Load model files + readme

README.md

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+---
+language:
+
+- de
+- en
+- es
+- fr
+
+---
+
+# Model Card for answer-finder-v1-multilingual
+
+This model is a question answering model developed by Sinequa. It produces two lists of logit scores corresponding to
+the start token and end token of an answer.
+
+Model name: `answer-finder-v1-multilingual`
+
+## Supported Languages
+
+The model was trained and tested in the following languages:
+
+- English
+- French
+- German
+- Spanish
+
+## Scores
+
+| Metric                                                        |  Value |
+|:--------------------------------------------------------------|-------:|
+| F1 Score on SQuAD v2 EN with Hugging Face evaluation pipeline |     75 |
+| F1 Score on SQuAD v2 EN with Haystack evaluation pipeline     |     75 |
+| F1 Score on SQuAD v2 FR with Haystack evaluation pipeline     |   73.4 |
+| F1 Score on SQuAD v2 DE with Haystack evaluation pipeline     |   90.8 |
+| F1 Score on SQuAD v2 ES with Haystack evaluation pipeline     |   67.1 |
+
+## Inference Time
+
+| GPU Info                                                      |  Batch size 1  |  Batch size 32 |
+|:--------------------------------------------------------------|---------------:|---------------:|
+| NVIDIA A10                                                    |           4 ms |          84 ms |
+| NVIDIA T4                                                     |          15 ms |         362 ms |
+
+**Note that the Answer Finder models are only used at query time.**
+
+## Requirements
+
+- Minimal Sinequa version: 11.10.0
+- GPU memory usage: 1060 MiB
+
+Note that GPU memory usage only includes how much GPU memory the actual model consumes on an NVIDIA T4 GPU with a batch
+size of 32. It does not include the fix amount of memory that is consumed by the ONNX Runtime upon initialization which
+can be around 0.5 to 1 GiB depending on the used GPU.
+
+## Model Details
+
+### Overview
+
+- Number of parameters: 110 million
+- Base language model: [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)
+  pre-trained by Sinequa in English, French, German and Spanish
+- Insensitive to casing and accents
+
+### Training Data
+
+- [SQuAD v2](https://rajpurkar.github.io/SQuAD-explorer/)
+- [French-SQuAD](https://github.com/Alikabbadj/French-SQuAD)
+- [GermanQuAD](https://www.deepset.ai/germanquad)
+- [SQuAD-es-v2](https://github.com/ccasimiro88/TranslateAlignRetrieve)

config.json

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+{
+  "_name_or_path": "./tmp/bert_model/multi_4_lang_uncased/",
+  "architectures": [
+    "BertForQuestionAnswering"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.21.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 50099
+}

pytorch_model.bin

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