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 | ['generated_from_trainer'] | false | distilgpt2-finetuned-custom-mail This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.1905 | 1aa348a36aeaaa24611bc2be0676f954 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 7 | 3.5915 | | No log | 2.0 | 14 | 3.4986 | | No log | 3.0 | 21 | 3.4418 | | No log | 4.0 | 28 | 3.3970 | | No log | 5.0 | 35 | 3.3569 | | No log | 6.0 | 42 | 3.3207 | | No log | 7.0 | 49 | 3.2972 | | No log | 8.0 | 56 | 3.2806 | | No log | 9.0 | 63 | 3.2620 | | No log | 10.0 | 70 | 3.2451 | | No log | 11.0 | 77 | 3.2302 | | No log | 12.0 | 84 | 3.2177 | | No log | 13.0 | 91 | 3.2083 | | No log | 14.0 | 98 | 3.2024 | | No log | 15.0 | 105 | 3.1984 | | No log | 16.0 | 112 | 3.1962 | | No log | 17.0 | 119 | 3.1938 | | No log | 18.0 | 126 | 3.1920 | | No log | 19.0 | 133 | 3.1913 | | No log | 20.0 | 140 | 3.1905 | | 2a1f7e428b3229e1051a6b5350ba40be |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-german-cased-finetuned-jl This model is a fine-tuned version of [distilbert-base-german-cased](https://huggingface.co/distilbert-base-german-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9427 | d5d9982840b2f2d341557f7ff3a446a5 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP | 74ce7c5b05befd4943f54d9ac195b803 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | No log | 0.1 | 1000 | 1.5731 | | No log | 0.19 | 2000 | 1.4019 | | No log | 0.29 | 3000 | 1.3042 | | No log | 0.39 | 4000 | 1.2398 | | No log | 0.48 | 5000 | 1.1949 | | No log | 0.58 | 6000 | 1.1584 | | No log | 0.68 | 7000 | 1.1296 | | No log | 0.77 | 8000 | 1.1055 | | No log | 0.87 | 9000 | 1.0842 | | No log | 0.97 | 10000 | 1.0680 | | No log | 1.06 | 11000 | 1.0521 | | No log | 1.16 | 12000 | 1.0388 | | No log | 1.26 | 13000 | 1.0248 | | No log | 1.35 | 14000 | 1.0154 | | No log | 1.45 | 15000 | 1.0051 | | No log | 1.55 | 16000 | 0.9981 | | No log | 1.64 | 17000 | 0.9891 | | No log | 1.74 | 18000 | 0.9827 | | No log | 1.84 | 19000 | 0.9765 | | No log | 1.93 | 20000 | 0.9714 | | 1.2477 | 2.03 | 21000 | 0.9672 | | 1.2477 | 2.13 | 22000 | 0.9613 | | 1.2477 | 2.22 | 23000 | 0.9582 | | 1.2477 | 2.32 | 24000 | 0.9548 | | 1.2477 | 2.42 | 25000 | 0.9508 | | 1.2477 | 2.51 | 26000 | 0.9491 | | 1.2477 | 2.61 | 27000 | 0.9466 | | 1.2477 | 2.71 | 28000 | 0.9458 | | 1.2477 | 2.8 | 29000 | 0.9446 | | 1.2477 | 2.9 | 30000 | 0.9431 | | 1.2477 | 3.0 | 31000 | 0.9427 | | 0956ef6035e72952670706d6df215dc1 |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'whisper-event'] | false | Fine-tuned whisper-large-v2 model for ASR in French This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2), trained on the mozilla-foundation/common_voice_11_0 fr dataset. When using the model make sure that your speech input is also sampled at 16Khz. **This model also predicts casing and punctuation.** | 09bc4bd95c668d72e7de3e842e267030 |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'whisper-event'] | false | Load model model = AutoModelForSpeechSeq2Seq.from_pretrained("bofenghuang/whisper-large-v2-cv11-french-punct").to(device) processor = AutoProcessor.from_pretrained("bofenghuang/whisper-large-v2-cv11-french-punct", language="french", task="transcribe") | 9c30df8c6ff29e0455fc4d7afea8e743 |
mit | ['generated_from_trainer'] | false | xlm-roberta-large-xnli-finetuned-mnli-SJP-v3 This model is a fine-tuned version of [joeddav/xlm-roberta-large-xnli](https://huggingface.co/joeddav/xlm-roberta-large-xnli) on the swiss_judgment_prediction dataset. It achieves the following results on the evaluation set: - eval_loss: 5.4348 - eval_accuracy: 0.3352 - eval_runtime: 588.81 - eval_samples_per_second: 8.492 - eval_steps_per_second: 4.246 - epoch: 14.0 - step: 70 | ec77f0e9df4c0e04c4d179ce830911b4 |
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: 20 | e2638c37b2130f9cfbfadff19dc39c91 |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-b0.02 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: 2.8126 - Bleu: 7.632 - Gen Len: 45.006 | fab475c915875a3fbeeeb95214389f80 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'ga-IE', 'robust-speech-event', 'hf-asr-leaderboard'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - GA-IE dataset. It achieves the following results on the evaluation set: - Loss: 0.8445 - Wer: 0.5585 | 48ece4e03933cdae460813436b0af0ac |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'ga-IE', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 60.0 - mixed_precision_training: Native AMP | bdd52b41e8aecad20e28fa1f5d36d8f6 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'ga-IE', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.7135 | 31.24 | 500 | 0.9609 | 0.6926 | | 395ed6a536149b10947b9d06b31ddf1e |
apache-2.0 | [] | false | Fine-tuned T5 small 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](https://framenet2.icsi.berkeley.edu/). | 4e7fd7e4df052569f8d929e09fd0c3ce |
apache-2.0 | [] | false | Tasks This model is trained to perform 3 tasks related to semantic frame parsing: 1. Identify frame trigger locations in the text 2. Classify the frame given a trigger location 3. Extract frame elements in the sentence | ffb93ba8bb808fd00d747644db816178 |
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 | 0.74 | 0.70 | | Frame Classification | 0.83 | 0.81 | | Argument Extraction | 0.68 | 0.70 | | 4a2cb0bae6679a737f18e7c91757d409 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_mrpc_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.1267 - Accuracy: 0.9926 - F1: 0.9947 - Combined Score: 0.9936 | dcf9044fec7c2192429e340ebfa3f283 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.3017 | 1.0 | 1959 | 0.2241 | 0.9608 | 0.9713 | 0.9661 | | 0.233 | 2.0 | 3918 | 0.2357 | 0.9828 | 0.9876 | 0.9852 | | 0.2241 | 3.0 | 5877 | 0.1908 | 0.9706 | 0.9786 | 0.9746 | | 0.2189 | 4.0 | 7836 | 0.1863 | 0.9755 | 0.9824 | 0.9789 | | 0.2149 | 5.0 | 9795 | 0.1868 | 0.9804 | 0.9858 | 0.9831 | | 0.211 | 6.0 | 11754 | 0.1735 | 0.9804 | 0.9859 | 0.9831 | | 0.2073 | 7.0 | 13713 | 0.1875 | 0.9828 | 0.9876 | 0.9852 | | 0.204 | 8.0 | 15672 | 0.1690 | 0.9853 | 0.9894 | 0.9873 | | 0.2014 | 9.0 | 17631 | 0.1597 | 0.9853 | 0.9893 | 0.9873 | | 0.1992 | 10.0 | 19590 | 0.1604 | 0.9877 | 0.9911 | 0.9894 | | 0.1975 | 11.0 | 21549 | 0.1563 | 0.9853 | 0.9894 | 0.9873 | | 0.1959 | 12.0 | 23508 | 0.1518 | 0.9853 | 0.9894 | 0.9873 | | 0.1948 | 13.0 | 25467 | 0.1429 | 0.9902 | 0.9929 | 0.9915 | | 0.1937 | 14.0 | 27426 | 0.1484 | 0.9853 | 0.9894 | 0.9873 | | 0.1928 | 15.0 | 29385 | 0.1527 | 0.9804 | 0.9856 | 0.9830 | | 0.1919 | 16.0 | 31344 | 0.1433 | 0.9926 | 0.9947 | 0.9936 | | 0.1913 | 17.0 | 33303 | 0.1445 | 0.9902 | 0.9929 | 0.9915 | | 0.1905 | 18.0 | 35262 | 0.1407 | 0.9926 | 0.9947 | 0.9936 | | 0.1899 | 19.0 | 37221 | 0.1402 | 0.9926 | 0.9947 | 0.9936 | | 0.1892 | 20.0 | 39180 | 0.1387 | 0.9926 | 0.9947 | 0.9936 | | 0.1887 | 21.0 | 41139 | 0.1384 | 0.9926 | 0.9947 | 0.9936 | | 0.1882 | 22.0 | 43098 | 0.1430 | 0.9951 | 0.9964 | 0.9958 | | 0.1877 | 23.0 | 45057 | 0.1384 | 0.9951 | 0.9964 | 0.9958 | | 0.1871 | 24.0 | 47016 | 0.1398 | 0.9951 | 0.9964 | 0.9958 | | 0.1867 | 25.0 | 48975 | 0.1336 | 0.9926 | 0.9947 | 0.9936 | | 0.1863 | 26.0 | 50934 | 0.1368 | 0.9951 | 0.9964 | 0.9958 | | 0.1859 | 27.0 | 52893 | 0.1337 | 0.9951 | 0.9964 | 0.9958 | | 0.1855 | 28.0 | 54852 | 0.1352 | 0.9926 | 0.9947 | 0.9936 | | 0.1851 | 29.0 | 56811 | 0.1314 | 0.9951 | 0.9964 | 0.9958 | | 0.1847 | 30.0 | 58770 | 0.1333 | 0.9951 | 0.9964 | 0.9958 | | 0.1844 | 31.0 | 60729 | 0.1368 | 0.9951 | 0.9964 | 0.9958 | | 0.184 | 32.0 | 62688 | 0.1310 | 0.9951 | 0.9964 | 0.9958 | | 0.1837 | 33.0 | 64647 | 0.1321 | 0.9951 | 0.9964 | 0.9958 | | 0.1834 | 34.0 | 66606 | 0.1302 | 0.9926 | 0.9947 | 0.9936 | | 0.183 | 35.0 | 68565 | 0.1320 | 0.9951 | 0.9964 | 0.9958 | | 0.1827 | 36.0 | 70524 | 0.1303 | 0.9951 | 0.9964 | 0.9958 | | 0.1825 | 37.0 | 72483 | 0.1273 | 0.9951 | 0.9964 | 0.9958 | | 0.1822 | 38.0 | 74442 | 0.1293 | 0.9951 | 0.9964 | 0.9958 | | 0.1819 | 39.0 | 76401 | 0.1296 | 0.9951 | 0.9964 | 0.9958 | | 0.1817 | 40.0 | 78360 | 0.1305 | 0.9926 | 0.9947 | 0.9936 | | 0.1814 | 41.0 | 80319 | 0.1267 | 0.9926 | 0.9947 | 0.9936 | | 0.1812 | 42.0 | 82278 | 0.1267 | 0.9951 | 0.9964 | 0.9958 | | 0.1809 | 43.0 | 84237 | 0.1278 | 0.9902 | 0.9929 | 0.9915 | | 0.1807 | 44.0 | 86196 | 0.1293 | 0.9951 | 0.9964 | 0.9958 | | 0.1805 | 45.0 | 88155 | 0.1269 | 0.9951 | 0.9964 | 0.9958 | | 0.1803 | 46.0 | 90114 | 0.1284 | 0.9951 | 0.9964 | 0.9958 | | 391b39c1a3a4882dc3cdc2f5254201e1 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-BASE-NL2 (Deep-Narrow version) T5-Efficient-BASE-NL2 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint and was released with the paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*. In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures of similar parameter count. To quote the paper: > We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased > before considering any other forms of uniform scaling across other dimensions. This is largely due to > how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a > tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise, > a tall base model might also generally more efficient compared to a large model. We generally find > that, regardless of size, even if absolute performance might increase as we continue to stack layers, > the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36 > layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e., > params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params, > FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to > consider. To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially. A sequence of word embeddings is therefore processed sequentially by each transformer block. | 0b5c7f3281ae19b678555ac0c42b8fd4 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-base-nl2** - is of model type **Base** with the following variations: - **nl** is **2** It has **57.72** million parameters and thus requires *ca.* **230.88 MB** of memory in full precision (*fp32*) or **115.44 MB** of memory in half precision (*fp16* or *bf16*). A summary of the *original* T5 model architectures can be seen here: | Model | nl (el/dl) | ff | dm | kv | nh | | 5b6c117cf15d2285d1e672bea560b36b |
apache-2.0 | [] | false | [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**. The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4), subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.08909.pdf)'s salient span masking objective on [Wikipedia](https://huggingface.co/datasets/wikipedia), and finally fine-tuned on [Natural Questions (NQ)](https://huggingface.co/datasets/natural_questions). **Note**: The model was fine-tuned on 90% of the train splits of [Natural Questions (NQ)](https://huggingface.co/datasets/natural_questions) for 20k steps and validated on the held-out 10% of the train split. Other community Checkpoints: [here](https://huggingface.co/models?search=ssm) Paper: [How Much Knowledge Can You Pack Into the Parameters of a Language Model?](https://arxiv.org/abs/1910.10683.pdf) Authors: *Adam Roberts, Colin Raffel, Noam Shazeer* | 1c76578b2efd447a2261ff0981b36743 |
apache-2.0 | [] | false | Results on Natural Questions - Test Set |Id | link | Exact Match | |---|---|---| |T5-large|https://huggingface.co/google/t5-large-ssm-nqo|29.0| |T5-xxl|https://huggingface.co/google/t5-xxl-ssm-nqo|35.2| |T5-3b|https://huggingface.co/google/t5-3b-ssm-nqo|31.7| |**T5-11b**|**https://huggingface.co/google/t5-11b-ssm-nqo**|**34.8**| | 33d0d870d791d182fe7c7580246e848f |
apache-2.0 | [] | false | Usage The model can be used as follows for **closed book question answering**: ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer t5_qa_model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-11b-ssm-nqo") t5_tok = AutoTokenizer.from_pretrained("google/t5-11b-ssm-nqo") input_ids = t5_tok("When was Franklin D. Roosevelt born?", return_tensors="pt").input_ids gen_output = t5_qa_model.generate(input_ids)[0] print(t5_tok.decode(gen_output, skip_special_tokens=True)) ``` | 2c0f055c35b78e06d3b52307b8128bb4 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-SMALL-EL4 (Deep-Narrow version) T5-Efficient-SMALL-EL4 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint and was released with the paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*. In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures of similar parameter count. To quote the paper: > We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased > before considering any other forms of uniform scaling across other dimensions. This is largely due to > how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a > tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise, > a tall base model might also generally more efficient compared to a large model. We generally find > that, regardless of size, even if absolute performance might increase as we continue to stack layers, > the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36 > layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e., > params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params, > FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to > consider. To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially. A sequence of word embeddings is therefore processed sequentially by each transformer block. | ab9399765eb4c128e472cb0741f688d2 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-small-el4** - is of model type **Small** with the following variations: - **el** is **4** It has **54.23** million parameters and thus requires *ca.* **216.9 MB** of memory in full precision (*fp32*) or **108.45 MB** of memory in half precision (*fp16* or *bf16*). A summary of the *original* T5 model architectures can be seen here: | Model | nl (el/dl) | ff | dm | kv | nh | | 628bc0af2690580ad0c2de31b5b7cb8b |
creativeml-openrail-m | ['text-to-image'] | false | DuskfallAi Dreambooth model trained by Duskfallcrew with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! If you want to donate towards costs and don't want to subscribe: https://ko-fi.com/DUSKFALLcrew If you want to monthly support the EARTH & DUSK media projects and not just AI: https://www.patreon.com/earthndusk WARNING: This is trained largely on a small data set of our own art with a focus on the fact that our art, and any stable/midjourney outputs we included in this are related to our Dissoicative Identity Disorder. May actually retrain a larger data set later on. Trained using the MultiModel Dreambooth App, sitting on a friday afternoon doing absolute squat. Please DO NOT re-upload the sample pictures that it was trained on, except in the instance you are inspired to use img2img.. In which we dutifully ask you to spam the community section with your outputs. DO NOT RESELL THIS MODEL, AS IT DOES HAVE A TON OF MY ART IN IT. You may: - Merge, use at will - SELL your generations - it's a STYLE after all! - Do credit when reuploading or merging if possible. - DO USE in any merged, OR home based model - cause that's what it's for! More information & output samples to all our models: [Civit AI -Duskfallcrew](https://civitai.com/user/duskfallcrew) lisdusk1 (use that on your prompt) lisdusk1 (use that on your prompt)  lisdusk2 (use that on your prompt) lisdusk2 (use that on your prompt)  | a48e5a835d85d37b59745c3149e47bae |
mit | [] | false | model by no3 This your the **waifu diffusion** model fine-tuned the pistachio from [vibrant venture](https://store.steampowered.com/app/1264520) taught to **waifu diffusion** with Dreambooth. It can be used by modifying the `instance_prompt`: **sks ps** 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) | a733d10fba15b7fa1e3faab7aa942510 |
mit | [] | false | Note If the output isn't that good using instance prompt you can use generic prompt like `a woman` or `a girl` you can add `, green hair` before `a woman` or `a girl` if that's doesn't give you good result. If you have issues or questions feel free to visit the Community Tab and start discussion about it. Here are the images used for training this concept:       [and this](https://huggingface.co/no3/pistachio-wd-1.3-beta1/resolve/main/concept_images/7.jpg) | 0de8600526f9af21710e7158d9142925 |
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: - eval_loss: 1.1563 - eval_runtime: 141.535 - eval_samples_per_second: 76.193 - eval_steps_per_second: 4.762 - epoch: 1.0 - step: 5533 | fa6ea8ea903ae7491a200c66aba9a2b7 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2251 - Accuracy: 0.9265 - F1: 0.9265 | db3f33701caaf76fa5c970821efd1553 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8432 | 1.0 | 250 | 0.3353 | 0.8975 | 0.8939 | | 0.2582 | 2.0 | 500 | 0.2251 | 0.9265 | 0.9265 | | 12a8f6a5e0f869cc10d0b3b5f6272cff |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-google-colab 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: 0.5079 - Wer: 0.3365 | cca2085dfaba46e51357940f683827f3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.4933 | 1.0 | 500 | 1.7711 | 0.9978 | | 0.8658 | 2.01 | 1000 | 0.6262 | 0.5295 | | 0.4405 | 3.01 | 1500 | 0.4841 | 0.4845 | | 0.3062 | 4.02 | 2000 | 0.4897 | 0.4215 | | 0.233 | 5.02 | 2500 | 0.4326 | 0.4101 | | 0.1896 | 6.02 | 3000 | 0.4924 | 0.4078 | | 0.1589 | 7.03 | 3500 | 0.4430 | 0.3896 | | 0.1391 | 8.03 | 4000 | 0.4334 | 0.3889 | | 0.1216 | 9.04 | 4500 | 0.4691 | 0.3828 | | 0.1063 | 10.04 | 5000 | 0.4726 | 0.3705 | | 0.0992 | 11.04 | 5500 | 0.4333 | 0.3690 | | 0.0872 | 12.05 | 6000 | 0.4986 | 0.3771 | | 0.0829 | 13.05 | 6500 | 0.4903 | 0.3685 | | 0.0713 | 14.06 | 7000 | 0.5293 | 0.3655 | | 0.068 | 15.06 | 7500 | 0.5039 | 0.3612 | | 0.0621 | 16.06 | 8000 | 0.5314 | 0.3665 | | 0.0571 | 17.07 | 8500 | 0.5038 | 0.3572 | | 0.0585 | 18.07 | 9000 | 0.4718 | 0.3550 | | 0.0487 | 19.08 | 9500 | 0.5482 | 0.3626 | | 0.0459 | 20.08 | 10000 | 0.5239 | 0.3545 | | 0.0419 | 21.08 | 10500 | 0.5096 | 0.3473 | | 0.0362 | 22.09 | 11000 | 0.5222 | 0.3500 | | 0.0331 | 23.09 | 11500 | 0.5062 | 0.3489 | | 0.0352 | 24.1 | 12000 | 0.4913 | 0.3459 | | 0.0315 | 25.1 | 12500 | 0.4701 | 0.3412 | | 0.028 | 26.1 | 13000 | 0.5178 | 0.3402 | | 0.0255 | 27.11 | 13500 | 0.5168 | 0.3405 | | 0.0228 | 28.11 | 14000 | 0.5154 | 0.3368 | | 0.0232 | 29.12 | 14500 | 0.5079 | 0.3365 | | ecd156eb58c6f44feebe8cf739290a04 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/paraphrase-xlm-r-multilingual-v1 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. | 3d576031fc96a52c3a3344f724860b2a |
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 = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/paraphrase-xlm-r-multilingual-v1') embeddings = model.encode(sentences) print(embeddings) ``` | 45379a7034a02eb4e96ecb49d3790d4f |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-xlm-r-multilingual-v1') model = AutoModel.from_pretrained('sentence-transformers/paraphrase-xlm-r-multilingual-v1') | 906d406ba654a3182847c5a872d19650 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/paraphrase-xlm-r-multilingual-v1) | 117c2541476321135d3dde7ade6676dc |
mit | ['spacy', 'token-classification'] | false | | Feature | Description | | --- | --- | | **Name** | `en_core_med7_trf` | | **Version** | `3.4.2.1` | | **spaCy** | `>=3.4.2,<3.5.0` | | **Default Pipeline** | `transformer`, `ner` | | **Components** | `transformer`, `ner` | | **Vectors** | 514157 keys, 514157 unique vectors (300 dimensions) | | **Sources** | n/a | | **License** | `MIT` | | **Author** | [Andrey Kormilitzin](https://www.kormilitzin.com/) | | 12e238ab08eebf0eb1c0db2300203d31 |
mit | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (7 labels for 1 components)</summary> | Component | Labels | | --- | --- | | **`ner`** | `DOSAGE`, `DRUG`, `DURATION`, `FORM`, `FREQUENCY`, `ROUTE`, `STRENGTH` | </details> | affe870be0feb70d9058b5db5cf1b5b7 |
mit | ['spacy', 'token-classification'] | false | BibTeX entry and citation info ```bibtex @article{kormilitzin2021med7, title={Med7: A transferable clinical natural language processing model for electronic health records}, author={Kormilitzin, Andrey and Vaci, Nemanja and Liu, Qiang and Nevado-Holgado, Alejo}, journal={Artificial Intelligence in Medicine}, volume={118}, pages={102086}, year={2021}, publisher={Elsevier} } ``` | 674acc822e0d04528b544a8e3c855349 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-tweeteval-hate-speech 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: 0.8397 - Accuracy: 0.0 - F1: 0.0 | 08528a2ad7780a600cce111b68385cbb |
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_cs-pharmaceutical", 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) ``` | a7981e4c04d2615f75fdbac4400296cf |
mit | ['generated_from_trainer'] | false | camembert-base-finetuned-Train_RAW20-dd This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2380 - Precision: 0.8661 - Recall: 0.8900 - F1: 0.8779 - Accuracy: 0.9209 | 003d7ee1501944a3391116ec6b8ee0bc |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.23 | 1.0 | 14269 | 0.2282 | 0.8446 | 0.8714 | 0.8578 | 0.9088 | | 0.1787 | 2.0 | 28538 | 0.2380 | 0.8661 | 0.8900 | 0.8779 | 0.9209 | | baefef5b0a8cddb8321b899015274bda |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_80k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 2, Step 80k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different random seeds, which causes variations in the initial weights and order of training instances. The aim is to distinguish findings that apply to a specific artifact (i.e., a particular instance of the model) from those that apply to the more general procedure. We also provide 140 intermediate checkpoints captured during the course of pre-training (we saved 28 checkpoints for the first 5 runs). The models were originally released through [http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our paper [The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163). This is model | 35024b6c8c03116bc857423d0297d766 |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_80k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_80k') model = TFBertModel.from_pretrained("google/multiberts-seed_2-step_80k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` PyTorch version: ``` from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_80k') model = BertModel.from_pretrained("google/multiberts-seed_2-step_80k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | 48114b8a4116c50abece8f5a5979f25a |
mit | ['bert', 'cloze', 'distractor', 'generation'] | false | Model description This model is a Candidate Set Generator in **"CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model", Findings of EMNLP 2022**. Its input are stem and answer, and output is candidate set of distractors. It is fine-tuned by [**CLOTH**](https://www.cs.cmu.edu/~glai1/data/cloth/) dataset based on [**bert-base-uncased**](https://huggingface.co/bert-base-uncased) model. For more details, you can see our **paper** or [**GitHub**](https://github.com/AndyChiangSH/CDGP). | 3b2afe63122d28b68bcea72ec2209bf4 |
mit | ['bert', 'cloze', 'distractor', 'generation'] | false | How to use? 1. Download the model by hugging face transformers. ```python from transformers import BertTokenizer, BertForMaskedLM, pipeline tokenizer = BertTokenizer.from_pretrained("AndyChiang/cdgp-csg-bert-cloth") csg_model = BertForMaskedLM.from_pretrained("AndyChiang/cdgp-csg-bert-cloth") ``` 2. Create a unmasker. ```python unmasker = pipeline("fill-mask", tokenizer=tokenizer, model=csg_model, top_k=10) ``` 3. Use the unmasker to generate the candidate set of distractors. ```python sent = "I feel [MASK] now. [SEP] happy" cs = unmasker(sent) print(cs) ``` | fdfb7648cc877f018762668a7f3fb1fb |
mit | ['bert', 'cloze', 'distractor', 'generation'] | false | Training hyperparameters The following hyperparameters were used during training: - Pre-train language model: [bert-base-uncased](https://huggingface.co/bert-base-uncased) - Optimizer: adam - Learning rate: 0.0001 - Max length of input: 64 - Batch size: 64 - Epoch: 1 - Device: NVIDIA® Tesla T4 in Google Colab | 7f9b07d099af76d7d09fc7f572d436bf |
mit | ['bert', 'cloze', 'distractor', 'generation'] | false | Testing The evaluations of this model as a Candidate Set Generator in CDGP is as follows: | P@1 | F1@3 | F1@10 | MRR | NDCG@10 | | ----- | ----- | ----- | ----- | ------- | | 18.50 | 13.80 | 15.37 | 29.96 | 37.82 | | edea1126afdd48baf7bf2a0a0a059f0d |
mit | ['bert', 'cloze', 'distractor', 'generation'] | false | Candidate Set Generator | Models | CLOTH | DGen | | ----------- | ----------------------------------------------------------------------------------- | -------------------------------------------------------------------------------- | | **BERT** | [*cdgp-csg-bert-cloth*](https://huggingface.co/AndyChiang/cdgp-csg-bert-cloth) | [cdgp-csg-bert-dgen](https://huggingface.co/AndyChiang/cdgp-csg-bert-dgen) | | **SciBERT** | [cdgp-csg-scibert-cloth](https://huggingface.co/AndyChiang/cdgp-csg-scibert-cloth) | [cdgp-csg-scibert-dgen](https://huggingface.co/AndyChiang/cdgp-csg-scibert-dgen) | | **RoBERTa** | [cdgp-csg-roberta-cloth](https://huggingface.co/AndyChiang/cdgp-csg-roberta-cloth) | [cdgp-csg-roberta-dgen](https://huggingface.co/AndyChiang/cdgp-csg-roberta-dgen) | | **BART** | [cdgp-csg-bart-cloth](https://huggingface.co/AndyChiang/cdgp-csg-bart-cloth) | [cdgp-csg-bart-dgen](https://huggingface.co/AndyChiang/cdgp-csg-bart-dgen) | | cdeccaf6c47a648b0f981e1424d09607 |
creativeml-openrail-m | ['text-to-image', 'stable-Diffusion', 'stable-diffusion-diffusers', 'diffusers', 'safetensors'] | false | <p align="center"> <img src="https://s1.fileditch.ch/iMqyjOnUtxntHolBiNgT.png" width=35% height=35%> <p> <p align="center"> AniReal - A latent diffusion model fine-tuned to output High Quality Photorealistic anime illustrations! <img src="https://m1.afileditch.ch/uJoodjDNVWxDqhhQHeRH.png"> <p> ________ | 62c1f0e8a028221866a007ec5407f49b |
creativeml-openrail-m | ['text-to-image', 'stable-Diffusion', 'stable-diffusion-diffusers', 'diffusers', 'safetensors'] | false | 【 AniReal 】 Welcome to AniReal! a latent diffusion model Trained and Fine-Tuned on **Photorealistic High Quality** anime illustrations using the **Danbooru** tagging dataset aswell as **Blip** I made it so that it understands some natural text description alongside danbooru tags It may not work as well though but give it a shot! The model itself its made to output generally anything with an anime art style if u can think of it u can prompt it! ________ | 9a9a8ccd88d8bd3047fbfa4e73184c6d |
creativeml-openrail-m | ['text-to-image', 'stable-Diffusion', 'stable-diffusion-diffusers', 'diffusers', 'safetensors'] | false | This project would be impossible without - [Haisenberg](https://huggingface.co/haisenberguwu) - [Thiros](https://huggingface.co/thiros) - [Closertodeath](https://huggingface.co/closertodeath) <img src="https://s1.fileditch.ch/FjLFnEcKHAFpEEEAawMP.png"> Many thanks, Hosioka. | 234da254f8807c3d417c5c199f3e8e62 |
creativeml-openrail-m | ['text-to-image', 'stable-Diffusion', 'stable-diffusion-diffusers', 'diffusers', 'safetensors'] | false | 0bb03505150d9b4b39975a9da8589b40190e7078 ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ba052021c71edb8879537478d7897bb6 |
creativeml-openrail-m | ['text-to-image', 'stable-Diffusion', 'stable-diffusion-diffusers', 'diffusers', 'safetensors'] | false | License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the **Model** to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the **Model** commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) | d0ba5e5845639eeaa6ad7dc9b5776817 |
mit | ['generated_from_keras_callback'] | false | turkishReviews-ds-mini This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 9.1632 - Validation Loss: 9.2525 - Epoch: 2 | 607f5462ce6160c7c083086f0f44878c |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': -896, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 | eedacee0263d6a953a3f62c92b3fcc74 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 10.2835 | 9.9707 | 0 | | 9.6408 | 9.6241 | 1 | | 9.1632 | 9.2525 | 2 | | cf50719f3280f565427a3c0100f77a5d |
apache-2.0 | ['vision', 'deep-stereo', 'depth-estimation', 'Tensorflow2', 'Keras'] | false | MADNet Keras
MADNet is a deep stereo depth estimation model. Its key defining features are:
1. It has a light-weight architecture which means it has low latency.
2. It supports self-supervised training, so it can be conveniently adapted in the field with no training data.
3. It's a stereo depth model, which means it's capable of high accuracy.
The MADNet weights in this repository were trained using a Tensorflow 2 / Keras implementation of the original code. The model was created using the Keras Functional API, which enables the following features:
1. Good optimization.
2. High level Keras methods (.fit, .predict and .evaluate).
3. Little boilerplate code.
4. Decent support from external packages (like Weights and Biases).
5. Callbacks.
The weights provided were either trained on the 2012 / 2015 kitti stereo dataset or flyingthings-3d dataset. The weights of the pretrained models from the original paper (tf1_conversion_kitti.h5 and tf1_conversion_synthetic.h5) are provided in tensorflow 2 format. The TF1 weights help speed up fine-tuning, but its recommended to use either synthetic.h5 (trained on flyingthings-3d) or kitti.h5 (trained on 2012 and 2015 kitti stereo datasets).
**Abstract**:
Deep convolutional neural networks trained end-to-end are the undisputed state-of-the-art methods to regress dense disparity maps directly from stereo pairs. However, such methods suffer from notable accuracy drops when exposed to scenarios significantly different from those seen in the training phase (e.g.real vs synthetic images, indoor vs outdoor, etc). As it is unlikely to be able to gather enough samples to achieve effective training/ tuning in any target domain, we propose to perform unsupervised and continuous online adaptation of a deep stereo network in order to preserve its accuracy independently of the sensed environment. However, such a strategy can be extremely demanding regarding computational resources and thus not enabling real-time performance. Therefore, we address this side effect by introducing a new lightweight, yet effective, deep stereo architecture Modularly ADaptive Network (MADNet) and by developing Modular ADaptation (MAD), an algorithm to train independently only sub-portions of our model. By deploying MADNet together with MAD we propose the first ever realtime self-adaptive deep stereo system.
| b038d4a2ca241194b53898de6734da28 |
apache-2.0 | ['vision', 'deep-stereo', 'depth-estimation', 'Tensorflow2', 'Keras'] | false | Usage Instructions
See the accompanying codes readme for details on how to perform training and inferencing with the model: [madnet-deep-stereo-with-keras](https://github.com/ChristianOrr/madnet-deep-stereo-with-keras).
| 23c04ae883f05ad74a5b50dec8833afd |
apache-2.0 | ['vision', 'deep-stereo', 'depth-estimation', 'Tensorflow2', 'Keras'] | false | TF1 Kitti and TF1 Synthetic
Training details for the TF1 weights are available in the supplementary material (at the end) of this paper: [Real-time self-adaptive deep stereo](https://arxiv.org/abs/1810.05424)
| 2601127bff7c5f482c92846c163b9cff |
apache-2.0 | ['vision', 'deep-stereo', 'depth-estimation', 'Tensorflow2', 'Keras'] | false | Synthetic
The synthetic model was finetuned using the tf1 synthetic weights. It was trained on the flyingthings-3d dataset with the following parameters:
- Steps: 1.5 million
- Learning Rate: 0.0001
- Decay Rate: 0.999
- Minimum Learning Rate Cap: 0.000001
- Batch Size: 1
- Optimizer: Adam
- Image Height: 480
- Image Width: 640
| 6350416db0032debe26e6af0f9d35d44 |
apache-2.0 | ['vision', 'deep-stereo', 'depth-estimation', 'Tensorflow2', 'Keras'] | false | Kitti
The kitti model was finetuned using the synthetic weights. Tensorboard events file is available in the logs directory. It was trained on the 2012 and 2015 kitti stereo dataset with the following parameters:
- Steps: 0.5 million
- Learning Rate: 0.0001
- Decay Rate: 0.999
- Minimum Learning Rate Cap: 0.0000001
- Batch Size: 1
- Optimizer: Adam
- Image Height: 480
- Image Width: 640
| 0b77d18040c86f2eb1c34a3d4ecc1266 |
apache-2.0 | ['vision', 'deep-stereo', 'depth-estimation', 'Tensorflow2', 'Keras'] | false | BibTeX entry and citation info
```bibtex
@InProceedings{Tonioni_2019_CVPR,
author = {Tonioni, Alessio and Tosi, Fabio and Poggi, Matteo and Mattoccia, Stefano and Di Stefano, Luigi},
title = {Real-time self-adaptive deep stereo},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}
```
```bibtex
@article{Poggi2021continual,
author={Poggi, Matteo and Tonioni, Alessio and Tosi, Fabio
and Mattoccia, Stefano and Di Stefano, Luigi},
title={Continual Adaptation for Deep Stereo},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
year={2021}
}
```
```bibtex
@InProceedings{MIFDB16,
author = "N. Mayer and E. Ilg and P. Hausser and P. Fischer and D. Cremers and A. Dosovitskiy and T. Brox",
title = "A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation",
booktitle = "IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)",
year = "2016",
note = "arXiv:1512.02134",
url = "http://lmb.informatik.uni-freiburg.de/Publications/2016/MIFDB16"
}
```
```bibtex
@INPROCEEDINGS{Geiger2012CVPR,
author = {Andreas Geiger and Philip Lenz and Raquel Urtasun},
title = {Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite},
booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2012}
}
```
```bibtex
@INPROCEEDINGS{Menze2015CVPR,
author = {Moritz Menze and Andreas Geiger},
title = {Object Scene Flow for Autonomous Vehicles},
booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2015}
}
``` | 3d10f3114a26f676619dcc9922b2e9d8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 40 | 4.5807 | | No log | 2.0 | 80 | 4.4023 | | No log | 3.0 | 120 | 4.3666 | | 1f8ae353d16d4b624a123621a6b910a4 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_xls-r_accent_france-2_belgium-8_s587 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | f8a8182153954713f1a87ee8b51ef1e5 |
apache-2.0 | ['generated_from_keras_callback'] | false | nlp-esg-scoring/bert-base-finetuned-cleaned-esg-plus This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.7242 - Validation Loss: 2.5107 - Epoch: 9 | 7491a11c6608cf506d74a06f6164be92 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -146, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 | 3ed3a1d4c8cf5a6028ee8e086136602f |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.7185 | 2.5414 | 0 | | 2.7167 | 2.5223 | 1 | | 2.7161 | 2.5627 | 2 | | 2.7189 | 2.5305 | 3 | | 2.7248 | 2.5103 | 4 | | 2.7173 | 2.5095 | 5 | | 2.7272 | 2.5135 | 6 | | 2.7215 | 2.5447 | 7 | | 2.7247 | 2.5632 | 8 | | 2.7242 | 2.5107 | 9 | | ac5cbd89b3fe3a2e8bcec47303b8f7f2 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-work-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: 2.3586 - Accuracy: 0.3689 | 47cd3512bd0bb542386ebc96df30d1d8 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3211 - Accuracy: 0.8633 - F1: 0.8638 | d3c592e07348f889720d20b2705c5be1 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.6551 - Accuracy: 0.6633 - F1: 0.7248 | 2059e7fe253b11f798695c06e6cf291b |
cc0-1.0 | ['kaggle', 'rembert', 'pytorch', 'question-answering'] | false | <div align = "center"> <img src = "https://github.com/SauravMaheshkar/chaii-Hindi-Tamil-QA/blob/main/assets/Coffee%20Banner.png?raw=true"> </div> This dataset contains the [**google/rembert**](https://huggingface.co/transformers/model_doc/rembert.html) model weights according to my team's experimentation strategy during the [**chaii - Hindi and Tamil Question Answering**](https://www.kaggle.com/c/chaii-hindi-and-tamil-question-answering) competition. They are listed below with their corresponding public LB score:- | Huggingface Hub Link | Public LB Score | | :---: | :---: | | [**SauravMaheshkar/rembert-maxseq-400-docstride-128-chaii**](https://huggingface.co/SauravMaheshkar/rembert-maxseq-400-docstride-128-chaii) | 0.724 | | [**SauravMaheshkar/rembert-maxseq-384-docstride-135-chaii**](https://huggingface.co/SauravMaheshkar/rembert-maxseq-384-docstride-135-chaii) | 0.723 | | [**SauravMaheshkar/rembert-maxseq-400-docstride-135-chaii**](https://huggingface.co/SauravMaheshkar/rembert-maxseq-400-docstride-135-chaii) | 0.737 | | [**SauravMaheshkar/rembert-maxseq-384-docstride-128-chaii**](https://huggingface.co/SauravMaheshkar/rembert-maxseq-384-docstride-128-chaii) | 0.725 | | 45b8913ff5870c38c59dbf44a073b612 |
apache-2.0 | ['bart', 'biobart', 'biomedical'] | false | Paper: [BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model](https://arxiv.org/pdf/2204.03905.pdf) V2 adopts a new biomedical vocab. ``` @misc{BioBART, title={BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model}, author={Hongyi Yuan and Zheng Yuan and Ruyi Gan and Jiaxing Zhang and Yutao Xie and Sheng Yu}, year={2022}, eprint={2204.03905}, archivePrefix={arXiv} } ``` | 39e1a171bc516b5ff0b5eb102db14239 |
mit | ['generated_from_trainer'] | false | pubmedbert-fulltext-cord19 This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the pritamdeka/cord-19-fulltext dataset. It achieves the following results on the evaluation set: - Loss: 1.2667 - Accuracy: 0.7175 | fd7dfbe8d2b181ba574c7913aa775754 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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 - lr_scheduler_warmup_steps: 10000 - num_epochs: 3.0 - mixed_precision_training: Native AMP | 692cd361a4c64d425f086f14a5d07afd |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.7985 | 0.27 | 5000 | 1.2710 | 0.7176 | | 1.7542 | 0.53 | 10000 | 1.3359 | 0.7070 | | 1.7462 | 0.8 | 15000 | 1.3489 | 0.7034 | | 1.8371 | 1.07 | 20000 | 1.4361 | 0.6891 | | 1.7102 | 1.33 | 25000 | 1.3502 | 0.7039 | | 1.6596 | 1.6 | 30000 | 1.3341 | 0.7065 | | 1.6265 | 1.87 | 35000 | 1.3228 | 0.7087 | | 1.605 | 2.13 | 40000 | 1.3079 | 0.7099 | | 1.5731 | 2.4 | 45000 | 1.2986 | 0.7121 | | 1.5602 | 2.67 | 50000 | 1.2929 | 0.7136 | | 1.5447 | 2.93 | 55000 | 1.2875 | 0.7143 | | 4ff7a712e63c610d994ebb6800165b3b |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-qnli-target-glue-sst2 This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-qnli](https://huggingface.co/muhtasham/tiny-mlm-glue-qnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5008 - Accuracy: 0.8211 | 68dbab67afa5ea9b6940719ad38941f5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5757 | 0.24 | 500 | 0.4901 | 0.7775 | | 0.4436 | 0.48 | 1000 | 0.4673 | 0.7833 | | 0.3947 | 0.71 | 1500 | 0.4434 | 0.7970 | | 0.3751 | 0.95 | 2000 | 0.4601 | 0.7970 | | 0.3326 | 1.19 | 2500 | 0.4463 | 0.8005 | | 0.316 | 1.43 | 3000 | 0.4510 | 0.8005 | | 0.2981 | 1.66 | 3500 | 0.4367 | 0.8142 | | 0.2929 | 1.9 | 4000 | 0.4383 | 0.8108 | | 0.2746 | 2.14 | 4500 | 0.4873 | 0.8016 | | 0.256 | 2.38 | 5000 | 0.4395 | 0.8165 | | 0.246 | 2.61 | 5500 | 0.4444 | 0.8280 | | 0.2522 | 2.85 | 6000 | 0.4478 | 0.8245 | | 0.2371 | 3.09 | 6500 | 0.4556 | 0.8291 | | 0.2299 | 3.33 | 7000 | 0.4655 | 0.8326 | | 0.2143 | 3.56 | 7500 | 0.4581 | 0.8314 | | 0.2153 | 3.8 | 8000 | 0.4869 | 0.8291 | | 0.2134 | 4.04 | 8500 | 0.5008 | 0.8211 | | 02c1467b53b79e88956004d82cbab465 |
cc-by-sa-4.0 | [] | false | LegalBERT Tokenizer **LegalBERT** tokenizer is a word level byte-pair encoding with vocabulary size of 52k tokens (containing the most common words in legal documents), based on the [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) tokenizer. The tokenizer was trained on data provided by the **BRAZILIAN SUPREME FEDERAL TRIBUNAL**, through the terms of use: [LREC 2020](https://ailab.unb.br/victor/lrec2020). Tokenizer utilize `BertTokenizer` implementation from [transformers](https://github.com/huggingface/transformers). **NOTE**: The results of this project do not imply in any way the position of the BRAZILIAN SUPREME FEDERAL TRIBUNAL, all being the sole and exclusive responsibility of the author. | cd0c8c3f4f83651106d1d9a90aacb61f |
cc-by-sa-4.0 | [] | false | Tokenizer usage ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("dominguesm/legal-bert-tokenizer") example = "" tokens = tokenizer.tokenize(example) ``` | a130e410c8953927ffb91059c4562e48 |
cc-by-sa-4.0 | [] | false | Comparison of results **Original Text**: ```De ordem, a Secretaria Judiciária do Supremo Tribunal Federal INTIMA a parte abaixo identificada, ou quem as suas vezes fizer, do inteiro teor do(a) despacho/decisão presente nos autos (art. 270 do Código de Processo Cívil e art 5º da Lei 11.419/2006).``` | Tokenizer | Tokens | Num. Tokens | | --------- | ------ | ----------- | | BERTimbau | ```['De', 'ordem', ',', 'a', 'Secretaria', 'Judic', ' | 6b1ae6fed00ac6719681666ce766549f |
cc-by-sa-4.0 | [] | false | 9', '/', '2006', ')', '.']``` | 66 | | LegalBERT | ```['De', 'ordem', ',', 'a', 'Secretaria', 'Judiciária', 'do', 'Supremo', 'Tribunal', 'Federal', 'INTIMA', 'a', 'parte', 'abaixo', 'identificada', ',', 'ou', 'quem', 'as', 'suas', 'vezes', 'fizer', ',', 'do', 'inteiro', 'teor', 'do', '(', 'a', ')', 'despacho', '/', 'decisão', 'presente', 'nos', 'autos', '(', 'art', '.', '270', 'do', 'Código', 'de', 'Processo', 'Cív', ' | 60e282175d0d84aa9952ccadcffd30e3 |
cc-by-sa-4.0 | [] | false | Citation If you use this tokenizer, please cite: ``` @misc {maicon_domingues_2022, author = { {Maicon Domingues} }, title = { legal-bert-tokenizer (Revision d8e9d4a) }, year = 2022, url = { https://huggingface.co/dominguesm/legal-bert-tokenizer }, doi = { 10.57967/hf/0110 }, publisher = { Hugging Face } } ``` | 2b4590f582cac8488d3825fcc7683251 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small GL - Santiago Paramés-Estévez 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: 0.3179 - Wer: 15.2334 | 6f81530707c79548d49ed265b961ad24 |
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: 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 - training_steps: 4000 - mixed_precision_training: Native AMP | fb92dcfe0dec92458a6bc54f90fbc577 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0707 | 2.69 | 1000 | 0.2596 | 16.4915 | | 0.0063 | 5.38 | 2000 | 0.2952 | 15.8583 | | 0.0014 | 8.06 | 3000 | 0.3105 | 15.2624 | | 0.0011 | 10.75 | 4000 | 0.3179 | 15.2334 | | c777721c290eafae5857b28c262e1aa5 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-hindi_commonvoice This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 3.5947 - Wer: 1.0 | be732c9372c134ea5fbfd8e3e2e1148c |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 30 - mixed_precision_training: Native AMP | 546deac694ef8c63832d7287d0a92fe5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 24.0069 | 4.0 | 20 | 40.3956 | 1.0 | | 18.1097 | 8.0 | 40 | 15.3603 | 1.0 | | 7.1344 | 12.0 | 60 | 5.2695 | 1.0 | | 4.0032 | 16.0 | 80 | 3.7403 | 1.0 | | 3.4894 | 20.0 | 100 | 3.5724 | 1.0 | | 3.458 | 24.0 | 120 | 3.6164 | 1.0 | | 3.4412 | 28.0 | 140 | 3.5947 | 1.0 | | 3066bb000cd5b75b1c0b68d48549665c |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-2'] | false | MultiBERTs Seed 2 Checkpoint 80k (uncased) Seed 2 intermediate checkpoint 80k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint. The final checkpoint can be found at [multiberts-seed-2](https://hf.co/multberts-seed-2). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing MultiBERTs did not write a model card for this model so this model card has been written by [gchhablani](https://hf.co/gchhablani). | fe04412a17596d49a9686d3d88cc3d42 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-2'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-2-80k') model = BertModel.from_pretrained("multiberts-seed-2-80k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | 27de633082c526c00c91244fb5f3b194 |
mit | [] | false | model by MrHidden This your the Stable Diffusion model fine-tuned the mexican_concha concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks Mexican Concha** 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:         | 44d56e750186b107c485e49147ddb738 |
apache-2.0 | ['generated_from_trainer'] | false | 84rry-xlsr-53-arabic This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.0025 - Wer: 0.4977 | 0e0299c3198ead166986a75fafe7b8bf |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP | 46f7ba23b20e3f01f6144f7dfc182422 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.4906 | 2.25 | 500 | 1.3179 | 0.8390 | | 0.8851 | 4.5 | 1000 | 0.7385 | 0.6221 | | 0.6884 | 6.76 | 1500 | 0.7005 | 0.5765 | | 0.5525 | 9.01 | 2000 | 0.6931 | 0.5610 | | 0.474 | 11.26 | 2500 | 0.7977 | 0.5560 | | 0.3976 | 13.51 | 3000 | 0.7750 | 0.5375 | | 0.343 | 15.76 | 3500 | 0.7553 | 0.5206 | | 0.2838 | 18.02 | 4000 | 0.8162 | 0.5099 | | 0.2369 | 20.27 | 4500 | 0.8574 | 0.5124 | | 0.2298 | 22.52 | 5000 | 0.8848 | 0.5057 | | 0.1727 | 24.77 | 5500 | 0.9193 | 0.5070 | | 0.1675 | 27.03 | 6000 | 0.9959 | 0.4988 | | 0.1457 | 29.28 | 6500 | 1.0025 | 0.4977 | | 2b542f58c0d1b56f376f61a1f3721ecf |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 150 - eval_batch_size: 40 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 1200 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6.0 | ba922b0cf37625351a6aa2c822039c9a |
mit | ['pytorch', 'diffusers', 'unconditional-image-generation', 'diffusion-models-class'] | false | Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. The model was trained with 1000 images using the [DDPM](https://arxiv.org/abs/2006.11239) architecture. Images generated are of 64x64 pixel size. The model was trained for 50 epochs with a batch size of 64, using around 10 GB of GPU memory. | 7ad8cb8bdc10689e9bcb1700f55aa856 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-0505-2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4277 - Accuracy: 0.9206 - F1: 0.9205 | 803b60dd32f1297991093c892802f1b4 |
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