Upload TFT5ForConditionalGeneration

README.md

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+---
+tags:
+- generated_from_keras_callback
+model-index:
+- name: ASL_t5_movinet
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information Keras had access to. You should
+probably proofread and complete it, then remove this comment. -->
+
+# ASL_t5_movinet
+
+This model was trained from scratch on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Train Loss: 0.5272
+- Train Top 1: 0.8966
+- Train Top 5: 0.9383
+- Validation Loss: 0.7459
+- Validation Top 1: 0.8710
+- Validation Top 5: 0.9142
+- Train Bleu: 0.0
+- Train Gen Len: 2.0
+- Epoch: 2
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- optimizer: {'name': 'Adafactor', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 1e-04, 'beta_2_decay': -0.8, 'epsilon_1': 1e-30, 'epsilon_2': 0.001, 'clip_threshold': 1.0, 'relative_step': True}
+- training_precision: float32
+
+### Training results
+
+| Train Loss | Train Top 1 | Train Top 5 | Validation Loss | Validation Top 1 | Validation Top 5 | Train Bleu | Train Gen Len | Epoch |
+|:----------:|:-----------:|:-----------:|:---------------:|:----------------:|:----------------:|:----------:|:-------------:|:-----:|
+| 0.5971     | 0.8879      | 0.9305      | 0.7228          | 0.8744           | 0.9170           | 0.0        | 2.0           | 0     |
+| 0.5567     | 0.8928      | 0.9353      | 0.7311          | 0.8736           | 0.9156           | 0.0        | 2.0           | 1     |
+| 0.5272     | 0.8966      | 0.9383      | 0.7459          | 0.8710           | 0.9142           | 0.0        | 2.0           | 2     |
+
+
+### Framework versions
+
+- Transformers 4.34.1
+- TensorFlow 2.13.0
+- Datasets 2.15.0
+- Tokenizers 0.14.1

config.json

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+{
+  "_name_or_path": "t5_word_epoch12_1203",
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "classifier_dropout": 0.0,
+  "d_ff": 3072,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "relu",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": false,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 12,
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "transformers_version": "4.34.1",
+  "use_cache": true,
+  "vocab_size": 32128
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "decoder_start_token_id": 0,
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "4.34.1"
+}

tf_model.h5

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+oid sha256:bddeb4e33b99cd8600528f7ea5b4f6c7ba12180911efa819d59ab059ce5d73bd
+size 1089544048