epo/opus-2020-06-16.zip) * test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/lit-epo/opus-2020-06-16.test.txt) * test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/lit-epo/opus-2020-06-16.eval.txt)
4dc76db4a8ed3dc159e70a9ddefb5e13
apache-2.0
['translation']
false
System Info: - hf_name: lit-epo - source_languages: lit - target_languages: epo - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/lit-epo/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['lt', 'eo'] - src_constituents: {'lit'} - tgt_constituents: {'epo'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm4k,spm4k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/lit-epo/opus-2020-06-16.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/lit-epo/opus-2020-06-16.test.txt - src_alpha3: lit - tgt_alpha3: epo - short_pair: lt-eo - chrF2_score: 0.313 - bleu: 13.0 - brevity_penalty: 1.0 - ref_len: 70340.0 - src_name: Lithuanian - tgt_name: Esperanto - train_date: 2020-06-16 - src_alpha2: lt - tgt_alpha2: eo - prefer_old: False - long_pair: lit-epo - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
c807e13fec4b916aae132147825a0b15
apache-2.0
['automatic-speech-recognition', 'fr']
false
exp_w2v2r_fr_vp-100k_age_teens-8_sixties-2_s607 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](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 been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
da25df9372f65d4a9087c55fc0f6c625
apache-2.0
['generated_from_trainer']
false
flan-t5-base-juraqanda This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0784 - Rouge1: 9.5491 - Rouge2: 1.4927 - Rougel: 8.828 - Rougelsum: 9.2708 - Gen Len: 18.5260
5c6d135105ac9c879d602fcd815d59c1
apache-2.0
['generated_from_trainer']
false
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: 5
1dfee99edb7096dd7255628f4e8af898
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:| | 4.0303 | 1.0 | 712 | 3.3466 | 9.4455 | 1.2684 | 8.8558 | 9.1832 | 18.7577 | | 3.6049 | 2.0 | 1424 | 3.1931 | 10.0714 | 1.4116 | 9.4163 | 9.8024 | 18.6461 | | 3.3464 | 3.0 | 2136 | 3.1246 | 9.6542 | 1.4317 | 8.9441 | 9.36 | 18.5485 | | 3.2831 | 4.0 | 2848 | 3.0910 | 9.6676 | 1.4584 | 8.9533 | 9.3876 | 18.6706 | | 3.2176 | 5.0 | 3560 | 3.0784 | 9.5491 | 1.4927 | 8.828 | 9.2708 | 18.5260 |
1aa25836a59a9d7589d0350e0019282c
apache-2.0
['generated_from_trainer']
false
mt5-base-finetuned-rabbi-kook This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3861
4b43730ad356142e72b9cb8d230737b5
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2
766307dc09a647c2c26511921c4a5dce
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.2102 | 1.0 | 3567 | 2.4526 | | 3.0283 | 2.0 | 7134 | 2.3861 |
d1a61acdaf78985fb1081ac2b30545eb
apache-2.0
['generated_from_trainer']
false
t5-small-finetuned-eli5 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the eli5 dataset. It achieves the following results on the evaluation set: - Loss: 3.6782 - Rouge1: 13.0163 - Rouge2: 1.9263 - Rougel: 10.484 - Rougelsum: 11.8234 - Gen Len: 18.9951
6330aef12c4bed8f6b8aec9114679c90
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:| | 3.8841 | 1.0 | 17040 | 3.6782 | 13.0163 | 1.9263 | 10.484 | 11.8234 | 18.9951 |
2b80d6f2f69a3c60a46e26be578a32e2
creativeml-openrail-m
['text-to-image']
false
white-walker-style Dreambooth model trained by sztanki with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Sample pictures of: white (use that on your prompt) ![white 0](https://huggingface.co/sztanki/white-walker-style/resolve/main/concept_images/white_walker_style_%281%29.jpg)![white 1](https://huggingface.co/sztanki/white-walker-style/resolve/main/concept_images/white_walker_style_%282%29.jpg)![white 2](https://huggingface.co/sztanki/white-walker-style/resolve/main/concept_images/white_walker_style_%283%29.jpg)![white 3](https://huggingface.co/sztanki/white-walker-style/resolve/main/concept_images/white_walker_style_%284%29.jpg)![white 4](https://huggingface.co/sztanki/white-walker-style/resolve/main/concept_images/white_walker_style_%285%29.jpg)![white 5](https://huggingface.co/sztanki/white-walker-style/resolve/main/concept_images/white_walker_style_%286%29.jpg)![white 6](https://huggingface.co/sztanki/white-walker-style/resolve/main/concept_images/white_walker_style_%287%29.jpg)![white 7](https://huggingface.co/sztanki/white-walker-style/resolve/main/concept_images/white_walker_style_%288%29.jpg)![white 8](https://huggingface.co/sztanki/white-walker-style/resolve/main/concept_images/white_walker_style_%289%29.jpg)![white 9](https://huggingface.co/sztanki/white-walker-style/resolve/main/concept_images/white_walker_style_%2810%29.jpg)![white 10](https://huggingface.co/sztanki/white-walker-style/resolve/main/concept_images/white_walker_style_%2811%29.jpg)![white 11](https://huggingface.co/sztanki/white-walker-style/resolve/main/concept_images/white_walker_style_%2812%29.jpg)![white 12](https://huggingface.co/sztanki/white-walker-style/resolve/main/concept_images/white_walker_style_%2813%29.jpg)![white 13](https://huggingface.co/sztanki/white-walker-style/resolve/main/concept_images/white_walker_style_%2814%29.jpg)![white 14](https://huggingface.co/sztanki/white-walker-style/resolve/main/concept_images/white_walker_style_%2815%29.jpg)![white 15](https://huggingface.co/sztanki/white-walker-style/resolve/main/concept_images/white_walker_style_%2816%29.jpg)
5c3c1004782940524db365347c3ef85d
cc-by-4.0
['generated_from_trainer']
false
roberta-base-squad2-finetuned This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0010
fa206ba6460bb0cb62e1284bbf5b2d81
cc-by-4.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 27 | 0.0023 | | No log | 2.0 | 54 | 0.0010 |
39433e92701ffccb85e8f9bdc0acffce
apache-2.0
['whisper-event', 'generated_from_trainer']
false
Whisper Small Lithuanian and Serbian sequentially trained 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:
cd46f80cc1b71b07cfb10fbbfd295e63
apache-2.0
['whisper-event', 'generated_from_trainer']
false
Training hyperparameters per fine-tune The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - 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 - training_steps: 2000 - mixed_precision_training: Native AMP
94c6257c863f6a171b780cf6bb8c2f08
apache-2.0
['generated_from_trainer']
false
all-roberta-large-v1-banking-16-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7470 - Accuracy: 0.0756
79bfd8fca61ff67ac86ee4309739b36d
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.8182 | 1.0 | 1 | 2.7709 | 0.0356 | | 2.6751 | 2.0 | 2 | 2.7579 | 0.0578 | | 2.5239 | 3.0 | 3 | 2.7509 | 0.0622 | | 2.4346 | 4.0 | 4 | 2.7470 | 0.0756 | | 2.4099 | 5.0 | 5 | 2.7452 | 0.0756 |
8e2f555b15eaf0d8211534d878febe93
mit
['generated_from_trainer']
false
dreamy_poitras 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.
44f2f03cb90c05e69eba98d031a5156c
mit
['generated_from_trainer']
false
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, 'skip_tokens': 1649999872}, 'generation': {'force_call_on': [25177], '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': [25177], 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<|aligned|>'}, 'model': {'from_scratch': False, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'revision': '9e6c78543a6ff1e4089002c38864d5a9cf71ec90'}, 'num_additional_tokens': 2, 'path_or_name': 'tomekkorbak/nervous_wozniak'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2', 'special_tokens': ['<|aligned|>', '<|misaligned|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 128, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'dreamy_poitras', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0001, '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': 25177, 'save_strategy': 'steps', 'seed': 42, 'tokens_already_seen': 1649999872, 'warmup_ratio': 0.01, 'weight_decay': 0.1}}
6b0eb87871efd02b17dba016b79bbb9f
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 8.7297 | 0.19 | 500 | 8.5541 | | 8.5592 | 0.39 | 1000 | 8.5536 | | 8.4892 | 0.58 | 1500 | 8.5554 | | 8.5288 | 0.77 | 2000 | 8.4786 | | 8.5034 | 0.97 | 2500 | 8.4756 | | 8.3497 | 1.16 | 3000 | 8.4821 | | 8.4516 | 1.36 | 3500 | 8.4742 | | 8.4224 | 1.55 | 4000 | 8.3972 | | 8.3356 | 1.74 | 4500 | 8.4158 | | 8.3805 | 1.94 | 5000 | 8.3800 | | 8.2947 | 2.13 | 5500 | 8.4242 | | 8.2475 | 2.32 | 6000 | 8.4334 | | 8.2708 | 2.52 | 6500 | 8.3504 | | 8.2559 | 2.71 | 7000 | 8.4211 | | 8.3676 | 2.9 | 7500 | 8.3744 |
b580dcecb75ece9ec12ef9e93ef958e5
apache-2.0
['generated_from_trainer']
false
clinical_trial_stop_reasons_custom 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: 0.1448 - Accuracy Thresh: 0.9570 - F1 Micro: 0.5300 - F1 Macro: 0.1254 - Confusion Matrix: [[5940 15] [ 270 150]]
9f39cc48a6cc035a3ffa360e0b59fe61
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7
a06fdf7fe6c7517ac7ac3472f262689d
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy Thresh | F1 Micro | F1 Macro | Confusion Matrix | |:-------------:|:-----:|:----:|:---------------:|:---------------:|:--------:|:--------:|:--------------------------:| | No log | 1.0 | 106 | 0.2812 | 0.8328 | 0.0 | 0.0 | [[5955 0] [ 420 0]] | | No log | 2.0 | 212 | 0.2189 | 0.9382 | 0.0 | 0.0 | [[5955 0] [ 420 0]] | | No log | 3.0 | 318 | 0.1840 | 0.9489 | 0.0 | 0.0 | [[5955 0] [ 420 0]] | | No log | 4.0 | 424 | 0.1638 | 0.9485 | 0.4940 | 0.0989 | [[5943 12] [ 288 132]] | | 0.239 | 5.0 | 530 | 0.1526 | 0.9533 | 0.5060 | 0.1018 | [[5943 12] [ 277 143]] | | 0.239 | 6.0 | 636 | 0.1467 | 0.9564 | 0.5077 | 0.1020 | [[5938 17] [ 275 145]] | | 0.239 | 7.0 | 742 | 0.1448 | 0.9570 | 0.5300 | 0.1254 | [[5940 15] [ 270 150]] |
8962b5c0c8cbb02b16a87cd6e5c5bbe7
mit
['generated_from_trainer']
false
finetuned_gpt2_sst2_negation0.2_pretrainedFalse This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 5.3370
87c262282bb6039a8abdbd95a22a6ec3
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.9034 | 1.0 | 1072 | 5.5636 | | 4.5404 | 2.0 | 2144 | 5.3854 | | 4.368 | 3.0 | 3216 | 5.3370 |
eecea2cf59961a0f3af46c2b6c732f23
apache-2.0
[]
false
模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 通用 General | 自然语言转换 NLT | 燃灯 Randeng | Transformer | 1.1B | 中文-改写 Chinese-Paraphrase |
67b41f8936b032d31600f9a44a2714de
apache-2.0
[]
false
加载模型 Loading Models ```shell git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git ``` ```python from fengshen.models.transfo_xl_paraphrase import TransfoXLModel from transformers import T5Tokenizer as TransfoXLTokenizer model = TransfoXLModel.from_pretrained('IDEA-CCNL/Randeng-TransformerXL-1.1B-Paraphrasing-Chinese') tokenizer = TransfoXLTokenizer.from_pretrained('IDEA-CCNL/Randeng-TransformerXL-1.1B-Paraphrasing-Chinese', eos_token = '<|endoftext|>', extra_ids=0) ```
6ecdab57f16b62b7fd5519a489184371
apache-2.0
[]
false
使用示例 Usage Examples ```python from fengshen.models.transfo_xl_paraphrase import paraphrase_generatete input_text = "年轻教师选择农村学校，还是县城学校？" res = paraphrase_generate(model, tokenizer, input_text, device=0) print(res)
ccb3c866aa7d90dd7e42b5ae34bdd9fa
apache-2.0
[]
false
引用 Citation 如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2209.02970)： If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2209.02970): ```text @article{fengshenbang, author = {Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen and Ruyi Gan and Jiaxing Zhang}, title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence}, journal = {CoRR}, volume = {abs/2209.02970}, year = {2022} } ``` 也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/): You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/): ```text @misc{Fengshenbang-LM, title={Fengshenbang-LM}, author={IDEA-CCNL}, year={2021}, howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}}, } ```
6462d4adc2b2f58a57e23f33c97340be
cc-by-4.0
['espnet', 'audio', 'text-to-speech']
false
`kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4381098/ This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
072cae3a56105822c8530f7ac56b9056
apache-2.0
['generated_from_trainer']
false
mobilebert_add_GLUE_Experiment_logit_kd_pretrain_wnli This model is a fine-tuned version of [gokuls/mobilebert_add_pre-training-complete](https://huggingface.co/gokuls/mobilebert_add_pre-training-complete) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: nan - Accuracy: 0.5634
e1f2743199b467bfb78c5daf93d79f85
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0 | 1.0 | 5 | nan | 0.5634 | | 0.0 | 2.0 | 10 | nan | 0.5634 | | 0.0 | 3.0 | 15 | nan | 0.5634 | | 0.0 | 4.0 | 20 | nan | 0.5634 | | 0.0 | 5.0 | 25 | nan | 0.5634 | | 0.0 | 6.0 | 30 | nan | 0.5634 |
41d9ecfc097f438fd6f9a4d3438d5871
apache-2.0
['audio', 'automatic-speech-recognition', 'speech', 'asr', 'hubert']
false
Usage download file ```shell wget https://raw.githubusercontent.com/voidful/hubert-cluster-code/main/km_feat_100_layer_20 wget https://cdn-media.huggingface.co/speech_samples/sample1.flac ``` Hubert kmeans code ```python import joblib import torch from transformers import Wav2Vec2FeatureExtractor, HubertModel import soundfile as sf class HubertCode(object): def __init__(self, hubert_model, km_path, km_layer): self.processor = Wav2Vec2FeatureExtractor.from_pretrained(hubert_model) self.model = HubertModel.from_pretrained(hubert_model) self.km_model = joblib.load(km_path) self.km_layer = km_layer self.C_np = self.km_model.cluster_centers_.transpose() self.Cnorm_np = (self.C_np ** 2).sum(0, keepdims=True) self.C = torch.from_numpy(self.C_np) self.Cnorm = torch.from_numpy(self.Cnorm_np) if torch.cuda.is_available(): self.C = self.C.cuda() self.Cnorm = self.Cnorm.cuda() self.model = self.model.cuda() def __call__(self, filepath, sampling_rate=None): speech, sr = sf.read(filepath) input_values = self.processor(speech, return_tensors="pt", sampling_rate=sr).input_values if torch.cuda.is_available(): input_values = input_values.cuda() hidden_states = self.model(input_values, output_hidden_states=True).hidden_states x = hidden_states[self.km_layer].squeeze() dist = ( x.pow(2).sum(1, keepdim=True) - 2 * torch.matmul(x, self.C) + self.Cnorm ) return dist.argmin(dim=1).cpu().numpy() ``` input ```python hc = HubertCode("facebook/hubert-large-ll60k", './km_feat_100_layer_20', 20) voice_ids = hc('./sample1.flac') ``` bart model ````python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("voidful/asr_hubert_cluster_bart_base") model = AutoModelForSeq2SeqLM.from_pretrained("voidful/asr_hubert_cluster_bart_base") ```` generate output ```python gen_output = model.generate(input_ids=tokenizer("".join([f":vtok{i}:" for i in voice_ids]),return_tensors='pt').input_ids,max_length=1024) print(tokenizer.decode(gen_output[0], skip_special_tokens=True)) ```
32b0533f888e59d1c2f4e5322dfff90e
apache-2.0
['audio', 'automatic-speech-recognition', 'speech', 'asr', 'hubert']
false
Result `going along slushy country roads and speaking to damp audience in drifty school rooms day after day for a fortnight he'll have to put in an appearance at some place of worship on sunday morning and he can come to ask immediately afterwards`
02ebbc811d34c7d461253ee8a2ce4e3c
mit
['generated_from_trainer']
false
xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2763 - F1: 0.8346
1ae68f2dd45cc41bc5be3a01017ef53d
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5779 | 1.0 | 191 | 0.3701 | 0.7701 | | 0.2735 | 2.0 | 382 | 0.2908 | 0.8254 | | 0.1769 | 3.0 | 573 | 0.2763 | 0.8346 |
8454f3d1b3753d0e792b9432bc38c8b5
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP
ac8ecaebc4da708736a0313623ecdc7b
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 188 | 2.1169 | 7.6948 | 17.4103 |
4e18a9639c7e740f33ae7c9e0dce01c7
apache-2.0
['generated_from_trainer']
false
vit-base-patch32-224-in21k-finetuned-eurosat This model is a fine-tuned version of [google/vit-base-patch32-224-in21k](https://huggingface.co/google/vit-base-patch32-224-in21k) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 1.6175 - Accuracy: 0.7321
8db70de80f6a854fa6359fb35f9bec34
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.6483 | 1.0 | 532 | 2.5574 | 0.6605 | | 1.8885 | 2.0 | 1064 | 1.8063 | 0.7182 | | 1.6371 | 3.0 | 1596 | 1.6175 | 0.7321 |
c0cb1d3370da221965469dcf2349904f
apache-2.0
[]
false
Mengzi-T5-MT model This is a Multi-Task model trained on the multitask mixture of 27 datasets and 301 prompts, based on [Mengzi-T5-base](https://huggingface.co/Langboat/mengzi-t5-base). [Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese](https://arxiv.org/abs/2110.06696)
2ff7792249d073942a0a0c6cefb83172
apache-2.0
[]
false
Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("Langboat/mengzi-t5-base-mt") model = T5ForConditionalGeneration.from_pretrained("Langboat/mengzi-t5-base-mt") ```
83bd60e387cadb7a4fd096d80488848e
apache-2.0
[]
false
This model is a fine-tune checkpoint of [T5-base](https://huggingface.co/t5-base), fine-tuned on the [Wiki Neutrality Corpus (WNC)](https://github.com/rpryzant/neutralizing-bias), a labeled dataset composed of 180,000 biased and neutralized sentence pairs that are generated from Wikipedia edits tagged for “neutral point of view”. This model reaches an accuracy of 0.39 on a dev split of the WNC. For more details about T5, check out this [model card](https://huggingface.co/t5-base).
2ad47ac71a16d32a7514584b68448d7d
apache-2.0
['generated_from_trainer']
false
beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05 This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.7881 - Accuracy: 0.7221
ff4d5273c2708ae28bea93d1632391d0
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-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: 10
c8f34f799b111211bd0b2bdc66e57e85
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.2307 | 1.0 | 224 | 1.0863 | 0.5874 | | 1.0893 | 2.0 | 448 | 0.9700 | 0.6362 | | 1.0244 | 3.0 | 672 | 0.8859 | 0.6757 | | 1.016 | 4.0 | 896 | 0.8804 | 0.6787 | | 0.9089 | 5.0 | 1120 | 0.8611 | 0.6897 | | 0.8935 | 6.0 | 1344 | 0.8283 | 0.7028 | | 0.8403 | 7.0 | 1568 | 0.8116 | 0.7102 | | 0.8179 | 8.0 | 1792 | 0.7934 | 0.7166 | | 0.7764 | 9.0 | 2016 | 0.7865 | 0.7208 | | 0.771 | 10.0 | 2240 | 0.7881 | 0.7221 |
48c7ab7a7419368a6618c9e258210a91
apache-2.0
['generated_from_trainer']
false
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: 0.2284 - Accuracy: 0.9195 - F1: 0.9195
ec1c3a40f728b8dd63240a47175a0125
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8441 | 1.0 | 250 | 0.3260 | 0.9 | 0.8970 | | 0.2551 | 2.0 | 500 | 0.2284 | 0.9195 | 0.9195 |
9cf76e4feb12af0f936984f861fcca7d
apache-2.0
['automatic-speech-recognition', 'id']
false
exp_w2v2t_id_xlsr-53_s149 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (id)](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 been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
06ce85a2c715229510e89776181b14ee
other
['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers']
false
Official Repository Read more about this model here: https://civitai.com/models/4384/dreamshaper You can run this model on: - https://huggingface.co/spaces/Lykon/DreamShaper-webui - https://sinkin.ai/m/4zdwGOB Some sample output: ![sample 1](https://huggingface.co/Lykon/DreamShaper/resolve/main/1.png) ![sample 2](https://huggingface.co/Lykon/DreamShaper/resolve/main/2.png) ![sample 3](https://huggingface.co/Lykon/DreamShaper/resolve/main/3.png) ![sample 4](https://huggingface.co/Lykon/DreamShaper/resolve/main/4.png) ![sample 5](https://huggingface.co/Lykon/DreamShaper/resolve/main/5.png)
8e7f54b3ee948c531e6892a39c2d8f27
apache-2.0
['text-classification', 'generated_from_trainer']
false
custom-textcat-model This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the custom dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.3305 - Accuracy: 0.9541
76d84e5e75db72af4e284df3df51026b
apache-2.0
['text-classification', 'generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 209 | 0.3650 | 0.9514 | | No log | 2.0 | 418 | 0.3371 | 0.9568 | | 0.0108 | 3.0 | 627 | 0.3305 | 0.9541 | | 0.0108 | 4.0 | 836 | 0.3465 | 0.9568 | | 0.0056 | 5.0 | 1045 | 0.3498 | 0.9541 |
cd2037356332f74764ddc2ba9030c44a
mit
['spacy', 'token-classification']
false
English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer. | Feature | Description | | --- | --- | | **Name** | `en_food_entity_extractor_v2` | | **Version** | `3.4.1` | | **spaCy** | `>=3.4.0,<3.5.0` | | **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` | | **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` | | **Vectors** | 514157 keys, 514157 unique vectors (300 dimensions) | | **Sources** | [OntoNotes 5](https://catalog.ldc.upenn.edu/LDC2013T19) (Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston)<br />[ClearNLP Constituent-to-Dependency Conversion](https://github.com/clir/clearnlp-guidelines/blob/master/md/components/dependency_conversion.md) (Emory University)<br />[WordNet 3.0](https://wordnet.princeton.edu/) (Princeton University)<br />[Explosion Vectors (OSCAR 2109 + Wikipedia + OpenSubtitles + WMT News Crawl)](https://github.com/explosion/spacy-vectors-builder) (Explosion) | | **License** | `MIT` | | **Author** | [Explosion](https://explosion.ai) |
5851ca1869b3924f438e1d62add2be8b
mit
['spacy', 'token-classification']
false
Label Scheme <details> <summary>View label scheme (114 labels for 3 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, `_SP`, ```` | | **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `csubj`, `csubjpass`, `dative`, `dep`, `det`, `dobj`, `expl`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `oprd`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` | | **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `FOOD`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` | </details>
fedb12cfc702d815bd6442873f36c9f2
mit
['spacy', 'token-classification']
false
Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.93 | | `TOKEN_P` | 99.57 | | `TOKEN_R` | 99.58 | | `TOKEN_F` | 99.57 | | `TAG_ACC` | 97.34 | | `SENTS_P` | 91.79 | | `SENTS_R` | 89.14 | | `SENTS_F` | 90.44 | | `DEP_UAS` | 92.04 | | `DEP_LAS` | 90.23 | | `ENTS_P` | 85.35 | | `ENTS_R` | 85.93 | | `ENTS_F` | 85.64 |
f287e5daea6ac9a7c24d9a6a5f54119d
mit
['generated_from_trainer']
false
xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2403 - F1: 0.8358
dfcf6b546110c1e531ba4e489765e22f
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.7053 | 1.0 | 70 | 0.3077 | 0.7587 | | 0.2839 | 2.0 | 140 | 0.2692 | 0.8007 | | 0.1894 | 3.0 | 210 | 0.2403 | 0.8358 |
a6840a35873fa97be60e89b124bc3905
apache-2.0
['generated_from_trainer']
false
ec_model This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9323
5e9a86b815fcecb98f75b45a6fbbffb4
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 497 | 1.1985 | | 1.578 | 2.0 | 994 | 1.0032 | | 1.187 | 3.0 | 1491 | 0.9479 |
4c86c635c3fd4760e49746f5d53ed323
cc-by-4.0
['translation']
false
🇳🇴 Bokmål ⇔ Nynorsk 🇳🇴 Norwegian has two relatively similar written languages; Bokmål and Nynorsk. Historically Nynorsk is a written norm based on dialects curated by the linguist Ivar Aasen in the mid-to-late 1800s, whereas Bokmål is a gradual 'Norwegization' of written Danish. The two written languages are considered equal and citizens have a right to receive public service information in their primary and prefered language. Even though this right has been around for a long time only between 5-10% of Norwegian texts are written in Nynorsk. Nynorsk is therefore a low-resource language within a low-resource language. Apart from some word-list based engines, there are not any working off-the-shelf machine learning-based translation models. Translation between Bokmål and Nynorsk is not available in Google Translate.
87b7acaed849ad4fda3e6fec8bc5b38e
cc-by-4.0
['translation']
false
Demo | | | |---|---| | Widget | Try the widget in the top right corner | | Huggingface Spaces | [Spaces Demo](https://huggingface.co/spaces/NbAiLab/nb2nn) | | | |
b240270a76a4b8fa0e05a19fa889a93c
cc-by-4.0
['translation']
false
Pretraining a T5-base There is an [mt5](https://huggingface.co/google/mt5-base) that includes Norwegian. Unfortunately a very small part of this is Nynorsk; there is only around 1GB Nynorsk text in mC4. Despite this, the mt5 also gives a BLEU score above 80. During the project we extracted all available Nynorsk text from the [Norwegian Colossal Corpus](https://github.com/NBAiLab/notram/blob/master/guides/corpus_v2_summary.md) at the National Library of Norway, and matched it (by material type i.e. book, newspapers and so on) with an equal amount of Bokmål. The corpus collection is described [here](https://github.com/NBAiLab/notram/blob/master/guides/nb_nn_balanced_corpus.md) and the total size is 19GB.
dbc05025fea8ab4cf86e815ae74e7072
cc-by-4.0
['translation']
false
Finetuning - BLEU-SCORE 88.17 🎉 The central finetuning data of the project have been 200k translation units (TU) i.e. aligned pairs of sentences in the respective languages extracted from textbooks of various subjects and newspapers. Training for [10] epochs with a learning rate of [7e-4], a batch size of [32] and a max source and target length of [512] fine tuning reached a SACREBLEU score of [88.03] at training and a test score of [**88.17**] after training.
ebc00e8d0c1e31bc3ddb262f168b8cd3
cc-by-4.0
['translation']
false
This is not a translator We found out that we were able to get almost identical BLEU-score with training it both ways, and letting the model decide if the input is in Bokmål or Nynorsk. This way we can train one model instead of two. We call it a language switcher.
af689f15e0d045e1e10c6ea94f308286
apache-2.0
['generated_from_trainer']
false
wav2vec2-large-xls-r-300m-turkish-colab2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3738 - Wer: 0.3532
060c228ee494b1f420fd9121d9248643
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP
6dd140a1eea31695310f04d6298b0d38
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.9022 | 3.7 | 400 | 0.6778 | 0.7414 | | 0.4106 | 7.4 | 800 | 0.4123 | 0.5049 | | 0.1862 | 11.11 | 1200 | 0.4260 | 0.4232 | | 0.1342 | 14.81 | 1600 | 0.3951 | 0.4097 | | 0.0997 | 18.51 | 2000 | 0.4100 | 0.3999 | | 0.0782 | 22.22 | 2400 | 0.3918 | 0.3875 | | 0.059 | 25.92 | 2800 | 0.3803 | 0.3698 | | 0.0474 | 29.63 | 3200 | 0.3738 | 0.3532 |
8412136505ee5d31c9f5cd7f117cb546
mit
['generated_from_trainer']
false
rubert-tiny2_finetuned_emotion_experiment_augmented_anger_fear_no_emojis This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5820 - Accuracy: 0.7881 - F1: 0.7886 - Precision: 0.7906 - Recall: 0.7881
83be75c00670618c238242f8f4cd41cb
mit
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-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: 40
49bb6bb19ac65676953b89072cf6b258
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 1.0996 | 1.0 | 69 | 1.0013 | 0.6879 | 0.6779 | 0.7070 | 0.6879 | | 0.9524 | 2.0 | 138 | 0.8651 | 0.7265 | 0.7245 | 0.7322 | 0.7265 | | 0.8345 | 3.0 | 207 | 0.7821 | 0.7422 | 0.7413 | 0.7445 | 0.7422 | | 0.7573 | 4.0 | 276 | 0.7222 | 0.7484 | 0.7473 | 0.7482 | 0.7484 | | 0.6923 | 5.0 | 345 | 0.6828 | 0.7568 | 0.7562 | 0.7562 | 0.7568 | | 0.6412 | 6.0 | 414 | 0.6531 | 0.7568 | 0.7559 | 0.7556 | 0.7568 | | 0.5982 | 7.0 | 483 | 0.6320 | 0.7610 | 0.7601 | 0.7597 | 0.7610 | | 0.5593 | 8.0 | 552 | 0.6133 | 0.7651 | 0.7655 | 0.7664 | 0.7651 | | 0.5183 | 9.0 | 621 | 0.6036 | 0.7714 | 0.7708 | 0.7709 | 0.7714 | | 0.5042 | 10.0 | 690 | 0.5951 | 0.7756 | 0.7755 | 0.7760 | 0.7756 | | 0.483 | 11.0 | 759 | 0.5878 | 0.7766 | 0.7768 | 0.7774 | 0.7766 | | 0.4531 | 12.0 | 828 | 0.5855 | 0.7850 | 0.7841 | 0.7839 | 0.7850 | | 0.4386 | 13.0 | 897 | 0.5828 | 0.7797 | 0.7790 | 0.7786 | 0.7797 | | 0.4238 | 14.0 | 966 | 0.5788 | 0.7777 | 0.7780 | 0.7786 | 0.7777 | | 0.4018 | 15.0 | 1035 | 0.5793 | 0.7839 | 0.7842 | 0.7855 | 0.7839 | | 0.3998 | 16.0 | 1104 | 0.5801 | 0.7850 | 0.7844 | 0.7841 | 0.7850 | | 0.3747 | 17.0 | 1173 | 0.5791 | 0.7839 | 0.7836 | 0.7833 | 0.7839 | | 0.3595 | 18.0 | 1242 | 0.5799 | 0.7891 | 0.7891 | 0.7894 | 0.7891 | | 0.3575 | 19.0 | 1311 | 0.5820 | 0.7881 | 0.7886 | 0.7906 | 0.7881 |
e59fe003e652eff89a025269d7ccc057