license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0121 | 0.99 | 140 | 0.0001 | 1.0 | | 0.0103 | 1.99 | 280 | 0.0001 | 1.0 | | 0.0049 | 2.99 | 420 | 0.0000 | 1.0 | | f1ca9d24e25d81d1a2569304da791f6a |
mit | ['generated_from_trainer'] | false | xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-9 This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on the Turkish squad dataset. It achieves the following results on the evaluation set: - Loss: 2.2340 | fa214500ae508cba9ddcc459246228f3 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 9 | e42358b6bfbdc5b9b6cb0af7afe2f3b0 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.5236 | 1.0 | 1050 | 3.0042 | | 2.8489 | 2.0 | 2100 | 2.5866 | | 2.5485 | 3.0 | 3150 | 2.3526 | | 2.4067 | 4.0 | 4200 | 2.3535 | | 2.3091 | 5.0 | 5250 | 2.2862 | | 2.2401 | 6.0 | 6300 | 2.3989 | | 2.1715 | 7.0 | 7350 | 2.2284 | | 2.1414 | 8.0 | 8400 | 2.2298 | | 2.1221 | 9.0 | 9450 | 2.2340 | | b565d31ebab447b1b949fcc56a7ed74f |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True}, 'generation': {'batch_size': 64, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 640, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 512}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 272, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_samples': 512, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'codeparrot/codeparrot-small'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'kejian/final-mle', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0008, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 5000, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} | fe4780759e800af071d92649f7f5f2bc |
apache-2.0 | ['generated_from_trainer'] | false | xlsr-english This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the librispeech_asr dataset. It achieves the following results on the evaluation set: - Loss: 0.3098 - Wer: 0.1451 | 4806cb228be0a6e04df6c665b186af65 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.2453 | 2.37 | 400 | 0.5789 | 0.4447 | | 0.3736 | 4.73 | 800 | 0.3737 | 0.2850 | | 0.1712 | 7.1 | 1200 | 0.3038 | 0.2136 | | 0.117 | 9.47 | 1600 | 0.3016 | 0.2072 | | 0.0897 | 11.83 | 2000 | 0.3158 | 0.1920 | | 0.074 | 14.2 | 2400 | 0.3137 | 0.1831 | | 0.0595 | 16.57 | 2800 | 0.2967 | 0.1745 | | 0.0493 | 18.93 | 3200 | 0.3192 | 0.1670 | | 0.0413 | 21.3 | 3600 | 0.3176 | 0.1644 | | 0.0322 | 23.67 | 4000 | 0.3079 | 0.1598 | | 0.0296 | 26.04 | 4400 | 0.2978 | 0.1511 | | 0.0235 | 28.4 | 4800 | 0.3098 | 0.1451 | | c8e693ef90a8c5d014d729b81d08c78d |
mit | [] | false | Party girl on Stable Diffusion This is the `<party-girl>` 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`:       | 0129035a934d62a10df09eaf9ca62a76 |
unknown | [] | false | Age estimation in supermarkets The model analyzed in this card estimates someone's age. This project has been done for the master Applied Artificial Intelligence and is about estimating ages in supermarkets when a person wants to buy alcohol. This model's goal is to only estimate ages in an image. It will not cover ethnicities or gender. | ef221e285d3dc1d158c5c8df01af98b4 |
unknown | [] | false | Model description **Used dataset:** UTKFace images - This dataset contains roughly 24K face images. - The age of a person on the picture is labeled in the filename of that image. - Since we do not have use for baby images, we decided to cut these out of the dataset, so there are 21K images left. **Model input:** Facial images **Model output:** For a face in a picture, the model will return the estimated age of that person. The model output also gives a confidence score for the estimation. **Model architecture:** A Convolutional Neural Network. This CNN will perform a regression analysis to estimates the ages. | bad43b2b3bc03a59c643764e3ea7b254 |
unknown | [] | false | Performance To determine the performance of the model, the following metrics have been used: - MSE, this metric measures how close the regression line is to the data points. <br>   - *Our model's MSE:* 60.9 - RMSE, this metric measures the mean error that can be made. <br>   - *Our model's RMSE:* 7.8 - MAE, this is a measure for model accuracy. The MAE is the average error that the model's predictions have in comparison with their corresponding actual targets. <br>   - *Our model's MAE:* 5.2 Ideally, the RMSE and the MAE should be close to each other. When there is a big difference in these two numbers, it is an indication of variance in the individually errors. Our results show that the prediction model can be around 8 years off of the actual age of a person. We also looked at how the model performs in different age, gender and race classes. It seemed the model predicted the ages of people between 20 and 30 better than the rest. The model could also predict the ages of females better than males. The race that the model can predict the best is East Asian. | 77dcf2bbf4c109025b613226dfc2e0fb |
unknown | [] | false | Limitations - **Lighting** <br> When the lighting is poor, the age estimation can be poor as well - **Occlusion** <br> Partially hidden or obstructed faces might not be detected. (e.g. face masks) - **UTKFace** <br> The ages in this dataset are in itself estimation from a previous model. Since we do not know the exact ages of the people in the images, our model will not be the most reliable. | df8e997ea9a7c319e6707545e2e6e117 |
unknown | [] | false | Training and evaluation data Train data: 70% Test data: 30% Our model has been made by trial and error. The following architecture is the outcome: - Hidden layers: 7 - Batch size: 128 - Epochs: 65 - Optimizer: adam - Activation: ReLu & Linear | c05919f66f397faaab0bd0b6aad6e4ea |
apache-2.0 | ['speech'] | false | Wav2Vec2-Conformer-Large with Rotary Position Embeddings Wav2Vec2 Conformer with rotary position embeddings, pretrained on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more in-detail explanation of how to fine-tune the model. **Paper**: [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) **Authors**: Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino The results of Wav2Vec2-Conformer can be found in Table 3 and Table 4 of the [official paper](https://arxiv.org/abs/2010.05171). The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec | ed48e8a6b11e9f4a91e3eae520fad5aa |
mit | ['roberta', 'gottbert'] | false | Overview **Language model:** uklfr/gottbert-base **Language:** German **Training & Eval data:** [GARFAB2022Weighted](https://huggingface.co/datasets/julius-br/GARFAB) <br> **Published**: September 21th, 2022 <br> **Author**: Julius Breiholz | d239fd910b90b95575644560452d013e |
mit | ['roberta', 'gottbert'] | false | Performance | Label | Precision | Recall | F1-Score | | --- | --- | --- | --- | | Irrelevant | 0,95 | 0,91 | 0,93 | | Bug Report | 0,82 | 0,91 | 0,86 | | Feature Request | 0,87 | 0,82 | 0,85 | | all classes (avg.) | 0,88 | 0,88 | 0,88 | | a30913e87fc8d8fd2701bb789ed72a1f |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the NST dataset. Aborted after 6000 steps / 0.4 epochs as it wasen't promising when manualy evaluated on an SVT broadcast. The punctation, capitalization and entities like Norge seems worse than original so probably need to fix dataset before more training. Re-split the test dataset to contain a thousand samples so evaluate didn't take hours. | 3349274a73d5474b14636f03ad1d15b8 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Step | Wer | |:----:|:----:| | 1000 | 9.42 | | 2000 | 8.13 | | 3000 | 7.27 | | 4000 | 7.05 | | 5000 | 6.60 | | 6000 | 6.49 | Source audio: https://www.youtube.com/watch?v=9XLHas6oD_E This model: ``` [00:00:00.000 --> 00:00:03.040] Ta nu ett djupt andetag för er kan inte alla göra. [00:00:03.040 --> 00:00:11.840] För de allra flesta så är det en självklarhet att kunna andas utan större problem, men har man lomsjukdomens hysterisk fibrås är det inte så. [00:00:11.840 --> 00:00:16.240] Nu finns en ny medicin, men den är inte subventionerad i Sverige. [00:00:16.240 --> 00:00:22.960] Nej, om man vill kunna andas i sverige så får man söka sig till svarta marknaden i mindre noggräknade länder som är norrje. [00:00:22.960 --> 00:00:39.360] Nu ska vi åka till norrje och så ska vi möta upp då en person som ska jag köpa då kafttrio av honom som han får då gratis från norska staten och som han då säljer vidare. [00:00:39.360 --> 00:00:54.560] Okej, i norrje delar läkarna ut medicin i kafttri och gratis till vilken jävla gud som helst och det är bra för nu kan helen andas ut och in.Det ser okej bra att hon får hosta upp inte bara slemme utan även tjugosex tusen i kontanter. [00:00:54.560 --> 00:01:00.320] Jag fattar inte, sverige är ju världsbäst på subventioner, i alla fall i södra sverige, ja när det gäller äl. ``` Whisper medium: ``` [00:00:00.000 --> 00:00:03.080] Ta ett djupt antal, för det kan inte alla göra. [00:00:03.080 --> 00:00:08.000] För de flesta är det självklar att kunna andas utan problem. [00:00:08.000 --> 00:00:12.120] Men har man Lundsjukdomens fibros, är det inte så. [00:00:12.120 --> 00:00:16.200] Nu finns en ny medicin, men den är inte subventionerad i Sverige. [00:00:16.200 --> 00:00:20.160] Om man vill andas i Sverige, så får man söka sig till svarta marknaden- [00:00:20.160 --> 00:00:22.920] -i mindre noggräknade länder som Norge. [00:00:22.920 --> 00:00:29.840] Nu ska vi åka till Norge och möta upp en person som jag ska köpa. [00:00:29.840 --> 00:00:37.480] Ja, kaffetrio av honom. Som han får gratis från Norska staten. [00:00:37.480 --> 00:00:40.200] -Och som han säljer vidare. -Okej. [00:00:40.200 --> 00:00:44.560] I Norge delar läkarna ut medicinen kaffetrio gratis till vilken gud som helst. [00:00:44.560 --> 00:00:49.360] Det är bra, för nu kan Helen andas ut och in. [00:00:49.360 --> 00:00:54.280] Det är inte bara att hon får rosta upp, utan även 26 000 kontanter. [00:00:54.280 --> 00:00:59.320] Sverige är världsbäst på subventioner, i alla fall i södra Sverige. ``` | a97c6fce43ba4e08b39d34522186248e |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab647 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5534 - Wer: 0.4799 | 63067da4491d2835f033c4886b5f8beb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.2072 | 7.04 | 500 | 3.7757 | 1.0 | | 1.2053 | 14.08 | 1000 | 0.6128 | 0.5648 | | 0.3922 | 21.13 | 1500 | 0.5547 | 0.5035 | | 0.2157 | 28.17 | 2000 | 0.5534 | 0.4799 | | 01edb54b32b7edfefd88976abec7c16d |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/bart-base-subjqa-books-qg` This model is fine-tuned version of [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: books) via [`lmqg`](https://github.com/asahi417/lm-question-generation). | 532420a3459630a15fd205e922201ee7 |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad) - **Language:** en - **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (books) - **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) | 1dfab9306fdff92c1dc082bd422f40db |
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", "lmqg/bart-base-subjqa-books-qg") output = pipe("<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` | d7030f02f2cdff33d74203e4e0718ab5 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/bart-base-subjqa-books-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.books.json) | | Score | Type | Dataset | |:-----------|--------:|:-------|:-----------------------------------------------------------------| | BERTScore | 92.96 | books | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_1 | 22.47 | books | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_2 | 13.03 | books | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_3 | 4.52 | books | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_4 | 2.03 | books | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | METEOR | 20.57 | books | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | MoverScore | 62.85 | books | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | ROUGE_L | 23.24 | books | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | d915e13939b1a966752e53722b7c1b11 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_subjqa - dataset_name: books - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: lmqg/bart-base-squad - max_length: 512 - max_length_output: 32 - epoch: 2 - batch: 32 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 4 - label_smoothing: 0.0 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/bart-base-subjqa-books-qg/raw/main/trainer_config.json). | 1c721cc595bc589f12e1d6444f26f16d |
apache-2.0 | ['translation'] | false | opus-mt-fr-sv * source languages: fr * target languages: sv * OPUS readme: [fr-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-24.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-sv/opus-2020-01-24.zip) * test set translations: [opus-2020-01-24.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-sv/opus-2020-01-24.test.txt) * test set scores: [opus-2020-01-24.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-sv/opus-2020-01-24.eval.txt) | cd400e167479a4fbc1d481ef4e0493d9 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6949 - Matthews Correlation: 0.5410 | e7ff10546fff6f179ee68e6d111b69a7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5241 | 1.0 | 535 | 0.5322 | 0.3973 | | 0.356 | 2.0 | 1070 | 0.5199 | 0.4836 | | 0.2402 | 3.0 | 1605 | 0.6086 | 0.5238 | | 0.166 | 4.0 | 2140 | 0.6949 | 0.5410 | | 0.134 | 5.0 | 2675 | 0.8254 | 0.5253 | | a9a234ad50df3794bcd96ef24bff124c |
apache-2.0 | ['automatic-speech-recognition', 'it'] | false | exp_w2v2t_it_vp-es_s496 Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (it)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | c37657965f3fb3d11ef4e66e2aa0c186 |
mit | [] | false | model by Fedeya This your the Stable Diffusion model fine-tuned the federico minaya concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks federicominaya** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can 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), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept:       | 42bb1b9a54a54024af9ba919bdc9f4ae |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-response-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9774 - Matthews Correlation: 0.3330 | cc5f4b68bc8dfcdbb4031dcc8d214ff4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | No log | 1.0 | 23 | 1.0662 | 0.0 | | No log | 2.0 | 46 | 1.0175 | 0.0 | | No log | 3.0 | 69 | 1.0001 | 0.0 | | No log | 4.0 | 92 | 0.9852 | 0.1196 | | No log | 5.0 | 115 | 0.9836 | 0.2326 | | No log | 6.0 | 138 | 0.9680 | 0.1808 | | No log | 7.0 | 161 | 0.9774 | 0.3330 | | No log | 8.0 | 184 | 0.9786 | 0.2881 | | No log | 9.0 | 207 | 0.9974 | 0.2235 | | No log | 10.0 | 230 | 0.9957 | 0.2031 | | a6367b91e462025b5e6b2c88659db19d |
apache-2.0 | ['automatic-speech-recognition', 'it'] | false | exp_w2v2t_it_wav2vec2_s211 Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition using the train split of [Common Voice 7.0 (it)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 1c9de4adec4b851a280d6952072ea3c7 |
apache-2.0 | ['Quality Estimation', 'monotransquest', 'hter'] | false | Using Pre-trained Models ```python import torch from transquest.algo.sentence_level.monotransquest.run_model import MonoTransQuestModel model = MonoTransQuestModel("xlmroberta", "TransQuest/monotransquest-hter-en_lv-it-nmt", num_labels=1, use_cuda=torch.cuda.is_available()) predictions, raw_outputs = model.predict([["Reducerea acestor conflicte este importantă pentru conservare.", "Reducing these conflicts is not important for preservation."]]) print(predictions) ``` | 222be06fcf6a2f2f8bed40c432656afd |
apache-2.0 | ['image-segmentation', 'vision', 'generated_from_trainer'] | false | segformer-trainer-test This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 1.3886 - Mean Iou: 0.1391 - Mean Accuracy: 0.1905 - Overall Accuracy: 0.7192 | b8c0e221b73e83d71bdf165a64a083fc |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | indo-sentence-bert-base This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> | 327ee422123ec4f49dec46072a8c06df |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["Ibukota Perancis adalah Paris", "Menara Eifel terletak di Paris, Perancis", "Pizza adalah makanan khas Italia", "Saya kuliah di Carneige Mellon University"] model = SentenceTransformer('firqaaa/indo-sentence-bert-base') embeddings = model.encode(sentences) print(embeddings) ``` | 0d7ef8bdd93351928dd9ba93b0bbe0b6 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Sentences we want sentence embeddings for sentences = ["Ibukota Perancis adalah Paris", "Menara Eifel terletak di Paris, Perancis", "Pizza adalah makanan khas Italia", "Saya kuliah di Carneige Mellon University"] | 6343efdcde436459f9478ae11982bc3b |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Training The model was trained with the parameters: **DataLoader**: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 19644 with parameters: ``` {'batch_size': 16} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 5, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 9930, "weight_decay": 0.01 } ``` | cb031dd6c515905d5f4d18192410816f |
apache-2.0 | ['generated_from_trainer'] | false | mini-mlm-imdb-target-tweet This model is a fine-tuned version of [muhtasham/mini-mlm-imdb](https://huggingface.co/muhtasham/mini-mlm-imdb) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.3042 - Accuracy: 0.7674 - F1: 0.7669 | fe9884544dee7edf56dbd4ca6ccc5770 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8543 | 4.9 | 500 | 0.6920 | 0.7674 | 0.7571 | | 0.3797 | 9.8 | 1000 | 0.7231 | 0.7727 | 0.7709 | | 0.1668 | 14.71 | 1500 | 0.9171 | 0.7594 | 0.7583 | | 0.068 | 19.61 | 2000 | 1.1558 | 0.7647 | 0.7642 | | 0.0409 | 24.51 | 2500 | 1.3042 | 0.7674 | 0.7669 | | 4da08762b1f57112ae18ac65ece29d0e |
apache-2.0 | [] | false | Fine-tuned T5 base model for use as a frame semantic parser in the [Frame Semantic Transformer](https://github.com/chanind/frame-semantic-transformer) project. This model is trained on data from [FrameNet 1.7](https://framenet2.icsi.berkeley.edu/). | 1a704eed7f1a9cfb6cc52fc3ebfa0a60 |
apache-2.0 | [] | false | Performance This model is trained and evaluated using the same train/dev/test splits from FrameNet 1.7 annotated corpora as used by [Open Sesame](https://github.com/swabhs/open-sesame). | Task | F1 Score (Dev) | F1 Score (Test) | | ---------------------- | -------------- | --------------- | | Trigger identification | 0.78 | 0.71 | | Frame Classification | 0.89 | 0.87 | | Argument Extraction | 0.74 | 0.72 | | f2b2e2ecb7db084eccc475375f3e630b |
apache-2.0 | ['generated_from_trainer'] | false | mis_515_bert This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3636 - Accuracy: 0.9073 | 057f17ee3e81fd4545be08a6aea12ed3 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-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: 2 | 39705bae8ee9556de11607cf339413be |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4773 | 1.0 | 1125 | 0.3741 | 0.8777 | | 0.2705 | 2.0 | 2250 | 0.3636 | 0.9073 | | 465ecc0399aec0a33032471f188c3d1c |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-finetuned-wikitext2 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: 8.3687 | f53a46c44ee1ce44b77a6acb7f3b6a76 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 6 | 8.8622 | | No log | 2.0 | 12 | 8.4576 | | No log | 3.0 | 18 | 8.4412 | | 7176a58774a14d113cc49486f4631906 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-mlm-finetuned-emotion This model is a fine-tuned version of [google/bert_uncased_L-12_H-768_A-12](https://huggingface.co/google/bert_uncased_L-12_H-768_A-12) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.3374 | d56b2bd467861bfb6e00510cb43dcda6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.4247 | 5.75 | 500 | 2.3526 | | 2.1825 | 11.49 | 1000 | 2.2778 | | 2.0578 | 17.24 | 1500 | 2.3802 | | 1.9059 | 22.99 | 2000 | 2.3358 | | 1.7966 | 28.74 | 2500 | 2.3374 | | e098dd64fc7181f2d48c56253113882d |
apache-2.0 | ['generated_from_trainer'] | false | bert-small-finetuned-wnut17-ner-longer6 This model is a fine-tuned version of [muhtasham/bert-small-finetuned-wnut17-ner](https://huggingface.co/muhtasham/bert-small-finetuned-wnut17-ner) on the wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.4037 - Precision: 0.5667 - Recall: 0.4270 - F1: 0.4870 - Accuracy: 0.9268 | 3de32d18600677fecab1dde9d7030df8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 425 | 0.3744 | 0.5626 | 0.4139 | 0.4769 | 0.9248 | | 0.085 | 2.0 | 850 | 0.3914 | 0.5814 | 0.4270 | 0.4924 | 0.9271 | | 0.0652 | 3.0 | 1275 | 0.4037 | 0.5667 | 0.4270 | 0.4870 | 0.9268 | | e97f6de6e2cc8e3e144153dcc7d7aeb8 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | nes-cover-art-image-generator Dreambooth model trained by SergenK with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:     | 3f6d00c6e93afe9bfce91b63bdb8bc85 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-kr-jw4169 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the fleurs dataset. It achieves the following results on the evaluation set: - Loss: 0.9752 - Wer: 0.5196 | 1421131583785f787bcb8217d1ace7a7 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 | 505c3202f674d588db2d956e5b73961b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 35.084 | 1.39 | 200 | 6.8536 | 1.0 | | 4.853 | 2.78 | 400 | 4.6246 | 1.0 | | 4.5491 | 4.17 | 600 | 4.3815 | 1.0 | | 2.799 | 5.55 | 800 | 1.7402 | 0.8642 | | 1.3872 | 6.94 | 1000 | 1.2019 | 0.7448 | | 0.9599 | 8.33 | 1200 | 1.0594 | 0.7134 | | 0.675 | 9.72 | 1400 | 0.9321 | 0.6404 | | 0.4775 | 11.11 | 1600 | 0.9088 | 0.5911 | | 0.3479 | 12.5 | 1800 | 0.9430 | 0.6010 | | 0.2712 | 13.89 | 2000 | 0.8948 | 0.5854 | | 0.2283 | 15.28 | 2200 | 0.9009 | 0.5495 | | 0.1825 | 16.67 | 2400 | 0.9079 | 0.5501 | | 0.161 | 18.06 | 2600 | 0.9518 | 0.5390 | | 0.1394 | 19.44 | 2800 | 0.9529 | 0.5399 | | 0.1266 | 20.83 | 3000 | 0.9505 | 0.5283 | | 0.1102 | 22.22 | 3200 | 0.9748 | 0.5328 | | 0.101 | 23.61 | 3400 | 0.9593 | 0.5316 | | 0.0907 | 25.0 | 3600 | 0.9832 | 0.5292 | | 0.0833 | 26.39 | 3800 | 0.9773 | 0.5181 | | 0.0781 | 27.78 | 4000 | 0.9736 | 0.5163 | | 0.0744 | 29.17 | 4200 | 0.9752 | 0.5196 | | 56e21bbcd55a24af0964575d317c9554 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-Regression-Edmunds_Car_Reviews-Non_European_Imports This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2240 - Mae: 0.3140 - Mse: 0.2240 - Rmse: 0.4733 | 6d8aae9ad066235c15c64b67a156489c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mae | Mse | Rmse | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.6594 | 1.0 | 715 | 0.2436 | 0.3319 | 0.2436 | 0.4935 | | 0.2324 | 2.0 | 1430 | 0.2274 | 0.3210 | 0.2274 | 0.4769 | | 0.1975 | 3.0 | 2145 | 0.2303 | 0.3198 | 0.2303 | 0.4799 | | f85e352f28c914643bf97639dc8e433a |
apache-2.0 | ['generated_from_trainer'] | false | DistilBERT-POWO_MGH_Epiphyte_Finetuned This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0749 | d80bb22b59f7a6eb74c0ddd50464e877 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0824 | 1.0 | 1931 | 0.0807 | | 0.0768 | 2.0 | 3862 | 0.0747 | | 0.0664 | 3.0 | 5793 | 0.0749 | | bf97e7b9f06964c87d056452b172f49a |
mit | [] | false | German BERT large fine-tuned to predict educational requirements This is a fine-tuned version of the German BERT large language model [deepset/gbert-large](https://huggingface.co/deepset/gbert-large). The multilabel task this model was trained on was to predict education requirements from job ad texts. The dataset used for training is not available to the public. The 7 labels in the task are (in the classification head order): - `'Bachelor'` - `'Berufsausbildung'` - `'Doktorat oder äquivalent'` - `'Höhere Berufsausbildung'` - `'Master'` - `'Sonstiges'` - `'keine Ausbildungserfordernisse'` The number of representatives of these labels in each of the splits (train/test/val) of the dataset is summarized in the following table: | Label name | All data | Training | Validation | Test | |------------|----------|----------|------------|------| | Bachelor | 521 | 365 | 52 | 104 | | Berufsausbildung | 1854 | 1298 | 185 | 371 | | Doktorat oder äquivalent | 38 | 27 | 4 | 7 | | Höhere Berufsausbildung | 564 | 395 | 56 | 113 | | Master | 245 | 171 | 25 | 49 | | Sonstiges | 819 | 573 | 82 | 164 | | keine Ausbildungserfordernisse | 176 | 123 | 18 | 35 | | 7d18b1f0436cc452552d51833c6c3767 |
mit | [] | false | Cross-entropy_minimization) loss between the model's predictions and the actual labels in the training set. During training, a weighted version of the [label ranking average precision (LRAP)](https://scikit-learn.org/stable/modules/model_evaluation.html | da7fe36f45a5a3a17e7083c2d2a3a40f |
mit | [] | false | label-ranking-average-precision) was tracked for the testing set. LRAP measures what fraction of higher-ranked labels produced by the model were true labels. To account for the label imbalance, the rankings were weighted so that improperly ranked rare labels are penalized more than their more frequent counterparts. After training was complete, the model with highest weighted LRAP was saved. ``` LRAP: 0.96 ``` | 4c52f84b211e592ef3d371c894ac5c1b |
mit | [] | false | See also: - [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) - [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) - [gonzpen/gbert-base-ft-edu-redux](https://huggingface.co/gonzpen/gbert-base-ft-edu-redux) | cb0d8bbd2ca7b6287fb9d5756e155855 |
mit | ['generated_from_trainer'] | false | bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-earlystopping This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8347 - Rouge1: 53.9049 - Rouge2: 35.5953 - Rougel: 39.788 - Rougelsum: 51.4101 - Gen Len: 142.0 | 877bedbabd840890e796381044c7de79 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 200 - mixed_precision_training: Native AMP | 84f40a80eb7b7e7a8c316579938d1333 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | No log | 0.31 | 125 | 1.0240 | 52.5632 | 32.977 | 34.672 | 49.9905 | 142.0 | | No log | 0.63 | 250 | 1.0056 | 52.5508 | 32.4826 | 34.6851 | 49.835 | 141.6852 | | No log | 0.94 | 375 | 0.8609 | 53.0475 | 32.9384 | 35.3322 | 50.272 | 141.6481 | | 0.8255 | 1.26 | 500 | 0.9022 | 52.2493 | 31.5622 | 33.389 | 49.6612 | 142.0 | | 0.8255 | 1.57 | 625 | 0.8706 | 53.3568 | 33.2533 | 35.7531 | 50.4568 | 141.8889 | | 0.8255 | 1.88 | 750 | 0.8186 | 52.7375 | 33.4439 | 37.1094 | 50.5323 | 142.0 | | 0.8255 | 2.2 | 875 | 0.8041 | 53.4992 | 34.6929 | 37.9614 | 51.091 | 142.0 | | 0.5295 | 2.51 | 1000 | 0.7907 | 52.6185 | 33.8053 | 37.1725 | 50.4881 | 142.0 | | 0.5295 | 2.83 | 1125 | 0.7740 | 52.7107 | 33.1023 | 36.0865 | 50.0365 | 142.0 | | 0.5295 | 3.14 | 1250 | 0.8200 | 52.5607 | 33.7948 | 37.2312 | 50.3345 | 142.0 | | 0.5295 | 3.45 | 1375 | 0.8188 | 53.9233 | 34.446 | 36.7566 | 51.3135 | 142.0 | | 0.351 | 3.77 | 1500 | 0.8071 | 53.9096 | 35.5977 | 38.6832 | 51.4986 | 142.0 | | 0.351 | 4.08 | 1625 | 0.8347 | 53.9049 | 35.5953 | 39.788 | 51.4101 | 142.0 | | 1dba19eeed13c96a58c977d034e3eb1e |
cc-by-4.0 | ['hi', 'en', 'codemix'] | false | HingRoBERTa HingRoBERTa is a Hindi-English code-mixed RoBERTa model trained on roman text. It is an xlm-RoBERTa model fine-tuned on L3Cube-HingCorpus. <br> [dataset link] (https://github.com/l3cube-pune/code-mixed-nlp) More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2204.08398) ``` @inproceedings{nayak-joshi-2022-l3cube, title = "{L}3{C}ube-{H}ing{C}orpus and {H}ing{BERT}: A Code Mixed {H}indi-{E}nglish Dataset and {BERT} Language Models", author = "Nayak, Ravindra and Joshi, Raviraj", booktitle = "Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.wildre-1.2", pages = "7--12", } ``` | 238cc1568e60d6d27ce897df7e830184 |
apache-2.0 | [] | false | FrALBERT Base Pretrained model on French language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1909.11942) and first released in [this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not make a difference between french and French. | 93dd4516ffa7292af32713bd81dd05b4 |
apache-2.0 | [] | false | Model description FrALBERT is a transformers model pretrained on 4Go of French Wikipedia in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Sentence Ordering Prediction (SOP): FrALBERT uses a pretraining loss based on predicting the ordering of two consecutive segments of text. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the FrALBERT model as inputs. FrALBERT is particular in that it shares its layers across its Transformer. Therefore, all layers have the same weights. Using repeating layers results in a small memory footprint, however, the computational cost remains similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same number of (repeating) layers. This is the first version of the base model. This model has the following configuration: - 12 repeating layers - 128 embedding dimension - 768 hidden dimension - 12 attention heads - 11M parameters | 73cab1004f0d18372bcaa7b55a3ebee9 |
apache-2.0 | [] | false | Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=fralbert) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. | 81bd14110709faf1150393a09fa2daff |
apache-2.0 | [] | false | How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='qwant/fralbert-base') >>> unmasker("Paris est la capitale de la [MASK] .") [ { "sequence": "paris est la capitale de la france.", "score": 0.6231236457824707, "token": 3043, "token_str": "france" }, { "sequence": "paris est la capitale de la region.", "score": 0.2993471622467041, "token": 10531, "token_str": "region" }, { "sequence": "paris est la capitale de la societe.", "score": 0.02028230018913746, "token": 24622, "token_str": "societe" }, { "sequence": "paris est la capitale de la bretagne.", "score": 0.012089950032532215, "token": 24987, "token_str": "bretagne" }, { "sequence": "paris est la capitale de la chine.", "score": 0.010002839379012585, "token": 14860, "token_str": "chine" } ] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AlbertTokenizer, AlbertModel tokenizer = AlbertTokenizer.from_pretrained('qwant/fralbert-base') model = AlbertModel.from_pretrained("qwant/fralbert-base") text = "Remplacez-moi par le texte en français que vous souhaitez." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import AlbertTokenizer, TFAlbertModel tokenizer = AlbertTokenizer.from_pretrained('qwant/fralbert-base') model = TFAlbertModel.from_pretrained("qwant/fralbert-base") text = "Remplacez-moi par le texte en français que vous souhaitez." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` | d10b12db160dd32ef332723dd4385b51 |
apache-2.0 | [] | false | Preprocessing The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 32,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` | 8a6b187db6b7f971447b2e02423f1650 |
apache-2.0 | [] | false | Training The FrALBERT procedure follows the BERT setup. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. | efb23eeb285ca1e18a709c7c7baa0b60 |
apache-2.0 | [] | false | Evaluation results When fine-tuned on downstream tasks, the ALBERT models achieve the following results: | | FQuAD1.0 | PIAF_dev |----------------|----------|---------- |frALBERT-base |72.6/55.1 |61.0 / 38.9 | 3705fad2498880c146303d2a4d7484ea |
apache-2.0 | [] | false | BibTeX entry and citation info ```bibtex @inproceedings{cattan2021fralbert, author = {Oralie Cattan and Christophe Servan and Sophie Rosset}, booktitle = {Recent Advances in Natural Language Processing, RANLP 2021}, title = {{On the Usability of Transformers-based models for a French Question-Answering task}}, year = {2021}, address = {Online}, month = sep, } ``` Link to the paper: [PDF](https://hal.archives-ouvertes.fr/hal-03336060) | ebb12879c3772986977375ee367839ed |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small hy This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.6376 - Wer: 116.0855 | e32b51f4f67fec7c05a24c81b3a278c1 |
apache-2.0 | ['whisper-event', '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 - 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: 5 - training_steps: 50 - mixed_precision_training: Native AMP | 65775acadd4e486e4b87c6c6ac346b98 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7891 | 0.2 | 10 | 0.9031 | 184.375 | | 0.6573 | 0.4 | 20 | 0.7425 | 149.0789 | | 0.647 | 0.6 | 30 | 0.6797 | 138.125 | | 0.551 | 0.8 | 40 | 0.6483 | 127.5329 | | 0.5477 | 1.0 | 50 | 0.6376 | 116.0855 | | 8b0674253720538261db5db9825c74de |
mit | [] | false | ChefBERTo 👨🍳 **chefberto-italian-cased** is a BERT model obtained by MLM adaptive-tuning [**bert-base-italian-xxl-cased**](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on Italian cooking recipes, approximately 50k sentences (2.6M words). **Author:** Cristiano De Nobili ([@denocris](https://twitter.com/denocris) on Twitter, [LinkedIn](https://www.linkedin.com/in/cristiano-de-nobili/)) for [VINHOOD](https://www.vinhood.com/en/). <p> <img src="https://drive.google.com/uc?export=view&id=1u5aY2wKu-X5DAzbOq7rsgGFW5_lGUAQn" width="400"> </br> </p> | abadc049f33c538c17f56c5ccd0d640f |
mit | [] | false | Usage ```python from transformers import AutoModel, AutoTokenizer model_name = "vinhood/chefberto-italian-cased" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModel.from_pretrained(model_name) ``` | 7a3553e5128e7cb7f4da51078955f60c |
mit | ['generated_from_trainer'] | false | roberta-base-finetuned-schizophreniaReddit2 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7785 | a5b7746ec2cb7ebe6f6b2f18e09e8980 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 490 | 1.8093 | | 1.9343 | 2.0 | 980 | 1.7996 | | 1.8856 | 3.0 | 1470 | 1.7966 | | 1.8552 | 4.0 | 1960 | 1.7844 | | 1.8267 | 5.0 | 2450 | 1.7839 | | e89924ec0b3dbfe7106c79975c02ba55 |
apache-2.0 | ['exbert'] | false | BERT base model (uncased) Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference between english and English. | 3e42d553e59f0d95aa0e56620b449178 |
apache-2.0 | ['exbert'] | false | Model description [sbcBI/sentiment_analysis] This is a fine-tuned downstream version of the bert-base-uncased model for sentiment analysis, this model is not intended for further downstream fine-tuning for any other tasks. This model is trained on a classified dataset for text-classification. | f2915e8bd9cef41be40de990543c25ea |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper medium Turkish CV 3K This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 tr dataset. It achieves the following results on the evaluation set: - Loss: 0.3611 - Wer: 15.9012 | c37b96910a18d26ec45b1ab5c81f6bd7 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 32 - 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: 3000 - mixed_precision_training: Native AMP | fc1f4ac6d4e70a9456c7ca30641e4085 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0856 | 3.02 | 1000 | 0.3732 | 20.6764 | | 0.0119 | 6.03 | 2000 | 0.3684 | 17.5353 | | 0.001 | 9.05 | 3000 | 0.3611 | 15.9012 | | a7a8e3ac478238931e7020618831d836 |
apache-2.0 | ['generated_from_trainer'] | false | platzi_vit_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0328 - Accuracy: 0.9925 | 1e3eee3afe69665d3b1fb0ab19a4048a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1427 | 3.85 | 500 | 0.0328 | 0.9925 | | 40d00b2e670065ee9bd7fa6441aece14 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'science'] | false | DreamBooth model for glxy trained by lewtun on the lewtun/galaxies dataset. This your the Stable Diffusion model fine-tuned the glxy concept taught to Stable Diffusion with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of glxy galaxy** This model was created as part of the DreamBooth Hackathon. Visit the organisation page for instructions on how to take part! | dc3884f87759bb78ce34ae959e6b0adc |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-0.6-1 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: 2.8345 - Bleu: 6.7165 - Gen Len: 46.3377 | 6fc4e81619252b8326789f29f18eb2d0 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | 56a625b652cee2311127147dcfed23e7 |
apache-2.0 | ['generated_from_trainer'] | false | roberta-base-bne-finetuned-detests This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0716 - Accuracy: 0.8396 | 20a84349dec2e1d7c5017d044e0110ba |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2972 | 1.0 | 153 | 0.3359 | 0.8462 | | 0.2924 | 2.0 | 306 | 0.4509 | 0.8249 | | 0.0663 | 3.0 | 459 | 0.7186 | 0.8527 | | 0.0018 | 4.0 | 612 | 0.8081 | 0.8314 | | 0.0004 | 5.0 | 765 | 0.8861 | 0.8560 | | 0.0003 | 6.0 | 918 | 0.9940 | 0.8380 | | 0.0002 | 7.0 | 1071 | 1.0330 | 0.8396 | | 0.0002 | 8.0 | 1224 | 1.0545 | 0.8396 | | 0.0002 | 9.0 | 1377 | 1.0673 | 0.8396 | | 0.0002 | 10.0 | 1530 | 1.0716 | 0.8396 | | 4e8763b87eba7aa71e652112c755918d |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | fnet-base-finetuned-qqp This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.3686 - Accuracy: 0.8847 - F1: 0.8466 - Combined Score: 0.8657 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). | 9e36fcfb0a5b4618d07cf8855500feb1 |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | !/usr/bin/bash python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name qqp \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir fnet-base-finetuned-qqp \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` | c4ef2b475817b6fff93dabb7576c9975 |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.3484 | 1.0 | 22741 | 0.3014 | 0.8676 | 0.8297 | 0.8487 | | 0.2387 | 2.0 | 45482 | 0.3011 | 0.8801 | 0.8429 | 0.8615 | | 0.1739 | 3.0 | 68223 | 0.3686 | 0.8847 | 0.8466 | 0.8657 | | df0f01f5c4fa74223a313b6ad0831346 |
apache-2.0 | [] | false | distilbert-base-en-ro-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). | ab39616eededb348d152a2ceabd291ed |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-ro-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-ro-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). | 8de72ebd2f20d93d7b29adecdbf00122 |
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