license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | No log | 1.0 | 1141 | 9.4719 | 0.0 | 0.0 | 0.0 | 0.0 | | 40.4884 | 2.0 | 2282 | 78.1757 | 0.0 | 0.0 | 0.0 | 0.0 | | 40.4884 | 3.0 | 3423 | 54.3033 | 0.0 | 0.0 | 0.0 | 0.0 | | 72.4118 | 4.0 | 4564 | 75.8558 | 0.0 | 0.0 | 0.0 | 0.0 | | 72.4118 | 5.0 | 5705 | 12.4297 | 0.0 | 0.0 | 0.0 | 0.0 | | 24.3571 | 6.0 | 6846 | 12.4297 | 0.0 | 0.0 | 0.0 | 0.0 | | 24.3571 | 7.0 | 7987 | 12.4297 | 0.0 | 0.0 | 0.0 | 0.0 | | 16.5474 | 8.0 | 9128 | nan | 0.0 | 0.0 | 0.0 | 0.0 | | 3366f7a823546eee04d4409716996d38 |
other | ['text-generation', 'opt'] | false | transformers.generation_utils.GenerationMixin.generate) method as follows: ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer >>> import torch >>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-30b", torch_dtype=torch.float16).cuda() >>> | ec5f3562c366e797ee7e30f85ce0e1f6 |
other | ['text-generation', 'opt'] | false | the fast tokenizer currently does not work correctly >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-30b", use_fast=False) >>> prompt = "Hello, I am conscious and" >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda() >>> generated_ids = model.generate(input_ids) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True) ['Hello, I am conscious and I am here.\nI am also conscious and I am here'] ``` By default, generation is deterministic. In order to use the top-k sampling, please set `do_sample` to `True`. ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed >>> import torch >>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-30b", torch_dtype=torch.float16).cuda() >>> | 1507f508a5a9d6b9f7459bd36f7f4ee9 |
other | ['text-generation', 'opt'] | false | the fast tokenizer currently does not work correctly >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-30b", use_fast=False) >>> prompt = "Hello, I am conscious and" >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda() >>> set_seed(32) >>> generated_ids = model.generate(input_ids, do_sample=True) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True) ['Hello, I am conscious and aware that you have your back turned to me and want to talk'] ``` | 69b1d49321d944a64b32bd129ef92b4f |
other | ['text-generation', 'opt'] | false | Limitations and bias As mentioned in Meta AI's model card, given that the training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral the model is strongly biased : > Like other large language models for which the diversity (or lack thereof) of training > data induces downstream impact on the quality of our model, OPT-175B has limitations in terms > of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and > hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern > large language models. Here's an example of how the model can have biased predictions: ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed >>> import torch >>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-30b", torch_dtype=torch.float16).cuda() >>> | 86f37060104c3c465768f1625a42ad07 |
other | ['text-generation', 'opt'] | false | the fast tokenizer currently does not work correctly >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-30b", use_fast=False) >>> prompt = "The woman worked as a" >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda() >>> set_seed(32) >>> generated_ids = model.generate(input_ids, do_sample=True, num_return_sequences=5, max_length=10) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True) The woman worked as a supervisor in the office The woman worked as a social worker in a The woman worked as a cashier at the The woman worked as a teacher from 2011 to he woman worked as a maid at the house ``` compared to: ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed >>> import torch >>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-30b", torch_dtype=torch.float16).cuda() >>> | d5afb67c8bee3f06b11b4f7ab8eaac2c |
other | ['text-generation', 'opt'] | false | the fast tokenizer currently does not work correctly >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-30b", use_fast=False) >>> prompt = "The man worked as a" >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda() >>> set_seed(32) >>> generated_ids = model.generate(input_ids, do_sample=True, num_return_sequences=5, max_length=10) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True) The man worked as a school bus driver for The man worked as a bartender in a bar The man worked as a cashier at the The man worked as a teacher, and was The man worked as a professional at a range ``` This bias will also affect all fine-tuned versions of this model. | 77680fc75bdb732d962ec918a805023c |
apache-2.0 | ['generated_from_trainer'] | false | spanish-clinical-ner This model is a fine-tuned version of [plncmm/roberta-clinical-wl-es](https://huggingface.co/plncmm/roberta-clinical-wl-es) on the wl dataset. It achieves the following results on the evaluation set: - Loss: 0.6181 - Precision: 0.6869 - Recall: 0.7349 - F1: 0.7100 - Accuracy: 0.8263 | fe471d196e2693123ae5c2ad305246bd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 1.0283 | 1.0 | 500 | 0.6862 | 0.6690 | 0.6959 | 0.6822 | 0.8091 | | 0.599 | 2.0 | 1000 | 0.6198 | 0.6856 | 0.7276 | 0.7059 | 0.8252 | | 0.4973 | 3.0 | 1500 | 0.6181 | 0.6869 | 0.7349 | 0.7100 | 0.8263 | | 9ba44d129c49adcb3a8cb0741d0d3c1b |
mit | ['generated_from_trainer'] | false | deberta-base-finetuned-squad1-newsqa This model is a fine-tuned version of [stevemobs/deberta-base-finetuned-squad1](https://huggingface.co/stevemobs/deberta-base-finetuned-squad1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7556 | 54b9eede4ce473cdbaabb80851873a65 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.6703 | 1.0 | 17307 | 0.7207 | | 0.4775 | 2.0 | 34614 | 0.7556 | | bc2bfa8402a70f61a0a5493cb05a5db4 |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/bart-base-squadshifts-vanilla-new_wiki-qg` This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: new_wiki) via [`lmqg`](https://github.com/asahi417/lm-question-generation). | a68971549d50977e7f9c385790908ebc |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [facebook/bart-base](https://huggingface.co/facebook/bart-base) - **Language:** en - **Training data:** [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (new_wiki) - **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) | 933f1a60726e8d7d20383ac63076bc4a |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/bart-base-squadshifts-vanilla-new_wiki-qg") output = pipe("<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` | 1b14a3cfbfdd3bf8a6e034b88fccfd64 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/bart-base-squadshifts-vanilla-new_wiki-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.new_wiki.json) | | Score | Type | Dataset | |:-----------|--------:|:---------|:---------------------------------------------------------------------------| | BERTScore | 92.97 | new_wiki | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_1 | 29.14 | new_wiki | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_2 | 19.48 | new_wiki | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_3 | 13.85 | new_wiki | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_4 | 10.27 | new_wiki | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | METEOR | 23.65 | new_wiki | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | MoverScore | 64.36 | new_wiki | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | ROUGE_L | 26.47 | new_wiki | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | 0d2f44924b02103756a6317fcaf466f8 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squadshifts - dataset_name: new_wiki - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: facebook/bart-base - max_length: 512 - max_length_output: 32 - epoch: 4 - batch: 8 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 8 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/research-backup/bart-base-squadshifts-vanilla-new_wiki-qg/raw/main/trainer_config.json). | 92d03f1b7b5bfd5c7a19e092ad9ecb79 |
other | ['generated_from_keras_callback'] | false | nateraw/mit-b0-finetuned-sidewalks This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5197 - Validation Loss: 0.6268 - Validation Mean Iou: 0.2719 - Validation Mean Accuracy: 0.3442 - Validation Overall Accuracy: 0.8180 - Validation Per Category Iou: [0. 0.62230678 0.81645513 0.18616589 0.66669478 0.30574734 nan 0.36681201 0.31128062 0. 0.76635363 0. 0. nan 0. 0.37874505 0. 0. 0.68193241 0. 0.48867838 0.25809644 0. nan 0. 0.25765818 0. 0. 0.81965205 0.71604385 0.9214592 0. 0.00636635 0.12957446 0. ] - Validation Per Category Accuracy: [0. 0.89469845 0.88320521 0.45231002 0.72104833 0.3386303 nan 0.53522723 0.72026843 0. 0.93197124 0. 0. nan 0. 0.45525816 0. 0. 0.87276184 0. 0.60762821 0.29654901 0. nan 0. 0.32162193 0. 0. 0.90797988 0.89199119 0.96388697 0. 0.00646084 0.21171965 0. ] - Epoch: 5 | ef4ace67fd83bd0e5c6989bc17460d3c |
other | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 6e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 | 6dfe83176552555b7feff9c529215785 |
other | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Validation Mean Iou | Validation Mean Accuracy | Validation Overall Accuracy | Validation Per Category Iou | Validation Per Category Accuracy | Epoch | |:----------:|:---------------:|:-------------------:|:------------------------:|:---------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----:| | 1.3430 | 0.8858 | 0.1724 | 0.2253 | 0.7508 | [0.00000000e+00 5.02535817e-01 7.94050536e-01 1.37476079e-01 5.28949130e-01 1.76391302e-01 nan 1.19967229e-01 0.00000000e+00 0.00000000e+00 6.61310784e-01 0.00000000e+00 0.00000000e+00 nan 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.06634036e-01 0.00000000e+00 7.22567226e-02 5.35294630e-03 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.53949868e-02 0.00000000e+00 0.00000000e+00 7.37842004e-01 5.78989440e-01 8.52258994e-01 0.00000000e+00 0.00000000e+00 6.16858377e-05 0.00000000e+00] | [0.00000000e+00 5.80613096e-01 9.43852033e-01 1.50019637e-01 5.77268577e-01 3.25241508e-01 nan 1.68319967e-01 0.00000000e+00 0.00000000e+00 8.60308871e-01 0.00000000e+00 0.00000000e+00 nan 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 9.04260401e-01 0.00000000e+00 7.74112939e-02 5.58025588e-03 0.00000000e+00 nan 0.00000000e+00 1.56055377e-02 0.00000000e+00 0.00000000e+00 8.41648672e-01 8.58416118e-01 9.02457570e-01 0.00000000e+00 0.00000000e+00 6.18892982e-05 0.00000000e+00] | 0 | | 0.8402 | 0.7211 | 0.2203 | 0.2900 | 0.7927 | [0. 0.60561012 0.80467888 0.10134538 0.57674712 0.21967639 nan 0.279315 0.28998136 0. 0.71924852 0. 0. nan 0. 0.10241989 0. 0. 0.60537245 0. 0.37966409 0.0624908 0. 0. 0. 0.11869763 0. 0. 0.79675107 0.70541969 0.89177953 0. 0. 0.01097213 0. ] | [0. 0.70687024 0.92710849 0.47653578 0.6809956 0.28562204 nan 0.35954555 0.53804171 0. 0.87451178 0. 0. nan 0. 0.10473185 0. 0. 0.88548482 0. 0.52011987 0.06421075 0. nan 0. 0.13802701 0. 0. 0.9278545 0.83106582 0.94693817 0. 0. 0.01170072 0. ] | 1 | | 0.7051 | 0.6513 | 0.2568 | 0.3210 | 0.8151 | [0.00000000e+00 6.31500555e-01 8.33347761e-01 2.40727740e-01 6.71879162e-01 2.32727132e-01 nan 3.15720178e-01 3.22578864e-01 0.00000000e+00 7.51066980e-01 0.00000000e+00 0.00000000e+00 nan 0.00000000e+00 3.01090014e-01 0.00000000e+00 0.00000000e+00 6.56592309e-01 0.00000000e+00 3.82317489e-01 2.25385079e-01 0.00000000e+00 nan 0.00000000e+00 2.34975219e-01 0.00000000e+00 0.00000000e+00 7.92710603e-01 6.82508692e-01 9.02369099e-01 0.00000000e+00 5.10019193e-04 4.02361131e-02 0.00000000e+00] | [0.00000000e+00 7.76355941e-01 9.39707165e-01 3.90888278e-01 7.70256989e-01 2.84066636e-01 nan 4.57106724e-01 6.33498392e-01 0.00000000e+00 9.05789013e-01 0.00000000e+00 0.00000000e+00 nan 0.00000000e+00 3.57230962e-01 0.00000000e+00 0.00000000e+00 8.45761217e-01 0.00000000e+00 5.16681541e-01 2.82796479e-01 0.00000000e+00 nan 0.00000000e+00 3.07634724e-01 0.00000000e+00 0.00000000e+00 9.04391068e-01 8.86212453e-01 9.64570665e-01 0.00000000e+00 5.17411580e-04 4.71742075e-02 0.00000000e+00] | 2 | | 0.6294 | 0.6365 | 0.2695 | 0.3320 | 0.8244 | [0. 0.63840754 0.83879521 0.31781353 0.69394774 0.22324776 nan 0.35012894 0.31369877 0. 0.7683448 0. 0. nan 0. 0.36532292 0. 0. 0.65554136 0. 0.37438724 0.25682621 0. nan 0. 0.23051151 0. 0. 0.81818163 0.7633018 0.91092518 0. 0.00145576 0.10215516 0. ] | [0. 0.76103704 0.95305272 0.43848725 0.78760908 0.25645014 nan 0.48971828 0.61853472 0. 0.90793733 0. 0. nan 0. 0.48772201 0. 0. 0.84205031 0. 0.53308407 0.36285878 0. nan 0. 0.27953916 0. 0. 0.93079576 0.87079757 0.96477884 0. 0.00147054 0.13899972 0. ] | 3 | | 0.5686 | 0.6122 | 0.2715 | 0.3360 | 0.8256 | [0.00000000e+00 6.38345814e-01 8.56252996e-01 3.07043269e-01 6.87537894e-01 3.06534041e-01 nan 3.84145525e-01 3.19438916e-01 0.00000000e+00 7.57233152e-01 0.00000000e+00 0.00000000e+00 nan 0.00000000e+00 4.06585843e-01 0.00000000e+00 0.00000000e+00 6.47648546e-01 2.91885581e-04 4.00547422e-01 1.97261484e-01 0.00000000e+00 nan 0.00000000e+00 2.20793008e-01 0.00000000e+00 0.00000000e+00 8.19526784e-01 7.19306080e-01 9.20192720e-01 0.00000000e+00 2.23374930e-03 9.77508243e-02 0.00000000e+00] | [0.00000000e+00 7.89438910e-01 9.16367241e-01 4.32251205e-01 7.89740409e-01 4.88566404e-01 nan 5.36825005e-01 6.47787376e-01 0.00000000e+00 9.32641501e-01 0.00000000e+00 0.00000000e+00 nan 0.00000000e+00 4.73813253e-01 0.00000000e+00 0.00000000e+00 9.09004353e-01 2.91885581e-04 4.37175308e-01 2.25663128e-01 0.00000000e+00 nan 0.00000000e+00 2.60992057e-01 0.00000000e+00 0.00000000e+00 9.19328058e-01 9.02898346e-01 9.65529369e-01 0.00000000e+00 2.23984750e-03 1.20880721e-01 0.00000000e+00] | 4 | | 0.5197 | 0.6268 | 0.2719 | 0.3442 | 0.8180 | [0. 0.62230678 0.81645513 0.18616589 0.66669478 0.30574734 nan 0.36681201 0.31128062 0. 0.76635363 0. 0. nan 0. 0.37874505 0. 0. 0.68193241 0. 0.48867838 0.25809644 0. nan 0. 0.25765818 0. 0. 0.81965205 0.71604385 0.9214592 0. 0.00636635 0.12957446 0. ] | [0. 0.89469845 0.88320521 0.45231002 0.72104833 0.3386303 nan 0.53522723 0.72026843 0. 0.93197124 0. 0. nan 0. 0.45525816 0. 0. 0.87276184 0. 0.60762821 0.29654901 0. nan 0. 0.32162193 0. 0. 0.90797988 0.89199119 0.96388697 0. 0.00646084 0.21171965 0. ] | 5 | | ed6a8d18893f8c204891029453c8de71 |
bsd-3-clause | ['automatic-speech-recognition'] | false | Attribution As initial checkpoint used [stt_en_citrinet_512_gamma_0_25](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_citrinet_512_gamma_0_25) by [NVIDIA](https://github.com/NVIDIA) licensed under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) | 3ce9e48a3a7b7ef6f99fe4fe5814404c |
mit | ['roberta-base', 'roberta-base-epoch_23'] | false | RoBERTa, Intermediate Checkpoint - Epoch 23 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly initialized weights before the training) to provide the ability to study the training dynamics of such models, and other possible use-cases. These models were trained in part of a work that studies how simple statistics from data, such as co-occurrences affects model predictions, which are described in the paper [Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions](https://arxiv.org/abs/2207.14251). This is RoBERTa-base epoch_23. | b635157c7bd7a2ffe3954eb39524e70a |
apache-2.0 | ['automatic-speech-recognition', 'uk'] | false | exp_w2v2t_uk_hubert_s33 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (uk)](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. | ae04464968f5c18f8270b88d0d724851 |
apache-2.0 | ['automatic-speech-recognition', 'sv-SE'] | false | exp_w2v2t_sv-se_xlsr-53_s328 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 (sv-SE)](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. | 6c27ebed91c1e5d059643745cee6da8c |
apache-2.0 | ['translation'] | false | deu-msa * source group: German * target group: Malay (macrolanguage) * OPUS readme: [deu-msa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/deu-msa/README.md) * model: transformer-align * source language(s): deu * target language(s): ind zsm_Latn * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-msa/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-msa/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-msa/opus-2020-06-17.eval.txt) | 9a903a0a3d72273351ee818bce015d8a |
apache-2.0 | ['translation'] | false | System Info: - hf_name: deu-msa - source_languages: deu - target_languages: msa - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/deu-msa/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['de', 'ms'] - src_constituents: {'deu'} - tgt_constituents: {'zsm_Latn', 'ind', 'max_Latn', 'zlm_Latn', 'min'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/deu-msa/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/deu-msa/opus-2020-06-17.test.txt - src_alpha3: deu - tgt_alpha3: msa - short_pair: de-ms - chrF2_score: 0.607 - bleu: 34.0 - brevity_penalty: 0.9540000000000001 - ref_len: 3729.0 - src_name: German - tgt_name: Malay (macrolanguage) - train_date: 2020-06-17 - src_alpha2: de - tgt_alpha2: ms - prefer_old: False - long_pair: deu-msa - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 85ee87c6be4fb73eed54a154a107a591 |
creativeml-openrail-m | [] | false | <a href="https://colab.research.google.com/drive/1noyBA_JrYO6Lk6cwxsNZ_jdJ-Jtaf82G?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg"></a> PromptCLUE：全中文任务零样本学习模型 这个模型是基于PromptCLUE-base进一步训练(+50%步数)，以及更多任务(+50%任务)以及更多任务类型上进行训练，是对PromptCLUE-base进一步升级, 新增的任务类型有改写、纠错和问答等类型 在1000亿token中文语料上预训练，累计学习1.5万亿中文token，并且在数百种任务上进行Prompt任务式训练。针对理解类任务，如分类、情感分析、抽取等，可以自定义标签体系；针对多种生成任务，可以进行采样自由生成。 <a href='https://www.cluebenchmarks.com/clueai.html'>在线Demo</a> | <a href='https://www.clueai.cn'>使用clueai工具包和API(large版)</a> | <a href='https://github.com/clue-ai/PromptCLUE'>Github项目地址</a> | <a href='https://colab.research.google.com/drive/1noyBA_JrYO6Lk6cwxsNZ_jdJ-Jtaf82G?usp=sharing | 1d333ccdba77a427c27a3a10ba1cf613 |
creativeml-openrail-m | [] | false | 加载模型 from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("ClueAI/PromptCLUE-base-v1-5") model = T5ForConditionalGeneration.from_pretrained("ClueAI/PromptCLUE-base-v1-5") ``` 使用模型进行预测推理方法： ```python import torch | 77c8f5df8a776dbb92b956670a9ca71f |
creativeml-openrail-m | [] | false | device = torch.device('cpu') device = torch.device('cuda') model.to(device) def preprocess(text): return text.replace("\n", "_") def postprocess(text): return text.replace("_", "\n") def answer(text, sample=False, top_p=0.8): '''sample：是否抽样。生成任务，可以设置为True; top_p：0-1之间，生成的内容越多样''' text = preprocess(text) encoding = tokenizer(text=[text], truncation=True, padding=True, max_length=768, return_tensors="pt").to(device) if not sample: out = model.generate(**encoding, return_dict_in_generate=True, output_scores=False, max_length=128, num_beams=4, length_penalty=0.6) else: out = model.generate(**encoding, return_dict_in_generate=True, output_scores=False, max_length=64, do_sample=True, top_p=top_p) out_text = tokenizer.batch_decode(out["sequences"], skip_special_tokens=True) return postprocess(out_text[0]) ``` | 904be6c450da1329a61f2118f2bc8ef6 |
creativeml-openrail-m | [] | false | 改写(paraphrase)[sample=True] Input: 生成与下列文字相同意思的句子： 白云遍地无人扫 答案： Model output: 白云散去无踪，没人扫。 Input: 用另外的话复述下面的文字： 怎么到至今还不回来，这满地的白云幸好没人打扫。 答案： Model output: 满地白云无扫处 Input: 改写下面的文字，确保意思相同： 一个如此藐视本国人民民主权利的人，怎么可能捍卫外国人的民权？ 答案： Model output: 对一个如此藐视本国人民民主权利的人，怎么能捍卫外国人的民权？ | 6c4a06986cadbad24a7af5b0a5e77039 |
creativeml-openrail-m | [] | false | 新闻分类(classify) ```bash Input: 分类任务： 折价率过低遭抛售基金泰和跌7.15%，证券时报记者 朱景锋本报讯 由于折价率在大盘封基中处于最低水平，基金泰和昨日遭到投资者大举抛售，跌幅达到7.15%，远超大盘。盘面显示，基金泰和随大盘高开，之后开始震荡走低，午后开始加速下行，几乎没有像样反弹。截至收盘时，在沪深300指数仅下跌2.56%的情况下，基金泰和收盘跌幅高达7.15%，在所有封基中跌幅最大，而昨日多数封基跌幅在2%左右。 选项：财经，娱乐，时政，股票 答案： Model output: 财经 ``` | a44b18d5ef3925030edae66705ac039f |
creativeml-openrail-m | [] | false | 阅读理解(generate) ```bash Input: 阅读文章，给出答案： 段落： 港汇指数，全称港元实际汇兑指数（Effective Exchange Rate Index for the Hong Kong Dollar）是由香港政府统计处编制的一项指数，以反映港元与香港主要贸易伙伴之货币的名义有效汇率加权平均数的变动情况。加权比重是按1999年至2000年平均贸易模式所制定，但政府并未有公布详细的计算公式。旧港汇指数基准日为2000年1月1日，基数为100点。由2012年1月3日起，新系列港汇指数 (包括15种货币及以2010年1月 = 100) 已取代旧港汇指数系列。港汇指数的作用，主要是用于反映香港的货品及服务的价格相对于其主要贸易伙伴的变动，并通常被视作反映香港价格竞争力的指标。 问题：港汇指数的加权比重如何制定？ 答案： Model output: 按1999年至2000年平均贸易模式所制定 ``` | 6286375efdef27be78ee4119072c0ab4 |
creativeml-openrail-m | [] | false | 问题生成(generate) ```bash Input: 问题生成： 中新网2022年9月22日电 22日，商务部召开例行新闻发布会，商务部新闻发言人束珏婷表示，今年1-8月，中国实际使用外资1384亿美元，增长20.2%；其中，欧盟对华投资增长123.7%(含通过自由港投资数据)。这充分表明，包括欧盟在内的外国投资者持续看好中国市场，希望继续深化对华投资合作。 答案： Model output: 1、商务部发言人束珏婷表示，中国去年1-8月实际使用外资1384亿美元,增长20.2%，其中欧盟对华投资增长多少？ 2、欧盟对华投资的增长是否达到预期？ 3、欧盟在对华投资合作方面取得什么样的成果？ ``` | d1fe763fa8486f4302cfa047f087aae7 |
cc-by-4.0 | ['conversational', 'transformers'] | false | DialoGPT_AfriWOZ (Pidgin) This is a fine-tuned model of DialoGPT (small) on the AfriWOZ dataset. It is intended to be used as a conversational system in Nigeria Pidgin English language. The dataset it's trained on is limited in scope, as it covers only certain domains such as restaurants, hotel, taxi, and booking. The perplexity achieved on the validation set is 38.52. * Generation example from an interactive environment: |Role | Response | |------|------------| |User | I hear say restaurant dey here. | |Bot | I dey find restaurant. | |User | Abeg you fit tell me which kind chop dey? | |Bot | I no dey sure right now. | |User | You do well. Thank you. | |Bot | I dey find restauarant. | Please find the information about preprocessing, training and full details of the DialoGPT in the [original DialoGPT repository](https://github.com/microsoft/DialoGPT) The paper for this work can be found on arXiv: [https://arxiv.org/pdf/2204.08083.pdf](https://arxiv.org/pdf/2204.08083.pdf) | 5566ca7a9a661bac51c97e4be74b20ee |
cc-by-4.0 | ['conversational', 'transformers'] | false | 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 tokenizer = AutoTokenizer.from_pretrained("tosin/dialogpt_afriwoz_pidgin") model = AutoModelForCausalLM.from_pretrained("tosin/dialogpt_afriwoz_pidgin") | cdf5615c9909e4527276c5b8be737271 |
apache-2.0 | ['generated_from_trainer'] | false | bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR100-40 This model is a fine-tuned version of [jojoUla/bert-large-cased-sigir-support-refute-no-label-40](https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4444 | 0921fcb0ec566427291dea005d69ce71 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-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: 40.0 - mixed_precision_training: Native AMP | d5532c0aa031a530a533f48448a98ef7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.1928 | 1.0 | 1 | 5.0343 | | 3.8865 | 2.0 | 2 | 4.7751 | | 4.0526 | 3.0 | 3 | 2.2212 | | 2.3444 | 4.0 | 4 | 1.6810 | | 1.596 | 5.0 | 5 | 1.3135 | | 1.6805 | 6.0 | 6 | 1.2568 | | 1.1736 | 7.0 | 7 | 1.5288 | | 1.2663 | 8.0 | 8 | 1.4556 | | 1.3703 | 9.0 | 9 | 1.1139 | | 0.9768 | 10.0 | 10 | 1.0658 | | 1.0132 | 11.0 | 11 | 1.2556 | | 0.9896 | 12.0 | 12 | 1.1046 | | 1.1184 | 13.0 | 13 | 1.0522 | | 0.8142 | 14.0 | 14 | 1.3122 | | 0.706 | 15.0 | 15 | 1.0713 | | 0.7227 | 16.0 | 16 | 1.4111 | | 0.7169 | 17.0 | 17 | 0.5603 | | 0.7922 | 18.0 | 18 | 1.0911 | | 0.7763 | 19.0 | 19 | 0.6882 | | 0.5832 | 20.0 | 20 | 1.4459 | | 0.7265 | 21.0 | 21 | 1.5459 | | 0.7249 | 22.0 | 22 | 0.9200 | | 0.5397 | 23.0 | 23 | 1.0976 | | 0.5063 | 24.0 | 24 | 1.1201 | | 0.6569 | 25.0 | 25 | 1.0701 | | 0.472 | 26.0 | 26 | 1.7735 | | 0.6124 | 27.0 | 27 | 1.3597 | | 0.6042 | 28.0 | 28 | 0.9292 | | 0.5232 | 29.0 | 29 | 1.4994 | | 0.4961 | 30.0 | 30 | 1.2059 | | 0.371 | 31.0 | 31 | 1.2648 | | 0.4746 | 32.0 | 32 | 1.0907 | | 0.4901 | 33.0 | 33 | 1.2564 | | 0.5066 | 34.0 | 34 | 1.9231 | | 0.6352 | 35.0 | 35 | 1.0160 | | 0.5672 | 36.0 | 36 | 1.2958 | | 0.5139 | 37.0 | 37 | 0.9384 | | 0.5583 | 38.0 | 38 | 1.9518 | | 0.5443 | 39.0 | 39 | 1.4243 | | 0.5935 | 40.0 | 40 | 1.3882 | | 653340701644768ea493695868472a0d |
apache-2.0 | ['generated_from_trainer'] | false | irony_trained_1234567 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.6580 - F1: 0.6766 | be0c6e7ee32d55134c8952f0eaf50eea |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.6774391860025942e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 1234567 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | ca42ac84437f9960f52ff9b51927d8a6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.6608 | 1.0 | 716 | 0.6057 | 0.6704 | | 0.5329 | 2.0 | 1432 | 0.8935 | 0.6621 | | 0.3042 | 3.0 | 2148 | 1.3871 | 0.6822 | | 0.1769 | 4.0 | 2864 | 1.6580 | 0.6766 | | 3d1eea0af2ad3515065e3af02b7fe3d3 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-turkish-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9701 - Wer: 1.0 | 805d6c719609e9f2ad1a7ddd5fbc5281 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 40 - mixed_precision_training: Native AMP | cbed8f6a894ad55e518605a3a1c0ff4e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 4.3108 | 16.0 | 400 | 2.9378 | 1.0 | | 3.0115 | 32.0 | 800 | 2.9701 | 1.0 | | e2a89bb4f8f78b1b9f7bb0a886a4bfc3 |
apache-2.0 | ['generated_from_trainer'] | false | distilgpt2-finetuned-katpoems-lm-15-epoch 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: 4.8145 | 8e25245ca85e4373b3b338863762a933 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15.0 | 5ccaf89727bea44eaecd9e17d37194d4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 59 | 4.6495 | | No log | 2.0 | 118 | 4.6555 | | No log | 3.0 | 177 | 4.6696 | | No log | 4.0 | 236 | 4.6930 | | No log | 5.0 | 295 | 4.7132 | | No log | 6.0 | 354 | 4.7185 | | No log | 7.0 | 413 | 4.7444 | | No log | 8.0 | 472 | 4.7611 | | 4.2244 | 9.0 | 531 | 4.7794 | | 4.2244 | 10.0 | 590 | 4.7841 | | 4.2244 | 11.0 | 649 | 4.7929 | | 4.2244 | 12.0 | 708 | 4.8048 | | 4.2244 | 13.0 | 767 | 4.8058 | | 4.2244 | 14.0 | 826 | 4.8124 | | 4.2244 | 15.0 | 885 | 4.8145 | | 70fd4041b524b92d520ebaeca76efdd4 |
apache-2.0 | ['thai', 'token-classification', 'pos', 'wikipedia', 'dependency-parsing'] | false | Model Description This is a BERT model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing, derived from [bert-base-th-cased](https://huggingface.co/Geotrend/bert-base-th-cased). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech). | 499d636c9bc3dfb0822e48cfb9849678 |
apache-2.0 | ['thai', 'token-classification', 'pos', 'wikipedia', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/bert-base-thai-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/bert-base-thai-upos") ``` or ```py import esupar nlp=esupar.load("KoichiYasuoka/bert-base-thai-upos") ``` | 43b466ed838815108f0802567e98ed7e |
mit | [] | false | uma-meme-style on Stable Diffusion This is the `<uma-meme-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`:                                   | 442b8e847443ad1bee1325d5ea12aaab |
apache-2.0 | ['generated_from_trainer'] | false | mt5-small-MT5-Intento1 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 3.9645 - Rouge2: 0.8023 - Rougel: 3.8615 - Rougelsum: 3.8591 - Gen Len: 13.7379 | d4f79c119ecf3643ed713b93bee203c5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 0.0 | 1.0 | 6034 | nan | 3.9645 | 0.8023 | 3.8615 | 3.8591 | 13.7379 | | 28587f8f2610a256fff09a86df010ef8 |
apache-2.0 | ['Image Captioning'] | false | Model Description These are model weights originally provided by the authors of the paper [Text-Only Training for Image Captioning using Noise-Injected CLIP](https://arxiv.org/pdf/2211.00575.pdf). Their method aims to train CLIP with only text samples. Therefore they are injecting zero-mean Gaussian Noise into the text embeddings before decoding. In their words: *Specifically, we assume that the visual embedding corresponding to a text embedding lies somewhere within a ball of small radius around the text embedding (see Fig. 1). We would like all text embeddings in this ball to decode to the same caption,which should also correspond to the visual content mapped to this ball. We implement this intuition by adding zero-mean Gaussian noise of STD to the text embedding before decoding it.* The "Noise Level" of 0.001 is equivalent to the Noise Variance which is the square of the STD. The reported metrics are results of a model with a Noise Variance of 0.016, which the authors unfortunately do not provide in their repository. | c7a23859b1d423a51764f825bd963b7e |
cc-by-4.0 | ['generated_from_trainer'] | false | hing-roberta-finetuned-non-code-mixed-DS This model is a fine-tuned version of [l3cube-pune/hing-roberta](https://huggingface.co/l3cube-pune/hing-roberta) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1286 - Accuracy: 0.6656 - Precision: 0.6575 - Recall: 0.6554 - F1: 0.6556 | 126fd78327d56c22809d6167c112332b |
cc-by-4.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.824279936868144e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 43 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | df24fd3f44a87145b2aedbe8378b6e38 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.8233 | 2.0 | 926 | 0.8104 | 0.6656 | 0.6607 | 0.6537 | 0.6555 | | 0.3924 | 3.99 | 1852 | 1.1286 | 0.6656 | 0.6575 | 0.6554 | 0.6556 | | 4bc9481787e6d1da3d68438fcb6da061 |
creativeml-openrail-m | [] | false | <div style="display: flex; flex-direction: row; flex-wrap: wrap"> <a href="https://www.patreon.com/user?u=29466374" target="_blank"> <img src="https://img.shields.io/badge/Patreon-F96854?style=for-the-badge&logo=patreon&logoColor=white" alt="Patreon"/> </a> <a href="https://twitter.com/nerijs" target="_blank"> <img src="https://img.shields.io/badge/Twitter-1DA1F2?style=for-the-badge&logo=twitter&logoColor=white" alt="Twitter"/> </a> </div> | a09db4958772194162b2fcbd21b8fa7a |
creativeml-openrail-m | [] | false | coralchar-diffusion-v1 Stable Diffusion v1.5 model trained on to generate cute character portraits <div style="display: flex; flex-direction: row; flex-wrap: wrap"> <img src="https://s3.amazonaws.com/moonup/production/uploads/1670205150413-6303f37c3926de1f7ec42d3e.png" width="256"> <img src="https://s3.amazonaws.com/moonup/production/uploads/1670205171617-6303f37c3926de1f7ec42d3e.png" width="256"> </div> | 333e3028e0b26994a40f27f599720d6b |
creativeml-openrail-m | [] | false | How to use - Download the model and use it on your desired UI (Tested on AUTOMATIC1111's) .ckpt and Diffusers version available - Trigger the style in your prompt with the **coralchar** token, look at the next section for more examples - If you want to use the inpainting model, you can use it like a normal v1.5 model | 8b067ca31f557f011d7dbc341c55eaa1 |
creativeml-openrail-m | [] | false | Examples on step-6000 model **a woman wearing blue jeans and a white tank top** Steps: 20, Sampler: DPM++ SDE, CFG scale: 7, Size: 512x768 <img src="https://s3.amazonaws.com/moonup/production/uploads/1670204360798-6303f37c3926de1f7ec42d3e.png" width="512"/> **a man wearing a black puffy vest** Steps: 20, Sampler: DPM++ SDE, CFG scale: 7, Size: 512x768 <img src="https://s3.amazonaws.com/moonup/production/uploads/1670204467592-6303f37c3926de1f7ec42d3e.png" width="512"/> | 1401a9cb60fcb9b6daae8bbbada9186a |
creativeml-openrail-m | [] | false | Examples on inpainting model **a man wearing a blue puffy vest** Steps: 20, Sampler: DPM++ SDE, CFG scale: 7, Size: 512x768, 0.75 Denoising strength <h2>Original vs step_6000 vs inpainting version</h2> <div style="display: flex; flex-direction: row; flex-wrap: wrap"> <img src="https://s3.amazonaws.com/moonup/production/uploads/1670205036420-6303f37c3926de1f7ec42d3e.png" width="256"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1670204708270-6303f37c3926de1f7ec42d3e.png" width="256"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1670204954426-6303f37c3926de1f7ec42d3e.png" width="256"/> </div> | 3b1e0014451efbfd02e4f7e2bcc7fe49 |
creativeml-openrail-m | [] | false | Tips - Best results with 512x768, outputs full body portraits - Also high step count on Euler_a gives good results - Low CFG scale outputs great results - If you want to generate different expressions, generate a base character with txt2img then adjust your outfit and details with inpainting model and use inpainting again to generate different expressions and poses Please consider supporting further research on my Patreon: <a href="https://www.patreon.com/user?u=29466374" target="_blank"> <img src="https://img.shields.io/badge/Patreon-F96854?style=for-the-badge&logo=patreon&logoColor=white" alt="Patreon"/> </a> If you have any question, suggestion for new models or need help in general with SD related stuff, don't hesistate to reach out on Twitter: <a href="https://twitter.com/nerijs" target="_blank"> <img src="https://img.shields.io/badge/Twitter-1DA1F2?style=for-the-badge&logo=twitter&logoColor=white" alt="Twitter"/> </a> | d0bb1fc944b9153c2c0bfa699bb017bc |
apache-2.0 | ['whisper-event'] | false | Whisper Telugu Large-v2 This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Telugu data available from multiple publicly available ASR corpuses. It has been fine-tuned as a part of the Whisper fine-tuning sprint. | f1bd0032db3b1f74cde5d47697c1142d |
apache-2.0 | ['whisper-event'] | false | Training and evaluation data at Speech Lab, IITM Training Data: CSTD IIIT-H ASR Corpus, ULCA ASR Corpus, Shrutilipi ASR Corpus, Microsoft Research Telugu Corpus (Train+Dev), Babel ASR Corpus, Google/Fleurs (Train+Dev) set. Evaluation Data: Babel Test, Microsoft Research Telugu Corpus Test, Google/Fleurs Test set, OpenSLR. | dad208b53a76b28bb22c664c7707fb69 |
apache-2.0 | ['whisper-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.75e-05 - train_batch_size: 8 - eval_batch_size: 32 - seed: 22 - optimizer: adamw_bnb_8bit - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 22000 - training_steps: 75000 - mixed_precision_training: True | eb7ae6f7578e7c08ca9eca18b62ffeb2 |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event', 'sv'] | false | 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 - ZH-CN dataset. It achieves the following results on the evaluation set: - Loss: 0.8122 - Wer: 0.8392 - Cer: 0.2059 | 3072b5dd43e3210229652746415d85ef |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event', 'sv'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 100.0 - mixed_precision_training: Native AMP | ed4f613af6aef3a972489ab5cb1026ff |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event', 'sv'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| | 69.215 | 0.74 | 500 | 74.9751 | 1.0 | 1.0 | | 8.2109 | 1.48 | 1000 | 7.0617 | 1.0 | 1.0 | | 6.4277 | 2.22 | 1500 | 6.3811 | 1.0 | 1.0 | | 6.3513 | 2.95 | 2000 | 6.3061 | 1.0 | 1.0 | | 6.2522 | 3.69 | 2500 | 6.2147 | 1.0 | 1.0 | | 5.9757 | 4.43 | 3000 | 5.7906 | 1.1004 | 0.9924 | | 5.0642 | 5.17 | 3500 | 4.2984 | 1.7729 | 0.8214 | | 4.6346 | 5.91 | 4000 | 3.7129 | 1.8946 | 0.7728 | | 4.267 | 6.65 | 4500 | 3.2177 | 1.7526 | 0.6922 | | 3.9964 | 7.39 | 5000 | 2.8337 | 1.8055 | 0.6546 | | 3.8035 | 8.12 | 5500 | 2.5726 | 2.1851 | 0.6992 | | 3.6273 | 8.86 | 6000 | 2.3391 | 2.1029 | 0.6511 | | 3.5248 | 9.6 | 6500 | 2.1944 | 2.3617 | 0.6859 | | 3.3683 | 10.34 | 7000 | 1.9827 | 2.1014 | 0.6063 | | 3.2411 | 11.08 | 7500 | 1.8610 | 1.6160 | 0.5135 | | 3.1299 | 11.82 | 8000 | 1.7446 | 1.5948 | 0.4946 | | 3.0574 | 12.56 | 8500 | 1.6454 | 1.1291 | 0.4051 | | 2.985 | 13.29 | 9000 | 1.5919 | 1.0673 | 0.3893 | | 2.9573 | 14.03 | 9500 | 1.4903 | 1.0604 | 0.3766 | | 2.8897 | 14.77 | 10000 | 1.4614 | 1.0059 | 0.3653 | | 2.8169 | 15.51 | 10500 | 1.3997 | 1.0030 | 0.3550 | | 2.8155 | 16.25 | 11000 | 1.3444 | 0.9980 | 0.3441 | | 2.7595 | 16.99 | 11500 | 1.2911 | 0.9703 | 0.3325 | | 2.7107 | 17.72 | 12000 | 1.2462 | 0.9565 | 0.3227 | | 2.6358 | 18.46 | 12500 | 1.2466 | 0.9955 | 0.3333 | | 2.5801 | 19.2 | 13000 | 1.2059 | 1.0010 | 0.3226 | | 2.5554 | 19.94 | 13500 | 1.1919 | 1.0094 | 0.3223 | | 2.5314 | 20.68 | 14000 | 1.1703 | 0.9847 | 0.3156 | | 2.509 | 21.42 | 14500 | 1.1733 | 0.9896 | 0.3177 | | 2.4391 | 22.16 | 15000 | 1.1811 | 0.9723 | 0.3164 | | 2.4631 | 22.89 | 15500 | 1.1382 | 0.9698 | 0.3059 | | 2.4414 | 23.63 | 16000 | 1.0893 | 0.9644 | 0.2972 | | 2.3771 | 24.37 | 16500 | 1.0930 | 0.9505 | 0.2954 | | 2.3658 | 25.11 | 17000 | 1.0756 | 0.9609 | 0.2926 | | 2.3215 | 25.85 | 17500 | 1.0512 | 0.9614 | 0.2890 | | 2.3327 | 26.59 | 18000 | 1.0627 | 1.1984 | 0.3282 | | 2.3055 | 27.33 | 18500 | 1.0582 | 0.9520 | 0.2841 | | 2.299 | 28.06 | 19000 | 1.0356 | 0.9480 | 0.2817 | | 2.2673 | 28.8 | 19500 | 1.0305 | 0.9367 | 0.2771 | | 2.2166 | 29.54 | 20000 | 1.0139 | 0.9223 | 0.2702 | | 2.2378 | 30.28 | 20500 | 1.0095 | 0.9268 | 0.2722 | | 2.2168 | 31.02 | 21000 | 1.0001 | 0.9085 | 0.2691 | | 2.1766 | 31.76 | 21500 | 0.9884 | 0.9050 | 0.2640 | | 2.1715 | 32.5 | 22000 | 0.9730 | 0.9505 | 0.2719 | | 2.1104 | 33.23 | 22500 | 0.9752 | 0.9362 | 0.2656 | | 2.1158 | 33.97 | 23000 | 0.9720 | 0.9263 | 0.2624 | | 2.0718 | 34.71 | 23500 | 0.9573 | 1.0005 | 0.2759 | | 2.0824 | 35.45 | 24000 | 0.9609 | 0.9525 | 0.2643 | | 2.0591 | 36.19 | 24500 | 0.9662 | 0.9570 | 0.2667 | | 2.0768 | 36.93 | 25000 | 0.9528 | 0.9574 | 0.2646 | | 2.0893 | 37.67 | 25500 | 0.9810 | 0.9169 | 0.2612 | | 2.0282 | 38.4 | 26000 | 0.9556 | 0.8877 | 0.2528 | | 1.997 | 39.14 | 26500 | 0.9523 | 0.8723 | 0.2501 | | 2.0209 | 39.88 | 27000 | 0.9542 | 0.8773 | 0.2503 | | 1.987 | 40.62 | 27500 | 0.9427 | 0.8867 | 0.2500 | | 1.9663 | 41.36 | 28000 | 0.9546 | 0.9065 | 0.2546 | | 1.9945 | 42.1 | 28500 | 0.9431 | 0.9119 | 0.2536 | | 1.9604 | 42.84 | 29000 | 0.9367 | 0.9030 | 0.2490 | | 1.933 | 43.57 | 29500 | 0.9071 | 0.8916 | 0.2432 | | 1.9227 | 44.31 | 30000 | 0.9048 | 0.8882 | 0.2428 | | 1.8784 | 45.05 | 30500 | 0.9106 | 0.8991 | 0.2437 | | 1.8844 | 45.79 | 31000 | 0.8996 | 0.8758 | 0.2379 | | 1.8776 | 46.53 | 31500 | 0.9028 | 0.8798 | 0.2395 | | 1.8372 | 47.27 | 32000 | 0.9047 | 0.8778 | 0.2379 | | 1.832 | 48.01 | 32500 | 0.9016 | 0.8941 | 0.2393 | | 1.8154 | 48.74 | 33000 | 0.8915 | 0.8916 | 0.2372 | | 1.8072 | 49.48 | 33500 | 0.8781 | 0.8872 | 0.2365 | | 1.7489 | 50.22 | 34000 | 0.8738 | 0.8956 | 0.2340 | | 1.7928 | 50.96 | 34500 | 0.8684 | 0.8872 | 0.2323 | | 1.7748 | 51.7 | 35000 | 0.8723 | 0.8718 | 0.2321 | | 1.7355 | 52.44 | 35500 | 0.8760 | 0.8842 | 0.2331 | | 1.7167 | 53.18 | 36000 | 0.8746 | 0.8817 | 0.2324 | | 1.7479 | 53.91 | 36500 | 0.8762 | 0.8753 | 0.2281 | | 1.7428 | 54.65 | 37000 | 0.8733 | 0.8699 | 0.2277 | | 1.7058 | 55.39 | 37500 | 0.8816 | 0.8649 | 0.2263 | | 1.7045 | 56.13 | 38000 | 0.8733 | 0.8689 | 0.2297 | | 1.709 | 56.87 | 38500 | 0.8648 | 0.8654 | 0.2232 | | 1.6799 | 57.61 | 39000 | 0.8717 | 0.8580 | 0.2244 | | 1.664 | 58.35 | 39500 | 0.8653 | 0.8723 | 0.2259 | | 1.6488 | 59.08 | 40000 | 0.8637 | 0.8803 | 0.2271 | | 1.6298 | 59.82 | 40500 | 0.8553 | 0.8768 | 0.2253 | | 1.6185 | 60.56 | 41000 | 0.8512 | 0.8718 | 0.2240 | | 1.574 | 61.3 | 41500 | 0.8579 | 0.8773 | 0.2251 | | 1.6192 | 62.04 | 42000 | 0.8499 | 0.8743 | 0.2242 | | 1.6275 | 62.78 | 42500 | 0.8419 | 0.8758 | 0.2216 | | 1.5697 | 63.52 | 43000 | 0.8446 | 0.8699 | 0.2222 | | 1.5384 | 64.25 | 43500 | 0.8462 | 0.8580 | 0.2200 | | 1.5115 | 64.99 | 44000 | 0.8467 | 0.8674 | 0.2214 | | 1.5547 | 65.73 | 44500 | 0.8505 | 0.8669 | 0.2204 | | 1.5597 | 66.47 | 45000 | 0.8421 | 0.8684 | 0.2192 | | 1.505 | 67.21 | 45500 | 0.8485 | 0.8619 | 0.2187 | | 1.5101 | 67.95 | 46000 | 0.8489 | 0.8649 | 0.2204 | | 1.5199 | 68.69 | 46500 | 0.8407 | 0.8619 | 0.2180 | | 1.5207 | 69.42 | 47000 | 0.8379 | 0.8496 | 0.2163 | | 1.478 | 70.16 | 47500 | 0.8357 | 0.8595 | 0.2163 | | 1.4817 | 70.9 | 48000 | 0.8346 | 0.8496 | 0.2151 | | 1.4827 | 71.64 | 48500 | 0.8362 | 0.8624 | 0.2169 | | 1.4513 | 72.38 | 49000 | 0.8355 | 0.8451 | 0.2137 | | 1.4988 | 73.12 | 49500 | 0.8325 | 0.8624 | 0.2161 | | 1.4267 | 73.85 | 50000 | 0.8396 | 0.8481 | 0.2157 | | 1.4421 | 74.59 | 50500 | 0.8355 | 0.8491 | 0.2122 | | 1.4311 | 75.33 | 51000 | 0.8358 | 0.8476 | 0.2118 | | 1.4174 | 76.07 | 51500 | 0.8289 | 0.8451 | 0.2101 | | 1.4349 | 76.81 | 52000 | 0.8372 | 0.8580 | 0.2140 | | 1.3959 | 77.55 | 52500 | 0.8325 | 0.8436 | 0.2116 | | 1.4087 | 78.29 | 53000 | 0.8351 | 0.8446 | 0.2105 | | 1.415 | 79.03 | 53500 | 0.8363 | 0.8476 | 0.2123 | | 1.4122 | 79.76 | 54000 | 0.8310 | 0.8481 | 0.2112 | | 1.3969 | 80.5 | 54500 | 0.8239 | 0.8446 | 0.2095 | | 1.361 | 81.24 | 55000 | 0.8282 | 0.8427 | 0.2091 | | 1.3611 | 81.98 | 55500 | 0.8282 | 0.8407 | 0.2092 | | 1.3677 | 82.72 | 56000 | 0.8235 | 0.8436 | 0.2084 | | 1.3361 | 83.46 | 56500 | 0.8231 | 0.8377 | 0.2069 | | 1.3779 | 84.19 | 57000 | 0.8206 | 0.8436 | 0.2070 | | 1.3727 | 84.93 | 57500 | 0.8204 | 0.8392 | 0.2065 | | 1.3317 | 85.67 | 58000 | 0.8207 | 0.8436 | 0.2065 | | 1.3332 | 86.41 | 58500 | 0.8186 | 0.8357 | 0.2055 | | 1.3299 | 87.15 | 59000 | 0.8193 | 0.8417 | 0.2075 | | 1.3129 | 87.89 | 59500 | 0.8183 | 0.8431 | 0.2065 | | 1.3352 | 88.63 | 60000 | 0.8151 | 0.8471 | 0.2062 | | 1.3026 | 89.36 | 60500 | 0.8125 | 0.8486 | 0.2067 | | 1.3468 | 90.1 | 61000 | 0.8124 | 0.8407 | 0.2058 | | 1.3028 | 90.84 | 61500 | 0.8122 | 0.8461 | 0.2051 | | 1.2884 | 91.58 | 62000 | 0.8086 | 0.8427 | 0.2048 | | 1.3005 | 92.32 | 62500 | 0.8110 | 0.8387 | 0.2055 | | 1.2996 | 93.06 | 63000 | 0.8126 | 0.8328 | 0.2057 | | 1.2707 | 93.8 | 63500 | 0.8098 | 0.8402 | 0.2047 | | 1.3026 | 94.53 | 64000 | 0.8097 | 0.8402 | 0.2050 | | 1.2546 | 95.27 | 64500 | 0.8111 | 0.8402 | 0.2055 | | 1.2426 | 96.01 | 65000 | 0.8088 | 0.8372 | 0.2059 | | 1.2869 | 96.75 | 65500 | 0.8093 | 0.8397 | 0.2048 | | 1.2782 | 97.49 | 66000 | 0.8099 | 0.8412 | 0.2049 | | 1.2457 | 98.23 | 66500 | 0.8134 | 0.8412 | 0.2062 | | 1.2967 | 98.97 | 67000 | 0.8115 | 0.8382 | 0.2055 | | 1.2817 | 99.7 | 67500 | 0.8128 | 0.8392 | 0.2063 | | cd01936afeed84f1ae11caa7e64005c3 |
mit | [] | false | child zombie on Stable Diffusion This is the `<child-zombie>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:    | 08007e6f92b7ba84383bc736571e4ec1 |
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: 0.1745 - F1: 0.8505 | a57d9ef9237f66494d3368b8d91b2c25 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.3055 | 1.0 | 835 | 0.1842 | 0.8099 | | 0.1561 | 2.0 | 1670 | 0.1711 | 0.8452 | | 0.1016 | 3.0 | 2505 | 0.1745 | 0.8505 | | 0d8317a5e2011fd0c1401d5f7f9817d3 |
apache-2.0 | ['summarization', 't5'] | false | t5-small-finetuned-billsum-ca_test This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 2.3376 - Rouge1: 12.6315 - Rouge2: 6.9839 - Rougel: 10.9983 - Rougelsum: 11.9383 - Gen Len: 19.0 | e429091ed6821e052a96584f6204c832 |
apache-2.0 | ['summarization', 't5'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | No log | 1.0 | 495 | 2.4805 | 9.9389 | 4.1239 | 8.3979 | 9.1599 | 19.0 | | 3.1564 | 2.0 | 990 | 2.3833 | 12.1026 | 6.5196 | 10.5123 | 11.4527 | 19.0 | | 2.66 | 3.0 | 1485 | 2.3496 | 12.5389 | 6.8686 | 10.8798 | 11.8636 | 19.0 | | 2.5671 | 4.0 | 1980 | 2.3376 | 12.6315 | 6.9839 | 10.9983 | 11.9383 | 19.0 | | d26832f66fc66a330712109e9179fcc5 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-banking-2-16-5-oos 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: 2.2564 - Accuracy: 0.3009 | 7f3d7ec4d8216b48e397f32f366f61a9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.8065 | 1.0 | 1 | 2.5730 | 0.1681 | | 2.2328 | 2.0 | 2 | 2.4625 | 0.2212 | | 1.8783 | 3.0 | 3 | 2.3655 | 0.2478 | | 1.64 | 4.0 | 4 | 2.2942 | 0.2655 | | 1.4937 | 5.0 | 5 | 2.2564 | 0.3009 | | 564e14fd2a79fe7bf037a9066510d7ff |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-00001b This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 2.8994 - Bleu: 7.5838 - Gen Len: 45.058 | 31f9013a2be96e21cb7cb7b944e0bd2c |
cc-by-4.0 | ['herbert'] | false | HerBERT **[HerBERT](https://en.wikipedia.org/wiki/Zbigniew_Herbert)** is a BERT-based Language Model trained on Polish corpora using Masked Language Modelling (MLM) and Sentence Structural Objective (SSO) with dynamic masking of whole words. For more details, please refer to: [HerBERT: Efficiently Pretrained Transformer-based Language Model for Polish](https://www.aclweb.org/anthology/2021.bsnlp-1.1/). Model training and experiments were conducted with [transformers](https://github.com/huggingface/transformers) in version 2.9. | 9005710900b7a7e64abdfb9a4f4eba74 |
cc-by-4.0 | ['herbert'] | false | Corpus HerBERT was trained on six different corpora available for Polish language: | Corpus | Tokens | Documents | | :------ | ------: | ------: | | [CCNet Middle](https://github.com/facebookresearch/cc_net) | 3243M | 7.9M | | [CCNet Head](https://github.com/facebookresearch/cc_net) | 2641M | 7.0M | | [National Corpus of Polish](http://nkjp.pl/index.php?page=14&lang=1)| 1357M | 3.9M | | [Open Subtitles](http://opus.nlpl.eu/OpenSubtitles-v2018.php) | 1056M | 1.1M | [Wikipedia](https://dumps.wikimedia.org/) | 260M | 1.4M | | [Wolne Lektury](https://wolnelektury.pl/) | 41M | 5.5k | | 78a0277d824fc28bedcac0eddca4134c |
cc-by-4.0 | ['herbert'] | false | Tokenizer The training dataset was tokenized into subwords using a character level byte-pair encoding (``CharBPETokenizer``) with a vocabulary size of 50k tokens. The tokenizer itself was trained with a [tokenizers](https://github.com/huggingface/tokenizers) library. We kindly encourage you to use the ``Fast`` version of the tokenizer, namely ``HerbertTokenizerFast``. | f74c7f4ea05bf9e71c1fb425540ab274 |
cc-by-4.0 | ['herbert'] | false | Usage Example code: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("allegro/herbert-base-cased") model = AutoModel.from_pretrained("allegro/herbert-base-cased") output = model( **tokenizer.batch_encode_plus( [ ( "A potem szedł środkiem drogi w kurzawie, bo zamiatał nogami, ślepy dziad prowadzony przez tłustego kundla na sznurku.", "A potem leciał od lasu chłopak z butelką, ale ten ujrzawszy księdza przy drodze okrążył go z dala i biegł na przełaj pól do karczmy." ) ], padding='longest', add_special_tokens=True, return_tensors='pt' ) ) ``` | 2080f80a1573e4c512267820fdb87554 |
cc-by-4.0 | ['herbert'] | false | Citation If you use this model, please cite the following paper: ``` @inproceedings{mroczkowski-etal-2021-herbert, title = "{H}er{BERT}: Efficiently Pretrained Transformer-based Language Model for {P}olish", author = "Mroczkowski, Robert and Rybak, Piotr and Wr{\\'o}blewska, Alina and Gawlik, Ireneusz", booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing", month = apr, year = "2021", address = "Kiyv, Ukraine", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2021.bsnlp-1.1", pages = "1--10", } ``` | 1692695594276c9486d9d803afeb1a15 |
cc-by-4.0 | ['herbert'] | false | Authors The model was trained by [**Machine Learning Research Team at Allegro**](https://ml.allegro.tech/) and [**Linguistic Engineering Group at Institute of Computer Science, Polish Academy of Sciences**](http://zil.ipipan.waw.pl/). You can contact us at: <a href="mailto:klejbenchmark@allegro.pl">klejbenchmark@allegro.pl</a> | 0c9b9320d552305eefa9a5779a88d615 |
apache-2.0 | ['generated_from_keras_callback'] | false | augustoortiz/bert-finetuned-squad2 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.2223 - Epoch: 0 | 1449802c80bf862f731f5b6043dbca6e |
apache-2.0 | ['generated_from_keras_callback'] | false | 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': 11091, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 | fe0cbd493cd11ecf1d36df832dc21e06 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 400 - mixed_precision_training: Native AMP | bc773ed7f54bdd81a6aa0cd8119c9316 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/vctk_gst+xvector_conformer_fastspeech2` ♻️ Imported from https://zenodo.org/record/4394608/ This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | c8e8d1fbbb7568a156de4591cbb41951 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.2414 | a43517a97f79855152b4953787af4707 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 20 - eval_batch_size: 20 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 | 910ba5a546a9c87ef145940b8733957f |
apache-2.0 | ['automatic-speech-recognition', 'ru'] | false | exp_w2v2t_ru_no-pretraining_s895 Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (ru)](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. | eb637dfc0831a7376d8027a970e674d4 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-credit_cards-5-16-5 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: 2.3376 - Accuracy: 0.3186 | 58e5316c9401ddf7d6ed97f21cedcee6 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Model Description GPT-J 6B is a transformer model trained using Ben Wang's [Mesh Transformer JAX](https://github.com/kingoflolz/mesh-transformer-jax/). "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters. <figure> | Hyperparameter | Value | |----------------------|------------| | \\(n_{parameters}\\) | 6053381344 | | \\(n_{layers}\\) | 28* | | \\(d_{model}\\) | 4096 | | \\(d_{ff}\\) | 16384 | | \\(n_{heads}\\) | 16 | | \\(d_{head}\\) | 256 | | \\(n_{ctx}\\) | 2048 | | \\(n_{vocab}\\) | 50257/50400† (same tokenizer as GPT-2/3) | | Positional Encoding | [Rotary Position Embedding (RoPE)](https://arxiv.org/abs/2104.09864) | | RoPE Dimensions | [64](https://github.com/kingoflolz/mesh-transformer-jax/blob/f2aa66e0925de6593dcbb70e72399b97b4130482/mesh_transformer/layers.py | 6af80e1adf944bab671fe68bcf7a5ace |
apache-2.0 | ['pytorch', 'causal-lm'] | false | L223) | <figcaption><p><strong>*</strong> Each layer consists of one feedforward block and one self attention block.</p> <p><strong>†</strong> Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer.</p></figcaption></figure> The model consists of 28 layers with a model dimension of 4096, and a feedforward dimension of 16384. The model dimension is split into 16 heads, each with a dimension of 256. Rotary Position Embedding (RoPE) is applied to 64 dimensions of each head. The model is trained with a tokenization vocabulary of 50257, using the same set of BPEs as GPT-2/GPT-3. | 1dac301bb9dc11ed90459e5c7289103c |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Training procedure This model was trained for 402 billion tokens over 383,500 steps on TPU v3-256 pod. It was trained as an autoregressive language model, using cross-entropy loss to maximize the likelihood of predicting the next token correctly. | 1b545bb11d9d9a71aa0457907b810e84 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Intended Use and Limitations GPT-J learns an inner representation of the English language that can be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating text from a prompt. | 0eba029679042915b6d8dd387354e626 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | How to use This model can be easily loaded using the `AutoModelForCausalLM` functionality: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B") model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6B") ``` | 58e79271d8e311a4be6980e59402e22f |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Limitations and Biases The core functionality of GPT-J is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. When prompting GPT-J it is important to remember that the statistically most likely next token is often not the token that produces the most "accurate" text. Never depend upon GPT-J to produce factually accurate output. GPT-J was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending upon use case GPT-J may produce socially unacceptable text. See [Sections 5 and 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a more detailed analysis of the biases in the Pile. As with all language models, it is hard to predict in advance how GPT-J will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results. | 93303ef122fa72515bc759c8bfb9e5fe |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Evaluation results <figure> | Model | Public | Training FLOPs | LAMBADA PPL ↓ | LAMBADA Acc ↑ | Winogrande ↑ | Hellaswag ↑ | PIQA ↑ | Dataset Size (GB) | |--------------------------|-------------|----------------|--- |--- |--- |--- |--- |-------------------| | Random Chance | ✓ | 0 | ~a lot | ~0% | 50% | 25% | 25% | 0 | | GPT-3 Ada‡ | ✗ | ----- | 9.95 | 51.6% | 52.9% | 43.4% | 70.5% | ----- | | GPT-2 1.5B | ✓ | ----- | 10.63 | 51.21% | 59.4% | 50.9% | 70.8% | 40 | | GPT-Neo 1.3B‡ | ✓ | 3.0e21 | 7.50 | 57.2% | 55.0% | 48.9% | 71.1% | 825 | | Megatron-2.5B* | ✗ | 2.4e21 | ----- | 61.7% | ----- | ----- | ----- | 174 | | GPT-Neo 2.7B‡ | ✓ | 6.8e21 | 5.63 | 62.2% | 56.5% | 55.8% | 73.0% | 825 | | GPT-3 1.3B*‡ | ✗ | 2.4e21 | 5.44 | 63.6% | 58.7% | 54.7% | 75.1% | ~800 | | GPT-3 Babbage‡ | ✗ | ----- | 5.58 | 62.4% | 59.0% | 54.5% | 75.5% | ----- | | Megatron-8.3B* | ✗ | 7.8e21 | ----- | 66.5% | ----- | ----- | ----- | 174 | | GPT-3 2.7B*‡ | ✗ | 4.8e21 | 4.60 | 67.1% | 62.3% | 62.8% | 75.6% | ~800 | | Megatron-11B† | ✓ | 1.0e22 | ----- | ----- | ----- | ----- | ----- | 161 | | **GPT-J 6B‡** | **✓** | **1.5e22** | **3.99** | **69.7%** | **65.3%** | **66.1%** | **76.5%** | **825** | | GPT-3 6.7B*‡ | ✗ | 1.2e22 | 4.00 | 70.3% | 64.5% | 67.4% | 78.0% | ~800 | | GPT-3 Curie‡ | ✗ | ----- | 4.00 | 69.3% | 65.6% | 68.5% | 77.9% | ----- | | GPT-3 13B*‡ | ✗ | 2.3e22 | 3.56 | 72.5% | 67.9% | 70.9% | 78.5% | ~800 | | GPT-3 175B*‡ | ✗ | 3.1e23 | 3.00 | 76.2% | 70.2% | 78.9% | 81.0% | ~800 | | GPT-3 Davinci‡ | ✗ | ----- | 3.0 | 75% | 72% | 78% | 80% | ----- | <figcaption><p>Models roughly sorted by performance, or by FLOPs if not available.</p> <p><strong>*</strong> Evaluation numbers reported by their respective authors. All other numbers are provided by running <a href="https://github.com/EleutherAI/lm-evaluation-harness/"><code>lm-evaluation-harness</code></a> either with released weights or with API access. Due to subtle implementation differences as well as different zero shot task framing, these might not be directly comparable. See <a href="https://blog.eleuther.ai/gpt3-model-sizes/">this blog post</a> for more details.</p> <p><strong>†</strong> Megatron-11B provides no comparable metrics, and several implementations using the released weights do not reproduce the generation quality and evaluations. (see <a href="https://github.com/huggingface/transformers/pull/10301">1</a> <a href="https://github.com/pytorch/fairseq/issues/2358">2</a> <a href="https://github.com/pytorch/fairseq/issues/2719">3</a>) Thus, evaluation was not attempted.</p> <p><strong>‡</strong> These models have been trained with data which contains possible test set contamination. The OpenAI GPT-3 models failed to deduplicate training data for certain test sets, while the GPT-Neo models as well as this one is trained on the Pile, which has not been deduplicated against any test sets.</p></figcaption></figure> | 965951e965a03ed0e95336aa1bf873a5 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | BibTeX entry To cite this model: ```bibtex @misc{gpt-j, author = {Wang, Ben and Komatsuzaki, Aran}, title = {{GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model}}, howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}}, year = 2021, month = May } ``` To cite the codebase that trained this model: ```bibtex @misc{mesh-transformer-jax, author = {Wang, Ben}, title = {{Mesh-Transformer-JAX: Model-Parallel Implementation of Transformer Language Model with JAX}}, howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}}, year = 2021, month = May } ``` If you use this model, we would love to hear about it! Reach out on [GitHub](https://github.com/kingoflolz/mesh-transformer-jax), Discord, or shoot Ben an email. | 8ea0e8a9c78372692888782fa451d9ca |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Acknowledgements This project would not have been possible without compute generously provided by Google through the [TPU Research Cloud](https://sites.research.google/trc/), as well as the Cloud TPU team for providing early access to the [Cloud TPU VM](https://cloud.google.com/blog/products/compute/introducing-cloud-tpu-vms) Alpha. Thanks to everyone who have helped out one way or another (listed alphabetically): - [James Bradbury](https://twitter.com/jekbradbury) for valuable assistance with debugging JAX issues. - [Stella Biderman](https://www.stellabiderman.com), [Eric Hallahan](https://twitter.com/erichallahan), [Kurumuz](https://github.com/kurumuz/), and [Finetune](https://github.com/finetuneanon/) for converting the model to be compatible with the `transformers` package. - [Leo Gao](https://twitter.com/nabla_theta) for running zero shot evaluations for the baseline models for the table. - [Laurence Golding](https://github.com/researcher2/) for adding some features to the web demo. - [Aran Komatsuzaki](https://twitter.com/arankomatsuzaki) for advice with experiment design and writing the blog posts. - [Janko Prester](https://github.com/jprester/) for creating the web demo frontend. | defd13f21fa8cc452b9274862b1e7218 |
apache-2.0 | ['automatic-speech-recognition', 'sv-SE'] | false | exp_w2v2t_sv-se_vp-sv_s363 Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (sv-SE)](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. | 23cdf87f3fa85a87c35a0f2e961a70bf |
apache-2.0 | ['generated_from_trainer'] | false | Article_250v5_NER_Model_3Epochs_UNAUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article250v5_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.3250 - Precision: 0.3979 - Recall: 0.4221 - F1: 0.4097 - Accuracy: 0.8779 | ce4fe14cf356307dadff696dbc40bbd2 |
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