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README.md

@@ -1,3 +1,97 @@
 ---
+language: de
 license: mit
+metrics:
+- accuracy
+model-index:
+- name: GePaBERT
 ---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# GePaBERT
+
+This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on a corpus of parliamentary speeches held in the German Bundestag.
+It was specifically designed for the KONVENS 2023 shared task on speaker attribution.
+It achieves the following results on the evaluation set: 
+- Loss: 0.7997
+- Accuracy: 0.8020
+
+## Training and evaluation data
+
+The corpus of parliamentary speeches covers speeches held in the German Bundestag during the 9th-20th legislative period, from 1980 to April 2023. (757 MB)
+The speeches were automatically prepared from the publicly available [plenary protocols](https://www.bundestag.de/services/opendata), using the
+extraction pipeline [Open Discourse](https://opendiscourse.de) ([GitHub code](https://github.com/open-discourse/open-discourse)).
+Evaluation was done on a randomly-sampled 5% held-out dataset.
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- `learning_rate`: 2e-05
+- `train_batch_size`: 8
+- `optimizer`: Adam with `betas=(0.9,0.999)` and `epsilon=1e-08`
+- `lr_scheduler_type`: linear
+- `num_epochs`: 5
+
+### Training results
+
+| Training Loss | Epoch | Step   | Accuracy | Validation Loss |
+|:-------------:|:-----:|:------:|:--------:|:---------------:|
+| 1.0697        | 0.1   | 3489   | 0.7697   | 0.9802          |
+| 1.0339        | 0.2   | 6978   | 0.7727   | 0.9562          |
+| 1.0203        | 0.3   | 10467  | 0.7739   | 0.9463          |
+| 1.0215        | 0.4   | 13956  | 0.7743   | 0.9477          |
+| 1.0046        | 0.5   | 17445  | 0.7779   | 0.9299          |
+| 1.0036        | 0.6   | 20934  | 0.7764   | 0.9372          |
+| 1.2439        | 0.7   | 24423  | 0.7352   | 1.2473          |
+| 1.4382        | 0.8   | 27912  | 0.6947   | 1.5782          |
+| 1.1744        | 0.9   | 31401  | 0.7764   | 0.9360          |
+| 0.9718        | 1.0   | 34890  | 0.7799   | 0.9179          |
+| 0.9557        | 1.1   | 38379  | 0.7824   | 0.9038          |
+| 0.947         | 1.2   | 41868  | 0.7830   | 0.9000          |
+| 0.9487        | 1.3   | 45357  | 0.7833   | 0.8982          |
+| 0.9457        | 1.4   | 48846  | 0.7851   | 0.8862          |
+| 0.9442        | 1.5   | 52335  | 0.7863   | 0.8839          |
+| 0.9473        | 1.6   | 55824  | 0.7850   | 0.8855          |
+| 0.9388        | 1.7   | 59313  | 0.7865   | 0.8771          |
+| 0.9293        | 1.8   | 62802  | 0.7868   | 0.8805          |
+| 0.9242        | 1.9   | 66291  | 0.7873   | 0.8738          |
+| 0.9241        | 2.0   | 69780  | 0.7872   | 0.8757          |
+| 0.9127        | 2.1   | 73269  | 0.7896   | 0.8641          |
+| 0.9114        | 2.2   | 76758  | 0.7900   | 0.8627          |
+| 0.9095        | 2.3   | 80247  | 0.7913   | 0.8540          |
+| 0.9042        | 2.4   | 83736  | 0.7920   | 0.8518          |
+| 0.8999        | 2.5   | 87225  | 0.7919   | 0.8514          |
+| 0.899         | 2.6   | 90714  | 0.7918   | 0.8543          |
+| 0.8945        | 2.7   | 94203  | 0.7935   | 0.8418          |
+| 0.8867        | 2.8   | 97692  | 0.7934   | 0.8437          |
+| 0.893         | 2.9   | 101181 | 0.7938   | 0.8414          |
+| 0.8798        | 3.0   | 104670 | 0.7951   | 0.8359          |
+| 0.868         | 3.1   | 108159 | 0.7943   | 0.8375          |
+| 0.8736        | 3.2   | 111648 | 0.7956   | 0.8323          |
+| 0.8756        | 3.3   | 115137 | 0.7959   | 0.8315          |
+| 0.8681        | 3.4   | 118626 | 0.7964   | 0.8258          |
+| 0.8726        | 3.5   | 122115 | 0.7966   | 0.8266          |
+| 0.8594        | 3.6   | 125604 | 0.7967   | 0.8246          |
+| 0.8515        | 3.7   | 129093 | 0.7973   | 0.8227          |
+| 0.8568        | 3.8   | 132582 | 0.7979   | 0.8195          |
+| 0.8626        | 3.9   | 136071 | 0.7983   | 0.8173          |
+| 0.8585        | 4.0   | 139560 | 0.7978   | 0.8190          |
+| 0.8497        | 4.1   | 143049 | 0.7991   | 0.8127          |
+| 0.8383        | 4.2   | 146538 | 0.7992   | 0.8154          |
+| 0.8457        | 4.3   | 150027 | 0.8002   | 0.8080          |
+| 0.8353        | 4.4   | 153516 | 0.8005   | 0.8077          |
+| 0.8393        | 4.5   | 157005 | 0.8009   | 0.8027          |
+| 0.8417        | 4.6   | 160494 | 0.8050   | 0.8007          |
+| 0.836         | 4.7   | 163983 | 0.8004   | 0.8017          |
+| 0.8317        | 4.8   | 167472 | 0.7993   | 0.8021          |
+| 0.832         | 4.9   | 170961 | 0.8011   | 0.8013          |
+
+
+### Framework versions
+
+- Transformers 4.30.2
+- Pytorch 2.0.1+cu117
+- Datasets 2.13.1
+- Tokenizers 0.13.3

all_results.json

@@ -0,0 +1,15 @@
+{
+    "epoch": 5.0,
+    "eval_accuracy": 0.8020157582306698,
+    "eval_loss": 0.7997363209724426,
+    "eval_runtime": 358.801,
+    "eval_samples": 14660,
+    "eval_samples_per_second": 40.858,
+    "eval_steps_per_second": 0.641,
+    "perplexity": 2.2249541773850336,
+    "train_loss": 0.08348739328776694,
+    "train_runtime": 12934.391,
+    "train_samples": 279078,
+    "train_samples_per_second": 107.882,
+    "train_steps_per_second": 13.485
+}

config.json

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+{
+  "_name_or_path": "aehrm/gepabert",
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.30.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 31102
+}

eval_results.json

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+{
+    "epoch": 5.0,
+    "eval_accuracy": 0.8020157582306698,
+    "eval_loss": 0.7997363209724426,
+    "eval_runtime": 358.801,
+    "eval_samples": 14660,
+    "eval_samples_per_second": 40.858,
+    "eval_steps_per_second": 0.641,
+    "perplexity": 2.2249541773850336
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "pad_token_id": 0,
+  "transformers_version": "4.30.2"
+}

license.txt

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+Copyright 2022 Anton Ehrmanntraut
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2504f0d17ef31a692a1bc5326b7dab5255f10d2500dcd766bf3b44be4e54408f
+size 1343215289

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "max_len": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": false,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

train_results.json

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+{
+    "epoch": 5.0,
+    "train_loss": 0.08348739328776694,
+    "train_runtime": 12934.391,
+    "train_samples": 279078,
+    "train_samples_per_second": 107.882,
+    "train_steps_per_second": 13.485
+}